Double-loop Framework Implementation with Fossil Case: Integrated Ultra-supercritical Power Plant Model
Author: Naresh Susarla (naresh.susarla@netl.doe.gov)
This notebook presents an implementation of the double-loop framework for the fossil case study using the integrated ultra-supercritical power plant model with a molten-salt based thermal energy storage. In order to run this notebook, we need multiple packages including, DISPATCHES, IDAES, and gridx-prescient.
For a given integrated energy system, the double loop workflow has six key steps:
Forecasting: Generate plausible price forcasts for the bidding horizon (chosen to be 48 hours).
Bidding: Formulate and solve a multi-period bidding problem for the generated set of forcasted price signals. The solution of the bidding problem yields a time-varying bid curve.
Day Ahead (DA) Market: Using the bid curve generated in the previous step, solve the unit commitment problem for the day ahead market.
Real Time Market: Fix unit commitment decisions and solve the unit commitment problem for the real time market. The solution yields real time local marginal prices and the optimal dispatch profile for each generator.
Tracking: Formulate and solve a tracking problem to track the optimal dispatch profile obtained in the previous step, and minimize under-production/over-production of electricity.
Settlement: Compute all the cash flows
To start the analysis, first import all necessary modules and packages including Prescient simulator and MultiPeriodUsc class. MultiPeriodUsc builds a multi-period model of the integrated ultra-supercritical power plant model with a molten salt energy storage.
[1]:
# Import Python library packages
from types import ModuleType
import pandas as pd
from importlib import resources
# Import Pyomo packages
import pyomo.environ as pyo
# Import IDAES packages
from idaes.apps.grid_integration import Tracker
from idaes.apps.grid_integration import Bidder
from idaes.apps.grid_integration.forecaster import Backcaster
from idaes.apps.grid_integration import DoubleLoopCoordinator
from idaes.apps.grid_integration.model_data import ThermalGeneratorModelData
# Import Prescient simulator
from prescient.simulator import Prescient
# Import integrated ultra-supercritical power plant with energy storage model
from dispatches.case_studies.fossil_case.ultra_supercritical_plant.storage.multiperiod_double_loop_usc import MultiPeriodUsc
Interactive Python mode detected; using default matplotlib backend for plotting.
Following the imports, set up the fossil generator characteristics using a model_data object as shown below. The fossil generator used in this analysis is the integrated ultra-supercritical power plant, refered to in this analysis as the fossil-based integrated energy system (IES). A more detailed explanation of the integrated model is given in the notebook integrated_storage_with_powerplant. The double-loop model parameters are the number of hours for the bidding and tracking models,
number of scenarios to use, and the number of days for simulation. The fossil generator data included in model_data and needed during this analysis is given and described in the list below:
gen_name: The ID of the generator in the RTS - GMLC that is being replaced by the fossil-based IES. The chosen generator name
gen_nameis102_STEAM_3generator_type: Accepts either
thermalorrenewable. We choosethermalfor both fossil generatorsp_min: Minimum power output to the grid in MW. The minimum power output to the grid for this IES is 286 MW
p_max: Maximum power output to the grid in MW. The maximum power output to the grid for this IES is 460 MW
min_down_time: Mininum downtime after the generator is shutdown. The minimum downtime for this IES is 4 hours
min_up_time: Minimum uptime after the generator is turned on. The minimum uptime for this IES is 8 hours
ramp_up_60min: Ramp up rate in MW/hr. The default ramp up rate for this IES is 60 MW/hr
ramp_down_60min: Ramp down rate in MW/hr. The default ramp down rate for this IES is the same as the ramp up rate, 60 MW/hr
shutdown_capacity: Power output when shutdown is initiated in MW. For this IES, during shutdown the plant operates at p_min
startup_capacity: Power output when the generator is turned on. For this IES, during startup, the plant operates at p_min as considered during shotdown
production_cost_bid_pairs: Cost of production of electricity in \$/MW as function of power output in MW
startup_cost_pairs: (Number of hours after shutdown, cost of startup)
fixed_commitment: 0/1 indicates that plant shutdown is allowed/not allowed
[2]:
# Import the historical price data from 'original rts-gmlc run' output folder:
bus_name = "Attlee"
da_lmp_historical = [21.288312, 20.419032, 19.689677, 19.983571, 19.983571, 20.419032,
21.843871, 23.437857, 18.072549, -0. , -0. , -0. ,
-0. , -0. , -0. , -0. , 18.861 , 22.51634 ,
33.752674, 33.752674, 27.050323, 24.617429, 23.07 , 19.689677]
rt_lmp_historical = [23.07 , 22.968387, 24.617429, 24.617429, 27.050323, 22.51634 ,
22.492903, 23.657742, 21.843871, -0. , -0. , -0. ,
-0. , -0. , -0. , -0. , -0. , 21.916765,
24.079344, 23.40518 , 22.683303, 21.916765, 20.244451, 18.861 ]
[3]:
generator_data = {
"gen_name": "102_STEAM_3",
"p_min": 286,
"p_max": 460,
"min_down_time": 4,
"min_up_time": 8,
"ramp_up_60min": 60,
"ramp_down_60min": 60,
"shutdown_capacity": 286,
"startup_capacity": 286,
"initial_status": 9999,
"initial_p_output": 286+60,
"production_cost_bid_pairs": [
(286, 22.16602),
(350, 19.0723),
(400, 18.29703),
(430, 17.71727),
(460, 17.71727),
],
"startup_cost_pairs": [(4, 7355.42), (10, 10488.35), (12, 11383.41)],
"fixed_commitment": 1,
"bus": bus_name,
}
model_data = ThermalGeneratorModelData(**generator_data)
Next, we set the following parameters needed for the double loop workflow: - tracking_horizon: Length of the tracking horizon. We choose it to be 10 hours - bidding_horizon: Length of the bidding horizon. We choose it to be 48 hours - n_scenario: Number of scenarios to be considered for the bidding problem. The file idaes.apps.grid_integration.examples.lmp_forecasts_concat.csv includes the data for ten scenarios, so we can choose any number between 1 and 10 - n_tracking_hour: Frequency at
which the tracking problem needs to be solved (every hour) - num_days: Simulation horizon (number of days). For this demonstration, 2 days is chosen for this simulation
[4]:
tracking_horizon = 4 # hours
bidding_horizon = 48 # hours
hourly_bidding_horizon = 4 # hours
n_scenario = 1 # for bidding
n_tracking_hour = 1 # advance n_tracking_hour (i.e. assume we solve every hour)
num_days = 1
Set up the solver and the forecaster objects.
[5]:
# Create solver
solver = pyo.SolverFactory("ipopt")
solver.options = {
"max_iter": 200,
}
# Create forecaster object
forecaster = Backcaster({bus_name: da_lmp_historical}, {bus_name: rt_lmp_historical})
Set up the objects for Tracker, Bidder, and Coordinator.
[6]:
# Tracker
mp_usc_tracker = MultiPeriodUsc(
model_data=model_data
)
thermal_tracker = Tracker(
tracking_model_object=mp_usc_tracker,
n_tracking_hour=n_tracking_hour,
solver=solver,
tracking_horizon=tracking_horizon,
)
# Projection Tracker
mp_usc_projection_tracker = MultiPeriodUsc(
model_data=model_data
)
thermal_projection_tracker = Tracker(
tracking_model_object=mp_usc_projection_tracker,
n_tracking_hour=n_tracking_hour,
solver=solver,
tracking_horizon=tracking_horizon,
)
# Bidder
mp_usc_bidder = MultiPeriodUsc(
model_data=model_data
)
thermal_bidder = Bidder(
bidding_model_object=mp_usc_bidder,
n_scenario=n_scenario,
solver=solver,
forecaster=forecaster,
day_ahead_horizon=bidding_horizon,
real_time_horizon=tracking_horizon,
)
# Coordinator
coordinator = DoubleLoopCoordinator(
bidder=thermal_bidder,
tracker=thermal_tracker,
projection_tracker=thermal_projection_tracker,
)
[+ 0.00] Beginning the formulation of the multiperiod optimization problem.
2022-11-02 16:23:19 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[0]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[0]
2022-11-02 16:23:24 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[1]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[1]
2022-11-02 16:23:29 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[2]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[2]
2022-11-02 16:23:34 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[3]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[3]
[+ 20.01] Completed the formulation of the multiperiod optimization problem.
2022-11-02 16:23:49 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete
2022-11-02 16:23:49 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:49 [INFO] idaes.init.fs.turbine_splitter[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:49 [INFO] idaes.init.fs.turbine_splitter[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:49 [INFO] idaes.init.fs.reheater[1].control_volume: Initialization Complete
2022-11-02 16:23:49 [INFO] idaes.init.fs.reheater[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:49 [INFO] idaes.init.fs.turbine_splitter[3]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:49 [INFO] idaes.init.fs.turbine_splitter[4]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:49 [INFO] idaes.init.fs.reheater[2].control_volume: Initialization Complete
2022-11-02 16:23:50 [INFO] idaes.init.fs.reheater[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:50 [INFO] idaes.init.fs.turbine_splitter[5]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:50 [INFO] idaes.init.fs.turbine_splitter[6]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:50 [INFO] idaes.init.fs.turbine_splitter[7]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:50 [INFO] idaes.init.fs.turbine_splitter[8]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:50 [INFO] idaes.init.fs.turbine_splitter[9]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:51 [INFO] idaes.init.fs.turbine_splitter[10]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:51 [INFO] idaes.init.fs.condenser_mix: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:51 [INFO] idaes.init.fs.condenser.control_volume: Initialization Complete
2022-11-02 16:23:51 [INFO] idaes.init.fs.condenser: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:51 [INFO] idaes.init.fs.fwh_mixer[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:51 [INFO] idaes.init.fs.fwh[1].hot_side: Initialization Complete
2022-11-02 16:23:51 [INFO] idaes.init.fs.fwh[1].cold_side: Initialization Complete
2022-11-02 16:23:51 [INFO] idaes.init.fs.fwh[1]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:23:51 [INFO] idaes.init.fs.fwh_mixer[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:51 [INFO] idaes.init.fs.fwh[2].hot_side: Initialization Complete
2022-11-02 16:23:51 [INFO] idaes.init.fs.fwh[2].cold_side: Initialization Complete
2022-11-02 16:23:52 [INFO] idaes.init.fs.fwh[2]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:23:52 [INFO] idaes.init.fs.fwh_mixer[3]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:52 [INFO] idaes.init.fs.fwh[3].hot_side: Initialization Complete
2022-11-02 16:23:52 [INFO] idaes.init.fs.fwh[3].cold_side: Initialization Complete
2022-11-02 16:23:52 [INFO] idaes.init.fs.fwh[3]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:23:52 [INFO] idaes.init.fs.fwh_mixer[4]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:52 [INFO] idaes.init.fs.fwh[4].hot_side: Initialization Complete
2022-11-02 16:23:52 [INFO] idaes.init.fs.fwh[4].cold_side: Initialization Complete
2022-11-02 16:23:52 [INFO] idaes.init.fs.fwh[4]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:23:52 [INFO] idaes.init.fs.fwh[5].hot_side: Initialization Complete
2022-11-02 16:23:52 [INFO] idaes.init.fs.fwh[5].cold_side: Initialization Complete
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh[5]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:23:53 [INFO] idaes.init.fs.deaerator: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh_mixer[6]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh[6].hot_side: Initialization Complete
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh[6].cold_side: Initialization Complete
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh[6]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh_mixer[7]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh[7].hot_side: Initialization Complete
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh[7].cold_side: Initialization Complete
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh[7]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh_mixer[8]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh[8].hot_side: Initialization Complete
2022-11-02 16:23:53 [INFO] idaes.init.fs.fwh[8].cold_side: Initialization Complete
2022-11-02 16:23:54 [INFO] idaes.init.fs.fwh[8]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:23:54 [INFO] idaes.init.fs.fwh[9].hot_side: Initialization Complete
2022-11-02 16:23:54 [INFO] idaes.init.fs.fwh[9].cold_side: Initialization Complete
2022-11-02 16:23:54 [INFO] idaes.init.fs.fwh[9]: Initialization Completed, optimal - Optimal Solution Found
Model Initialization = optimal
******************* USC Model Initialized ********************
2022-11-02 16:23:56 [INFO] idaes.init.fs.ess_hp_split: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:57 [INFO] idaes.init.fs.hxc.hot_side: Initialization Complete
2022-11-02 16:23:57 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxc.cold_side.properties_in Initialisation Step 1 Complete.
2022-11-02 16:23:57 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:23:57 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxc.cold_side.properties_out Initialisation Step 1 Complete.
2022-11-02 16:23:57 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:23:57 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:23:57 [INFO] idaes.init.fs.hxc.cold_side: Initialization Complete
2022-11-02 16:23:57 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:23:57 [INFO] idaes.init.fs.hxc: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:23:57 [INFO] idaes.init.fs.cooler.control_volume: Initialization Complete
2022-11-02 16:23:57 [INFO] idaes.init.fs.cooler: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:57 [INFO] idaes.init.fs.hx_pump.control_volume: Initialization Complete
2022-11-02 16:23:57 [INFO] idaes.init.fs.hx_pump: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:58 [INFO] idaes.init.fs.ess_bfp_split: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:58 [INFO] idaes.init.fs.recycle_mixer: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:23:58 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxd.hot_side.properties_in Initialisation Step 1 Complete.
2022-11-02 16:23:58 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:23:58 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxd.hot_side.properties_out Initialisation Step 1 Complete.
2022-11-02 16:23:58 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:23:58 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:23:58 [INFO] idaes.init.fs.hxd.hot_side: Initialization Complete
2022-11-02 16:23:58 [INFO] idaes.init.fs.hxd.cold_side: Initialization Complete
2022-11-02 16:23:58 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:23:58 [INFO] idaes.init.fs.hxd: Initialization Completed, optimal - Optimal Solution Found
Integrated Model Initialization = optimal
*************** Integrated Model Initialized ***************
Cost Initialization = optimal
******************** Costing Initialized *************************
WARNING:pyomo.core:Setting Var 'fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.0629934683431687` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[0].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
[+ 26.45] Created an instance of the flowsheet and initialized it.
WARNING:pyomo.core:Setting Var 'blocks[1].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[2].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[3].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
[+ 0.63] Initialized the entire multiperiod optimization model.
[+ 0.01] Unfixed the degrees of freedom from each period model.
[+ 0.00] Beginning the formulation of the multiperiod optimization problem.
2022-11-02 16:24:06 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[0]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[0]
2022-11-02 16:24:11 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[1]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[1]
2022-11-02 16:24:16 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[2]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[2]
2022-11-02 16:24:21 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[3]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[3]
[+ 20.30] Completed the formulation of the multiperiod optimization problem.
2022-11-02 16:24:36 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete
2022-11-02 16:24:36 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:36 [INFO] idaes.init.fs.turbine_splitter[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:37 [INFO] idaes.init.fs.turbine_splitter[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:37 [INFO] idaes.init.fs.reheater[1].control_volume: Initialization Complete
2022-11-02 16:24:37 [INFO] idaes.init.fs.reheater[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:37 [INFO] idaes.init.fs.turbine_splitter[3]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:37 [INFO] idaes.init.fs.turbine_splitter[4]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:37 [INFO] idaes.init.fs.reheater[2].control_volume: Initialization Complete
2022-11-02 16:24:37 [INFO] idaes.init.fs.reheater[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:37 [INFO] idaes.init.fs.turbine_splitter[5]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:37 [INFO] idaes.init.fs.turbine_splitter[6]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:38 [INFO] idaes.init.fs.turbine_splitter[7]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:38 [INFO] idaes.init.fs.turbine_splitter[8]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:38 [INFO] idaes.init.fs.turbine_splitter[9]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:38 [INFO] idaes.init.fs.turbine_splitter[10]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:38 [INFO] idaes.init.fs.condenser_mix: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:38 [INFO] idaes.init.fs.condenser.control_volume: Initialization Complete
2022-11-02 16:24:38 [INFO] idaes.init.fs.condenser: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:38 [INFO] idaes.init.fs.fwh_mixer[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:38 [INFO] idaes.init.fs.fwh[1].hot_side: Initialization Complete
2022-11-02 16:24:38 [INFO] idaes.init.fs.fwh[1].cold_side: Initialization Complete
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh[1]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh_mixer[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh[2].hot_side: Initialization Complete
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh[2].cold_side: Initialization Complete
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh[2]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh_mixer[3]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh[3].hot_side: Initialization Complete
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh[3].cold_side: Initialization Complete
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh[3]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh_mixer[4]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh[4].hot_side: Initialization Complete
2022-11-02 16:24:39 [INFO] idaes.init.fs.fwh[4].cold_side: Initialization Complete
2022-11-02 16:24:40 [INFO] idaes.init.fs.fwh[4]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:24:40 [INFO] idaes.init.fs.fwh[5].hot_side: Initialization Complete
2022-11-02 16:24:40 [INFO] idaes.init.fs.fwh[5].cold_side: Initialization Complete
2022-11-02 16:24:40 [INFO] idaes.init.fs.fwh[5]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:24:40 [INFO] idaes.init.fs.deaerator: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:40 [INFO] idaes.init.fs.fwh_mixer[6]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:40 [INFO] idaes.init.fs.fwh[6].hot_side: Initialization Complete
2022-11-02 16:24:40 [INFO] idaes.init.fs.fwh[6].cold_side: Initialization Complete
2022-11-02 16:24:40 [INFO] idaes.init.fs.fwh[6]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:24:40 [INFO] idaes.init.fs.fwh_mixer[7]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:40 [INFO] idaes.init.fs.fwh[7].hot_side: Initialization Complete
2022-11-02 16:24:40 [INFO] idaes.init.fs.fwh[7].cold_side: Initialization Complete
2022-11-02 16:24:41 [INFO] idaes.init.fs.fwh[7]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:24:41 [INFO] idaes.init.fs.fwh_mixer[8]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:41 [INFO] idaes.init.fs.fwh[8].hot_side: Initialization Complete
2022-11-02 16:24:41 [INFO] idaes.init.fs.fwh[8].cold_side: Initialization Complete
2022-11-02 16:24:41 [INFO] idaes.init.fs.fwh[8]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:24:41 [INFO] idaes.init.fs.fwh[9].hot_side: Initialization Complete
2022-11-02 16:24:41 [INFO] idaes.init.fs.fwh[9].cold_side: Initialization Complete
2022-11-02 16:24:41 [INFO] idaes.init.fs.fwh[9]: Initialization Completed, optimal - Optimal Solution Found
Model Initialization = optimal
******************* USC Model Initialized ********************
2022-11-02 16:24:44 [INFO] idaes.init.fs.ess_hp_split: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:44 [INFO] idaes.init.fs.hxc.hot_side: Initialization Complete
2022-11-02 16:24:44 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxc.cold_side.properties_in Initialisation Step 1 Complete.
2022-11-02 16:24:44 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:24:44 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxc.cold_side.properties_out Initialisation Step 1 Complete.
2022-11-02 16:24:44 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:24:44 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:24:44 [INFO] idaes.init.fs.hxc.cold_side: Initialization Complete
2022-11-02 16:24:44 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:24:44 [INFO] idaes.init.fs.hxc: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:24:44 [INFO] idaes.init.fs.cooler.control_volume: Initialization Complete
2022-11-02 16:24:44 [INFO] idaes.init.fs.cooler: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:44 [INFO] idaes.init.fs.hx_pump.control_volume: Initialization Complete
2022-11-02 16:24:45 [INFO] idaes.init.fs.hx_pump: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:45 [INFO] idaes.init.fs.ess_bfp_split: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:45 [INFO] idaes.init.fs.recycle_mixer: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:24:45 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxd.hot_side.properties_in Initialisation Step 1 Complete.
2022-11-02 16:24:45 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:24:45 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxd.hot_side.properties_out Initialisation Step 1 Complete.
2022-11-02 16:24:45 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:24:45 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:24:45 [INFO] idaes.init.fs.hxd.hot_side: Initialization Complete
2022-11-02 16:24:45 [INFO] idaes.init.fs.hxd.cold_side: Initialization Complete
2022-11-02 16:24:46 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:24:46 [INFO] idaes.init.fs.hxd: Initialization Completed, optimal - Optimal Solution Found
Integrated Model Initialization = optimal
*************** Integrated Model Initialized ***************
Cost Initialization = optimal
******************** Costing Initialized *************************
WARNING:pyomo.core:Setting Var 'fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.0629934683431687` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[0].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
[+ 26.05] Created an instance of the flowsheet and initialized it.
WARNING:pyomo.core:Setting Var 'blocks[1].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[2].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[3].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
[+ 0.68] Initialized the entire multiperiod optimization model.
[+ 0.01] Unfixed the degrees of freedom from each period model.
[+ 0.00] Beginning the formulation of the multiperiod optimization problem.
2022-11-02 16:24:53 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[0]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[0]
2022-11-02 16:24:57 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[1]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[1]
2022-11-02 16:25:02 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[2]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[2]
2022-11-02 16:25:08 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[3]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[3]
2022-11-02 16:25:12 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[4]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[4]
2022-11-02 16:25:18 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[5]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[5]
2022-11-02 16:25:23 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[6]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[6]
2022-11-02 16:25:27 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[7]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[7]
2022-11-02 16:25:32 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[8]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[8]
2022-11-02 16:25:37 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[9]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[9]
2022-11-02 16:25:42 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[10]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[10]
2022-11-02 16:25:46 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[11]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[11]
2022-11-02 16:25:51 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[12]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[12]
2022-11-02 16:25:57 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[13]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[13]
2022-11-02 16:26:02 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[14]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[14]
2022-11-02 16:26:06 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[15]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[15]
2022-11-02 16:26:11 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[16]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[16]
2022-11-02 16:26:15 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[17]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[17]
2022-11-02 16:26:22 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[18]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[18]
2022-11-02 16:26:26 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[19]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[19]
2022-11-02 16:26:31 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[20]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[20]
2022-11-02 16:26:35 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[21]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[21]
2022-11-02 16:26:40 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[22]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[22]
2022-11-02 16:26:45 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[23]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[23]
2022-11-02 16:26:49 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[24]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[24]
2022-11-02 16:26:56 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[25]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[25]
2022-11-02 16:27:00 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[26]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[26]
2022-11-02 16:27:05 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[27]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[27]
2022-11-02 16:27:10 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[28]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[28]
2022-11-02 16:27:15 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[29]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[29]
2022-11-02 16:27:20 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[30]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[30]
2022-11-02 16:27:24 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[31]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[31]
2022-11-02 16:27:29 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[32]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[32]
2022-11-02 16:27:37 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[33]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[33]
2022-11-02 16:27:42 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[34]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[34]
2022-11-02 16:27:46 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[35]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[35]
2022-11-02 16:27:51 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[36]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[36]
2022-11-02 16:27:56 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[37]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[37]
2022-11-02 16:28:00 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[38]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[38]
2022-11-02 16:28:05 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[39]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[39]
2022-11-02 16:28:10 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[40]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[40]
2022-11-02 16:28:14 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[41]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[41]
2022-11-02 16:28:19 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[42]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[42]
2022-11-02 16:28:24 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[43]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[43]
2022-11-02 16:28:33 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[44]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[44]
2022-11-02 16:28:37 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[45]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[45]
2022-11-02 16:28:42 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[46]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[46]
2022-11-02 16:28:47 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[47]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[47]
[+ 238.17] Completed the formulation of the multiperiod optimization problem.
2022-11-02 16:29:00 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete
2022-11-02 16:29:00 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:00 [INFO] idaes.init.fs.turbine_splitter[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:01 [INFO] idaes.init.fs.turbine_splitter[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:01 [INFO] idaes.init.fs.reheater[1].control_volume: Initialization Complete
2022-11-02 16:29:01 [INFO] idaes.init.fs.reheater[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:01 [INFO] idaes.init.fs.turbine_splitter[3]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:01 [INFO] idaes.init.fs.turbine_splitter[4]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:01 [INFO] idaes.init.fs.reheater[2].control_volume: Initialization Complete
2022-11-02 16:29:01 [INFO] idaes.init.fs.reheater[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:01 [INFO] idaes.init.fs.turbine_splitter[5]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:01 [INFO] idaes.init.fs.turbine_splitter[6]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:02 [INFO] idaes.init.fs.turbine_splitter[7]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:02 [INFO] idaes.init.fs.turbine_splitter[8]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:02 [INFO] idaes.init.fs.turbine_splitter[9]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:02 [INFO] idaes.init.fs.turbine_splitter[10]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:02 [INFO] idaes.init.fs.condenser_mix: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:02 [INFO] idaes.init.fs.condenser.control_volume: Initialization Complete
2022-11-02 16:29:02 [INFO] idaes.init.fs.condenser: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:02 [INFO] idaes.init.fs.fwh_mixer[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:02 [INFO] idaes.init.fs.fwh[1].hot_side: Initialization Complete
2022-11-02 16:29:02 [INFO] idaes.init.fs.fwh[1].cold_side: Initialization Complete
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh[1]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh_mixer[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh[2].hot_side: Initialization Complete
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh[2].cold_side: Initialization Complete
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh[2]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh_mixer[3]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh[3].hot_side: Initialization Complete
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh[3].cold_side: Initialization Complete
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh[3]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh_mixer[4]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh[4].hot_side: Initialization Complete
2022-11-02 16:29:03 [INFO] idaes.init.fs.fwh[4].cold_side: Initialization Complete
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh[4]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh[5].hot_side: Initialization Complete
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh[5].cold_side: Initialization Complete
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh[5]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:29:04 [INFO] idaes.init.fs.deaerator: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh_mixer[6]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh[6].hot_side: Initialization Complete
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh[6].cold_side: Initialization Complete
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh[6]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh_mixer[7]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh[7].hot_side: Initialization Complete
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh[7].cold_side: Initialization Complete
2022-11-02 16:29:04 [INFO] idaes.init.fs.fwh[7]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:29:05 [INFO] idaes.init.fs.fwh_mixer[8]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:05 [INFO] idaes.init.fs.fwh[8].hot_side: Initialization Complete
2022-11-02 16:29:05 [INFO] idaes.init.fs.fwh[8].cold_side: Initialization Complete
2022-11-02 16:29:05 [INFO] idaes.init.fs.fwh[8]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:29:05 [INFO] idaes.init.fs.fwh[9].hot_side: Initialization Complete
2022-11-02 16:29:05 [INFO] idaes.init.fs.fwh[9].cold_side: Initialization Complete
2022-11-02 16:29:05 [INFO] idaes.init.fs.fwh[9]: Initialization Completed, optimal - Optimal Solution Found
Model Initialization = optimal
******************* USC Model Initialized ********************
2022-11-02 16:29:08 [INFO] idaes.init.fs.ess_hp_split: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:08 [INFO] idaes.init.fs.hxc.hot_side: Initialization Complete
2022-11-02 16:29:08 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxc.cold_side.properties_in Initialisation Step 1 Complete.
2022-11-02 16:29:08 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:29:08 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxc.cold_side.properties_out Initialisation Step 1 Complete.
2022-11-02 16:29:08 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:29:08 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:29:08 [INFO] idaes.init.fs.hxc.cold_side: Initialization Complete
2022-11-02 16:29:09 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:29:09 [INFO] idaes.init.fs.hxc: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:29:09 [INFO] idaes.init.fs.cooler.control_volume: Initialization Complete
2022-11-02 16:29:09 [INFO] idaes.init.fs.cooler: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:09 [INFO] idaes.init.fs.hx_pump.control_volume: Initialization Complete
2022-11-02 16:29:09 [INFO] idaes.init.fs.hx_pump: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:09 [INFO] idaes.init.fs.ess_bfp_split: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:09 [INFO] idaes.init.fs.recycle_mixer: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:09 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxd.hot_side.properties_in Initialisation Step 1 Complete.
2022-11-02 16:29:09 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:29:09 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxd.hot_side.properties_out Initialisation Step 1 Complete.
2022-11-02 16:29:09 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:29:09 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:29:09 [INFO] idaes.init.fs.hxd.hot_side: Initialization Complete
2022-11-02 16:29:09 [INFO] idaes.init.fs.hxd.cold_side: Initialization Complete
2022-11-02 16:29:10 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:29:10 [INFO] idaes.init.fs.hxd: Initialization Completed, optimal - Optimal Solution Found
Integrated Model Initialization = optimal
*************** Integrated Model Initialized ***************
Cost Initialization = optimal
******************** Costing Initialized *************************
WARNING:pyomo.core:Setting Var 'fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.0629934683431687` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[0].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
[+ 25.39] Created an instance of the flowsheet and initialized it.
WARNING:pyomo.core:Setting Var 'blocks[1].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[2].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[3].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[4].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[5].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[6].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[7].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[8].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[9].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[10].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[11].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[12].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[13].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[14].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[15].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[16].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[17].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[18].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[19].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[20].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[21].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[22].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[23].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[24].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[25].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[26].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[27].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[28].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[29].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[30].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[31].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[32].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[33].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[34].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[35].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[36].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[37].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[38].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[39].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[40].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[41].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[42].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[43].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[44].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[45].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[46].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[47].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
[+ 7.47] Initialized the entire multiperiod optimization model.
[+ 0.04] Unfixed the degrees of freedom from each period model.
[+ 0.00] Beginning the formulation of the multiperiod optimization problem.
2022-11-02 16:29:24 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[0]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[0]
2022-11-02 16:29:29 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[1]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[1]
2022-11-02 16:29:33 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[2]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[2]
2022-11-02 16:29:38 [INFO] idaes.apps.grid_integration.multiperiod.multiperiod: ...Constructing the flowsheet model for blocks[3]
INFO:idaes.apps.grid_integration.multiperiod.multiperiod:...Constructing the flowsheet model for blocks[3]
[+ 18.71] Completed the formulation of the multiperiod optimization problem.
2022-11-02 16:29:57 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete
2022-11-02 16:29:57 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:57 [INFO] idaes.init.fs.turbine_splitter[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:57 [INFO] idaes.init.fs.turbine_splitter[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:57 [INFO] idaes.init.fs.reheater[1].control_volume: Initialization Complete
2022-11-02 16:29:57 [INFO] idaes.init.fs.reheater[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:57 [INFO] idaes.init.fs.turbine_splitter[3]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:57 [INFO] idaes.init.fs.turbine_splitter[4]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:57 [INFO] idaes.init.fs.reheater[2].control_volume: Initialization Complete
2022-11-02 16:29:57 [INFO] idaes.init.fs.reheater[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:57 [INFO] idaes.init.fs.turbine_splitter[5]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:58 [INFO] idaes.init.fs.turbine_splitter[6]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:58 [INFO] idaes.init.fs.turbine_splitter[7]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:58 [INFO] idaes.init.fs.turbine_splitter[8]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:58 [INFO] idaes.init.fs.turbine_splitter[9]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:58 [INFO] idaes.init.fs.turbine_splitter[10]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:58 [INFO] idaes.init.fs.condenser_mix: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:58 [INFO] idaes.init.fs.condenser.control_volume: Initialization Complete
2022-11-02 16:29:59 [INFO] idaes.init.fs.condenser: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh_mixer[1]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh[1].hot_side: Initialization Complete
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh[1].cold_side: Initialization Complete
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh[1]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh_mixer[2]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh[2].hot_side: Initialization Complete
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh[2].cold_side: Initialization Complete
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh[2]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh_mixer[3]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh[3].hot_side: Initialization Complete
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh[3].cold_side: Initialization Complete
2022-11-02 16:29:59 [INFO] idaes.init.fs.fwh[3]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:30:00 [INFO] idaes.init.fs.fwh_mixer[4]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:30:00 [INFO] idaes.init.fs.fwh[4].hot_side: Initialization Complete
2022-11-02 16:30:00 [INFO] idaes.init.fs.fwh[4].cold_side: Initialization Complete
2022-11-02 16:30:00 [INFO] idaes.init.fs.fwh[4]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:30:00 [INFO] idaes.init.fs.fwh[5].hot_side: Initialization Complete
2022-11-02 16:30:00 [INFO] idaes.init.fs.fwh[5].cold_side: Initialization Complete
2022-11-02 16:30:00 [INFO] idaes.init.fs.fwh[5]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:30:00 [INFO] idaes.init.fs.deaerator: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:30:00 [INFO] idaes.init.fs.fwh_mixer[6]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:30:00 [INFO] idaes.init.fs.fwh[6].hot_side: Initialization Complete
2022-11-02 16:30:00 [INFO] idaes.init.fs.fwh[6].cold_side: Initialization Complete
2022-11-02 16:30:00 [INFO] idaes.init.fs.fwh[6]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:30:01 [INFO] idaes.init.fs.fwh_mixer[7]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:30:01 [INFO] idaes.init.fs.fwh[7].hot_side: Initialization Complete
2022-11-02 16:30:01 [INFO] idaes.init.fs.fwh[7].cold_side: Initialization Complete
2022-11-02 16:30:01 [INFO] idaes.init.fs.fwh[7]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:30:01 [INFO] idaes.init.fs.fwh_mixer[8]: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:30:01 [INFO] idaes.init.fs.fwh[8].hot_side: Initialization Complete
2022-11-02 16:30:01 [INFO] idaes.init.fs.fwh[8].cold_side: Initialization Complete
2022-11-02 16:30:01 [INFO] idaes.init.fs.fwh[8]: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:30:01 [INFO] idaes.init.fs.fwh[9].hot_side: Initialization Complete
2022-11-02 16:30:01 [INFO] idaes.init.fs.fwh[9].cold_side: Initialization Complete
2022-11-02 16:30:01 [INFO] idaes.init.fs.fwh[9]: Initialization Completed, optimal - Optimal Solution Found
Model Initialization = optimal
******************* USC Model Initialized ********************
2022-11-02 16:30:04 [INFO] idaes.init.fs.ess_hp_split: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:30:04 [INFO] idaes.init.fs.hxc.hot_side: Initialization Complete
2022-11-02 16:30:04 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxc.cold_side.properties_in Initialisation Step 1 Complete.
2022-11-02 16:30:04 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:30:04 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxc.cold_side.properties_out Initialisation Step 1 Complete.
2022-11-02 16:30:04 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:30:04 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:30:04 [INFO] idaes.init.fs.hxc.cold_side: Initialization Complete
2022-11-02 16:30:05 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:30:05 [INFO] idaes.init.fs.hxc: Initialization Completed, optimal - Optimal Solution Found
2022-11-02 16:30:05 [INFO] idaes.init.fs.cooler.control_volume: Initialization Complete
2022-11-02 16:30:05 [INFO] idaes.init.fs.cooler: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:30:05 [INFO] idaes.init.fs.hx_pump.control_volume: Initialization Complete
2022-11-02 16:30:05 [INFO] idaes.init.fs.hx_pump: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:30:05 [INFO] idaes.init.fs.ess_bfp_split: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:30:05 [INFO] idaes.init.fs.recycle_mixer: Initialization Complete: optimal - Optimal Solution Found
2022-11-02 16:30:05 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxd.hot_side.properties_in Initialisation Step 1 Complete.
2022-11-02 16:30:05 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:30:05 [INFO] idaes.init.dispatches.properties.solarsalt_properties: fs.hxd.hot_side.properties_out Initialisation Step 1 Complete.
2022-11-02 16:30:05 [INFO] idaes.init.dispatches.properties.solarsalt_properties: Initialization Step 1 Complete.
2022-11-02 16:30:05 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:30:05 [INFO] idaes.init.fs.hxd.hot_side: Initialization Complete
2022-11-02 16:30:05 [INFO] idaes.init.fs.hxd.cold_side: Initialization Complete
2022-11-02 16:30:06 [INFO] idaes.init.dispatches.properties.solarsalt_properties: State Released.
2022-11-02 16:30:06 [INFO] idaes.init.fs.hxd: Initialization Completed, optimal - Optimal Solution Found
Integrated Model Initialization = optimal
*************** Integrated Model Initialized ***************
Cost Initialization = optimal
******************** Costing Initialized *************************
WARNING:pyomo.core:Setting Var 'fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.0629934683431687` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[0].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
[+ 29.92] Created an instance of the flowsheet and initialized it.
WARNING:pyomo.core:Setting Var 'blocks[1].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[2].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
WARNING:pyomo.core:Setting Var 'blocks[3].process.fs.hxd.delta_temperature_in[0.0]' to a numeric value `0.06299346834317004` outside the bounds (4.9, 300).
[+ 0.61] Initialized the entire multiperiod optimization model.
[+ 0.01] Unfixed the degrees of freedom from each period model.
Set up the plugin_module needed for the Prescient simulator
[7]:
class PrescientPluginModule(ModuleType):
def __init__(self, get_configuration, register_plugins):
self.get_configuration = get_configuration
self.register_plugins = register_plugins
plugin_module = PrescientPluginModule(
get_configuration=coordinator.get_configuration,
register_plugins=coordinator.register_plugins,
)
IMPORTANT Before proceeding with the rest of the notebook: 1. Install the rts_gmlc data package by running pip install dispatches-rts-gmlc-data in the working conda environment
OR
Download the RTS-GMLC dataset from [https://github.com/GridMod/RTS-GMLC] (or directly as a zipfile here) to your local machine
Update the value of the
rst_gmlc_data_dirvariable in the cell below to the appropriate location of theRTS_Data/SourceDatasubfolder on your local machine
[8]:
# If installing the `rts_gmlc` data package
from dispatches_data.api import path
rts_gmlc_data_dir = str(path("rts_gmlc") / "SourceData")
# If downloading the data to the local machine use the following option with appropriate path
# rts_gmlc_data_dir = "/projects/gmihybridsys/dguittet/data/RTS-GMLC/RTS_Data/SourceData"
Declare the options for Prescient simulator
[9]:
options = {
"data_path": rts_gmlc_data_dir,
"input_format": "rts-gmlc",
"simulate_out_of_sample": True,
"run_sced_with_persistent_forecast_errors": True,
"output_directory": "bidding_multiperiod_usc",
"start_date": "2020-07-10",
"num_days": num_days,
"sced_horizon": tracking_horizon,
"ruc_horizon": bidding_horizon,
"compute_market_settlements": True,
"day_ahead_pricing": "LMP",
"ruc_mipgap": 0.01,
"symbolic_solver_labels": True,
"reserve_factor": 0.0,
"deterministic_ruc_solver": "gurobi",
"output_ruc_solutions": True,
"sced_solver": "gurobi",
"print_sced": True,
"output_solver_logs": True,
"plugin": {
"doubleloop": {
"module": plugin_module,
"bidding_generator": "102_STEAM_3",
}
}
}
Run Prescient simulator.
[10]:
Prescient().simulate(**options)
Initializing simulation...
Did not find reserves.csv; assuming no reserves
WARNING:egret:Did not find reserves.csv; assuming no reserves
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:304: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:304: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:304: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:304: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:304: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:304: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
C:\Users\susarlan\Anaconda3\envs\gmlc\lib\site-packages\egret\parsers\rts_gmlc\parser.py:256: FutureWarning:
Use pd.to_datetime instead.
df = pd.read_csv(file_name,
Setting default t0 state in RTS-GMLC parser
WARNING:egret:Setting default t0 state in RTS-GMLC parser
Dates to simulate: 2020-07-10 to 2020-07-10
RUC activation hours: 0
Final RUC date: 2020-07-10
Using current day's forecasts for RUC solves
Using persistent forecast error model when projecting demand and renewables in SCED
Extracting scenario to simulate
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 76699
Number of nonzeros in inequality constraint Jacobian.: 716
Number of nonzeros in Lagrangian Hessian.............: 23760
Total number of variables............................: 29421
variables with only lower bounds: 336
variables with lower and upper bounds: 20109
variables with only upper bounds: 0
Total number of equality constraints.................: 29134
Total number of inequality constraints...............: 384
inequality constraints with only lower bounds: 48
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 336
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.1311302e+09 2.83e+08 2.71e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 3.1248284e+09 2.82e+08 5.22e+01 -1.0 4.17e+08 - 5.07e-02 2.84e-03f 1
2 3.1018934e+09 2.80e+08 3.00e+02 -1.0 4.26e+08 - 5.32e-04 8.58e-03f 1
3 3.0773563e+09 2.77e+08 5.53e+02 -1.0 4.22e+08 - 4.45e-05 9.13e-03f 1
4 2.9761183e+09 2.67e+08 4.56e+03 -1.0 4.19e+08 - 4.06e-02 3.76e-02f 1
5 2.9744249e+09 2.67e+08 4.56e+03 -1.0 4.14e+08 - 4.41e-04 7.30e-04f 1
6 2.9735828e+09 2.67e+08 4.56e+03 -1.0 4.26e+08 - 5.68e-05 4.85e-04f 1
7 2.9727907e+09 2.67e+08 4.56e+03 -1.0 4.26e+08 - 6.53e-03 5.08e-04f 1
8 2.9637978e+09 2.65e+08 4.96e+03 -1.0 4.31e+08 - 8.30e-05 5.87e-03f 1
9 2.9540949e+09 2.63e+08 5.41e+03 -1.0 4.30e+08 - 3.11e-03 6.34e-03f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 2.9083730e+09 2.56e+08 1.60e+04 -1.0 4.30e+08 - 1.72e-02 2.99e-02f 1
11 2.8476151e+09 2.46e+08 3.38e+04 -1.0 4.27e+08 - 1.00e-01 3.96e-02f 1
12 2.5845274e+09 2.11e+08 3.44e+05 -1.0 4.40e+08 - 1.18e-03 1.64e-01f 1
13 2.5817572e+09 2.11e+08 3.44e+05 -1.0 3.98e+08 - 1.25e-01 1.80e-03f 1
14 2.5803763e+09 2.10e+08 3.43e+05 -1.0 4.20e+08 - 1.66e-03 8.68e-04f 1
15 2.5763865e+09 2.10e+08 3.43e+05 -1.0 4.20e+08 - 6.70e-04 2.51e-03f 1
16 2.3872042e+09 1.87e+08 4.38e+05 -1.0 4.19e+08 - 6.79e-03 1.19e-01f 1
17 2.3852502e+09 1.87e+08 4.38e+05 -1.0 3.87e+08 - 1.30e-02 1.30e-03f 1
18 2.3850512e+09 1.87e+08 4.38e+05 -1.0 3.89e+08 - 3.10e-03 2.15e-04f 1
19 2.3847938e+09 1.87e+08 4.37e+05 -1.0 3.89e+08 - 2.30e-01 1.24e-03f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 2.3839366e+09 1.84e+08 4.33e+05 -1.0 4.11e+08 - 5.82e-03 1.43e-02f 1
21 2.3839107e+09 1.84e+08 4.32e+05 -1.0 7.15e+07 - 1.43e-02 8.37e-04f 1
22 2.3835947e+09 1.82e+08 4.27e+05 -1.0 3.87e+07 - 2.41e-01 1.25e-02f 1
23 2.3755596e+09 1.40e+08 3.35e+05 -1.0 3.85e+07 - 3.62e-03 2.96e-01f 1
24 2.3753755e+09 1.38e+08 3.32e+05 -1.0 3.42e+07 - 1.11e-01 1.02e-02f 1
25 2.3735451e+09 1.26e+08 3.02e+05 -1.0 3.40e+07 - 3.22e-03 9.70e-02f 1
26 2.3715101e+09 1.12e+08 2.68e+05 -1.0 3.20e+07 - 8.86e-02 1.18e-01f 1
27 2.3630602e+09 7.21e+07 1.70e+05 -1.0 2.94e+07 - 1.03e-02 5.03e-01f 1
28 2.3579060e+09 4.75e+07 1.10e+05 -1.0 1.79e+07 - 2.15e-01 4.16e-01f 1
29 2.3477175e+09 2.31e+07 6.20e+04 -1.0 1.12e+07 - 7.39e-01 6.73e-01f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
30 2.3007298e+09 1.02e+07 6.14e+04 -1.0 1.70e+07 - 4.53e-01 9.42e-01f 1
31 2.2467541e+09 1.19e+07 1.88e+04 -1.0 1.81e+07 - 6.10e-01 1.00e+00f 1
32 2.2064254e+09 1.12e+07 1.04e+04 -1.0 1.60e+07 - 8.62e-01 1.00e+00f 1
33 2.1941641e+09 4.94e+06 2.30e+03 -1.0 8.04e+06 - 4.70e-01 9.11e-01f 1
34 2.1938838e+09 4.11e+06 1.91e+03 -1.0 1.05e+06 - 9.52e-03 1.71e-01f 1
35 2.1938774e+09 4.09e+06 1.90e+03 -1.0 8.64e+05 - 1.39e-03 4.67e-03f 1
36 2.1934146e+09 2.72e+06 1.26e+03 -1.0 8.58e+05 - 9.86e-01 3.39e-01f 1
37 2.1933491e+09 2.53e+06 1.17e+03 -1.0 5.07e+05 - 3.11e-01 7.03e-02f 1
38 2.1927682e+09 8.63e+05 3.87e+02 -1.0 4.66e+05 - 7.06e-01 6.71e-01f 1
39 2.1924951e+09 3.69e+04 1.38e+01 -1.0 1.44e+05 - 9.50e-01 9.66e-01f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
40 2.1924901e+09 1.28e+04 1.95e+01 -1.0 7.85e+05 - 2.54e-01 6.53e-01f 1
41 2.1924901e+09 6.30e+03 3.88e+01 -1.0 8.44e+05 - 1.29e-01 5.09e-01h 1
42 2.1924902e+09 2.81e+03 4.39e+01 -1.0 7.94e+05 - 3.42e-01 5.54e-01h 1
43 2.1924902e+09 3.56e-02 7.55e+01 -1.0 8.62e+05 - 5.63e-03 1.00e+00f 1
44 2.1924881e+09 7.84e-01 2.92e+00 -1.0 5.34e+05 - 7.22e-02 9.95e-01f 1
45 2.1924879e+09 9.59e-03 3.92e-02 -1.0 3.57e+05 - 9.30e-01 1.00e+00f 1
46 2.1924879e+09 2.38e-07 1.00e-06 -1.0 5.52e+05 - 1.00e+00 1.00e+00f 1
47 2.1924879e+09 3.05e-06 2.57e-07 -2.5 2.74e+03 - 1.00e+00 1.00e+00f 1
48 2.1924879e+09 2.01e-07 1.12e-08 -3.8 5.05e+03 - 1.00e+00 1.00e+00f 1
49 2.1924879e+09 1.86e-07 1.36e-08 -5.7 1.10e+03 - 1.00e+00 1.00e+00f 1
In iteration 49, 48 Slacks too small, adjusting variable bounds
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
50 2.1924879e+09 1.94e-07 1.28e-08 -8.6 1.21e+02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 50
(scaled) (unscaled)
Objective...............: 2.1924878859498258e+09 2.1924878859498258e+09
Dual infeasibility......: 1.2774603908291213e-08 1.2774603908291213e-08
Constraint violation....: 2.5611370801925659e-09 1.9371509552001953e-07
Complementarity.........: 2.5457100086287576e-09 2.5457100086287576e-09
Overall NLP error.......: 2.5611370801925659e-09 1.9371509552001953e-07
Number of objective function evaluations = 51
Number of objective gradient evaluations = 51
Number of equality constraint evaluations = 51
Number of inequality constraint evaluations = 51
Number of equality constraint Jacobian evaluations = 51
Number of inequality constraint Jacobian evaluations = 51
Number of Lagrangian Hessian evaluations = 50
Total CPU secs in IPOPT (w/o function evaluations) = 6.704
Total CPU secs in NLP function evaluations = 246.346
EXIT: Optimal Solution Found.
Calculating PTDF Matrix Factorization
INFO:egret:Calculating PTDF Matrix Factorization
Pyomo model construction time: 5.27
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 79110 rows, 80702 columns and 323767 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 79110 rows, 80702 columns and 323767 nonzeros
Model fingerprint: 0xcd49f112
INFO:gurobipy.gurobipy:Model fingerprint: 0xcd49f112
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [7e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [7e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [1e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 1e+01]
RHS range [7e-03, 6e+01]
INFO:gurobipy.gurobipy: RHS range [7e-03, 6e+01]
INFO:gurobipy.gurobipy:
Concurrent LP optimizer: dual simplex and barrier
INFO:gurobipy.gurobipy:Concurrent LP optimizer: dual simplex and barrier
Showing barrier log only...
INFO:gurobipy.gurobipy:Showing barrier log only...
INFO:gurobipy.gurobipy:
Presolve removed 27040 rows and 31620 columns
INFO:gurobipy.gurobipy:Presolve removed 27040 rows and 31620 columns
Presolve time: 0.33s
INFO:gurobipy.gurobipy:Presolve time: 0.33s
Presolved: 52070 rows, 49132 columns, 254529 nonzeros
INFO:gurobipy.gurobipy:Presolved: 52070 rows, 49132 columns, 254529 nonzeros
INFO:gurobipy.gurobipy:
Ordering time: 0.03s
INFO:gurobipy.gurobipy:Ordering time: 0.03s
INFO:gurobipy.gurobipy:
Barrier statistics:
INFO:gurobipy.gurobipy:Barrier statistics:
AA' NZ : 3.601e+05
INFO:gurobipy.gurobipy: AA' NZ : 3.601e+05
Factor NZ : 1.325e+06 (roughly 34 MB of memory)
INFO:gurobipy.gurobipy: Factor NZ : 1.325e+06 (roughly 34 MB of memory)
Factor Ops : 1.859e+08 (less than 1 second per iteration)
INFO:gurobipy.gurobipy: Factor Ops : 1.859e+08 (less than 1 second per iteration)
Threads : 3
INFO:gurobipy.gurobipy: Threads : 3
INFO:gurobipy.gurobipy:
Objective Residual
INFO:gurobipy.gurobipy: Objective Residual
Iter Primal Dual Primal Dual Compl Time
INFO:gurobipy.gurobipy:Iter Primal Dual Primal Dual Compl Time
0 5.33808833e+11 -2.34570518e+12 6.02e+02 2.14e+04 1.44e+09 1s
INFO:gurobipy.gurobipy: 0 5.33808833e+11 -2.34570518e+12 6.02e+02 2.14e+04 1.44e+09 1s
1 4.07717538e+11 -2.18465557e+12 2.65e+02 5.18e+07 6.56e+08 1s
INFO:gurobipy.gurobipy: 1 4.07717538e+11 -2.18465557e+12 2.65e+02 5.18e+07 6.56e+08 1s
2 4.32538599e+11 -2.27148948e+12 6.84e+01 3.44e+06 1.77e+08 1s
INFO:gurobipy.gurobipy: 2 4.32538599e+11 -2.27148948e+12 6.84e+01 3.44e+06 1.77e+08 1s
3 2.83374059e+11 -1.54374132e+12 2.49e+01 1.18e+06 7.24e+07 1s
INFO:gurobipy.gurobipy: 3 2.83374059e+11 -1.54374132e+12 2.49e+01 1.18e+06 7.24e+07 1s
4 5.62470350e+10 -8.59194555e+11 7.35e+00 4.17e-07 2.30e+07 1s
INFO:gurobipy.gurobipy: 4 5.62470350e+10 -8.59194555e+11 7.35e+00 4.17e-07 2.30e+07 1s
5 2.22303300e+10 -4.56998974e+11 4.52e+00 1.75e-07 1.12e+07 1s
INFO:gurobipy.gurobipy: 5 2.22303300e+10 -4.56998974e+11 4.52e+00 1.75e-07 1.12e+07 1s
6 9.73319662e+09 -2.24424650e+11 2.13e+00 1.84e-07 4.74e+06 1s
INFO:gurobipy.gurobipy: 6 9.73319662e+09 -2.24424650e+11 2.13e+00 1.84e-07 4.74e+06 1s
7 3.18020843e+09 -1.01230629e+11 5.99e-01 1.81e-07 1.68e+06 1s
INFO:gurobipy.gurobipy: 7 3.18020843e+09 -1.01230629e+11 5.99e-01 1.81e-07 1.68e+06 1s
8 6.49310024e+08 -3.49765221e+10 1.24e-01 1.05e-07 4.99e+05 1s
INFO:gurobipy.gurobipy: 8 6.49310024e+08 -3.49765221e+10 1.24e-01 1.05e-07 4.99e+05 1s
9 1.48903138e+08 -1.47129885e+10 2.52e-02 1.04e-07 1.95e+05 1s
INFO:gurobipy.gurobipy: 9 1.48903138e+08 -1.47129885e+10 2.52e-02 1.04e-07 1.95e+05 1s
10 1.57349695e+07 -1.93802789e+09 9.88e-04 8.77e-08 2.49e+04 1s
INFO:gurobipy.gurobipy: 10 1.57349695e+07 -1.93802789e+09 9.88e-04 8.77e-08 2.49e+04 1s
11 7.82407520e+06 -6.85712046e+07 7.62e-06 1.64e-07 9.73e+02 1s
INFO:gurobipy.gurobipy: 11 7.82407520e+06 -6.85712046e+07 7.62e-06 1.64e-07 9.73e+02 1s
12 7.52881469e+06 -3.11040550e+07 1.23e-06 2.38e-07 4.92e+02 1s
INFO:gurobipy.gurobipy: 12 7.52881469e+06 -3.11040550e+07 1.23e-06 2.38e-07 4.92e+02 1s
13 7.10389227e+06 -1.07276650e+07 1.69e-07 3.88e-07 2.27e+02 1s
INFO:gurobipy.gurobipy: 13 7.10389227e+06 -1.07276650e+07 1.69e-07 3.88e-07 2.27e+02 1s
14 6.87934171e+06 -5.08844277e+06 1.34e-07 1.53e-06 1.52e+02 1s
INFO:gurobipy.gurobipy: 14 6.87934171e+06 -5.08844277e+06 1.34e-07 1.53e-06 1.52e+02 1s
15 6.43008251e+06 -2.25491702e+06 8.50e-08 1.51e-06 1.11e+02 1s
INFO:gurobipy.gurobipy: 15 6.43008251e+06 -2.25491702e+06 8.50e-08 1.51e-06 1.11e+02 1s
16 6.18139307e+06 -7.60143032e+05 7.05e-08 1.57e-06 8.84e+01 2s
INFO:gurobipy.gurobipy: 16 6.18139307e+06 -7.60143032e+05 7.05e-08 1.57e-06 8.84e+01 2s
17 5.78976359e+06 1.32601911e+06 5.22e-08 2.12e-06 5.68e+01 2s
INFO:gurobipy.gurobipy: 17 5.78976359e+06 1.32601911e+06 5.22e-08 2.12e-06 5.68e+01 2s
18 5.28110757e+06 2.41269819e+06 3.43e-08 3.06e-06 3.65e+01 2s
INFO:gurobipy.gurobipy: 18 5.28110757e+06 2.41269819e+06 3.43e-08 3.06e-06 3.65e+01 2s
19 4.96245999e+06 3.13330254e+06 2.54e-08 3.23e-06 2.33e+01 2s
INFO:gurobipy.gurobipy: 19 4.96245999e+06 3.13330254e+06 2.54e-08 3.23e-06 2.33e+01 2s
20 4.80461032e+06 3.42000579e+06 2.12e-08 3.30e-06 1.76e+01 2s
INFO:gurobipy.gurobipy: 20 4.80461032e+06 3.42000579e+06 2.12e-08 3.30e-06 1.76e+01 2s
21 4.64834872e+06 3.58239828e+06 1.71e-08 2.58e-06 1.36e+01 2s
INFO:gurobipy.gurobipy: 21 4.64834872e+06 3.58239828e+06 1.71e-08 2.58e-06 1.36e+01 2s
22 4.40846336e+06 3.71041755e+06 1.09e-08 3.47e-06 8.89e+00 2s
INFO:gurobipy.gurobipy: 22 4.40846336e+06 3.71041755e+06 1.09e-08 3.47e-06 8.89e+00 2s
23 4.23833743e+06 3.79014137e+06 6.78e-09 5.38e-06 5.71e+00 2s
INFO:gurobipy.gurobipy: 23 4.23833743e+06 3.79014137e+06 6.78e-09 5.38e-06 5.71e+00 2s
24 4.12582389e+06 3.86627695e+06 4.19e-09 3.39e-06 3.31e+00 2s
INFO:gurobipy.gurobipy: 24 4.12582389e+06 3.86627695e+06 4.19e-09 3.39e-06 3.31e+00 2s
25 4.05624754e+06 3.89743487e+06 2.65e-09 4.74e-06 2.02e+00 2s
INFO:gurobipy.gurobipy: 25 4.05624754e+06 3.89743487e+06 2.65e-09 4.74e-06 2.02e+00 2s
26 4.01115065e+06 3.90562782e+06 1.67e-09 6.04e-06 1.34e+00 2s
INFO:gurobipy.gurobipy: 26 4.01115065e+06 3.90562782e+06 1.67e-09 6.04e-06 1.34e+00 2s
27 3.99574397e+06 3.91066290e+06 1.34e-09 6.05e-06 1.08e+00 2s
INFO:gurobipy.gurobipy: 27 3.99574397e+06 3.91066290e+06 1.34e-09 6.05e-06 1.08e+00 2s
28 3.96617425e+06 3.92111190e+06 7.00e-10 7.16e-06 5.74e-01 2s
INFO:gurobipy.gurobipy: 28 3.96617425e+06 3.92111190e+06 7.00e-10 7.16e-06 5.74e-01 2s
29 3.94835957e+06 3.92589055e+06 3.26e-10 6.44e-06 2.86e-01 2s
INFO:gurobipy.gurobipy: 29 3.94835957e+06 3.92589055e+06 3.26e-10 6.44e-06 2.86e-01 2s
30 3.94428436e+06 3.92788352e+06 2.41e-10 5.28e-06 2.09e-01 2s
INFO:gurobipy.gurobipy: 30 3.94428436e+06 3.92788352e+06 2.41e-10 5.28e-06 2.09e-01 2s
31 3.94036932e+06 3.92941755e+06 1.62e-10 6.15e-06 1.40e-01 2s
INFO:gurobipy.gurobipy: 31 3.94036932e+06 3.92941755e+06 1.62e-10 6.15e-06 1.40e-01 2s
32 3.93751080e+06 3.93087880e+06 1.05e-10 7.99e-06 8.45e-02 2s
INFO:gurobipy.gurobipy: 32 3.93751080e+06 3.93087880e+06 1.05e-10 7.99e-06 8.45e-02 2s
33 3.93429559e+06 3.93142639e+06 4.37e-11 7.36e-06 3.66e-02 2s
INFO:gurobipy.gurobipy: 33 3.93429559e+06 3.93142639e+06 4.37e-11 7.36e-06 3.66e-02 2s
34 3.93357323e+06 3.93177198e+06 3.02e-11 3.75e-04 2.30e-02 2s
INFO:gurobipy.gurobipy: 34 3.93357323e+06 3.93177198e+06 3.02e-11 3.75e-04 2.30e-02 2s
35 3.93268000e+06 3.93189444e+06 1.59e-11 6.10e-04 1.00e-02 2s
INFO:gurobipy.gurobipy: 35 3.93268000e+06 3.93189444e+06 1.59e-11 6.10e-04 1.00e-02 2s
36 3.93225609e+06 3.93194632e+06 7.94e-12 1.31e-04 3.95e-03 2s
INFO:gurobipy.gurobipy: 36 3.93225609e+06 3.93194632e+06 7.94e-12 1.31e-04 3.95e-03 2s
37 3.93199750e+06 3.93195974e+06 1.11e-10 1.54e-03 4.81e-04 3s
INFO:gurobipy.gurobipy: 37 3.93199750e+06 3.93195974e+06 1.11e-10 1.54e-03 4.81e-04 3s
INFO:gurobipy.gurobipy:
Barrier performed 37 iterations in 2.55 seconds (1.15 work units)
INFO:gurobipy.gurobipy:Barrier performed 37 iterations in 2.55 seconds (1.15 work units)
Barrier solve interrupted - model solved by another algorithm
INFO:gurobipy.gurobipy:Barrier solve interrupted - model solved by another algorithm
INFO:gurobipy.gurobipy:
INFO:gurobipy.gurobipy:
Solved with dual simplex
INFO:gurobipy.gurobipy:Solved with dual simplex
Solved in 16396 iterations and 2.57 seconds (1.33 work units)
INFO:gurobipy.gurobipy:Solved in 16396 iterations and 2.57 seconds (1.33 work units)
Optimal objective 3.931964722e+06
INFO:gurobipy.gurobipy:Optimal objective 3.931964722e+06
[LP warmstart phase] iteration 0, found 0 violation(s)
INFO:egret:[LP warmstart phase] iteration 0, found 0 violation(s)
[LP warmstart phase] added 0 flow constraint(s)
INFO:egret:[LP warmstart phase] added 0 flow constraint(s)
Discarded solution information
INFO:gurobipy.gurobipy:Discarded solution information
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 79110 rows, 80702 columns and 323767 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 79110 rows, 80702 columns and 323767 nonzeros
Model fingerprint: 0x0c47cab7
INFO:gurobipy.gurobipy:Model fingerprint: 0x0c47cab7
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [7e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [7e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [1e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 1e+01]
RHS range [7e-03, 6e+01]
INFO:gurobipy.gurobipy: RHS range [7e-03, 6e+01]
INFO:gurobipy.gurobipy:
Concurrent LP optimizer: dual simplex and barrier
INFO:gurobipy.gurobipy:Concurrent LP optimizer: dual simplex and barrier
Showing barrier log only...
INFO:gurobipy.gurobipy:Showing barrier log only...
INFO:gurobipy.gurobipy:
Presolve removed 27040 rows and 31620 columns
INFO:gurobipy.gurobipy:Presolve removed 27040 rows and 31620 columns
Presolve time: 0.30s
INFO:gurobipy.gurobipy:Presolve time: 0.30s
Presolved: 52070 rows, 49132 columns, 254529 nonzeros
INFO:gurobipy.gurobipy:Presolved: 52070 rows, 49132 columns, 254529 nonzeros
INFO:gurobipy.gurobipy:
Ordering time: 0.04s
INFO:gurobipy.gurobipy:Ordering time: 0.04s
INFO:gurobipy.gurobipy:
Barrier statistics:
INFO:gurobipy.gurobipy:Barrier statistics:
AA' NZ : 3.601e+05
INFO:gurobipy.gurobipy: AA' NZ : 3.601e+05
Factor NZ : 1.325e+06 (roughly 34 MB of memory)
INFO:gurobipy.gurobipy: Factor NZ : 1.325e+06 (roughly 34 MB of memory)
Factor Ops : 1.859e+08 (less than 1 second per iteration)
INFO:gurobipy.gurobipy: Factor Ops : 1.859e+08 (less than 1 second per iteration)
Threads : 3
INFO:gurobipy.gurobipy: Threads : 3
INFO:gurobipy.gurobipy:
Objective Residual
INFO:gurobipy.gurobipy: Objective Residual
Iter Primal Dual Primal Dual Compl Time
INFO:gurobipy.gurobipy:Iter Primal Dual Primal Dual Compl Time
0 5.33808833e+11 -2.34570518e+12 6.02e+02 2.14e+04 1.44e+09 1s
INFO:gurobipy.gurobipy: 0 5.33808833e+11 -2.34570518e+12 6.02e+02 2.14e+04 1.44e+09 1s
1 4.07717538e+11 -2.18465557e+12 2.65e+02 5.18e+07 6.56e+08 1s
INFO:gurobipy.gurobipy: 1 4.07717538e+11 -2.18465557e+12 2.65e+02 5.18e+07 6.56e+08 1s
2 4.32538599e+11 -2.27148948e+12 6.84e+01 3.44e+06 1.77e+08 1s
INFO:gurobipy.gurobipy: 2 4.32538599e+11 -2.27148948e+12 6.84e+01 3.44e+06 1.77e+08 1s
3 2.83374059e+11 -1.54374132e+12 2.49e+01 1.18e+06 7.24e+07 1s
INFO:gurobipy.gurobipy: 3 2.83374059e+11 -1.54374132e+12 2.49e+01 1.18e+06 7.24e+07 1s
4 5.62470350e+10 -8.59194555e+11 7.35e+00 4.17e-07 2.30e+07 1s
INFO:gurobipy.gurobipy: 4 5.62470350e+10 -8.59194555e+11 7.35e+00 4.17e-07 2.30e+07 1s
5 2.22303300e+10 -4.56998974e+11 4.52e+00 1.75e-07 1.12e+07 1s
INFO:gurobipy.gurobipy: 5 2.22303300e+10 -4.56998974e+11 4.52e+00 1.75e-07 1.12e+07 1s
6 9.73319662e+09 -2.24424650e+11 2.13e+00 1.84e-07 4.74e+06 1s
INFO:gurobipy.gurobipy: 6 9.73319662e+09 -2.24424650e+11 2.13e+00 1.84e-07 4.74e+06 1s
7 3.18020843e+09 -1.01230629e+11 5.99e-01 1.81e-07 1.68e+06 1s
INFO:gurobipy.gurobipy: 7 3.18020843e+09 -1.01230629e+11 5.99e-01 1.81e-07 1.68e+06 1s
8 6.49310024e+08 -3.49765221e+10 1.24e-01 1.05e-07 4.99e+05 1s
INFO:gurobipy.gurobipy: 8 6.49310024e+08 -3.49765221e+10 1.24e-01 1.05e-07 4.99e+05 1s
9 1.48903138e+08 -1.47129885e+10 2.52e-02 1.04e-07 1.95e+05 1s
INFO:gurobipy.gurobipy: 9 1.48903138e+08 -1.47129885e+10 2.52e-02 1.04e-07 1.95e+05 1s
10 1.57349695e+07 -1.93802789e+09 9.88e-04 8.77e-08 2.49e+04 1s
INFO:gurobipy.gurobipy: 10 1.57349695e+07 -1.93802789e+09 9.88e-04 8.77e-08 2.49e+04 1s
11 7.82407520e+06 -6.85712046e+07 7.62e-06 1.64e-07 9.73e+02 1s
INFO:gurobipy.gurobipy: 11 7.82407520e+06 -6.85712046e+07 7.62e-06 1.64e-07 9.73e+02 1s
12 7.52881469e+06 -3.11040550e+07 1.23e-06 2.38e-07 4.92e+02 1s
INFO:gurobipy.gurobipy: 12 7.52881469e+06 -3.11040550e+07 1.23e-06 2.38e-07 4.92e+02 1s
13 7.10389227e+06 -1.07276650e+07 1.69e-07 3.88e-07 2.27e+02 1s
INFO:gurobipy.gurobipy: 13 7.10389227e+06 -1.07276650e+07 1.69e-07 3.88e-07 2.27e+02 1s
14 6.87934171e+06 -5.08844277e+06 1.34e-07 1.53e-06 1.52e+02 1s
INFO:gurobipy.gurobipy: 14 6.87934171e+06 -5.08844277e+06 1.34e-07 1.53e-06 1.52e+02 1s
15 6.43008251e+06 -2.25491702e+06 8.50e-08 1.51e-06 1.11e+02 1s
INFO:gurobipy.gurobipy: 15 6.43008251e+06 -2.25491702e+06 8.50e-08 1.51e-06 1.11e+02 1s
16 6.18139307e+06 -7.60143032e+05 7.05e-08 1.57e-06 8.84e+01 1s
INFO:gurobipy.gurobipy: 16 6.18139307e+06 -7.60143032e+05 7.05e-08 1.57e-06 8.84e+01 1s
17 5.78976359e+06 1.32601911e+06 5.22e-08 2.12e-06 5.68e+01 2s
INFO:gurobipy.gurobipy: 17 5.78976359e+06 1.32601911e+06 5.22e-08 2.12e-06 5.68e+01 2s
18 5.28110757e+06 2.41269819e+06 3.43e-08 3.06e-06 3.65e+01 2s
INFO:gurobipy.gurobipy: 18 5.28110757e+06 2.41269819e+06 3.43e-08 3.06e-06 3.65e+01 2s
19 4.96245999e+06 3.13330254e+06 2.54e-08 3.23e-06 2.33e+01 2s
INFO:gurobipy.gurobipy: 19 4.96245999e+06 3.13330254e+06 2.54e-08 3.23e-06 2.33e+01 2s
20 4.80461032e+06 3.42000579e+06 2.12e-08 3.30e-06 1.76e+01 2s
INFO:gurobipy.gurobipy: 20 4.80461032e+06 3.42000579e+06 2.12e-08 3.30e-06 1.76e+01 2s
21 4.64834872e+06 3.58239828e+06 1.71e-08 2.58e-06 1.36e+01 2s
INFO:gurobipy.gurobipy: 21 4.64834872e+06 3.58239828e+06 1.71e-08 2.58e-06 1.36e+01 2s
22 4.40846336e+06 3.71041755e+06 1.09e-08 3.47e-06 8.89e+00 2s
INFO:gurobipy.gurobipy: 22 4.40846336e+06 3.71041755e+06 1.09e-08 3.47e-06 8.89e+00 2s
23 4.23833743e+06 3.79014137e+06 6.78e-09 5.38e-06 5.71e+00 2s
INFO:gurobipy.gurobipy: 23 4.23833743e+06 3.79014137e+06 6.78e-09 5.38e-06 5.71e+00 2s
24 4.12582389e+06 3.86627695e+06 4.19e-09 3.39e-06 3.31e+00 2s
INFO:gurobipy.gurobipy: 24 4.12582389e+06 3.86627695e+06 4.19e-09 3.39e-06 3.31e+00 2s
25 4.05624754e+06 3.89743487e+06 2.65e-09 4.74e-06 2.02e+00 2s
INFO:gurobipy.gurobipy: 25 4.05624754e+06 3.89743487e+06 2.65e-09 4.74e-06 2.02e+00 2s
26 4.01115065e+06 3.90562782e+06 1.67e-09 6.04e-06 1.34e+00 2s
INFO:gurobipy.gurobipy: 26 4.01115065e+06 3.90562782e+06 1.67e-09 6.04e-06 1.34e+00 2s
27 3.99574397e+06 3.91066290e+06 1.34e-09 6.05e-06 1.08e+00 2s
INFO:gurobipy.gurobipy: 27 3.99574397e+06 3.91066290e+06 1.34e-09 6.05e-06 1.08e+00 2s
28 3.96617425e+06 3.92111190e+06 7.00e-10 7.16e-06 5.74e-01 2s
INFO:gurobipy.gurobipy: 28 3.96617425e+06 3.92111190e+06 7.00e-10 7.16e-06 5.74e-01 2s
29 3.94835957e+06 3.92589055e+06 3.26e-10 6.44e-06 2.86e-01 2s
INFO:gurobipy.gurobipy: 29 3.94835957e+06 3.92589055e+06 3.26e-10 6.44e-06 2.86e-01 2s
30 3.94428436e+06 3.92788352e+06 2.41e-10 5.28e-06 2.09e-01 2s
INFO:gurobipy.gurobipy: 30 3.94428436e+06 3.92788352e+06 2.41e-10 5.28e-06 2.09e-01 2s
31 3.94036932e+06 3.92941755e+06 1.62e-10 6.15e-06 1.40e-01 2s
INFO:gurobipy.gurobipy: 31 3.94036932e+06 3.92941755e+06 1.62e-10 6.15e-06 1.40e-01 2s
32 3.93751080e+06 3.93087880e+06 1.05e-10 7.99e-06 8.45e-02 2s
INFO:gurobipy.gurobipy: 32 3.93751080e+06 3.93087880e+06 1.05e-10 7.99e-06 8.45e-02 2s
33 3.93429559e+06 3.93142639e+06 4.37e-11 7.36e-06 3.66e-02 2s
INFO:gurobipy.gurobipy: 33 3.93429559e+06 3.93142639e+06 4.37e-11 7.36e-06 3.66e-02 2s
34 3.93357323e+06 3.93177198e+06 3.02e-11 3.75e-04 2.30e-02 2s
INFO:gurobipy.gurobipy: 34 3.93357323e+06 3.93177198e+06 3.02e-11 3.75e-04 2.30e-02 2s
35 3.93268000e+06 3.93189444e+06 1.59e-11 6.10e-04 1.00e-02 2s
INFO:gurobipy.gurobipy: 35 3.93268000e+06 3.93189444e+06 1.59e-11 6.10e-04 1.00e-02 2s
36 3.93225609e+06 3.93194632e+06 7.94e-12 1.31e-04 3.95e-03 2s
INFO:gurobipy.gurobipy: 36 3.93225609e+06 3.93194632e+06 7.94e-12 1.31e-04 3.95e-03 2s
37 3.93199750e+06 3.93195974e+06 1.11e-10 1.54e-03 4.81e-04 2s
INFO:gurobipy.gurobipy: 37 3.93199750e+06 3.93195974e+06 1.11e-10 1.54e-03 4.81e-04 2s
INFO:gurobipy.gurobipy:
Barrier performed 37 iterations in 2.52 seconds (1.15 work units)
INFO:gurobipy.gurobipy:Barrier performed 37 iterations in 2.52 seconds (1.15 work units)
Barrier solve interrupted - model solved by another algorithm
INFO:gurobipy.gurobipy:Barrier solve interrupted - model solved by another algorithm
INFO:gurobipy.gurobipy:
INFO:gurobipy.gurobipy:
Solved with dual simplex
INFO:gurobipy.gurobipy:Solved with dual simplex
Solved in 16396 iterations and 2.54 seconds (1.33 work units)
INFO:gurobipy.gurobipy:Solved in 16396 iterations and 2.54 seconds (1.33 work units)
Optimal objective 3.931964722e+06
INFO:gurobipy.gurobipy:Optimal objective 3.931964722e+06
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
[LP phase] iteration 0, added 0 flow constraint(s)
INFO:egret:[LP phase] iteration 0, added 0 flow constraint(s)
[LP cleanup phase] removed 0 inactive flow constraint(s)
INFO:egret:[LP cleanup phase] removed 0 inactive flow constraint(s)
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 79110 rows, 80702 columns and 323767 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 79110 rows, 80702 columns and 323767 nonzeros
Model fingerprint: 0x7ca7e824
INFO:gurobipy.gurobipy:Model fingerprint: 0x7ca7e824
Variable types: 55197 continuous, 25505 integer (25505 binary)
INFO:gurobipy.gurobipy:Variable types: 55197 continuous, 25505 integer (25505 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [7e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [7e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [1e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 1e+01]
RHS range [7e-03, 6e+01]
INFO:gurobipy.gurobipy: RHS range [7e-03, 6e+01]
INFO:gurobipy.gurobipy:
User MIP start produced solution with objective 1.68796e+09 (0.37s)
INFO:gurobipy.gurobipy:User MIP start produced solution with objective 1.68796e+09 (0.37s)
Loaded user MIP start with objective 1.68796e+09
INFO:gurobipy.gurobipy:Loaded user MIP start with objective 1.68796e+09
INFO:gurobipy.gurobipy:
Presolve removed 28652 rows and 28289 columns
INFO:gurobipy.gurobipy:Presolve removed 28652 rows and 28289 columns
Presolve time: 1.20s
INFO:gurobipy.gurobipy:Presolve time: 1.20s
Presolved: 50458 rows, 52413 columns, 231147 nonzeros
INFO:gurobipy.gurobipy:Presolved: 50458 rows, 52413 columns, 231147 nonzeros
Variable types: 31741 continuous, 20672 integer (20672 binary)
INFO:gurobipy.gurobipy:Variable types: 31741 continuous, 20672 integer (20672 binary)
INFO:gurobipy.gurobipy:
Deterministic concurrent LP optimizer: primal and dual simplex
INFO:gurobipy.gurobipy:Deterministic concurrent LP optimizer: primal and dual simplex
Showing first log only...
INFO:gurobipy.gurobipy:Showing first log only...
INFO:gurobipy.gurobipy:
Concurrent spin time: 0.01s
INFO:gurobipy.gurobipy:Concurrent spin time: 0.01s
INFO:gurobipy.gurobipy:
Solved with dual simplex
INFO:gurobipy.gurobipy:Solved with dual simplex
INFO:gurobipy.gurobipy:
Root relaxation: objective 3.931965e+06, 15256 iterations, 1.29 seconds (0.59 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 3.931965e+06, 15256 iterations, 1.29 seconds (0.59 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
0 0 3931964.72 0 92 1.6880e+09 3931964.72 100% - 3s
INFO:gurobipy.gurobipy: 0 0 3931964.72 0 92 1.6880e+09 3931964.72 100% - 3s
H 0 0 3943422.3251 3931964.72 0.29% - 3s
INFO:gurobipy.gurobipy:H 0 0 3943422.3251 3931964.72 0.29% - 3s
INFO:gurobipy.gurobipy:
Explored 1 nodes (16977 simplex iterations) in 3.66 seconds (2.04 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (16977 simplex iterations) in 3.66 seconds (2.04 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 3.94342e+06 1.68796e+09
INFO:gurobipy.gurobipy:Solution count 2: 3.94342e+06 1.68796e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 3.943422325074e+06, best bound 3.931964722018e+06, gap 0.2905%
INFO:gurobipy.gurobipy:Best objective 3.943422325074e+06, best bound 3.931964722018e+06, gap 0.2905%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Pyomo model solve time: 22.15949058532715
Deterministic RUC Cost: 3943422.33
Generator Commitments:
101_CT_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
101_CT_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
101_STEAM_3 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
101_STEAM_4 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
102_CT_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
102_CT_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
102_STEAM_3 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
102_STEAM_4 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
107_CC_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
113_CT_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
113_CT_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
113_CT_3 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
113_CT_4 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
115_STEAM_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
115_STEAM_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
115_STEAM_3 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
116_STEAM_1 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
118_CC_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
123_STEAM_2 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
123_STEAM_3 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
123_CT_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
123_CT_4 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
123_CT_5 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
201_CT_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
201_CT_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
201_STEAM_3 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
202_CT_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
202_CT_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
202_STEAM_3 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
202_STEAM_4 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
207_CT_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
207_CT_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
213_CC_3 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
213_CT_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
213_CT_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
215_CT_4 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
215_CT_5 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
216_STEAM_1 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
218_CC_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
221_CC_1 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
223_STEAM_1 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
223_STEAM_2 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
223_STEAM_3 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
223_CT_4 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
223_CT_5 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
223_CT_6 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
301_CT_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
301_CT_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
301_CT_3 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
301_CT_4 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
302_CT_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
302_CT_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
302_CT_3 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
302_CT_4 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
307_CT_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
307_CT_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
313_CC_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
315_STEAM_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
315_STEAM_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
315_STEAM_3 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
315_STEAM_4 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
315_STEAM_5 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
315_CT_6 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
315_CT_7 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
315_CT_8 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
316_STEAM_1 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
318_CC_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
321_CC_1 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
322_CT_5 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
322_CT_6 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
323_CC_1 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
323_CC_2 : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
114_SYNC_COND_1: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
121_NUCLEAR_1 : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1
214_SYNC_COND_1: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
314_SYNC_COND_1: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 0 0 0 0 0 0 0 0 0 0 0 0
Generator Dispatch Levels:
101_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
101_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
101_STEAM_3 : 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 | 76.00 76.00 76.00 76.00 76.00 76.00 60.70 60.70 45.70 60.70 76.00 76.00
101_STEAM_4 : 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 | 76.00 76.00 76.00 76.00 76.00 76.00 63.30 47.30 45.30 75.21 76.00 76.00
102_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
102_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
102_STEAM_3 : 370.00 310.00 291.00 351.00 291.00 286.00 298.70 358.70 418.70 460.00 460.00 460.00 460.00 460.00 460.00 460.00 460.00 460.00 450.00 450.00 460.00 460.00 460.00 460.00 | 460.00 460.00 460.00 460.00 411.00 351.00 291.00 286.00 286.00 291.00 351.00 411.00
102_STEAM_4 : 45.30 40.00 45.30 45.30 45.30 45.30 45.30 45.30 45.30 60.70 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 | 76.00 60.70 45.30 45.30 45.30 45.30 40.00 40.00 40.00 40.00 45.30 45.30
107_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 170.00 231.70 231.70 253.61 337.59 355.00 355.00 355.00 293.30 231.70 170.00 | 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00
113_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
113_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
113_CT_3 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
113_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
115_STEAM_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
115_STEAM_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
115_STEAM_3 : 62.00 62.00 62.00 62.00 62.00 62.00 62.00 62.00 62.00 93.00 124.00 124.00 124.00 124.00 124.00 124.00 124.00 155.00 155.00 155.00 155.00 155.00 124.00 124.00 | 124.00 93.00 93.00 93.00 62.00 62.00 62.00 62.00 62.00 62.00 62.00 62.00
116_STEAM_1 : 93.00 62.00 62.00 62.00 62.00 62.00 62.00 62.00 93.00 93.00 124.00 124.00 133.70 155.00 124.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 124.00 | 124.00 124.00 93.00 93.00 93.00 62.00 62.00 62.00 62.00 62.00 62.00 93.00
118_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 170.00 231.70 293.30 313.60 327.71 297.99 231.70 231.70 231.70 | 188.06 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00
123_STEAM_2 : 93.00 62.00 62.00 86.19 62.00 62.00 62.00 62.00 93.00 93.00 93.00 124.00 124.00 124.00 124.00 124.00 124.00 124.00 125.00 125.00 124.00 124.00 124.00 124.00 | 93.00 93.00 93.00 93.00 93.00 62.00 62.00 62.00 62.00 62.00 62.00 93.00
123_STEAM_3 : 141.66 140.00 140.00 140.00 140.00 140.00 140.00 140.00 140.00 210.00 280.00 292.49 350.00 350.00 350.00 350.00 350.00 350.00 350.00 350.00 350.00 350.00 350.00 280.00 | 280.00 227.66 210.00 210.00 185.94 140.00 140.00 140.00 140.00 140.00 140.00 166.49
123_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
123_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
123_CT_5 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
201_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
201_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
201_STEAM_3 : 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 45.30 45.30 60.70 60.70 60.70 76.00 76.00 76.00 76.00 76.00 76.00 76.00 76.00 45.30 | 45.30 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00
202_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
202_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
202_STEAM_3 : 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 45.30 60.70 60.70 60.70 60.70 60.70 60.70 60.70 66.78 69.28 76.00 60.70 60.70 60.70 60.70 | 60.70 60.70 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00
202_STEAM_4 : 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 45.30 60.70 60.70 60.70 60.70 60.70 60.70 60.70 60.70 60.70 76.00 72.78 60.70 60.70 60.70 | 60.70 60.70 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00 30.00
207_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
207_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
213_CC_3 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
213_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
213_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
215_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
215_CT_5 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
216_STEAM_1 : 93.00 77.00 90.70 93.00 93.00 91.70 77.00 93.00 93.00 93.00 124.00 124.00 155.00 155.00 129.14 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 124.00 | 124.00 93.00 93.00 93.00 93.00 93.00 77.00 77.00 73.30 65.30 93.00 93.00
218_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
221_CC_1 : 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 231.70 231.70 231.70 231.70 293.30 293.30 293.30 293.30 293.30 293.30 293.30 231.70 184.55 | 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00
223_STEAM_1 : 124.00 62.00 62.00 93.00 88.64 68.00 65.00 62.00 98.63 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 | 155.00 155.00 137.00 140.00 124.00 62.00 62.00 62.00 62.00 62.00 81.24 124.00
223_STEAM_2 : 124.00 62.00 62.00 93.00 77.00 65.00 63.00 76.03 124.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 155.00 | 155.00 155.00 142.59 132.99 124.00 62.00 62.00 62.00 62.00 62.00 93.00 124.00
223_STEAM_3 : 210.00 157.00 157.00 210.00 210.00 160.00 160.00 210.00 210.00 210.00 210.00 280.00 280.00 331.32 280.00 350.00 350.00 350.00 350.00 350.00 350.00 350.00 350.00 280.00 | 210.00 210.00 210.00 210.00 210.00 159.73 143.30 141.30 140.00 140.00 210.00 210.00
223_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
223_CT_5 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
223_CT_6 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
301_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
301_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
301_CT_3 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
301_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
302_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
302_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
302_CT_3 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
302_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
307_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
307_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
313_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 170.00 231.70 293.30 293.30 298.86 294.67 293.30 293.30 231.70 231.70 | 231.70 231.70 231.70 231.70 231.70 231.70 231.70 231.70 231.70 231.70 231.70 231.70
315_STEAM_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_STEAM_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_STEAM_3 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_STEAM_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_STEAM_5 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_CT_6 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_CT_7 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_CT_8 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
316_STEAM_1 : 62.00 62.00 62.00 62.00 62.00 62.00 62.00 62.00 62.00 109.01 124.00 124.00 124.00 124.00 124.00 124.00 124.00 155.00 155.00 155.00 155.00 142.61 124.00 124.00 | 124.00 124.00 62.00 62.00 62.00 62.00 62.00 62.00 62.00 62.00 62.00 62.00
318_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
321_CC_1 : 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 184.64 231.70 231.70 231.70 231.70 293.30 293.30 293.30 293.30 293.30 293.30 293.30 247.52 231.70 | 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00
322_CT_5 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
322_CT_6 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
323_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
323_CC_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 170.00 176.90 231.70 293.30 351.19 355.00 351.06 231.70 170.00 170.00 | 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00 170.00
114_SYNC_COND_1: 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
121_NUCLEAR_1 : 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 | 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00 400.00
214_SYNC_COND_1: 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
314_SYNC_COND_1: 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Generator Reserve Headroom:
101_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
101_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
101_STEAM_3 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 15.30 15.30 30.30 15.30 0.00 0.00
101_STEAM_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 12.70 28.70 30.70 0.79 0.00 0.00
102_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
102_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
102_STEAM_3 : 36.00 120.00 79.00 0.00 120.00 65.00 47.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.00 10.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 49.00 109.00 120.00 65.00 60.00 55.00 0.00 0.00
102_STEAM_4 : 30.70 36.00 30.70 30.70 30.70 30.70 30.70 30.70 30.70 15.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 15.30 30.70 30.70 30.70 30.70 36.00 36.00 36.00 36.00 30.70 30.70
107_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 123.30 123.30 101.39 17.41 0.00 0.00 0.00 61.70 123.30 185.00 | 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00
113_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
113_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
113_CT_3 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
113_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
115_STEAM_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
115_STEAM_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
115_STEAM_3 : 93.00 93.00 93.00 93.00 93.00 93.00 93.00 93.00 93.00 62.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 0.00 0.00 0.00 0.00 0.00 31.00 31.00 | 31.00 62.00 62.00 62.00 93.00 93.00 93.00 93.00 93.00 93.00 93.00 93.00
116_STEAM_1 : 62.00 93.00 93.00 93.00 93.00 93.00 93.00 93.00 62.00 62.00 31.00 31.00 21.30 0.00 31.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 31.00 | 31.00 31.00 62.00 62.00 62.00 93.00 93.00 93.00 93.00 93.00 93.00 62.00
118_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 123.30 61.70 41.40 27.29 57.01 123.30 123.30 123.30 | 166.94 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00
123_STEAM_2 : 62.00 93.00 93.00 68.81 93.00 93.00 93.00 93.00 62.00 62.00 62.00 31.00 31.00 31.00 31.00 31.00 31.00 31.00 30.00 30.00 31.00 31.00 31.00 31.00 | 62.00 62.00 62.00 62.00 62.00 93.00 93.00 93.00 93.00 93.00 93.00 62.00
123_STEAM_3 : 208.34 210.00 210.00 210.00 210.00 210.00 210.00 210.00 210.00 140.00 70.00 57.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 70.00 | 70.00 122.34 140.00 140.00 164.06 210.00 210.00 210.00 210.00 210.00 210.00 183.51
123_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
123_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
123_CT_5 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
201_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
201_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
201_STEAM_3 : 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 30.70 30.70 15.30 15.30 15.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 30.70 | 30.70 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00
202_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
202_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
202_STEAM_3 : 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 30.70 15.30 15.30 15.30 15.30 15.30 15.30 15.30 9.22 6.72 0.00 15.30 15.30 15.30 15.30 | 15.30 15.30 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00
202_STEAM_4 : 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 30.70 15.30 15.30 15.30 15.30 15.30 15.30 15.30 15.30 15.30 0.00 3.22 15.30 15.30 15.30 | 15.30 15.30 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00 46.00
207_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
207_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
213_CC_3 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
213_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
213_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
215_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
215_CT_5 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
216_STEAM_1 : 62.00 78.00 64.30 62.00 62.00 63.30 78.00 62.00 62.00 62.00 31.00 31.00 0.00 0.00 25.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 31.00 | 31.00 62.00 62.00 62.00 62.00 62.00 78.00 78.00 81.70 89.70 62.00 62.00
218_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
221_CC_1 : 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 123.30 123.30 123.30 123.30 61.70 61.70 61.70 61.70 61.70 61.70 61.70 123.30 170.45 | 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00
223_STEAM_1 : 31.00 93.00 93.00 62.00 66.36 87.00 90.00 93.00 56.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 18.00 15.00 31.00 93.00 93.00 93.00 93.00 93.00 73.76 31.00
223_STEAM_2 : 31.00 93.00 93.00 62.00 78.00 90.00 92.00 78.97 31.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 12.41 22.01 31.00 93.00 93.00 93.00 93.00 93.00 62.00 31.00
223_STEAM_3 : 140.00 193.00 193.00 140.00 140.00 190.00 190.00 140.00 140.00 140.00 140.00 70.00 70.00 18.68 70.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 70.00 | 140.00 140.00 140.00 140.00 140.00 190.27 206.70 208.70 210.00 210.00 140.00 140.00
223_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
223_CT_5 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
223_CT_6 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
301_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
301_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
301_CT_3 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
301_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
302_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
302_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
302_CT_3 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
302_CT_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
307_CT_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
307_CT_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
313_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 123.30 61.70 61.70 56.14 60.33 61.70 61.70 123.30 123.30 | 123.30 123.30 123.30 123.30 123.30 123.30 123.30 123.30 123.30 123.30 123.30 123.30
315_STEAM_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_STEAM_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_STEAM_3 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_STEAM_4 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_STEAM_5 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_CT_6 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_CT_7 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
315_CT_8 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
316_STEAM_1 : 93.00 93.00 93.00 93.00 93.00 93.00 93.00 93.00 93.00 45.99 31.00 31.00 31.00 31.00 31.00 31.00 31.00 0.00 0.00 0.00 0.00 12.39 31.00 31.00 | 31.00 31.00 93.00 93.00 93.00 93.00 93.00 93.00 93.00 93.00 93.00 93.00
318_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
321_CC_1 : 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 170.36 123.30 123.30 123.30 123.30 61.70 61.70 61.70 61.70 61.70 61.70 61.70 107.48 123.30 | 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00
322_CT_5 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
322_CT_6 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
323_CC_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
323_CC_2 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 178.10 123.30 61.70 3.81 0.00 3.94 123.30 185.00 185.00 | 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00 185.00
114_SYNC_COND_1: 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
121_NUCLEAR_1 : 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
214_SYNC_COND_1: 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
314_SYNC_COND_1: 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 | 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Total : 1357.04 1703.00 1643.00 1422.51 1587.06 1616.00 1618.00 1494.67 1348.07 1066.69 812.66 590.41 476.80 404.18 635.66 671.70 656.69 381.43 286.76 251.03 295.56 567.39 909.28 1266.65 | 1487.54 1650.54 1868.41 1875.01 2004.06 2346.27 2423.00 2386.00 2403.00 2361.09 2136.76 1974.51
Fixed costs: 2747961.28
Variable costs: 1195461.05
Renewables curtailment summary (time-period, aggregate_quantity):
2020-07-10 01:00 50.74
2020-07-10 02:00 223.61
2020-07-10 05:00 148.57
2020-07-10 06:00 91.02
2020-07-11 06:00 81.52
2020-07-11 07:00 151.47
2020-07-11 08:00 102.07
Solving day-ahead market
Computing day-ahead prices using method LMP.
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 79110 rows, 80702 columns and 259128 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 79110 rows, 80702 columns and 259128 nonzeros
Model fingerprint: 0x867b9dc6
INFO:gurobipy.gurobipy:Model fingerprint: 0x867b9dc6
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [7e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [7e-03, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [1e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 1e+01]
RHS range [2e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [2e-03, 4e+01]
INFO:gurobipy.gurobipy:
Concurrent LP optimizer: dual simplex and barrier
INFO:gurobipy.gurobipy:Concurrent LP optimizer: dual simplex and barrier
Showing barrier log only...
INFO:gurobipy.gurobipy:Showing barrier log only...
INFO:gurobipy.gurobipy:
Presolve removed 75605 rows and 72199 columns
INFO:gurobipy.gurobipy:Presolve removed 75605 rows and 72199 columns
Presolve time: 0.16s
INFO:gurobipy.gurobipy:Presolve time: 0.16s
Presolved: 3505 rows, 8550 columns, 23903 nonzeros
INFO:gurobipy.gurobipy:Presolved: 3505 rows, 8550 columns, 23903 nonzeros
INFO:gurobipy.gurobipy:
Ordering time: 0.00s
INFO:gurobipy.gurobipy:Ordering time: 0.00s
INFO:gurobipy.gurobipy:
Barrier statistics:
INFO:gurobipy.gurobipy:Barrier statistics:
AA' NZ : 1.261e+04
INFO:gurobipy.gurobipy: AA' NZ : 1.261e+04
Factor NZ : 3.828e+04 (roughly 4 MB of memory)
INFO:gurobipy.gurobipy: Factor NZ : 3.828e+04 (roughly 4 MB of memory)
Factor Ops : 5.607e+05 (less than 1 second per iteration)
INFO:gurobipy.gurobipy: Factor Ops : 5.607e+05 (less than 1 second per iteration)
Threads : 1
INFO:gurobipy.gurobipy: Threads : 1
INFO:gurobipy.gurobipy:
Objective Residual
INFO:gurobipy.gurobipy: Objective Residual
Iter Primal Dual Primal Dual Compl Time
INFO:gurobipy.gurobipy:Iter Primal Dual Primal Dual Compl Time
0 4.96641303e+09 -2.52582290e+10 2.76e+01 1.86e-09 2.73e+07 0s
INFO:gurobipy.gurobipy: 0 4.96641303e+09 -2.52582290e+10 2.76e+01 1.86e-09 2.73e+07 0s
1 1.65217457e+09 -2.55274238e+10 4.66e+00 9.31e-09 5.41e+06 0s
INFO:gurobipy.gurobipy: 1 1.65217457e+09 -2.55274238e+10 4.66e+00 9.31e-09 5.41e+06 0s
2 3.04627054e+08 -1.30932975e+10 3.61e-01 1.04e+04 9.96e+05 0s
INFO:gurobipy.gurobipy: 2 3.04627054e+08 -1.30932975e+10 3.61e-01 1.04e+04 9.96e+05 0s
3 1.61702848e+08 -1.34204364e+09 1.94e-02 5.40e-08 9.17e+04 0s
INFO:gurobipy.gurobipy: 3 1.61702848e+08 -1.34204364e+09 1.94e-02 5.40e-08 9.17e+04 0s
4 5.83447672e+07 -6.72078823e+08 6.79e-03 2.79e-08 4.40e+04 0s
INFO:gurobipy.gurobipy: 4 5.83447672e+07 -6.72078823e+08 6.79e-03 2.79e-08 4.40e+04 0s
5 2.99502785e+07 -2.25316858e+08 3.68e-03 1.12e-08 1.53e+04 0s
INFO:gurobipy.gurobipy: 5 2.99502785e+07 -2.25316858e+08 3.68e-03 1.12e-08 1.53e+04 0s
6 1.83864064e+07 -1.08661786e+08 2.08e-03 6.52e-09 7.60e+03 0s
INFO:gurobipy.gurobipy: 6 1.83864064e+07 -1.08661786e+08 2.08e-03 6.52e-09 7.60e+03 0s
7 1.15949116e+07 -1.94001495e+07 1.05e-03 2.25e-09 1.85e+03 0s
INFO:gurobipy.gurobipy: 7 1.15949116e+07 -1.94001495e+07 1.05e-03 2.25e-09 1.85e+03 0s
8 8.55491049e+06 -5.97512641e+06 6.58e-04 1.59e-09 8.68e+02 0s
INFO:gurobipy.gurobipy: 8 8.55491049e+06 -5.97512641e+06 6.58e-04 1.59e-09 8.68e+02 0s
9 6.66157164e+06 -1.22945939e+06 3.89e-04 1.41e-09 4.71e+02 0s
INFO:gurobipy.gurobipy: 9 6.66157164e+06 -1.22945939e+06 3.89e-04 1.41e-09 4.71e+02 0s
10 4.69093957e+06 1.27484730e+06 8.41e-05 1.34e-09 2.04e+02 0s
INFO:gurobipy.gurobipy: 10 4.69093957e+06 1.27484730e+06 8.41e-05 1.34e-09 2.04e+02 0s
11 4.39114570e+06 3.15511560e+06 4.69e-05 1.95e-09 7.38e+01 0s
INFO:gurobipy.gurobipy: 11 4.39114570e+06 3.15511560e+06 4.69e-05 1.95e-09 7.38e+01 0s
12 4.20685459e+06 3.59927690e+06 2.60e-05 1.96e-09 3.63e+01 0s
INFO:gurobipy.gurobipy: 12 4.20685459e+06 3.59927690e+06 2.60e-05 1.96e-09 3.63e+01 0s
13 4.10677134e+06 3.78476581e+06 1.54e-05 1.85e-09 1.92e+01 0s
INFO:gurobipy.gurobipy: 13 4.10677134e+06 3.78476581e+06 1.54e-05 1.85e-09 1.92e+01 0s
14 4.06355917e+06 3.84615831e+06 1.10e-05 1.27e-09 1.30e+01 0s
INFO:gurobipy.gurobipy: 14 4.06355917e+06 3.84615831e+06 1.10e-05 1.27e-09 1.30e+01 0s
15 4.02263883e+06 3.89430494e+06 7.16e-06 2.43e-09 7.66e+00 0s
INFO:gurobipy.gurobipy: 15 4.02263883e+06 3.89430494e+06 7.16e-06 2.43e-09 7.66e+00 0s
INFO:gurobipy.gurobipy:
Barrier performed 15 iterations in 0.34 seconds (0.11 work units)
INFO:gurobipy.gurobipy:Barrier performed 15 iterations in 0.34 seconds (0.11 work units)
Barrier solve interrupted - model solved by another algorithm
INFO:gurobipy.gurobipy:Barrier solve interrupted - model solved by another algorithm
INFO:gurobipy.gurobipy:
INFO:gurobipy.gurobipy:
Solved with dual simplex
INFO:gurobipy.gurobipy:Solved with dual simplex
Solved in 2458 iterations and 0.35 seconds (0.10 work units)
INFO:gurobipy.gurobipy:Solved in 2458 iterations and 0.35 seconds (0.10 work units)
Optimal objective 3.943422325e+06
INFO:gurobipy.gurobipy:Optimal objective 3.943422325e+06
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Simulating time_step 2020-07-10 00:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.6093105e+08 2.83e+08 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.6092703e+08 2.82e+08 2.80e+02 -1.0 7.82e+06 - 7.63e-01 2.70e-03f 1
2 2.5682035e+08 2.03e+08 8.71e+03 -1.0 3.26e+07 - 5.00e-01 4.71e-01f 1
3 2.4970025e+08 1.44e+08 7.16e+03 -1.0 4.38e+07 - 3.99e-01 3.77e-01f 1
4 2.4948780e+08 1.43e+08 7.10e+03 -1.0 5.39e+07 - 4.39e-01 8.35e-03f 1
5 2.4748062e+08 1.34e+08 6.64e+03 -1.0 9.15e+07 - 4.78e-01 6.63e-02f 1
6 2.3528670e+08 1.04e+08 5.08e+03 -1.0 1.61e+08 - 3.82e-01 2.55e-01f 1
7 2.3514456e+08 1.04e+08 5.07e+03 -1.0 2.21e+08 - 5.54e-01 2.16e-03f 1
8 2.2912568e+08 9.69e+07 4.71e+03 -1.0 3.71e+08 - 9.05e-03 7.09e-02f 1
9 2.2417366e+08 9.20e+07 4.47e+03 -1.0 3.58e+08 - 1.70e-04 5.18e-02f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 2.2409874e+08 9.19e+07 4.47e+03 -1.0 3.59e+08 - 3.87e-02 7.45e-04f 1
11 2.2309546e+08 9.08e+07 4.42e+03 -1.0 3.66e+08 - 6.66e-02 1.21e-02f 1
12 2.1126516e+08 7.90e+07 3.82e+03 -1.0 3.77e+08 - 7.24e-04 1.38e-01f 1
13 2.1109294e+08 7.89e+07 3.81e+03 -1.0 3.63e+08 - 2.01e-03 1.63e-03f 1
14 2.1106477e+08 7.89e+07 3.81e+03 -1.0 3.64e+08 - 3.50e-01 3.38e-04f 1
15 2.0906950e+08 7.72e+07 3.73e+03 -1.0 4.40e+08 - 3.21e-03 2.09e-02f 1
16 2.0893514e+08 7.71e+07 3.72e+03 -1.0 4.38e+08 - 8.08e-02 1.99e-03f 1
17 2.0754384e+08 7.54e+07 3.64e+03 -1.0 4.51e+08 - 7.91e-04 2.22e-02f 1
18 2.0738771e+08 7.52e+07 3.63e+03 -1.0 4.43e+08 - 2.40e-04 2.41e-03f 1
19 2.0721004e+08 7.50e+07 3.62e+03 -1.0 4.43e+08 - 9.79e-02 2.83e-03f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 2.0594277e+08 7.34e+07 3.54e+03 -1.0 4.57e+08 - 6.95e-03 2.18e-02f 1
21 2.0574860e+08 7.30e+07 3.52e+03 -1.0 4.50e+08 - 7.72e-01 5.44e-03f 1
22 2.0246636e+08 6.80e+07 3.28e+03 -1.0 5.86e+08 - 1.03e-01 6.98e-02f 1
23 2.0240298e+08 6.76e+07 3.25e+03 -1.0 8.25e+07 - 3.99e-01 6.55e-03f 1
24 1.9980292e+08 4.66e+07 3.41e+03 -1.0 3.57e+07 - 7.87e-01 3.65e-01f 1
25 1.9116639e+08 4.62e+07 9.18e+03 -1.0 5.21e+07 - 3.31e-01 7.43e-01f 1
26 1.9110081e+08 4.51e+07 8.97e+03 -1.0 1.67e+07 - 9.04e-01 2.34e-02f 1
27 1.9109439e+08 4.50e+07 8.94e+03 -1.0 1.91e+07 - 5.12e-02 3.08e-03f 1
28 1.9024282e+08 3.13e+07 5.73e+03 -1.0 1.84e+07 - 7.76e-04 3.73e-01f 1
29 1.8918008e+08 1.71e+07 2.59e+03 -1.0 1.39e+07 - 9.26e-01 5.75e-01f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
30 1.8854064e+08 6.33e+06 4.12e+02 -1.0 5.83e+06 - 8.13e-01 8.66e-01f 1
31 1.8845937e+08 4.41e+05 1.05e+01 -1.0 1.30e+06 - 9.90e-01 9.82e-01f 1
32 1.8845882e+08 2.48e+04 1.34e+01 -1.0 1.87e+06 - 9.96e-01 9.44e-01h 1
33 1.8845878e+08 1.23e+00 2.08e+00 -1.0 3.85e+05 - 5.51e-01 1.00e+00f 1
34 1.8845873e+08 1.06e+00 1.93e+00 -1.0 1.02e+04 - 2.42e-01 1.38e-01f 1
35 1.8845869e+08 8.70e-01 5.65e-01 -1.0 8.81e+03 - 4.31e-01 1.00e+00f 1
36 1.8845850e+08 1.76e+01 4.54e-01 -1.0 1.44e+04 - 2.85e-01 1.00e+00f 1
37 1.8845741e+08 5.52e+02 1.01e-01 -1.0 8.09e+04 - 2.10e-01 1.00e+00f 1
38 1.8845683e+08 1.56e+02 3.23e-04 -1.0 4.26e+04 - 1.00e+00 1.00e+00f 1
39 1.8845664e+08 3.87e-02 2.96e-05 -2.5 6.70e+02 - 1.00e+00 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
40 1.8845663e+08 8.81e-05 3.17e-08 -3.8 3.22e+01 - 1.00e+00 1.00e+00f 1
41 1.8845663e+08 1.49e-07 1.16e-10 -5.7 1.11e+00 - 1.00e+00 1.00e+00f 1
42 1.8845663e+08 1.43e-07 1.16e-10 -8.6 1.36e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 42
(scaled) (unscaled)
Objective...............: 1.8845662950620535e+06 1.8845662950620535e+08
Dual infeasibility......: 1.1641532182693481e-10 1.1641532182693481e-08
Constraint violation....: 2.5611370801925659e-09 1.4342367649078369e-07
Complementarity.........: 2.5404901777587944e-09 2.5404901777587944e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5404901777587944e-07
Number of objective function evaluations = 43
Number of objective gradient evaluations = 43
Number of equality constraint evaluations = 43
Number of inequality constraint evaluations = 43
Number of equality constraint Jacobian evaluations = 43
Number of inequality constraint Jacobian evaluations = 43
Number of Lagrangian Hessian evaluations = 42
Total CPU secs in IPOPT (w/o function evaluations) = 0.660
Total CPU secs in NLP function evaluations = 18.084
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4482 rows, 5667 columns and 13286 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4482 rows, 5667 columns and 13286 nonzeros
Model fingerprint: 0xfeab7267
INFO:gurobipy.gurobipy:Model fingerprint: 0xfeab7267
Variable types: 3971 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3971 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 9e+00]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 9e+00]
RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 2e+01]
Found heuristic solution: objective 5.546193e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 5.546193e+09
Presolve removed 4297 rows and 5405 columns
INFO:gurobipy.gurobipy:Presolve removed 4297 rows and 5405 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 185 rows, 262 columns, 731 nonzeros
INFO:gurobipy.gurobipy:Presolved: 185 rows, 262 columns, 731 nonzeros
Found heuristic solution: objective 5.364254e+08
INFO:gurobipy.gurobipy:Found heuristic solution: objective 5.364254e+08
Variable types: 262 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 262 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 1.720155e+05, 67 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 1.720155e+05, 67 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 172015.48482 172015.485 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 172015.48482 172015.485 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (67 simplex iterations) in 0.06 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (67 simplex iterations) in 0.06 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 172015 5.54619e+09
INFO:gurobipy.gurobipy:Solution count 2: 172015 5.54619e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 1.720154848203e+05, best bound 1.720154848203e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 1.720154848203e+05, best bound 1.720154848203e+05, gap 0.0000%
[MIP phase] iteration 0, found 1 violation(s)
INFO:egret:[MIP phase] iteration 0, found 1 violation(s)
[MIP phase] iteration 0, added 1 flow constraint(s)
INFO:egret:[MIP phase] iteration 0, added 1 flow constraint(s)
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4483 rows, 5668 columns and 13359 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4483 rows, 5668 columns and 13359 nonzeros
Model fingerprint: 0xc518d6c2
INFO:gurobipy.gurobipy:Model fingerprint: 0xc518d6c2
Variable types: 3972 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3972 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy:
User MIP start did not produce a new incumbent solution
INFO:gurobipy.gurobipy:User MIP start did not produce a new incumbent solution
User MIP start violates constraint TransmissionBlock(3)_ineq_pf_branch_thermal_bounds(A27) by 0.054373808
INFO:gurobipy.gurobipy:User MIP start violates constraint TransmissionBlock(3)_ineq_pf_branch_thermal_bounds(A27) by 0.054373808
MIP start from previous solve produced solution with objective 172015 (0.03s)
INFO:gurobipy.gurobipy:MIP start from previous solve produced solution with objective 172015 (0.03s)
Loaded MIP start from previous solve with objective 172015
INFO:gurobipy.gurobipy:Loaded MIP start from previous solve with objective 172015
INFO:gurobipy.gurobipy:
Presolve removed 4282 rows and 5301 columns
INFO:gurobipy.gurobipy:Presolve removed 4282 rows and 5301 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 201 rows, 367 columns, 982 nonzeros
INFO:gurobipy.gurobipy:Presolved: 201 rows, 367 columns, 982 nonzeros
Variable types: 367 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 367 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: cutoff, 0 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: cutoff, 0 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Explored 1 nodes (0 simplex iterations) in 0.06 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (0 simplex iterations) in 0.06 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 1: 172015
INFO:gurobipy.gurobipy:Solution count 1: 172015
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 1.720154848203e+05, best bound 1.720154848203e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 1.720154848203e+05, best bound 1.720154848203e+05, gap 0.0000%
[MIP phase] iteration 1, found 1 violation(s)
INFO:egret:[MIP phase] iteration 1, found 1 violation(s)
[MIP phase] iteration 1, added 1 flow constraint(s)
INFO:egret:[MIP phase] iteration 1, added 1 flow constraint(s)
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4484 rows, 5669 columns and 13432 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4484 rows, 5669 columns and 13432 nonzeros
Model fingerprint: 0x7127bc2b
INFO:gurobipy.gurobipy:Model fingerprint: 0x7127bc2b
Variable types: 3973 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3973 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy:
User MIP start did not produce a new incumbent solution
INFO:gurobipy.gurobipy:User MIP start did not produce a new incumbent solution
User MIP start violates constraint TransmissionBlock(2)_ineq_pf_branch_thermal_bounds(A27) by 0.190195264
INFO:gurobipy.gurobipy:User MIP start violates constraint TransmissionBlock(2)_ineq_pf_branch_thermal_bounds(A27) by 0.190195264
MIP start from previous solve produced solution with objective 172015 (0.03s)
INFO:gurobipy.gurobipy:MIP start from previous solve produced solution with objective 172015 (0.03s)
Loaded MIP start from previous solve with objective 172015
INFO:gurobipy.gurobipy:Loaded MIP start from previous solve with objective 172015
INFO:gurobipy.gurobipy:
Presolve removed 4267 rows and 5197 columns
INFO:gurobipy.gurobipy:Presolve removed 4267 rows and 5197 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 217 rows, 472 columns, 1233 nonzeros
INFO:gurobipy.gurobipy:Presolved: 217 rows, 472 columns, 1233 nonzeros
Variable types: 472 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 472 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: cutoff, 0 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: cutoff, 0 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Explored 1 nodes (0 simplex iterations) in 0.06 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (0 simplex iterations) in 0.06 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 1: 172015
INFO:gurobipy.gurobipy:Solution count 1: 172015
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 1.720154848203e+05, best bound 1.720154848203e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 1.720154848203e+05, best bound 1.720154848203e+05, gap 0.0000%
[MIP phase] iteration 2, found 0 violation(s)
INFO:egret:[MIP phase] iteration 2, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4484 rows, 5669 columns and 13432 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4484 rows, 5669 columns and 13432 nonzeros
Model fingerprint: 0xfcc54e04
INFO:gurobipy.gurobipy:Model fingerprint: 0xfcc54e04
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 2e+01]
Presolve removed 4233 rows and 5137 columns
INFO:gurobipy.gurobipy:Presolve removed 4233 rows and 5137 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 251 rows, 535 columns, 1503 nonzeros
INFO:gurobipy.gurobipy:Presolved: 251 rows, 535 columns, 1503 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 1.6409421e+05 1.774951e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 1.6409421e+05 1.774951e+01 0.000000e+00 0s
88 1.7201548e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 88 1.7201548e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Use crossover to convert LP symmetric solution to basic solution...
INFO:gurobipy.gurobipy:Use crossover to convert LP symmetric solution to basic solution...
Crossover log...
INFO:gurobipy.gurobipy:Crossover log...
INFO:gurobipy.gurobipy:
54 PPushes remaining with PInf 0.0000000e+00 0s
INFO:gurobipy.gurobipy: 54 PPushes remaining with PInf 0.0000000e+00 0s
0 PPushes remaining with PInf 0.0000000e+00 0s
INFO:gurobipy.gurobipy: 0 PPushes remaining with PInf 0.0000000e+00 0s
INFO:gurobipy.gurobipy:
Push phase complete: Pinf 0.0000000e+00, Dinf 0.0000000e+00 0s
INFO:gurobipy.gurobipy: Push phase complete: Pinf 0.0000000e+00, Dinf 0.0000000e+00 0s
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
145 1.7201548e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 145 1.7201548e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 145 iterations and 0.06 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 145 iterations and 0.06 seconds (0.01 work units)
Optimal objective 1.720154848e+05
INFO:gurobipy.gurobipy:Optimal objective 1.720154848e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 323
Realized SOC (MPC) 76000
Implemented Power (MPC) 323
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 7921.27
Number on/offs: 57
Sum on/off ramps: 0.00
Sum nominal ramps: 489.29
Simulating time_step 2020-07-10 01:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8845669e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8735384e+08 9.84e+04 6.40e+01 -1.0 3.19e+06 - 9.60e-01 1.56e-02f 1
2 1.8665483e+08 9.45e+04 5.01e+01 -1.0 6.98e+07 - 2.81e-02 4.67e-02f 1
3 1.8656276e+08 9.40e+04 5.40e+01 -1.0 7.05e+07 - 4.70e-02 5.27e-03f 1
4 1.8628239e+08 9.23e+04 9.43e+01 -1.0 7.29e+07 - 7.27e-02 2.00e-02f 1
5 1.8608976e+08 9.03e+04 4.97e+02 -1.0 7.65e+07 - 3.56e-01 2.32e-02f 1
6 1.8490596e+08 9.48e+04 9.00e+02 -1.0 1.08e+08 - 1.46e-01 1.59e-01f 1
7 1.8254935e+08 2.33e+05 2.72e+03 -1.0 1.28e+08 - 1.47e-01 2.59e-01f 1
8 1.8252456e+08 2.32e+05 2.72e+03 -1.0 1.27e+08 - 6.98e-01 2.59e-03f 1
9 1.8255217e+08 2.22e+05 2.60e+03 -1.0 2.53e+06 - 9.85e-01 4.31e-02h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8311347e+08 1.67e+04 7.95e+02 -1.0 2.36e+06 - 9.85e-01 9.25e-01h 1
11 1.8314885e+08 3.81e+03 1.84e+02 -1.0 1.64e+05 - 5.89e-01 7.72e-01h 1
12 1.8315877e+08 5.73e+01 1.70e+00 -1.0 3.44e+04 - 5.82e-01 9.98e-01h 1
13 1.8315872e+08 4.34e-01 1.71e-02 -1.0 2.22e+03 - 9.90e-01 1.00e+00h 1
14 1.8315872e+08 4.77e-07 1.36e-04 -1.0 2.17e+00 - 9.92e-01 1.00e+00h 1
15 1.8315856e+08 2.65e-02 2.02e-05 -1.7 5.54e+02 - 1.00e+00 1.00e+00f 1
16 1.8315852e+08 1.85e-03 1.27e-06 -3.8 1.47e+02 - 1.00e+00 1.00e+00f 1
17 1.8315852e+08 2.09e-07 1.16e-10 -5.7 1.60e+00 - 1.00e+00 1.00e+00f 1
18 1.8315852e+08 1.96e-07 8.99e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 18
(scaled) (unscaled)
Objective...............: 1.8315851846997172e+06 1.8315851846997172e+08
Dual infeasibility......: 8.9868308122945562e-11 8.9868308122945562e-09
Constraint violation....: 2.5611370801925659e-09 1.9557774066925049e-07
Complementarity.........: 2.5404417952120364e-09 2.5404417952120365e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5404417952120365e-07
Number of objective function evaluations = 19
Number of objective gradient evaluations = 19
Number of equality constraint evaluations = 19
Number of inequality constraint evaluations = 19
Number of equality constraint Jacobian evaluations = 19
Number of inequality constraint Jacobian evaluations = 19
Number of Lagrangian Hessian evaluations = 18
Total CPU secs in IPOPT (w/o function evaluations) = 0.740
Total CPU secs in NLP function evaluations = 7.595
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4485 rows, 5670 columns and 13564 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4485 rows, 5670 columns and 13564 nonzeros
Model fingerprint: 0xb5076c3c
INFO:gurobipy.gurobipy:Model fingerprint: 0xb5076c3c
Variable types: 3974 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3974 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 2e+01]
Found heuristic solution: objective 5.576058e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 5.576058e+09
Presolve removed 4253 rows and 5011 columns
INFO:gurobipy.gurobipy:Presolve removed 4253 rows and 5011 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 232 rows, 659 columns, 1658 nonzeros
INFO:gurobipy.gurobipy:Presolved: 232 rows, 659 columns, 1658 nonzeros
Found heuristic solution: objective 2.416107e+08
INFO:gurobipy.gurobipy:Found heuristic solution: objective 2.416107e+08
Variable types: 659 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 659 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 1.652566e+05, 141 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 1.652566e+05, 141 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 165256.57240 165256.572 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 165256.57240 165256.572 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (141 simplex iterations) in 0.06 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (141 simplex iterations) in 0.06 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 165257 5.57606e+09
INFO:gurobipy.gurobipy:Solution count 2: 165257 5.57606e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 1.652565724033e+05, best bound 1.652565724033e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 1.652565724033e+05, best bound 1.652565724033e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4485 rows, 5670 columns and 13564 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4485 rows, 5670 columns and 13564 nonzeros
Model fingerprint: 0x4d567bfb
INFO:gurobipy.gurobipy:Model fingerprint: 0x4d567bfb
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 2e+01]
Presolve removed 4233 rows and 4970 columns
INFO:gurobipy.gurobipy:Presolve removed 4233 rows and 4970 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 252 rows, 702 columns, 1905 nonzeros
INFO:gurobipy.gurobipy:Presolved: 252 rows, 702 columns, 1905 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 1.6409421e+05 3.820761e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 1.6409421e+05 3.820761e+01 0.000000e+00 0s
148 1.6525657e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 148 1.6525657e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 148 iterations and 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 148 iterations and 0.04 seconds (0.01 work units)
Optimal objective 1.652565724e+05
INFO:gurobipy.gurobipy:Optimal objective 1.652565724e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 1162.36
Renewables curtailment reported at t=1 - total= 51.00
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 358.29
Simulating time_step 2020-07-10 02:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8315796e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8243123e+08 9.84e+04 6.39e+01 -1.0 2.84e+06 - 9.61e-01 1.56e-02f 1
2 1.8187723e+08 9.46e+04 5.06e+01 -1.0 5.91e+07 - 2.81e-02 4.46e-02f 1
3 1.8184513e+08 9.44e+04 5.97e+01 -1.0 6.13e+07 - 4.55e-02 2.79e-03f 1
4 1.8166811e+08 9.25e+04 1.01e+02 -1.0 5.99e+07 - 5.93e-02 2.02e-02f 1
5 1.8152846e+08 9.04e+04 1.72e+02 -1.0 4.81e+07 - 8.17e-02 2.19e-02f 1
6 1.8156535e+08 8.76e+04 1.10e+03 -1.0 3.74e+06 - 8.57e-01 3.15e-02h 1
7 1.8272001e+08 1.37e+04 1.84e+03 -1.0 3.97e+06 - 2.90e-01 8.49e-01h 1
8 1.8286743e+08 4.32e+03 6.04e+02 -1.0 5.94e+05 - 9.89e-01 6.85e-01h 1
9 1.8291745e+08 3.43e+02 3.41e+01 -1.0 1.53e+05 - 5.87e-01 9.49e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8291884e+08 5.79e+01 4.38e+00 -1.0 1.81e+04 - 3.78e-01 8.72e-01h 1
11 1.8291827e+08 3.80e+01 4.92e-01 -1.0 2.10e+04 - 6.96e-01 1.00e+00h 1
12 1.8291816e+08 5.90e-01 4.91e-03 -1.0 2.60e+03 - 9.90e-01 1.00e+00f 1
13 1.8291816e+08 1.65e-04 4.89e-05 -1.0 4.35e+01 - 9.90e-01 1.00e+00h 1
14 1.8291816e+08 1.56e-07 1.00e-06 -1.0 3.22e-01 - 1.00e+00 1.00e+00h 1
15 1.8291797e+08 3.90e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
16 1.8291796e+08 8.55e-05 3.49e-08 -3.8 3.15e+01 - 1.00e+00 1.00e+00f 1
17 1.8291796e+08 1.29e-07 1.04e-10 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
18 1.8291796e+08 2.09e-07 7.61e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 18
(scaled) (unscaled)
Objective...............: 1.8291796300242606e+06 1.8291796300242606e+08
Dual infeasibility......: 7.6114217444551063e-11 7.6114217444551063e-09
Constraint violation....: 2.5611370801925659e-09 2.0861625671386719e-07
Complementarity.........: 2.5405319617721238e-09 2.5405319617721240e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5405319617721240e-07
Number of objective function evaluations = 19
Number of objective gradient evaluations = 19
Number of equality constraint evaluations = 19
Number of inequality constraint evaluations = 19
Number of equality constraint Jacobian evaluations = 19
Number of inequality constraint Jacobian evaluations = 19
Number of Lagrangian Hessian evaluations = 18
Total CPU secs in IPOPT (w/o function evaluations) = 0.781
Total CPU secs in NLP function evaluations = 7.642
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4492 rows, 5669 columns and 13574 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4492 rows, 5669 columns and 13574 nonzeros
Model fingerprint: 0xe7654ef7
INFO:gurobipy.gurobipy:Model fingerprint: 0xe7654ef7
Variable types: 3973 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3973 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 2e+01]
Found heuristic solution: objective 5.996525e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 5.996525e+09
Presolve removed 4261 rows and 5013 columns
INFO:gurobipy.gurobipy:Presolve removed 4261 rows and 5013 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 231 rows, 656 columns, 1652 nonzeros
INFO:gurobipy.gurobipy:Presolved: 231 rows, 656 columns, 1652 nonzeros
Found heuristic solution: objective 3.120252e+08
INFO:gurobipy.gurobipy:Found heuristic solution: objective 3.120252e+08
Variable types: 656 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 656 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 1.665272e+05, 133 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 1.665272e+05, 133 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 166527.19260 166527.193 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 166527.19260 166527.193 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (133 simplex iterations) in 0.06 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (133 simplex iterations) in 0.06 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 166527 5.99653e+09
INFO:gurobipy.gurobipy:Solution count 2: 166527 5.99653e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 1.665271926044e+05, best bound 1.665271926044e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 1.665271926044e+05, best bound 1.665271926044e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4492 rows, 5669 columns and 13574 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4492 rows, 5669 columns and 13574 nonzeros
Model fingerprint: 0x2ff30d9a
INFO:gurobipy.gurobipy:Model fingerprint: 0x2ff30d9a
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 2e+01]
Presolve removed 4241 rows and 4973 columns
INFO:gurobipy.gurobipy:Presolve removed 4241 rows and 4973 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 251 rows, 698 columns, 1898 nonzeros
INFO:gurobipy.gurobipy:Presolved: 251 rows, 698 columns, 1898 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 1.6409421e+05 3.596518e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 1.6409421e+05 3.596518e+01 0.000000e+00 0s
128 1.6652719e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 128 1.6652719e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 128 iterations and 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 128 iterations and 0.04 seconds (0.01 work units)
Optimal objective 1.665271926e+05
INFO:gurobipy.gurobipy:Optimal objective 1.665271926e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 2432.98
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 74.38
Simulating time_step 2020-07-10 03:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8291813e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8249402e+08 9.88e+04 6.42e+01 -1.0 2.91e+06 - 9.62e-01 1.17e-02f 1
2 1.8184136e+08 9.51e+04 5.72e+01 -1.0 6.15e+07 - 3.87e-02 4.33e-02f 1
3 1.8181452e+08 9.48e+04 7.50e+01 -1.0 6.05e+07 - 4.94e-02 3.13e-03f 1
4 1.8172489e+08 9.30e+04 5.62e+01 -1.0 5.43e+07 - 5.16e-03 1.98e-02f 1
5 1.8164400e+08 9.18e+04 5.55e+01 -1.0 5.45e+07 - 3.93e-04 1.28e-02f 1
6 1.8161558e+08 9.12e+04 3.03e+02 -1.0 6.22e+07 - 2.25e-01 6.63e-03f 1
7 1.8159541e+08 8.80e+04 2.99e+02 -1.0 2.11e+07 - 4.17e-02 3.44e-02f 1
8 1.8167138e+08 8.32e+04 1.04e+03 -1.0 3.84e+06 - 8.52e-01 5.47e-02h 1
9 1.8269689e+08 1.95e+04 1.36e+03 -1.0 3.77e+06 - 2.36e-01 7.71e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8276326e+08 1.54e+04 1.07e+03 -1.0 8.60e+05 - 5.85e-01 2.11e-01h 1
11 1.8296371e+08 3.05e+03 2.58e+02 -1.0 6.68e+05 - 7.57e-01 8.03e-01h 1
12 1.8300370e+08 4.18e+02 3.74e+01 -1.0 1.04e+05 - 7.94e-01 8.61e-01h 1
13 1.8300392e+08 8.68e+01 1.52e+00 -1.0 3.58e+04 - 5.93e-01 9.97e-01h 1
14 1.8300230e+08 5.10e+01 5.67e-01 -1.0 2.43e+04 - 6.29e-01 1.00e+00f 1
15 1.8300205e+08 1.86e+00 5.64e-03 -1.0 4.62e+03 - 9.90e-01 1.00e+00f 1
16 1.8300204e+08 7.97e-03 5.55e-05 -1.0 3.03e+02 - 9.90e-01 1.00e+00h 1
17 1.8300204e+08 1.34e-07 1.32e-06 -1.0 4.75e-01 - 9.94e-01 1.00e+00h 1
18 1.8300188e+08 2.65e-02 2.02e-05 -1.7 5.54e+02 - 1.00e+00 1.00e+00f 1
19 1.8300184e+08 1.85e-03 1.27e-06 -3.8 1.47e+02 - 1.00e+00 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.8300184e+08 2.24e-07 1.16e-10 -5.7 1.60e+00 - 1.00e+00 1.00e+00f 1
21 1.8300184e+08 1.49e-07 8.48e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 21
(scaled) (unscaled)
Objective...............: 1.8300183902461270e+06 1.8300183902461269e+08
Dual infeasibility......: 8.4754674576375562e-11 8.4754674576375562e-09
Constraint violation....: 2.5611370801925659e-09 1.4901161193847656e-07
Complementarity.........: 2.5405324278449422e-09 2.5405324278449421e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5405324278449421e-07
Number of objective function evaluations = 22
Number of objective gradient evaluations = 22
Number of equality constraint evaluations = 22
Number of inequality constraint evaluations = 22
Number of equality constraint Jacobian evaluations = 22
Number of inequality constraint Jacobian evaluations = 22
Number of Lagrangian Hessian evaluations = 21
Total CPU secs in IPOPT (w/o function evaluations) = 0.830
Total CPU secs in NLP function evaluations = 9.004
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4499 rows, 5668 columns and 13572 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4499 rows, 5668 columns and 13572 nonzeros
Model fingerprint: 0x5a805a93
INFO:gurobipy.gurobipy:Model fingerprint: 0x5a805a93
Variable types: 3972 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3972 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 2e+01]
Found heuristic solution: objective 5.540050e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 5.540050e+09
Presolve removed 4268 rows and 5014 columns
INFO:gurobipy.gurobipy:Presolve removed 4268 rows and 5014 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 231 rows, 654 columns, 1648 nonzeros
INFO:gurobipy.gurobipy:Presolved: 231 rows, 654 columns, 1648 nonzeros
Found heuristic solution: objective 1.029315e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 1.029315e+09
Variable types: 654 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 654 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 1.816824e+05, 111 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 1.816824e+05, 111 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 181682.44047 181682.440 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 181682.44047 181682.440 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (111 simplex iterations) in 0.06 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (111 simplex iterations) in 0.06 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 181682 5.54005e+09
INFO:gurobipy.gurobipy:Solution count 2: 181682 5.54005e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 1.816824404718e+05, best bound 1.816824404718e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 1.816824404718e+05, best bound 1.816824404718e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4499 rows, 5668 columns and 13572 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4499 rows, 5668 columns and 13572 nonzeros
Model fingerprint: 0x169150fc
INFO:gurobipy.gurobipy:Model fingerprint: 0x169150fc
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 2e+01]
Presolve removed 4248 rows and 4974 columns
INFO:gurobipy.gurobipy:Presolve removed 4248 rows and 4974 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 251 rows, 696 columns, 1894 nonzeros
INFO:gurobipy.gurobipy:Presolved: 251 rows, 696 columns, 1894 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 1.6409421e+05 3.886030e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 1.6409421e+05 3.886030e+01 0.000000e+00 0s
128 1.8168244e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 128 1.8168244e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 128 iterations and 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 128 iterations and 0.04 seconds (0.01 work units)
Optimal objective 1.816824405e+05
INFO:gurobipy.gurobipy:Optimal objective 1.816824405e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 17588.23
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 739.98
Simulating time_step 2020-07-10 04:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8300205e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8257028e+08 9.88e+04 8.21e+01 -1.0 2.94e+06 - 9.59e-01 1.18e-02f 1
2 1.8177365e+08 9.49e+04 8.72e+01 -1.0 5.85e+07 - 9.65e-02 4.54e-02f 1
3 1.8173345e+08 9.45e+04 1.16e+02 -1.0 6.00e+07 - 9.48e-02 4.85e-03f 1
4 1.8162438e+08 9.16e+04 8.97e+01 -1.0 4.47e+07 - 1.23e-02 3.03e-02f 1
5 1.8164702e+08 9.02e+04 1.22e+02 -1.0 4.79e+06 - 6.51e-05 1.57e-02h 1
6 1.8169255e+08 8.69e+04 3.94e+02 -1.0 4.09e+06 - 2.62e-01 3.68e-02h 1
7 1.8209335e+08 6.17e+04 1.81e+02 -1.0 3.94e+06 - 5.69e-02 2.91e-01h 1
8 1.8272154e+08 2.24e+04 5.86e+02 -1.0 2.80e+06 - 9.10e-01 6.39e-01h 1
9 1.8272895e+08 2.19e+04 5.74e+02 -1.0 9.98e+05 - 9.88e-01 2.05e-02h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8303373e+08 2.74e+03 1.76e+02 -1.0 9.13e+05 - 4.36e-01 8.78e-01h 1
11 1.8307293e+08 4.12e+02 2.79e+01 -1.0 1.06e+05 - 5.24e-01 8.49e-01h 1
12 1.8307925e+08 5.14e+01 2.28e+00 -1.0 2.48e+04 - 7.24e-01 1.00e+00h 1
13 1.8307915e+08 1.07e+00 2.28e-02 -1.0 3.50e+03 - 9.90e-01 1.00e+00f 1
14 1.8307915e+08 2.79e-04 2.19e-04 -1.0 5.65e+01 - 9.90e-01 1.00e+00h 1
15 1.8307915e+08 1.56e-07 1.00e-06 -1.0 2.54e-01 - 1.00e+00 1.00e+00h 1
16 1.8307896e+08 3.91e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
17 1.8307895e+08 8.55e-05 3.49e-08 -3.8 3.15e+01 - 1.00e+00 1.00e+00f 1
18 1.8307895e+08 1.49e-07 1.23e-10 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
19 1.8307895e+08 1.19e-07 8.48e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 19
(scaled) (unscaled)
Objective...............: 1.8307895287729383e+06 1.8307895287729383e+08
Dual infeasibility......: 8.4754674590351606e-11 8.4754674590351606e-09
Constraint violation....: 2.5611370801925659e-09 1.1920928955078125e-07
Complementarity.........: 2.5405092628903092e-09 2.5405092628903093e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5405092628903093e-07
Number of objective function evaluations = 20
Number of objective gradient evaluations = 20
Number of equality constraint evaluations = 20
Number of inequality constraint evaluations = 20
Number of equality constraint Jacobian evaluations = 20
Number of inequality constraint Jacobian evaluations = 20
Number of Lagrangian Hessian evaluations = 19
Total CPU secs in IPOPT (w/o function evaluations) = 0.742
Total CPU secs in NLP function evaluations = 8.739
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4506 rows, 5668 columns and 13573 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4506 rows, 5668 columns and 13573 nonzeros
Model fingerprint: 0xbe1b2282
INFO:gurobipy.gurobipy:Model fingerprint: 0xbe1b2282
Variable types: 3972 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3972 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [2e-02, 3e+01]
INFO:gurobipy.gurobipy: RHS range [2e-02, 3e+01]
Found heuristic solution: objective 6.841153e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 6.841153e+09
Presolve removed 4275 rows and 5014 columns
INFO:gurobipy.gurobipy:Presolve removed 4275 rows and 5014 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 231 rows, 654 columns, 1648 nonzeros
INFO:gurobipy.gurobipy:Presolved: 231 rows, 654 columns, 1648 nonzeros
Found heuristic solution: objective 3.177582e+08
INFO:gurobipy.gurobipy:Found heuristic solution: objective 3.177582e+08
Variable types: 654 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 654 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 1.665073e+05, 137 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 1.665073e+05, 137 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 166507.31754 166507.318 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 166507.31754 166507.318 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (137 simplex iterations) in 0.06 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (137 simplex iterations) in 0.06 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 166507 6.84115e+09
INFO:gurobipy.gurobipy:Solution count 2: 166507 6.84115e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 1.665073175411e+05, best bound 1.665073175411e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 1.665073175411e+05, best bound 1.665073175411e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4506 rows, 5668 columns and 13573 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4506 rows, 5668 columns and 13573 nonzeros
Model fingerprint: 0x8086d1db
INFO:gurobipy.gurobipy:Model fingerprint: 0x8086d1db
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [2e-02, 3e+01]
INFO:gurobipy.gurobipy: RHS range [2e-02, 3e+01]
Presolve removed 4255 rows and 4974 columns
INFO:gurobipy.gurobipy:Presolve removed 4255 rows and 4974 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 251 rows, 696 columns, 1894 nonzeros
INFO:gurobipy.gurobipy:Presolved: 251 rows, 696 columns, 1894 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 1.6409421e+05 4.670622e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 1.6409421e+05 4.670622e+01 0.000000e+00 0s
166 1.6650732e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 166 1.6650732e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 166 iterations and 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 166 iterations and 0.04 seconds (0.01 work units)
Optimal objective 1.665073175e+05
INFO:gurobipy.gurobipy:Optimal objective 1.665073175e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 2413.10
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 741.06
Simulating time_step 2020-07-10 05:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8307911e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8298673e+08 9.90e+04 9.46e+01 -1.0 2.94e+06 - 9.60e-01 1.04e-02f 1
2 1.8222032e+08 9.51e+04 9.08e+01 -1.0 6.04e+07 - 9.33e-02 4.57e-02f 1
3 1.8215311e+08 9.44e+04 9.96e+01 -1.0 5.84e+07 - 1.21e-01 7.13e-03f 1
4 1.8212166e+08 9.19e+04 1.23e+02 -1.0 2.94e+07 - 8.37e-03 2.76e-02f 1
5 1.8216217e+08 8.98e+04 1.03e+02 -1.0 2.71e+06 - 5.17e-04 2.24e-02h 1
6 1.8221803e+08 8.74e+04 3.73e+02 -1.0 4.05e+06 - 2.50e-01 2.67e-02h 1
7 1.8282307e+08 6.28e+04 1.72e+02 -1.0 3.96e+06 - 5.51e-02 2.82e-01h 1
8 1.8340762e+08 3.91e+04 3.17e+02 -1.0 2.85e+06 - 7.06e-01 3.78e-01h 1
9 1.8387531e+08 2.02e+04 2.68e+02 -1.0 1.77e+06 - 8.65e-01 4.86e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8388249e+08 1.99e+04 3.27e+02 -1.0 8.89e+05 - 9.88e-01 1.46e-02h 1
11 1.8430882e+08 2.06e+03 1.19e+02 -1.0 8.21e+05 - 4.91e-01 8.99e-01h 1
12 1.8435194e+08 2.07e+02 1.30e+01 -1.0 7.99e+04 - 4.65e-01 8.99e-01h 1
13 1.8435624e+08 1.94e+01 7.66e-02 -1.0 1.52e+04 - 9.44e-01 1.00e+00h 1
14 1.8435621e+08 5.06e-02 7.59e-04 -1.0 7.57e+02 - 9.90e-01 1.00e+00f 1
15 1.8435621e+08 6.85e-07 5.76e-06 -1.0 2.81e+00 - 9.92e-01 1.00e+00h 1
16 1.8435605e+08 2.65e-02 2.02e-05 -1.7 5.54e+02 - 1.00e+00 1.00e+00f 1
17 1.8435601e+08 1.80e-03 1.27e-06 -3.8 1.45e+02 - 1.00e+00 1.00e+00f 1
18 1.8435601e+08 2.24e-07 1.02e-10 -5.7 1.45e+00 - 1.00e+00 1.00e+00f 1
19 1.8435601e+08 1.04e-07 7.16e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 19
(scaled) (unscaled)
Objective...............: 1.8435601313124662e+06 1.8435601313124663e+08
Dual infeasibility......: 7.1605774128846695e-11 7.1605774128846695e-09
Constraint violation....: 2.5611370801925659e-09 1.0430812835693359e-07
Complementarity.........: 2.5405453350331773e-09 2.5405453350331773e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5405453350331773e-07
Number of objective function evaluations = 20
Number of objective gradient evaluations = 20
Number of equality constraint evaluations = 20
Number of inequality constraint evaluations = 20
Number of equality constraint Jacobian evaluations = 20
Number of inequality constraint Jacobian evaluations = 20
Number of Lagrangian Hessian evaluations = 19
Total CPU secs in IPOPT (w/o function evaluations) = 0.772
Total CPU secs in NLP function evaluations = 8.350
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4506 rows, 5668 columns and 13579 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4506 rows, 5668 columns and 13579 nonzeros
Model fingerprint: 0xf3698578
INFO:gurobipy.gurobipy:Model fingerprint: 0xf3698578
Variable types: 3972 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3972 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [2e-04, 3e+01]
INFO:gurobipy.gurobipy: RHS range [2e-04, 3e+01]
Found heuristic solution: objective 7.044010e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 7.044010e+09
Presolve removed 4275 rows and 4998 columns
INFO:gurobipy.gurobipy:Presolve removed 4275 rows and 4998 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 231 rows, 670 columns, 1676 nonzeros
INFO:gurobipy.gurobipy:Presolved: 231 rows, 670 columns, 1676 nonzeros
Found heuristic solution: objective 2.769247e+08
INFO:gurobipy.gurobipy:Found heuristic solution: objective 2.769247e+08
Variable types: 670 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 670 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 8.216610e+07, 145 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 8.216610e+07, 145 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 8.216610e+07 8.2166e+07 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 8.216610e+07 8.2166e+07 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (145 simplex iterations) in 0.05 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (145 simplex iterations) in 0.05 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 8.21661e+07 7.04401e+09
INFO:gurobipy.gurobipy:Solution count 2: 8.21661e+07 7.04401e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 8.216610015352e+07, best bound 8.216610015352e+07, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 8.216610015352e+07, best bound 8.216610015352e+07, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4506 rows, 5668 columns and 13579 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4506 rows, 5668 columns and 13579 nonzeros
Model fingerprint: 0x44bc3a5d
INFO:gurobipy.gurobipy:Model fingerprint: 0x44bc3a5d
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [2e-04, 3e+01]
INFO:gurobipy.gurobipy: RHS range [2e-04, 3e+01]
Presolve removed 4255 rows and 4958 columns
INFO:gurobipy.gurobipy:Presolve removed 4255 rows and 4958 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 251 rows, 712 columns, 1922 nonzeros
INFO:gurobipy.gurobipy:Presolved: 251 rows, 712 columns, 1922 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 1.6409421e+05 4.668735e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 1.6409421e+05 4.668735e+01 0.000000e+00 0s
151 9.8535923e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 151 9.8535923e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 151 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 151 iterations and 0.03 seconds (0.01 work units)
Optimal objective 9.853592304e+05
INFO:gurobipy.gurobipy:Optimal objective 9.853592304e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 1257.53
Renewables curtailment reported at t=1 - total= 84.90
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 76.30
Simulating time_step 2020-07-10 06:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8434631e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8423846e+08 9.90e+04 9.61e+01 -1.0 2.94e+06 - 9.60e-01 1.02e-02f 1
2 1.8346365e+08 9.51e+04 9.20e+01 -1.0 6.06e+07 - 9.38e-02 4.54e-02f 1
3 1.8343208e+08 9.47e+04 1.72e+02 -1.0 5.86e+07 - 1.44e-01 4.84e-03f 1
4 1.8342241e+08 9.16e+04 1.49e+02 -1.0 2.97e+07 - 2.64e-03 3.27e-02f 1
5 1.8347434e+08 8.97e+04 1.23e+02 -1.0 2.70e+06 - 1.90e-03 2.16e-02h 1
6 1.8355767e+08 8.67e+04 7.70e+02 -1.0 4.05e+06 - 5.75e-01 3.28e-02h 1
7 1.8508794e+08 3.48e+04 8.93e+02 -1.0 3.93e+06 - 5.80e-02 6.02e-01h 1
8 1.8593506e+08 5.77e+03 4.18e+02 -1.0 1.56e+06 - 8.59e-01 8.36e-01h 1
9 1.8609764e+08 1.38e+01 9.12e+00 -1.0 2.43e+05 - 9.67e-01 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8608251e+08 3.02e+02 4.39e-01 -1.0 7.73e+04 - 7.63e-01 1.00e+00f 1
11 1.8608056e+08 4.76e+01 3.31e-01 -1.0 2.09e+04 - 2.73e-01 9.35e-01f 1
12 1.8607976e+08 1.89e+01 3.65e-03 -1.0 1.48e+04 - 9.90e-01 1.00e+00f 1
13 1.8607970e+08 1.09e-01 6.43e-05 -1.0 1.12e+03 - 9.90e-01 1.00e+00f 1
14 1.8607970e+08 6.26e-07 1.27e-06 -1.0 2.63e+00 - 9.94e-01 1.00e+00h 1
15 1.8607954e+08 2.65e-02 2.02e-05 -1.7 5.54e+02 - 1.00e+00 1.00e+00f 1
16 1.8607950e+08 1.85e-03 1.27e-06 -3.8 1.47e+02 - 1.00e+00 1.00e+00f 1
17 1.8607950e+08 2.38e-07 1.29e-10 -5.7 1.60e+00 - 1.00e+00 1.00e+00f 1
18 1.8607950e+08 1.49e-07 5.82e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 18
(scaled) (unscaled)
Objective...............: 1.8607950192003665e+06 1.8607950192003664e+08
Dual infeasibility......: 5.8207660913467407e-11 5.8207660913467407e-09
Constraint violation....: 2.5611370801925659e-09 1.4901161193847656e-07
Complementarity.........: 2.5409797403663985e-09 2.5409797403663983e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5409797403663983e-07
Number of objective function evaluations = 19
Number of objective gradient evaluations = 19
Number of equality constraint evaluations = 19
Number of inequality constraint evaluations = 19
Number of equality constraint Jacobian evaluations = 19
Number of inequality constraint Jacobian evaluations = 19
Number of Lagrangian Hessian evaluations = 18
Total CPU secs in IPOPT (w/o function evaluations) = 0.743
Total CPU secs in NLP function evaluations = 7.792
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4516 rows, 5708 columns and 13705 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4516 rows, 5708 columns and 13705 nonzeros
Model fingerprint: 0x70cb0e35
INFO:gurobipy.gurobipy:Model fingerprint: 0x70cb0e35
Variable types: 4012 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 4012 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [1e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 1e+01]
RHS range [1e-02, 3e+01]
INFO:gurobipy.gurobipy: RHS range [1e-02, 3e+01]
Found heuristic solution: objective 6.917757e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 6.917757e+09
Presolve removed 4284 rows and 4962 columns
INFO:gurobipy.gurobipy:Presolve removed 4284 rows and 4962 columns
Presolve time: 0.03s
INFO:gurobipy.gurobipy:Presolve time: 0.03s
Presolved: 232 rows, 746 columns, 1828 nonzeros
INFO:gurobipy.gurobipy:Presolved: 232 rows, 746 columns, 1828 nonzeros
Found heuristic solution: objective 2.265756e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 2.265756e+09
Variable types: 746 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 746 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 1.654089e+05, 146 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 1.654089e+05, 146 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 165408.85060 165408.851 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 165408.85060 165408.851 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (146 simplex iterations) in 0.08 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (146 simplex iterations) in 0.08 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 165409 6.91776e+09
INFO:gurobipy.gurobipy:Solution count 2: 165409 6.91776e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 1.654088506043e+05, best bound 1.654088506043e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 1.654088506043e+05, best bound 1.654088506043e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4516 rows, 5708 columns and 13705 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4516 rows, 5708 columns and 13705 nonzeros
Model fingerprint: 0x76898fba
INFO:gurobipy.gurobipy:Model fingerprint: 0x76898fba
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [1e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 1e+01]
RHS range [1e-02, 3e+01]
INFO:gurobipy.gurobipy: RHS range [1e-02, 3e+01]
Presolve removed 4264 rows and 4921 columns
INFO:gurobipy.gurobipy:Presolve removed 4264 rows and 4921 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 252 rows, 789 columns, 2075 nonzeros
INFO:gurobipy.gurobipy:Presolved: 252 rows, 789 columns, 2075 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 1.6409421e+05 5.126346e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 1.6409421e+05 5.126346e+01 0.000000e+00 0s
157 1.6540885e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 157 1.6540885e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 157 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 157 iterations and 0.03 seconds (0.01 work units)
Optimal objective 1.654088506e+05
INFO:gurobipy.gurobipy:Optimal objective 1.654088506e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 1314.64
Renewables curtailment reported at t=1 - total= 86.92
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 3.00
Simulating time_step 2020-07-10 07:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8606877e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8591680e+08 9.90e+04 9.65e+01 -1.0 2.77e+06 - 9.61e-01 1.02e-02f 1
2 1.8515789e+08 9.52e+04 9.24e+01 -1.0 6.22e+07 - 9.32e-02 4.42e-02f 1
3 1.8512491e+08 9.48e+04 1.71e+02 -1.0 5.99e+07 - 1.43e-01 4.89e-03f 1
4 1.8511493e+08 9.17e+04 7.56e+02 -1.0 2.96e+07 - 5.51e-01 3.35e-02f 1
5 1.8600498e+08 6.02e+04 6.12e+02 -1.0 4.12e+06 - 5.83e-01 3.45e-01h 1
6 1.8739771e+08 1.10e+04 8.53e+02 -1.0 2.72e+06 - 8.44e-01 8.20e-01h 1
7 1.8769726e+08 4.97e+01 3.51e+01 -1.0 4.68e+05 - 9.44e-01 1.00e+00h 1
8 1.8768459e+08 2.29e+02 1.37e+00 -1.0 6.82e+04 - 7.91e-01 9.62e-01f 1
9 1.8768292e+08 4.02e+01 4.97e-01 -1.0 1.91e+04 - 2.60e-01 9.14e-01f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8768203e+08 2.33e+01 5.60e-02 -1.0 1.64e+04 - 8.80e-01 1.00e+00f 1
11 1.8768197e+08 1.02e-01 5.91e-04 -1.0 1.08e+03 - 9.90e-01 1.00e+00f 1
12 1.8768197e+08 2.37e-06 5.82e-06 -1.0 5.24e+00 - 9.90e-01 1.00e+00h 1
13 1.8768197e+08 1.19e-07 1.00e-06 -1.0 1.43e-01 - 1.00e+00 1.00e+00h 1
14 1.8768178e+08 3.90e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
15 1.8768177e+08 8.54e-05 3.49e-08 -3.8 3.15e+01 - 1.00e+00 1.00e+00f 1
16 1.8768177e+08 1.25e-07 8.73e-11 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
17 1.8768177e+08 1.42e-07 7.53e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 17
(scaled) (unscaled)
Objective...............: 1.8768177445491008e+06 1.8768177445491007e+08
Dual infeasibility......: 7.5264354084187470e-11 7.5264354084187470e-09
Constraint violation....: 2.5611370801925659e-09 1.4156103134155273e-07
Complementarity.........: 2.5409797403663965e-09 2.5409797403663962e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5409797403663962e-07
Number of objective function evaluations = 18
Number of objective gradient evaluations = 18
Number of equality constraint evaluations = 18
Number of inequality constraint evaluations = 18
Number of equality constraint Jacobian evaluations = 18
Number of inequality constraint Jacobian evaluations = 18
Number of Lagrangian Hessian evaluations = 17
Total CPU secs in IPOPT (w/o function evaluations) = 0.839
Total CPU secs in NLP function evaluations = 8.140
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4512 rows, 5701 columns and 13406 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4512 rows, 5701 columns and 13406 nonzeros
Model fingerprint: 0xf6e4190d
INFO:gurobipy.gurobipy:Model fingerprint: 0xf6e4190d
Variable types: 4005 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 4005 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [6e-03, 9e+00]
INFO:gurobipy.gurobipy: Bounds range [6e-03, 9e+00]
RHS range [8e-03, 3e+01]
INFO:gurobipy.gurobipy: RHS range [8e-03, 3e+01]
Found heuristic solution: objective 6.796629e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 6.796629e+09
Presolve removed 4330 rows and 5439 columns
INFO:gurobipy.gurobipy:Presolve removed 4330 rows and 5439 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 182 rows, 262 columns, 723 nonzeros
INFO:gurobipy.gurobipy:Presolved: 182 rows, 262 columns, 723 nonzeros
Found heuristic solution: objective 2.746055e+08
INFO:gurobipy.gurobipy:Found heuristic solution: objective 2.746055e+08
Variable types: 262 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 262 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 1.654089e+05, 114 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 1.654089e+05, 114 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 165408.85060 165408.851 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 165408.85060 165408.851 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (114 simplex iterations) in 0.09 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (114 simplex iterations) in 0.09 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 165409 6.79663e+09
INFO:gurobipy.gurobipy:Solution count 2: 165409 6.79663e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 1.654088506043e+05, best bound 1.654088506043e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 1.654088506043e+05, best bound 1.654088506043e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4512 rows, 5701 columns and 13406 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4512 rows, 5701 columns and 13406 nonzeros
Model fingerprint: 0x2a69d077
INFO:gurobipy.gurobipy:Model fingerprint: 0x2a69d077
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [6e-03, 9e+00]
INFO:gurobipy.gurobipy: Bounds range [6e-03, 9e+00]
RHS range [8e-03, 3e+01]
INFO:gurobipy.gurobipy: RHS range [8e-03, 3e+01]
Presolve removed 4265 rows and 5339 columns
INFO:gurobipy.gurobipy:Presolve removed 4265 rows and 5339 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 247 rows, 364 columns, 1118 nonzeros
INFO:gurobipy.gurobipy:Presolved: 247 rows, 364 columns, 1118 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 1.6409421e+05 2.224127e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 1.6409421e+05 2.224127e+01 0.000000e+00 0s
151 1.6540885e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 151 1.6540885e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 151 iterations and 0.03 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Solved in 151 iterations and 0.03 seconds (0.00 work units)
Optimal objective 1.654088506e+05
INFO:gurobipy.gurobipy:Optimal objective 1.654088506e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 1314.64
Renewables curtailment reported at t=1 - total= 37.57
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 0.00
Simulating time_step 2020-07-10 08:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8767612e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8750224e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8668959e+08 9.52e+04 9.43e+01 -1.0 6.22e+07 - 4.00e-01 4.41e-02f 1
3 1.8663538e+08 9.39e+04 6.92e+02 -1.0 6.09e+07 - 5.50e-01 1.48e-02f 1
4 1.8703548e+08 7.44e+04 8.67e+02 -1.0 2.77e+06 - 7.57e-01 2.08e-01h 1
5 1.8841726e+08 1.33e+04 1.24e+03 -1.0 3.36e+06 - 9.03e-01 8.25e-01h 1
6 1.8869788e+08 9.14e+01 5.01e+01 -1.0 5.46e+05 - 8.00e-01 1.00e+00h 1
7 1.8869162e+08 7.16e+01 5.95e+00 -1.0 3.61e+04 - 8.10e-01 8.81e-01f 1
8 1.8868944e+08 9.17e+01 3.24e-01 -1.0 3.27e+04 - 5.39e-01 1.00e+00f 1
9 1.8868932e+08 4.65e-01 3.31e-03 -1.0 2.28e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8868932e+08 4.46e-04 3.51e-05 -1.0 7.17e+01 - 9.90e-01 1.00e+00h 1
11 1.8868932e+08 1.34e-07 1.00e-06 -1.0 3.76e-01 - 9.99e-01 1.00e+00h 1
12 1.8868912e+08 3.90e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8868912e+08 8.54e-05 3.49e-08 -3.8 3.15e+01 - 1.00e+00 1.00e+00f 1
14 1.8868912e+08 1.60e-07 9.91e-11 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8868912e+08 1.47e-07 7.53e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8868911678861408e+06 1.8868911678861406e+08
Dual infeasibility......: 7.5264354293828124e-11 7.5264354293828124e-09
Constraint violation....: 2.5611370801925659e-09 1.4714896678924561e-07
Complementarity.........: 2.5409797403663977e-09 2.5409797403663977e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5409797403663977e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.575
Total CPU secs in NLP function evaluations = 5.473
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4512 rows, 5698 columns and 13399 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4512 rows, 5698 columns and 13399 nonzeros
Model fingerprint: 0x4a585907
INFO:gurobipy.gurobipy:Model fingerprint: 0x4a585907
Variable types: 4002 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 4002 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [4e-03, 4e+01]
Found heuristic solution: objective 7.515216e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 7.515216e+09
Presolve removed 4330 rows and 5436 columns
INFO:gurobipy.gurobipy:Presolve removed 4330 rows and 5436 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 182 rows, 262 columns, 723 nonzeros
INFO:gurobipy.gurobipy:Presolved: 182 rows, 262 columns, 723 nonzeros
Found heuristic solution: objective 2.038554e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 2.038554e+09
Variable types: 262 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 262 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 1.665522e+05, 108 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 1.665522e+05, 108 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 166552.19696 166552.197 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 166552.19696 166552.197 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (108 simplex iterations) in 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (108 simplex iterations) in 0.04 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 166552 7.51522e+09
INFO:gurobipy.gurobipy:Solution count 2: 166552 7.51522e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 1.665521969583e+05, best bound 1.665521969583e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 1.665521969583e+05, best bound 1.665521969583e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4512 rows, 5698 columns and 13399 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4512 rows, 5698 columns and 13399 nonzeros
Model fingerprint: 0x035af0f3
INFO:gurobipy.gurobipy:Model fingerprint: 0x035af0f3
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [4e-03, 4e+01]
Presolve removed 4265 rows and 5336 columns
INFO:gurobipy.gurobipy:Presolve removed 4265 rows and 5336 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 247 rows, 364 columns, 1118 nonzeros
INFO:gurobipy.gurobipy:Presolved: 247 rows, 364 columns, 1118 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 1.6409421e+05 1.951245e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 1.6409421e+05 1.951245e+01 0.000000e+00 0s
155 1.6655220e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 155 1.6655220e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 155 iterations and 0.03 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Solved in 155 iterations and 0.03 seconds (0.00 work units)
Optimal objective 1.665521970e+05
INFO:gurobipy.gurobipy:Optimal objective 1.665521970e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 2457.98
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 67.42
Simulating time_step 2020-07-10 09:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8868349e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8849859e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8765884e+08 9.52e+04 9.45e+01 -1.0 6.22e+07 - 5.99e-01 4.41e-02f 1
3 1.8757201e+08 9.33e+04 7.99e+02 -1.0 6.15e+07 - 6.38e-01 2.14e-02f 1
4 1.8810138e+08 6.13e+04 7.48e+02 -1.0 4.06e+06 - 8.32e-01 3.44e-01h 1
5 1.8911323e+08 1.38e+03 1.22e+03 -1.0 2.75e+06 - 9.84e-01 1.00e+00h 1
6 1.8910002e+08 2.93e+02 2.09e+02 -1.0 8.68e+04 - 6.28e-01 8.29e-01f 1
7 1.8909812e+08 7.14e+01 4.23e+01 -1.0 1.66e+04 - 4.17e-01 7.98e-01f 1
8 1.8909681e+08 5.77e+01 6.01e-01 -1.0 2.59e+04 - 5.70e-01 1.00e+00f 1
9 1.8909669e+08 9.89e-01 6.11e-03 -1.0 3.36e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8909669e+08 1.06e-04 6.17e-05 -1.0 3.47e+01 - 9.90e-01 1.00e+00h 1
11 1.8909669e+08 1.19e-07 1.00e-06 -1.0 2.20e-01 - 1.00e+00 1.00e+00h 1
12 1.8909650e+08 3.90e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8909649e+08 8.53e-05 3.49e-08 -3.8 3.14e+01 - 1.00e+00 1.00e+00f 1
14 1.8909649e+08 1.79e-07 1.10e-10 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8909649e+08 1.64e-07 8.99e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8909649416889565e+06 1.8909649416889563e+08
Dual infeasibility......: 8.9868308130145342e-11 8.9868308130145342e-09
Constraint violation....: 2.5611370801925659e-09 1.6391277313232422e-07
Complementarity.........: 2.5409797403663936e-09 2.5409797403663935e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5409797403663935e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.570
Total CPU secs in NLP function evaluations = 5.376
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4519 rows, 5696 columns and 13401 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4519 rows, 5696 columns and 13401 nonzeros
Model fingerprint: 0xe84b7827
INFO:gurobipy.gurobipy:Model fingerprint: 0xe84b7827
Variable types: 4000 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 4000 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-02, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [2e-02, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [2e-02, 1e+01]
RHS range [2e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [2e-03, 4e+01]
Found heuristic solution: objective 7.577607e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 7.577607e+09
Presolve removed 4337 rows and 5435 columns
INFO:gurobipy.gurobipy:Presolve removed 4337 rows and 5435 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 182 rows, 261 columns, 722 nonzeros
INFO:gurobipy.gurobipy:Presolved: 182 rows, 261 columns, 722 nonzeros
Found heuristic solution: objective 5.166480e+08
INFO:gurobipy.gurobipy:Found heuristic solution: objective 5.166480e+08
Variable types: 261 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 261 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 1.692943e+05, 97 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 1.692943e+05, 97 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 169294.31274 169294.313 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 169294.31274 169294.313 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (97 simplex iterations) in 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (97 simplex iterations) in 0.04 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 169294 7.57761e+09
INFO:gurobipy.gurobipy:Solution count 2: 169294 7.57761e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 1.692943127399e+05, best bound 1.692943127399e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 1.692943127399e+05, best bound 1.692943127399e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4519 rows, 5696 columns and 13401 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4519 rows, 5696 columns and 13401 nonzeros
Model fingerprint: 0x72f5cc4f
INFO:gurobipy.gurobipy:Model fingerprint: 0x72f5cc4f
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-02, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [2e-02, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [2e-02, 1e+01]
RHS range [2e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [2e-03, 4e+01]
Presolve removed 4272 rows and 5335 columns
INFO:gurobipy.gurobipy:Presolve removed 4272 rows and 5335 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 247 rows, 363 columns, 1117 nonzeros
INFO:gurobipy.gurobipy:Presolved: 247 rows, 363 columns, 1117 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 1.6409421e+05 2.015260e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 1.6409421e+05 2.015260e+01 0.000000e+00 0s
145 1.6929431e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 145 1.6929431e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 145 iterations and 0.03 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Solved in 145 iterations and 0.03 seconds (0.00 work units)
Optimal objective 1.692943127e+05
INFO:gurobipy.gurobipy:Optimal objective 1.692943127e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 5200.10
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 145.80
Simulating time_step 2020-07-10 10:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8909496e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8890564e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8804765e+08 9.52e+04 9.42e+01 -1.0 6.22e+07 - 6.65e-01 4.41e-02f 1
3 1.8795036e+08 9.33e+04 8.02e+02 -1.0 6.18e+07 - 6.41e-01 2.20e-02f 1
4 1.8839718e+08 5.76e+04 6.95e+02 -1.0 4.18e+06 - 8.27e-01 3.84e-01h 1
5 1.8911084e+08 1.22e+03 1.07e+03 -1.0 2.58e+06 - 9.84e-01 1.00e+00h 1
6 1.8909835e+08 2.65e+02 1.91e+02 -1.0 8.25e+04 - 6.43e-01 8.22e-01f 1
7 1.8909650e+08 6.30e+01 3.61e+01 -1.0 1.70e+04 - 3.98e-01 8.11e-01f 1
8 1.8909526e+08 5.12e+01 5.16e-01 -1.0 2.44e+04 - 6.02e-01 1.00e+00f 1
9 1.8909516e+08 6.60e-01 5.24e-03 -1.0 2.74e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8909516e+08 3.15e-05 5.20e-05 -1.0 1.88e+01 - 9.90e-01 1.00e+00h 1
11 1.8909516e+08 1.27e-07 1.00e-06 -1.0 1.80e-01 - 1.00e+00 1.00e+00h 1
12 1.8909497e+08 3.90e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8909496e+08 8.53e-05 3.49e-08 -3.8 3.14e+01 - 1.00e+00 1.00e+00f 1
14 1.8909496e+08 1.79e-07 1.04e-10 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8909496e+08 1.56e-07 9.94e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8909496258314061e+06 1.8909496258314061e+08
Dual infeasibility......: 9.9358629199586387e-11 9.9358629199586387e-09
Constraint violation....: 2.5611370801925659e-09 1.5646219253540039e-07
Complementarity.........: 2.5409797403663961e-09 2.5409797403663962e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5409797403663962e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.608
Total CPU secs in NLP function evaluations = 5.377
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4527 rows, 5695 columns and 13401 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4527 rows, 5695 columns and 13401 nonzeros
Model fingerprint: 0x1a716ae3
INFO:gurobipy.gurobipy:Model fingerprint: 0x1a716ae3
Variable types: 3999 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3999 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [2e-02, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [2e-02, 1e+01]
RHS range [3e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [3e-03, 4e+01]
Found heuristic solution: objective 8.199662e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 8.199662e+09
Presolve removed 4342 rows and 5431 columns
INFO:gurobipy.gurobipy:Presolve removed 4342 rows and 5431 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 185 rows, 264 columns, 733 nonzeros
INFO:gurobipy.gurobipy:Presolved: 185 rows, 264 columns, 733 nonzeros
Found heuristic solution: objective 9.171827e+08
INFO:gurobipy.gurobipy:Found heuristic solution: objective 9.171827e+08
Variable types: 264 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 264 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 2.087628e+05, 99 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 2.087628e+05, 99 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 208762.80016 208762.800 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 208762.80016 208762.800 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (99 simplex iterations) in 0.05 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (99 simplex iterations) in 0.05 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 208763 8.19966e+09
INFO:gurobipy.gurobipy:Solution count 2: 208763 8.19966e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.087628001597e+05, best bound 2.087628001597e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.087628001597e+05, best bound 2.087628001597e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4527 rows, 5695 columns and 13401 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4527 rows, 5695 columns and 13401 nonzeros
Model fingerprint: 0x0d87ab4c
INFO:gurobipy.gurobipy:Model fingerprint: 0x0d87ab4c
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [2e-02, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [2e-02, 1e+01]
RHS range [3e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [3e-03, 4e+01]
Presolve removed 4277 rows and 5331 columns
INFO:gurobipy.gurobipy:Presolve removed 4277 rows and 5331 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 250 rows, 367 columns, 1129 nonzeros
INFO:gurobipy.gurobipy:Presolved: 250 rows, 367 columns, 1129 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 1.9691339e+05 4.293639e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 1.9691339e+05 4.293639e+01 0.000000e+00 0s
142 2.0876280e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 142 2.0876280e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 142 iterations and 0.03 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Solved in 142 iterations and 0.03 seconds (0.00 work units)
Optimal objective 2.087628002e+05
INFO:gurobipy.gurobipy:Optimal objective 2.087628002e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 11849.41
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 338.84
Simulating time_step 2020-07-10 11:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8909454e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8890522e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8804722e+08 9.52e+04 9.42e+01 -1.0 6.22e+07 - 6.65e-01 4.41e-02f 1
3 1.8794993e+08 9.33e+04 8.02e+02 -1.0 6.18e+07 - 6.41e-01 2.20e-02f 1
4 1.8839675e+08 5.76e+04 6.95e+02 -1.0 4.18e+06 - 8.27e-01 3.84e-01h 1
5 1.8911041e+08 1.22e+03 1.07e+03 -1.0 2.58e+06 - 9.84e-01 1.00e+00h 1
6 1.8909792e+08 2.65e+02 1.91e+02 -1.0 8.25e+04 - 6.43e-01 8.22e-01f 1
7 1.8909607e+08 6.30e+01 3.61e+01 -1.0 1.70e+04 - 3.98e-01 8.11e-01f 1
8 1.8909483e+08 5.12e+01 5.16e-01 -1.0 2.44e+04 - 6.02e-01 1.00e+00f 1
9 1.8909474e+08 6.60e-01 5.24e-03 -1.0 2.74e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8909474e+08 3.15e-05 5.20e-05 -1.0 1.88e+01 - 9.90e-01 1.00e+00h 1
11 1.8909474e+08 1.45e-07 1.00e-06 -1.0 1.80e-01 - 1.00e+00 1.00e+00h 1
12 1.8909454e+08 3.90e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8909454e+08 8.53e-05 3.49e-08 -3.8 3.14e+01 - 1.00e+00 1.00e+00f 1
14 1.8909454e+08 1.79e-07 8.58e-11 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8909454e+08 1.79e-07 8.48e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8909453697635629e+06 1.8909453697635630e+08
Dual infeasibility......: 8.4754674586963474e-11 8.4754674586963474e-09
Constraint violation....: 2.5611370801925659e-09 1.7881393432617188e-07
Complementarity.........: 2.5409797403663969e-09 2.5409797403663967e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5409797403663967e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.591
Total CPU secs in NLP function evaluations = 5.356
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4533 rows, 5695 columns and 13406 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4533 rows, 5695 columns and 13406 nonzeros
Model fingerprint: 0x7f4db548
INFO:gurobipy.gurobipy:Model fingerprint: 0x7f4db548
Variable types: 3999 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3999 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [5e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [5e-03, 1e+01]
RHS range [5e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [5e-03, 4e+01]
Found heuristic solution: objective 8.126569e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 8.126569e+09
Presolve removed 4345 rows and 5426 columns
INFO:gurobipy.gurobipy:Presolve removed 4345 rows and 5426 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 188 rows, 269 columns, 724 nonzeros
INFO:gurobipy.gurobipy:Presolved: 188 rows, 269 columns, 724 nonzeros
Found heuristic solution: objective 1.091901e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 1.091901e+09
Variable types: 269 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 269 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 2.850922e+05, 98 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 2.850922e+05, 98 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 285092.18141 285092.181 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 285092.18141 285092.181 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (98 simplex iterations) in 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (98 simplex iterations) in 0.04 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 285092 8.12657e+09
INFO:gurobipy.gurobipy:Solution count 2: 285092 8.12657e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.850921814108e+05, best bound 2.850921814108e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.850921814108e+05, best bound 2.850921814108e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4533 rows, 5695 columns and 13406 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4533 rows, 5695 columns and 13406 nonzeros
Model fingerprint: 0xa0236163
INFO:gurobipy.gurobipy:Model fingerprint: 0xa0236163
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [5e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [5e-03, 1e+01]
RHS range [5e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [5e-03, 4e+01]
Presolve removed 4280 rows and 5326 columns
INFO:gurobipy.gurobipy:Presolve removed 4280 rows and 5326 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 253 rows, 372 columns, 1104 nonzeros
INFO:gurobipy.gurobipy:Presolved: 253 rows, 372 columns, 1104 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 2.6789982e+05 4.096054e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 2.6789982e+05 4.096054e+01 0.000000e+00 0s
128 2.8509218e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 128 2.8509218e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 128 iterations and 0.03 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Solved in 128 iterations and 0.03 seconds (0.00 work units)
Optimal objective 2.850921814e+05
INFO:gurobipy.gurobipy:Optimal objective 2.850921814e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 17192.36
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 259.33
Simulating time_step 2020-07-10 12:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8909316e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8890384e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8804584e+08 9.52e+04 9.42e+01 -1.0 6.22e+07 - 6.65e-01 4.41e-02f 1
3 1.8794855e+08 9.33e+04 8.02e+02 -1.0 6.18e+07 - 6.41e-01 2.20e-02f 1
4 1.8839538e+08 5.76e+04 6.95e+02 -1.0 4.18e+06 - 8.27e-01 3.84e-01h 1
5 1.8910903e+08 1.22e+03 1.07e+03 -1.0 2.58e+06 - 9.84e-01 1.00e+00h 1
6 1.8909655e+08 2.65e+02 1.91e+02 -1.0 8.25e+04 - 6.43e-01 8.22e-01f 1
7 1.8909470e+08 6.30e+01 3.61e+01 -1.0 1.70e+04 - 3.98e-01 8.11e-01f 1
8 1.8909346e+08 5.12e+01 5.16e-01 -1.0 2.44e+04 - 6.02e-01 1.00e+00f 1
9 1.8909336e+08 6.60e-01 5.24e-03 -1.0 2.74e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8909336e+08 3.15e-05 5.20e-05 -1.0 1.88e+01 - 9.90e-01 1.00e+00h 1
11 1.8909336e+08 1.49e-07 1.00e-06 -1.0 1.80e-01 - 1.00e+00 1.00e+00h 1
12 1.8909317e+08 3.90e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8909316e+08 8.53e-05 3.49e-08 -3.8 3.14e+01 - 1.00e+00 1.00e+00f 1
14 1.8909316e+08 1.49e-07 1.04e-10 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8909316e+08 1.64e-07 9.94e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8909316088232985e+06 1.8909316088232985e+08
Dual infeasibility......: 9.9358629093707269e-11 9.9358629093707269e-09
Constraint violation....: 2.5611370801925659e-09 1.6391277313232422e-07
Complementarity.........: 2.5409797403663952e-09 2.5409797403663951e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5409797403663951e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.623
Total CPU secs in NLP function evaluations = 5.306
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4540 rows, 5697 columns and 13413 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4540 rows, 5697 columns and 13413 nonzeros
Model fingerprint: 0x9f18f6a2
INFO:gurobipy.gurobipy:Model fingerprint: 0x9f18f6a2
Variable types: 4001 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 4001 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [5e-04, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [5e-04, 1e+01]
RHS range [5e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [5e-03, 4e+01]
Found heuristic solution: objective 8.194722e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 8.194722e+09
Presolve removed 4353 rows and 5424 columns
INFO:gurobipy.gurobipy:Presolve removed 4353 rows and 5424 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 187 rows, 273 columns, 731 nonzeros
INFO:gurobipy.gurobipy:Presolved: 187 rows, 273 columns, 731 nonzeros
Found heuristic solution: objective 1.078151e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 1.078151e+09
Variable types: 273 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 273 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 3.350803e+05, 89 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 3.350803e+05, 89 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 335080.25383 335080.254 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 335080.25383 335080.254 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (89 simplex iterations) in 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (89 simplex iterations) in 0.04 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 335080 8.19472e+09
INFO:gurobipy.gurobipy:Solution count 2: 335080 8.19472e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 3.350802538336e+05, best bound 3.350802538336e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 3.350802538336e+05, best bound 3.350802538336e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4540 rows, 5697 columns and 13413 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4540 rows, 5697 columns and 13413 nonzeros
Model fingerprint: 0x00468c8e
INFO:gurobipy.gurobipy:Model fingerprint: 0x00468c8e
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [5e-04, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [5e-04, 1e+01]
RHS range [5e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [5e-03, 4e+01]
Presolve removed 4280 rows and 5316 columns
INFO:gurobipy.gurobipy:Presolve removed 4280 rows and 5316 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 260 rows, 383 columns, 1140 nonzeros
INFO:gurobipy.gurobipy:Presolved: 260 rows, 383 columns, 1140 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 3.1779665e+05 1.844684e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 3.1779665e+05 1.844684e+01 0.000000e+00 0s
128 3.3508025e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 128 3.3508025e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 128 iterations and 0.03 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Solved in 128 iterations and 0.03 seconds (0.00 work units)
Optimal objective 3.350802538e+05
INFO:gurobipy.gurobipy:Optimal objective 3.350802538e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 41023.55
Variable costs: 19445.06
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 104.01
Simulating time_step 2020-07-10 13:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8909182e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8890250e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8804450e+08 9.52e+04 9.42e+01 -1.0 6.22e+07 - 6.65e-01 4.41e-02f 1
3 1.8794721e+08 9.33e+04 8.02e+02 -1.0 6.18e+07 - 6.41e-01 2.20e-02f 1
4 1.8839404e+08 5.76e+04 6.95e+02 -1.0 4.18e+06 - 8.27e-01 3.84e-01h 1
5 1.8910769e+08 1.22e+03 1.07e+03 -1.0 2.58e+06 - 9.84e-01 1.00e+00h 1
6 1.8909521e+08 2.65e+02 1.91e+02 -1.0 8.25e+04 - 6.43e-01 8.22e-01f 1
7 1.8909336e+08 6.30e+01 3.61e+01 -1.0 1.70e+04 - 3.98e-01 8.11e-01f 1
8 1.8909212e+08 5.12e+01 5.16e-01 -1.0 2.44e+04 - 6.02e-01 1.00e+00f 1
9 1.8909202e+08 6.60e-01 5.24e-03 -1.0 2.74e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8909202e+08 3.15e-05 5.20e-05 -1.0 1.88e+01 - 9.90e-01 1.00e+00h 1
11 1.8909202e+08 1.42e-07 1.00e-06 -1.0 1.80e-01 - 1.00e+00 1.00e+00h 1
12 1.8909183e+08 3.90e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8909182e+08 8.53e-05 3.49e-08 -3.8 3.14e+01 - 1.00e+00 1.00e+00f 1
14 1.8909182e+08 1.64e-07 7.28e-11 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8909182e+08 1.19e-07 1.16e-10 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8909181962967315e+06 1.8909181962967315e+08
Dual infeasibility......: 1.1641532182693481e-10 1.1641532182693481e-08
Constraint violation....: 2.5611370801925659e-09 1.1920928955078125e-07
Complementarity.........: 2.5409797403663985e-09 2.5409797403663983e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5409797403663983e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.608
Total CPU secs in NLP function evaluations = 5.455
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4540 rows, 5698 columns and 13416 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4540 rows, 5698 columns and 13416 nonzeros
Model fingerprint: 0xbacc0931
INFO:gurobipy.gurobipy:Model fingerprint: 0xbacc0931
Variable types: 4002 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 4002 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-02, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-02, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [6e-04, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [6e-04, 1e+01]
RHS range [8e-04, 4e+01]
INFO:gurobipy.gurobipy: RHS range [8e-04, 4e+01]
Found heuristic solution: objective 8.087897e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 8.087897e+09
Presolve removed 4349 rows and 5421 columns
INFO:gurobipy.gurobipy:Presolve removed 4349 rows and 5421 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 191 rows, 277 columns, 746 nonzeros
INFO:gurobipy.gurobipy:Presolved: 191 rows, 277 columns, 746 nonzeros
Found heuristic solution: objective 1.186372e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 1.186372e+09
Variable types: 277 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 277 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 3.570932e+05, 82 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 3.570932e+05, 82 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 357093.15695 357093.157 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 357093.15695 357093.157 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (82 simplex iterations) in 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (82 simplex iterations) in 0.04 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 357093 8.0879e+09
INFO:gurobipy.gurobipy:Solution count 2: 357093 8.0879e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 3.570931569484e+05, best bound 3.570931569484e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 3.570931569484e+05, best bound 3.570931569484e+05, gap 0.0000%
[MIP phase] iteration 0, found 2 violation(s)
INFO:egret:[MIP phase] iteration 0, found 2 violation(s)
[MIP phase] iteration 0, added 2 flow constraint(s)
INFO:egret:[MIP phase] iteration 0, added 2 flow constraint(s)
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4542 rows, 5700 columns and 13562 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4542 rows, 5700 columns and 13562 nonzeros
Model fingerprint: 0x2cc0e79d
INFO:gurobipy.gurobipy:Model fingerprint: 0x2cc0e79d
Variable types: 4004 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 4004 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [6e-04, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [6e-04, 1e+01]
RHS range [8e-04, 4e+01]
INFO:gurobipy.gurobipy: RHS range [8e-04, 4e+01]
INFO:gurobipy.gurobipy:
User MIP start did not produce a new incumbent solution
INFO:gurobipy.gurobipy:User MIP start did not produce a new incumbent solution
User MIP start violates constraint TransmissionBlock(2)_ineq_pf_branch_thermal_bounds(AB1) by 0.195106987
INFO:gurobipy.gurobipy:User MIP start violates constraint TransmissionBlock(2)_ineq_pf_branch_thermal_bounds(AB1) by 0.195106987
MIP start from previous solve produced solution with objective 357093 (0.02s)
INFO:gurobipy.gurobipy:MIP start from previous solve produced solution with objective 357093 (0.02s)
Loaded MIP start from previous solve with objective 357093
INFO:gurobipy.gurobipy:Loaded MIP start from previous solve with objective 357093
INFO:gurobipy.gurobipy:
Presolve removed 4310 rows and 5166 columns
INFO:gurobipy.gurobipy:Presolve removed 4310 rows and 5166 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 232 rows, 534 columns, 1351 nonzeros
INFO:gurobipy.gurobipy:Presolved: 232 rows, 534 columns, 1351 nonzeros
Variable types: 534 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 534 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: cutoff, 3 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: cutoff, 3 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
0 0 cutoff 0 357093.157 357093.157 0.00% - 0s
INFO:gurobipy.gurobipy: 0 0 cutoff 0 357093.157 357093.157 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (3 simplex iterations) in 0.05 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (3 simplex iterations) in 0.05 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 1: 357093
INFO:gurobipy.gurobipy:Solution count 1: 357093
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 3.570931569484e+05, best bound 3.570931569484e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 3.570931569484e+05, best bound 3.570931569484e+05, gap 0.0000%
[MIP phase] iteration 1, found 1 violation(s)
INFO:egret:[MIP phase] iteration 1, found 1 violation(s)
[MIP phase] iteration 1, added 1 flow constraint(s)
INFO:egret:[MIP phase] iteration 1, added 1 flow constraint(s)
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4543 rows, 5701 columns and 13635 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4543 rows, 5701 columns and 13635 nonzeros
Model fingerprint: 0xa5e3b88a
INFO:gurobipy.gurobipy:Model fingerprint: 0xa5e3b88a
Variable types: 4005 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 4005 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [6e-04, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [6e-04, 1e+01]
RHS range [8e-04, 4e+01]
INFO:gurobipy.gurobipy: RHS range [8e-04, 4e+01]
INFO:gurobipy.gurobipy:
User MIP start did not produce a new incumbent solution
INFO:gurobipy.gurobipy:User MIP start did not produce a new incumbent solution
User MIP start violates constraint TransmissionBlock(3)_ineq_pf_branch_thermal_bounds(AB1) by 0.244007350
INFO:gurobipy.gurobipy:User MIP start violates constraint TransmissionBlock(3)_ineq_pf_branch_thermal_bounds(AB1) by 0.244007350
MIP start from previous solve produced solution with objective 357093 (0.03s)
INFO:gurobipy.gurobipy:MIP start from previous solve produced solution with objective 357093 (0.03s)
Loaded MIP start from previous solve with objective 357093
INFO:gurobipy.gurobipy:Loaded MIP start from previous solve with objective 357093
INFO:gurobipy.gurobipy:
Presolve removed 4288 rows and 5038 columns
INFO:gurobipy.gurobipy:Presolve removed 4288 rows and 5038 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 255 rows, 663 columns, 1665 nonzeros
INFO:gurobipy.gurobipy:Presolved: 255 rows, 663 columns, 1665 nonzeros
Variable types: 663 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 663 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: cutoff, 3 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: cutoff, 3 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
0 0 cutoff 0 357093.157 357093.157 0.00% - 0s
INFO:gurobipy.gurobipy: 0 0 cutoff 0 357093.157 357093.157 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (3 simplex iterations) in 0.06 seconds (0.02 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (3 simplex iterations) in 0.06 seconds (0.02 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 1: 357093
INFO:gurobipy.gurobipy:Solution count 1: 357093
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 3.570931569484e+05, best bound 3.570931569484e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 3.570931569484e+05, best bound 3.570931569484e+05, gap 0.0000%
[MIP phase] iteration 2, found 0 violation(s)
INFO:egret:[MIP phase] iteration 2, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4543 rows, 5701 columns and 13635 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4543 rows, 5701 columns and 13635 nonzeros
Model fingerprint: 0xc5c7fe87
INFO:gurobipy.gurobipy:Model fingerprint: 0xc5c7fe87
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [6e-04, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [6e-04, 1e+01]
RHS range [8e-04, 4e+01]
INFO:gurobipy.gurobipy: RHS range [8e-04, 4e+01]
Presolve removed 4268 rows and 4998 columns
INFO:gurobipy.gurobipy:Presolve removed 4268 rows and 4998 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 275 rows, 705 columns, 1868 nonzeros
INFO:gurobipy.gurobipy:Presolved: 275 rows, 705 columns, 1868 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 3.3748535e+05 5.057822e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 3.3748535e+05 5.057822e+01 0.000000e+00 0s
147 3.5709316e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 147 3.5709316e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 147 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 147 iterations and 0.03 seconds (0.01 work units)
Optimal objective 3.570931569e+05
INFO:gurobipy.gurobipy:Optimal objective 3.570931569e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 73842.73
Variable costs: 21769.27
Number on/offs: 1
Sum on/off ramps: 170.00
Sum nominal ramps: 103.54
Simulating time_step 2020-07-10 14:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8909292e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8890360e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8804568e+08 9.52e+04 9.42e+01 -1.0 6.22e+07 - 6.65e-01 4.41e-02f 1
3 1.8794838e+08 9.33e+04 8.02e+02 -1.0 6.18e+07 - 6.41e-01 2.20e-02f 1
4 1.8839503e+08 5.76e+04 6.96e+02 -1.0 4.18e+06 - 8.27e-01 3.84e-01h 1
5 1.8910879e+08 1.22e+03 1.07e+03 -1.0 2.58e+06 - 9.84e-01 1.00e+00h 1
6 1.8909631e+08 2.65e+02 1.91e+02 -1.0 8.25e+04 - 6.43e-01 8.22e-01f 1
7 1.8909446e+08 6.30e+01 3.62e+01 -1.0 1.70e+04 - 3.98e-01 8.11e-01f 1
8 1.8909322e+08 5.12e+01 5.16e-01 -1.0 2.44e+04 - 6.02e-01 1.00e+00f 1
9 1.8909312e+08 6.60e-01 5.24e-03 -1.0 2.75e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8909312e+08 3.16e-05 5.20e-05 -1.0 1.88e+01 - 9.90e-01 1.00e+00h 1
11 1.8909312e+08 1.04e-07 1.00e-06 -1.0 1.80e-01 - 1.00e+00 1.00e+00h 1
12 1.8909293e+08 3.91e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8909292e+08 8.53e-05 3.49e-08 -3.8 3.14e+01 - 1.00e+00 1.00e+00f 1
14 1.8909292e+08 1.42e-07 9.91e-11 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8909292e+08 1.19e-07 8.99e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8909292050223318e+06 1.8909292050223318e+08
Dual infeasibility......: 8.9868308071700068e-11 8.9868308071700068e-09
Constraint violation....: 2.5611370801925659e-09 1.1920928955078125e-07
Complementarity.........: 2.5405438854026358e-09 2.5405438854026357e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5405438854026357e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.631
Total CPU secs in NLP function evaluations = 5.393
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4544 rows, 5701 columns and 13702 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4544 rows, 5701 columns and 13702 nonzeros
Model fingerprint: 0xf7c470dd
INFO:gurobipy.gurobipy:Model fingerprint: 0xf7c470dd
Variable types: 4005 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 4005 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [8e-04, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [8e-04, 1e+01]
RHS range [4e-02, 4e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 4e+01]
Found heuristic solution: objective 9.906344e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 9.906344e+09
Presolve removed 4275 rows and 4916 columns
INFO:gurobipy.gurobipy:Presolve removed 4275 rows and 4916 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 269 rows, 785 columns, 1946 nonzeros
INFO:gurobipy.gurobipy:Presolved: 269 rows, 785 columns, 1946 nonzeros
Found heuristic solution: objective 3.904412e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 3.904412e+09
Variable types: 785 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 785 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 3.489427e+05, 128 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 3.489427e+05, 128 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 348942.71071 348942.711 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 348942.71071 348942.711 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (128 simplex iterations) in 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (128 simplex iterations) in 0.04 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 348943 9.90634e+09
INFO:gurobipy.gurobipy:Solution count 2: 348943 9.90634e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 3.489427107100e+05, best bound 3.489427107100e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 3.489427107100e+05, best bound 3.489427107100e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4544 rows, 5701 columns and 13702 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4544 rows, 5701 columns and 13702 nonzeros
Model fingerprint: 0xfad80a63
INFO:gurobipy.gurobipy:Model fingerprint: 0xfad80a63
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [8e-04, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [8e-04, 1e+01]
RHS range [4e-02, 4e+01]
INFO:gurobipy.gurobipy: RHS range [4e-02, 4e+01]
Presolve removed 4256 rows and 4877 columns
INFO:gurobipy.gurobipy:Presolve removed 4256 rows and 4877 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 288 rows, 826 columns, 2190 nonzeros
INFO:gurobipy.gurobipy:Presolved: 288 rows, 826 columns, 2190 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 3.2435486e+05 6.301201e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 3.2435486e+05 6.301201e+01 0.000000e+00 0s
154 3.4894271e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 154 3.4894271e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 154 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 154 iterations and 0.03 seconds (0.01 work units)
Optimal objective 3.489427107e+05
INFO:gurobipy.gurobipy:Optimal objective 3.489427107e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 112009.98
Variable costs: 26749.31
Number on/offs: 2
Sum on/off ramps: 340.00
Sum nominal ramps: 216.18
Simulating time_step 2020-07-10 15:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8909255e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8885218e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8799224e+08 9.52e+04 9.42e+01 -1.0 6.23e+07 - 6.65e-01 4.41e-02f 1
3 1.8789376e+08 9.33e+04 8.02e+02 -1.0 6.18e+07 - 6.41e-01 2.20e-02f 1
4 1.8832300e+08 5.76e+04 6.95e+02 -1.0 4.18e+06 - 8.27e-01 3.84e-01h 1
5 1.8900383e+08 1.21e+03 1.06e+03 -1.0 2.58e+06 - 9.84e-01 9.93e-01h 1
6 1.8899511e+08 2.63e+02 1.88e+02 -1.0 8.27e+04 - 6.45e-01 8.22e-01f 1
7 1.8899393e+08 6.25e+01 3.55e+01 -1.0 1.71e+04 - 3.99e-01 8.11e-01h 1
8 1.8899285e+08 5.09e+01 5.17e-01 -1.0 2.43e+04 - 6.04e-01 1.00e+00f 1
9 1.8899275e+08 6.45e-01 5.26e-03 -1.0 2.71e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8899275e+08 2.92e-05 5.21e-05 -1.0 1.81e+01 - 9.90e-01 1.00e+00h 1
11 1.8899275e+08 1.34e-07 1.00e-06 -1.0 1.77e-01 - 1.00e+00 1.00e+00h 1
12 1.8899256e+08 3.91e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8899255e+08 8.53e-05 3.49e-08 -3.8 3.14e+01 - 1.00e+00 1.00e+00f 1
14 1.8899255e+08 1.34e-07 1.04e-10 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8899255e+08 1.42e-07 7.61e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8899254931052686e+06 1.8899254931052685e+08
Dual infeasibility......: 7.6114217563559192e-11 7.6114217563559192e-09
Constraint violation....: 2.5611370801925659e-09 1.4156103134155273e-07
Complementarity.........: 2.5405008785632756e-09 2.5405008785632757e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5405008785632757e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.649
Total CPU secs in NLP function evaluations = 5.279
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4544 rows, 5691 columns and 13670 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4544 rows, 5691 columns and 13670 nonzeros
Model fingerprint: 0x8df9e47c
INFO:gurobipy.gurobipy:Model fingerprint: 0x8df9e47c
Variable types: 3995 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3995 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [6e-04, 7e+01]
INFO:gurobipy.gurobipy: Bounds range [6e-04, 7e+01]
RHS range [3e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [3e-03, 4e+01]
Found heuristic solution: objective 8.346309e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 8.346309e+09
Presolve removed 4263 rows and 4915 columns
INFO:gurobipy.gurobipy:Presolve removed 4263 rows and 4915 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 281 rows, 776 columns, 1981 nonzeros
INFO:gurobipy.gurobipy:Presolved: 281 rows, 776 columns, 1981 nonzeros
Found heuristic solution: objective 2.579545e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 2.579545e+09
Variable types: 776 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 776 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 2.944076e+05, 132 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 2.944076e+05, 132 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 294407.64600 294407.646 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 294407.64600 294407.646 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (132 simplex iterations) in 0.05 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (132 simplex iterations) in 0.05 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 294408 8.34631e+09
INFO:gurobipy.gurobipy:Solution count 2: 294408 8.34631e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.944076460034e+05, best bound 2.944076460034e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.944076460034e+05, best bound 2.944076460034e+05, gap 0.0000%
[MIP phase] iteration 0, found 9 violation(s)
INFO:egret:[MIP phase] iteration 0, found 9 violation(s)
[MIP phase] iteration 0, added 8 flow constraint(s)
INFO:egret:[MIP phase] iteration 0, added 8 flow constraint(s)
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4552 rows, 5699 columns and 14254 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4552 rows, 5699 columns and 14254 nonzeros
Model fingerprint: 0x4542bfbb
INFO:gurobipy.gurobipy:Model fingerprint: 0x4542bfbb
Variable types: 4003 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 4003 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-04, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [6e-04, 7e+01]
INFO:gurobipy.gurobipy: Bounds range [6e-04, 7e+01]
RHS range [3e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [3e-03, 4e+01]
INFO:gurobipy.gurobipy:
User MIP start did not produce a new incumbent solution
INFO:gurobipy.gurobipy:User MIP start did not produce a new incumbent solution
User MIP start violates constraint TransmissionBlock(2)_ineq_pf_branch_thermal_bounds(B24) by 0.025859337
INFO:gurobipy.gurobipy:User MIP start violates constraint TransmissionBlock(2)_ineq_pf_branch_thermal_bounds(B24) by 0.025859337
MIP start from previous solve produced solution with objective 294408 (0.03s)
INFO:gurobipy.gurobipy:MIP start from previous solve produced solution with objective 294408 (0.03s)
Loaded MIP start from previous solve with objective 294408
INFO:gurobipy.gurobipy:Loaded MIP start from previous solve with objective 294408
INFO:gurobipy.gurobipy:
Presolve removed 4197 rows and 4843 columns
INFO:gurobipy.gurobipy:Presolve removed 4197 rows and 4843 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 355 rows, 856 columns, 2561 nonzeros
INFO:gurobipy.gurobipy:Presolved: 355 rows, 856 columns, 2561 nonzeros
Variable types: 856 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 856 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: cutoff, 1 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: cutoff, 1 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
0 0 cutoff 0 294407.646 294407.646 0.00% - 0s
INFO:gurobipy.gurobipy: 0 0 cutoff 0 294407.646 294407.646 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (1 simplex iterations) in 0.08 seconds (0.02 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (1 simplex iterations) in 0.08 seconds (0.02 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 1: 294408
INFO:gurobipy.gurobipy:Solution count 1: 294408
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.944076460034e+05, best bound 2.944076460034e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.944076460034e+05, best bound 2.944076460034e+05, gap 0.0000%
[MIP phase] iteration 1, found 0 violation(s)
INFO:egret:[MIP phase] iteration 1, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4552 rows, 5699 columns and 14254 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4552 rows, 5699 columns and 14254 nonzeros
Model fingerprint: 0xf3866264
INFO:gurobipy.gurobipy:Model fingerprint: 0xf3866264
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [2e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [2e-04, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [6e-04, 7e+01]
INFO:gurobipy.gurobipy: Bounds range [6e-04, 7e+01]
RHS range [3e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [3e-03, 4e+01]
Presolve removed 4245 rows and 4877 columns
INFO:gurobipy.gurobipy:Presolve removed 4245 rows and 4877 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 307 rows, 824 columns, 2934 nonzeros
INFO:gurobipy.gurobipy:Presolved: 307 rows, 824 columns, 2934 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 handle free variables 0s
INFO:gurobipy.gurobipy: 0 handle free variables 0s
256 2.9440765e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 256 2.9440765e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 256 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 256 iterations and 0.03 seconds (0.01 work units)
Optimal objective 2.944076460e+05
INFO:gurobipy.gurobipy:Optimal objective 2.944076460e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 88758.93
Variable costs: 23511.99
Number on/offs: 1
Sum on/off ramps: 170.00
Sum nominal ramps: 243.94
Simulating time_step 2020-07-10 16:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8899334e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8875402e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8789424e+08 9.52e+04 9.42e+01 -1.0 6.21e+07 - 6.65e-01 4.42e-02f 1
3 1.8779568e+08 9.33e+04 8.02e+02 -1.0 6.18e+07 - 6.41e-01 2.20e-02f 1
4 1.8822417e+08 5.76e+04 6.96e+02 -1.0 4.18e+06 - 8.28e-01 3.84e-01h 1
5 1.8890443e+08 1.21e+03 1.06e+03 -1.0 2.58e+06 - 9.84e-01 9.93e-01h 1
6 1.8889587e+08 2.63e+02 1.88e+02 -1.0 8.27e+04 - 6.45e-01 8.22e-01f 1
7 1.8889471e+08 6.25e+01 3.55e+01 -1.0 1.71e+04 - 3.99e-01 8.11e-01h 1
8 1.8889364e+08 5.10e+01 5.18e-01 -1.0 2.43e+04 - 6.04e-01 1.00e+00f 1
9 1.8889354e+08 6.47e-01 5.27e-03 -1.0 2.72e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8889354e+08 2.95e-05 5.22e-05 -1.0 1.82e+01 - 9.90e-01 1.00e+00h 1
11 1.8889354e+08 1.49e-07 1.00e-06 -1.0 1.78e-01 - 1.00e+00 1.00e+00h 1
12 1.8889335e+08 3.91e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8889334e+08 8.53e-05 3.49e-08 -3.8 3.14e+01 - 1.00e+00 1.00e+00f 1
14 1.8889334e+08 1.25e-07 8.58e-11 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8889334e+08 1.49e-07 9.52e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8889334016499026e+06 1.8889334016499025e+08
Dual infeasibility......: 9.5177656314567499e-11 9.5177656314567499e-09
Constraint violation....: 2.5611370801925659e-09 1.4901161193847656e-07
Complementarity.........: 2.5405142855476452e-09 2.5405142855476451e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5405142855476451e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.576
Total CPU secs in NLP function evaluations = 5.294
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4544 rows, 5681 columns and 13638 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4544 rows, 5681 columns and 13638 nonzeros
Model fingerprint: 0xc83c03b4
INFO:gurobipy.gurobipy:Model fingerprint: 0xc83c03b4
Variable types: 3985 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3985 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [1e-03, 9e+00]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 9e+00]
RHS range [2e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [2e-03, 4e+01]
Found heuristic solution: objective 9.720384e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 9.720384e+09
Presolve removed 4259 rows and 4919 columns
INFO:gurobipy.gurobipy:Presolve removed 4259 rows and 4919 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 285 rows, 762 columns, 1958 nonzeros
INFO:gurobipy.gurobipy:Presolved: 285 rows, 762 columns, 1958 nonzeros
Found heuristic solution: objective 3.952182e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 3.952182e+09
Variable types: 762 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 762 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 2.777916e+05, 171 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 2.777916e+05, 171 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 277791.59964 277791.600 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 277791.59964 277791.600 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (171 simplex iterations) in 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (171 simplex iterations) in 0.04 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 277792 9.72038e+09
INFO:gurobipy.gurobipy:Solution count 2: 277792 9.72038e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.777915996353e+05, best bound 2.777915996353e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.777915996353e+05, best bound 2.777915996353e+05, gap 0.0000%
[MIP phase] iteration 0, found 1 violation(s)
INFO:egret:[MIP phase] iteration 0, found 1 violation(s)
[MIP phase] iteration 0, added 1 flow constraint(s)
INFO:egret:[MIP phase] iteration 0, added 1 flow constraint(s)
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4545 rows, 5682 columns and 13711 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4545 rows, 5682 columns and 13711 nonzeros
Model fingerprint: 0x3c7bc2c7
INFO:gurobipy.gurobipy:Model fingerprint: 0x3c7bc2c7
Variable types: 3986 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3986 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [1e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 1e+01]
RHS range [2e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [2e-03, 4e+01]
INFO:gurobipy.gurobipy:
User MIP start did not produce a new incumbent solution
INFO:gurobipy.gurobipy:User MIP start did not produce a new incumbent solution
User MIP start violates constraint TransmissionBlock(3)_ineq_pf_branch_thermal_bounds(CA_1) by 0.345137771
INFO:gurobipy.gurobipy:User MIP start violates constraint TransmissionBlock(3)_ineq_pf_branch_thermal_bounds(CA_1) by 0.345137771
MIP start from previous solve produced solution with objective 277792 (0.03s)
INFO:gurobipy.gurobipy:MIP start from previous solve produced solution with objective 277792 (0.03s)
Loaded MIP start from previous solve with objective 277792
INFO:gurobipy.gurobipy:Loaded MIP start from previous solve with objective 277792
INFO:gurobipy.gurobipy:
Presolve removed 4258 rows and 4918 columns
INFO:gurobipy.gurobipy:Presolve removed 4258 rows and 4918 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 287 rows, 764 columns, 2056 nonzeros
INFO:gurobipy.gurobipy:Presolved: 287 rows, 764 columns, 2056 nonzeros
Variable types: 764 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 764 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: cutoff, 3 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: cutoff, 3 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
0 0 cutoff 0 277791.600 277791.600 0.00% - 0s
INFO:gurobipy.gurobipy: 0 0 cutoff 0 277791.600 277791.600 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (3 simplex iterations) in 0.06 seconds (0.02 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (3 simplex iterations) in 0.06 seconds (0.02 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 1: 277792
INFO:gurobipy.gurobipy:Solution count 1: 277792
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.777915996353e+05, best bound 2.777915996353e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.777915996353e+05, best bound 2.777915996353e+05, gap 0.0000%
[MIP phase] iteration 1, found 0 violation(s)
INFO:egret:[MIP phase] iteration 1, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4545 rows, 5682 columns and 13711 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4545 rows, 5682 columns and 13711 nonzeros
Model fingerprint: 0x792e3eb8
INFO:gurobipy.gurobipy:Model fingerprint: 0x792e3eb8
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [1e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 1e+01]
RHS range [2e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [2e-03, 4e+01]
Presolve removed 4243 rows and 4883 columns
INFO:gurobipy.gurobipy:Presolve removed 4243 rows and 4883 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 302 rows, 801 columns, 2293 nonzeros
INFO:gurobipy.gurobipy:Presolved: 302 rows, 801 columns, 2293 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 handle free variables 0s
INFO:gurobipy.gurobipy: 0 handle free variables 0s
209 2.7779160e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 209 2.7779160e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 209 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 209 iterations and 0.03 seconds (0.01 work units)
Optimal objective 2.777915996e+05
INFO:gurobipy.gurobipy:Optimal objective 2.777915996e+05
[LP phase] iteration 0, found 1 violation(s)
INFO:egret:[LP phase] iteration 0, found 1 violation(s)
[LP phase] iteration 0, added 1 flow constraint(s)
INFO:egret:[LP phase] iteration 0, added 1 flow constraint(s)
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4546 rows, 5683 columns and 13784 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4546 rows, 5683 columns and 13784 nonzeros
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [1e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 1e+01]
RHS range [2e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [2e-03, 4e+01]
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 2.7779160e+05 1.416436e-03 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 2.7779160e+05 1.416436e-03 0.000000e+00 0s
1 2.7779160e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 1 2.7779160e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 1 iterations and 0.02 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Solved in 1 iterations and 0.02 seconds (0.00 work units)
Optimal objective 2.777915996e+05
INFO:gurobipy.gurobipy:Optimal objective 2.777915996e+05
[LP phase] iteration 1, found 0 violation(s)
INFO:egret:[LP phase] iteration 1, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 60712.24
Variable costs: 31439.25
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 343.58
Simulating time_step 2020-07-10 17:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8889466e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8865640e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8780090e+08 9.52e+04 9.43e+01 -1.0 6.22e+07 - 6.65e-01 4.41e-02f 1
3 1.8770448e+08 9.33e+04 8.02e+02 -1.0 6.18e+07 - 6.41e-01 2.20e-02f 1
4 1.8816866e+08 5.76e+04 6.95e+02 -1.0 4.18e+06 - 8.27e-01 3.84e-01h 1
5 1.8890535e+08 1.21e+03 1.06e+03 -1.0 2.58e+06 - 9.84e-01 9.93e-01h 1
6 1.8889712e+08 2.63e+02 1.88e+02 -1.0 8.27e+04 - 6.45e-01 8.22e-01f 1
7 1.8889602e+08 6.25e+01 3.55e+01 -1.0 1.70e+04 - 3.99e-01 8.11e-01h 1
8 1.8889496e+08 5.10e+01 5.18e-01 -1.0 2.43e+04 - 6.04e-01 1.00e+00f 1
9 1.8889486e+08 6.48e-01 5.27e-03 -1.0 2.72e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8889486e+08 2.95e-05 5.22e-05 -1.0 1.82e+01 - 9.90e-01 1.00e+00h 1
11 1.8889486e+08 1.49e-07 1.00e-06 -1.0 1.78e-01 - 1.00e+00 1.00e+00h 1
12 1.8889467e+08 3.91e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8889466e+08 8.53e-05 3.49e-08 -3.8 3.15e+01 - 1.00e+00 1.00e+00f 1
14 1.8889466e+08 1.71e-07 1.28e-10 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8889466e+08 1.79e-07 8.99e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8889466251968269e+06 1.8889466251968268e+08
Dual infeasibility......: 8.9868308108969518e-11 8.9868308108969518e-09
Constraint violation....: 2.5611370801925659e-09 1.7881393432617188e-07
Complementarity.........: 2.5405286413923016e-09 2.5405286413923013e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5405286413923013e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.581
Total CPU secs in NLP function evaluations = 5.459
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4544 rows, 5672 columns and 13610 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4544 rows, 5672 columns and 13610 nonzeros
Model fingerprint: 0xdf4d7f09
INFO:gurobipy.gurobipy:Model fingerprint: 0xdf4d7f09
Variable types: 3976 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3976 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [4e-03, 9e+00]
INFO:gurobipy.gurobipy: Bounds range [4e-03, 9e+00]
RHS range [2e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [2e-03, 4e+01]
Found heuristic solution: objective 9.677340e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 9.677340e+09
Presolve removed 4259 rows and 4932 columns
INFO:gurobipy.gurobipy:Presolve removed 4259 rows and 4932 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 285 rows, 740 columns, 1915 nonzeros
INFO:gurobipy.gurobipy:Presolved: 285 rows, 740 columns, 1915 nonzeros
Found heuristic solution: objective 1.897995e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 1.897995e+09
Variable types: 740 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 740 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 2.846217e+05, 177 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 2.846217e+05, 177 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 284621.71676 284621.717 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 284621.71676 284621.717 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (177 simplex iterations) in 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (177 simplex iterations) in 0.04 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 284622 9.67734e+09
INFO:gurobipy.gurobipy:Solution count 2: 284622 9.67734e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.846217167589e+05, best bound 2.846217167589e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.846217167589e+05, best bound 2.846217167589e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4544 rows, 5672 columns and 13610 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4544 rows, 5672 columns and 13610 nonzeros
Model fingerprint: 0x5e158368
INFO:gurobipy.gurobipy:Model fingerprint: 0x5e158368
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [4e-03, 9e+00]
INFO:gurobipy.gurobipy: Bounds range [4e-03, 9e+00]
RHS range [2e-03, 4e+01]
INFO:gurobipy.gurobipy: RHS range [2e-03, 4e+01]
Presolve removed 4243 rows and 4896 columns
INFO:gurobipy.gurobipy:Presolve removed 4243 rows and 4896 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 301 rows, 778 columns, 2177 nonzeros
INFO:gurobipy.gurobipy:Presolved: 301 rows, 778 columns, 2177 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 2.4885123e+05 1.703800e+02 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 2.4885123e+05 1.703800e+02 0.000000e+00 0s
187 2.8462172e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 187 2.8462172e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 187 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 187 iterations and 0.03 seconds (0.01 work units)
Optimal objective 2.846217168e+05
INFO:gurobipy.gurobipy:Optimal objective 2.846217168e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 60712.24
Variable costs: 38269.36
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 254.80
Simulating time_step 2020-07-10 18:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8889616e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8865793e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8780240e+08 9.52e+04 9.43e+01 -1.0 6.22e+07 - 6.66e-01 4.41e-02f 1
3 1.8770602e+08 9.33e+04 8.02e+02 -1.0 6.17e+07 - 6.41e-01 2.20e-02f 1
4 1.8817068e+08 5.76e+04 6.95e+02 -1.0 4.18e+06 - 8.27e-01 3.84e-01h 1
5 1.8890683e+08 1.21e+03 1.06e+03 -1.0 2.58e+06 - 9.84e-01 9.93e-01h 1
6 1.8889861e+08 2.63e+02 1.88e+02 -1.0 8.27e+04 - 6.45e-01 8.22e-01f 1
7 1.8889752e+08 6.24e+01 3.55e+01 -1.0 1.71e+04 - 3.99e-01 8.11e-01h 1
8 1.8889646e+08 5.09e+01 5.17e-01 -1.0 2.43e+04 - 6.04e-01 1.00e+00f 1
9 1.8889636e+08 6.45e-01 5.26e-03 -1.0 2.71e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8889636e+08 2.91e-05 5.21e-05 -1.0 1.81e+01 - 9.90e-01 1.00e+00h 1
11 1.8889636e+08 1.34e-07 1.00e-06 -1.0 1.77e-01 - 1.00e+00 1.00e+00h 1
12 1.8889617e+08 3.91e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8889616e+08 8.54e-05 3.49e-08 -3.8 3.15e+01 - 1.00e+00 1.00e+00f 1
14 1.8889616e+08 1.34e-07 8.88e-11 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8889616e+08 1.27e-07 7.61e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8889615930641363e+06 1.8889615930641362e+08
Dual infeasibility......: 7.6114217529677875e-11 7.6114217529677875e-09
Constraint violation....: 2.5611370801925659e-09 1.2665987014770508e-07
Complementarity.........: 2.5405438854026354e-09 2.5405438854026351e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5405438854026351e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.623
Total CPU secs in NLP function evaluations = 5.501
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4541 rows, 5669 columns and 13586 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4541 rows, 5669 columns and 13586 nonzeros
Model fingerprint: 0xe427fad4
INFO:gurobipy.gurobipy:Model fingerprint: 0xe427fad4
Variable types: 3973 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3973 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [1e-03, 9e+00]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 9e+00]
RHS range [1e-02, 3e+01]
INFO:gurobipy.gurobipy: RHS range [1e-02, 3e+01]
Found heuristic solution: objective 9.074440e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 9.074440e+09
Presolve removed 4256 rows and 4936 columns
INFO:gurobipy.gurobipy:Presolve removed 4256 rows and 4936 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 285 rows, 733 columns, 1901 nonzeros
INFO:gurobipy.gurobipy:Presolved: 285 rows, 733 columns, 1901 nonzeros
Found heuristic solution: objective 2.094008e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 2.094008e+09
Variable types: 733 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 733 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 2.899201e+05, 177 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 2.899201e+05, 177 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 289920.05117 289920.051 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 289920.05117 289920.051 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (177 simplex iterations) in 0.05 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (177 simplex iterations) in 0.05 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 289920 9.07444e+09
INFO:gurobipy.gurobipy:Solution count 2: 289920 9.07444e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.899200511721e+05, best bound 2.899200511721e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.899200511721e+05, best bound 2.899200511721e+05, gap 0.0000%
[MIP phase] iteration 0, found 1 violation(s)
INFO:egret:[MIP phase] iteration 0, found 1 violation(s)
[MIP phase] iteration 0, added 1 flow constraint(s)
INFO:egret:[MIP phase] iteration 0, added 1 flow constraint(s)
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4542 rows, 5670 columns and 13659 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4542 rows, 5670 columns and 13659 nonzeros
Model fingerprint: 0xf8594d75
INFO:gurobipy.gurobipy:Model fingerprint: 0xf8594d75
Variable types: 3974 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3974 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [1e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 1e+01]
RHS range [1e-02, 3e+01]
INFO:gurobipy.gurobipy: RHS range [1e-02, 3e+01]
INFO:gurobipy.gurobipy:
User MIP start did not produce a new incumbent solution
INFO:gurobipy.gurobipy:User MIP start did not produce a new incumbent solution
User MIP start violates constraint TransmissionBlock(2)_ineq_pf_branch_thermal_bounds(CA_1) by 0.266787575
INFO:gurobipy.gurobipy:User MIP start violates constraint TransmissionBlock(2)_ineq_pf_branch_thermal_bounds(CA_1) by 0.266787575
MIP start from previous solve produced solution with objective 289920 (0.04s)
INFO:gurobipy.gurobipy:MIP start from previous solve produced solution with objective 289920 (0.04s)
Loaded MIP start from previous solve with objective 289920
INFO:gurobipy.gurobipy:Loaded MIP start from previous solve with objective 289920
INFO:gurobipy.gurobipy:
Presolve removed 4255 rows and 4935 columns
INFO:gurobipy.gurobipy:Presolve removed 4255 rows and 4935 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 287 rows, 735 columns, 1999 nonzeros
INFO:gurobipy.gurobipy:Presolved: 287 rows, 735 columns, 1999 nonzeros
Variable types: 735 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 735 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: cutoff, 3 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: cutoff, 3 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
0 0 cutoff 0 289920.051 289920.051 0.00% - 0s
INFO:gurobipy.gurobipy: 0 0 cutoff 0 289920.051 289920.051 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (3 simplex iterations) in 0.07 seconds (0.02 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (3 simplex iterations) in 0.07 seconds (0.02 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 1: 289920
INFO:gurobipy.gurobipy:Solution count 1: 289920
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.899200511721e+05, best bound 2.899200511721e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.899200511721e+05, best bound 2.899200511721e+05, gap 0.0000%
[MIP phase] iteration 1, found 0 violation(s)
INFO:egret:[MIP phase] iteration 1, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4542 rows, 5670 columns and 13659 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4542 rows, 5670 columns and 13659 nonzeros
Model fingerprint: 0x36af9d15
INFO:gurobipy.gurobipy:Model fingerprint: 0x36af9d15
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [1e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [1e-03, 1e+01]
RHS range [1e-02, 3e+01]
INFO:gurobipy.gurobipy: RHS range [1e-02, 3e+01]
Presolve removed 4240 rows and 4900 columns
INFO:gurobipy.gurobipy:Presolve removed 4240 rows and 4900 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 302 rows, 772 columns, 2236 nonzeros
INFO:gurobipy.gurobipy:Presolved: 302 rows, 772 columns, 2236 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 handle free variables 0s
INFO:gurobipy.gurobipy: 0 handle free variables 0s
213 2.8992005e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 213 2.8992005e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 213 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 213 iterations and 0.03 seconds (0.01 work units)
Optimal objective 2.899200512e+05
INFO:gurobipy.gurobipy:Optimal objective 2.899200512e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 60712.24
Variable costs: 43567.70
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 185.37
Simulating time_step 2020-07-10 19:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8889900e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8871177e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8785830e+08 9.52e+04 9.43e+01 -1.0 6.22e+07 - 6.65e-01 4.41e-02f 1
3 1.8776304e+08 9.33e+04 8.02e+02 -1.0 6.17e+07 - 6.41e-01 2.20e-02f 1
4 1.8824495e+08 5.76e+04 6.95e+02 -1.0 4.18e+06 - 8.27e-01 3.84e-01h 1
5 1.8901487e+08 1.22e+03 1.07e+03 -1.0 2.58e+06 - 9.84e-01 1.00e+00h 1
6 1.8900239e+08 2.65e+02 1.91e+02 -1.0 8.25e+04 - 6.43e-01 8.22e-01f 1
7 1.8900054e+08 6.30e+01 3.62e+01 -1.0 1.70e+04 - 3.98e-01 8.11e-01f 1
8 1.8899930e+08 5.12e+01 5.16e-01 -1.0 2.44e+04 - 6.02e-01 1.00e+00f 1
9 1.8899920e+08 6.61e-01 5.24e-03 -1.0 2.75e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8899920e+08 3.17e-05 5.20e-05 -1.0 1.89e+01 - 9.90e-01 1.00e+00h 1
11 1.8899920e+08 1.86e-07 1.00e-06 -1.0 1.80e-01 - 1.00e+00 1.00e+00h 1
12 1.8899901e+08 3.91e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8899900e+08 8.53e-05 3.49e-08 -3.8 3.15e+01 - 1.00e+00 1.00e+00f 1
14 1.8899900e+08 1.49e-07 8.73e-11 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8899900e+08 1.49e-07 8.48e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8899900442576534e+06 1.8899900442576534e+08
Dual infeasibility......: 8.4754674624232923e-11 8.4754674624232923e-09
Constraint violation....: 2.5611370801925659e-09 1.4901161193847659e-07
Complementarity.........: 2.5405771424256430e-09 2.5405771424256430e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5405771424256430e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.654
Total CPU secs in NLP function evaluations = 5.267
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4534 rows, 5662 columns and 13565 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4534 rows, 5662 columns and 13565 nonzeros
Model fingerprint: 0x75e38aba
INFO:gurobipy.gurobipy:Model fingerprint: 0x75e38aba
Variable types: 3966 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3966 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 9e+00]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 9e+00]
RHS range [4e-03, 3e+01]
INFO:gurobipy.gurobipy: RHS range [4e-03, 3e+01]
Found heuristic solution: objective 8.056442e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 8.056442e+09
Presolve removed 4249 rows and 4942 columns
INFO:gurobipy.gurobipy:Presolve removed 4249 rows and 4942 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 285 rows, 720 columns, 1876 nonzeros
INFO:gurobipy.gurobipy:Presolved: 285 rows, 720 columns, 1876 nonzeros
Found heuristic solution: objective 2.128526e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 2.128526e+09
Variable types: 720 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 720 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 2.927638e+05, 130 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 2.927638e+05, 130 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 292763.83164 292763.832 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 292763.83164 292763.832 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (130 simplex iterations) in 0.04 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (130 simplex iterations) in 0.04 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 292764 8.05644e+09
INFO:gurobipy.gurobipy:Solution count 2: 292764 8.05644e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.927638316361e+05, best bound 2.927638316361e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.927638316361e+05, best bound 2.927638316361e+05, gap 0.0000%
[MIP phase] iteration 0, found 1 violation(s)
INFO:egret:[MIP phase] iteration 0, found 1 violation(s)
[MIP phase] iteration 0, added 1 flow constraint(s)
INFO:egret:[MIP phase] iteration 0, added 1 flow constraint(s)
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4535 rows, 5663 columns and 13638 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4535 rows, 5663 columns and 13638 nonzeros
Model fingerprint: 0x3b1f0918
INFO:gurobipy.gurobipy:Model fingerprint: 0x3b1f0918
Variable types: 3967 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3967 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-03, 3e+01]
INFO:gurobipy.gurobipy: RHS range [4e-03, 3e+01]
INFO:gurobipy.gurobipy:
User MIP start did not produce a new incumbent solution
INFO:gurobipy.gurobipy:User MIP start did not produce a new incumbent solution
User MIP start violates constraint TransmissionBlock(1)_ineq_pf_branch_thermal_bounds(CA_1) by 0.144047853
INFO:gurobipy.gurobipy:User MIP start violates constraint TransmissionBlock(1)_ineq_pf_branch_thermal_bounds(CA_1) by 0.144047853
MIP start from previous solve produced solution with objective 292817 (0.02s)
INFO:gurobipy.gurobipy:MIP start from previous solve produced solution with objective 292817 (0.02s)
Loaded MIP start from previous solve with objective 292817
INFO:gurobipy.gurobipy:Loaded MIP start from previous solve with objective 292817
INFO:gurobipy.gurobipy:
Presolve removed 4248 rows and 4941 columns
INFO:gurobipy.gurobipy:Presolve removed 4248 rows and 4941 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 287 rows, 722 columns, 1981 nonzeros
INFO:gurobipy.gurobipy:Presolved: 287 rows, 722 columns, 1981 nonzeros
Variable types: 722 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 722 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: cutoff, 138 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: cutoff, 138 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
0 0 cutoff 0 292816.571 292816.571 0.00% - 0s
INFO:gurobipy.gurobipy: 0 0 cutoff 0 292816.571 292816.571 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (138 simplex iterations) in 0.05 seconds (0.02 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (138 simplex iterations) in 0.05 seconds (0.02 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 1: 292817
INFO:gurobipy.gurobipy:Solution count 1: 292817
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.928165712058e+05, best bound 2.928165712058e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.928165712058e+05, best bound 2.928165712058e+05, gap 0.0000%
[MIP phase] iteration 1, found 0 violation(s)
INFO:egret:[MIP phase] iteration 1, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4535 rows, 5663 columns and 13638 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4535 rows, 5663 columns and 13638 nonzeros
Model fingerprint: 0xb3e2f70a
INFO:gurobipy.gurobipy:Model fingerprint: 0xb3e2f70a
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [4e-03, 3e+01]
INFO:gurobipy.gurobipy: RHS range [4e-03, 3e+01]
Presolve removed 4233 rows and 4906 columns
INFO:gurobipy.gurobipy:Presolve removed 4233 rows and 4906 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 302 rows, 759 columns, 2289 nonzeros
INFO:gurobipy.gurobipy:Presolved: 302 rows, 759 columns, 2289 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 2.4902109e+05 5.228251e+01 0.000000e+00 0s
INFO:gurobipy.gurobipy: 0 2.4902109e+05 5.228251e+01 0.000000e+00 0s
145 2.9281657e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 145 2.9281657e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 145 iterations and 0.02 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 145 iterations and 0.02 seconds (0.01 work units)
Optimal objective 2.928165712e+05
INFO:gurobipy.gurobipy:Optimal objective 2.928165712e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 64215.62
Variable costs: 46464.22
Number on/offs: 3
Sum on/off ramps: 24.00
Sum nominal ramps: 109.51
Simulating time_step 2020-07-10 20:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8900300e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8881474e+08 9.90e+04 9.65e+01 -1.0 2.74e+06 - 9.61e-01 1.02e-02f 1
2 1.8796130e+08 9.52e+04 9.43e+01 -1.0 6.22e+07 - 6.65e-01 4.41e-02f 1
3 1.8786606e+08 9.33e+04 8.02e+02 -1.0 6.17e+07 - 6.41e-01 2.20e-02f 1
4 1.8834837e+08 5.76e+04 6.95e+02 -1.0 4.18e+06 - 8.27e-01 3.84e-01h 1
5 1.8911887e+08 1.22e+03 1.07e+03 -1.0 2.58e+06 - 9.84e-01 1.00e+00h 1
6 1.8910639e+08 2.65e+02 1.91e+02 -1.0 8.25e+04 - 6.43e-01 8.22e-01f 1
7 1.8910454e+08 6.30e+01 3.61e+01 -1.0 1.70e+04 - 3.98e-01 8.11e-01f 1
8 1.8910330e+08 5.12e+01 5.16e-01 -1.0 2.44e+04 - 6.02e-01 1.00e+00f 1
9 1.8910320e+08 6.61e-01 5.24e-03 -1.0 2.75e+03 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8910320e+08 3.16e-05 5.20e-05 -1.0 1.89e+01 - 9.90e-01 1.00e+00h 1
11 1.8910320e+08 1.49e-07 1.00e-06 -1.0 1.80e-01 - 1.00e+00 1.00e+00h 1
12 1.8910300e+08 3.91e-02 2.98e-05 -2.5 6.73e+02 - 1.00e+00 1.00e+00f 1
13 1.8910300e+08 8.54e-05 3.49e-08 -3.8 3.15e+01 - 1.00e+00 1.00e+00f 1
14 1.8910300e+08 1.79e-07 1.16e-10 -5.7 1.05e+00 - 1.00e+00 1.00e+00f 1
15 1.8910300e+08 1.49e-07 1.16e-10 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8910299897808486e+06 1.8910299897808486e+08
Dual infeasibility......: 1.1641532182693481e-10 1.1641532182693481e-08
Constraint violation....: 2.5611370801925659e-09 1.4901161193847656e-07
Complementarity.........: 2.5406046548794875e-09 2.5406046548794877e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5406046548794877e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.643
Total CPU secs in NLP function evaluations = 5.327
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4538 rows, 5666 columns and 13848 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4538 rows, 5666 columns and 13848 nonzeros
Model fingerprint: 0x9fb920e0
INFO:gurobipy.gurobipy:Model fingerprint: 0x9fb920e0
Variable types: 3970 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3970 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [4e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [4e-03, 1e+01]
RHS range [2e-03, 3e+01]
INFO:gurobipy.gurobipy: RHS range [2e-03, 3e+01]
Found heuristic solution: objective 7.097189e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 7.097189e+09
Presolve removed 4245 rows and 4940 columns
INFO:gurobipy.gurobipy:Presolve removed 4245 rows and 4940 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 293 rows, 726 columns, 2274 nonzeros
INFO:gurobipy.gurobipy:Presolved: 293 rows, 726 columns, 2274 nonzeros
Found heuristic solution: objective 7.094689e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 7.094689e+09
Variable types: 726 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 726 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 2.887980e+05, 152 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 2.887980e+05, 152 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 288797.99752 288797.998 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 288797.99752 288797.998 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (152 simplex iterations) in 0.05 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (152 simplex iterations) in 0.05 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 288798 7.09719e+09
INFO:gurobipy.gurobipy:Solution count 2: 288798 7.09719e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.887979975131e+05, best bound 2.887979975128e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.887979975131e+05, best bound 2.887979975128e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4538 rows, 5666 columns and 13848 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4538 rows, 5666 columns and 13848 nonzeros
Model fingerprint: 0x22909c93
INFO:gurobipy.gurobipy:Model fingerprint: 0x22909c93
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [4e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [4e-03, 1e+01]
RHS range [2e-03, 3e+01]
INFO:gurobipy.gurobipy: RHS range [2e-03, 3e+01]
Presolve removed 4233 rows and 4908 columns
INFO:gurobipy.gurobipy:Presolve removed 4233 rows and 4908 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 305 rows, 760 columns, 2507 nonzeros
INFO:gurobipy.gurobipy:Presolved: 305 rows, 760 columns, 2507 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 handle free variables 0s
INFO:gurobipy.gurobipy: 0 handle free variables 0s
204 2.8879800e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 204 2.8879800e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 204 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 204 iterations and 0.03 seconds (0.01 work units)
Optimal objective 2.887979975e+05
INFO:gurobipy.gurobipy:Optimal objective 2.887979975e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 60712.24
Variable costs: 45949.02
Number on/offs: 3
Sum on/off ramps: 0.00
Sum nominal ramps: 88.09
Simulating time_step 2020-07-10 21:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8736553e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8722089e+08 9.90e+04 9.65e+01 -1.0 2.94e+06 - 9.62e-01 1.02e-02f 1
2 1.8632654e+08 9.54e+04 9.42e+01 -1.0 6.73e+07 - 6.64e-01 4.36e-02f 1
3 1.8623523e+08 9.35e+04 8.36e+02 -1.0 5.68e+07 - 6.66e-01 2.06e-02f 1
4 1.8673532e+08 5.30e+04 6.40e+02 -1.0 4.20e+06 - 8.27e-01 4.35e-01h 1
5 1.8738049e+08 1.02e+03 9.03e+02 -1.0 2.37e+06 - 8.02e-01 1.00e+00h 1
6 1.8736909e+08 1.44e+02 1.97e+01 -1.0 5.62e+04 - 9.16e-02 1.00e+00f 1
7 1.8736628e+08 2.07e+01 7.30e+00 -1.0 1.45e+04 - 6.30e-01 9.77e-01f 1
8 1.8736489e+08 3.80e+01 3.60e+00 -1.0 2.10e+04 - 5.07e-01 1.00e+00f 1
9 1.8736439e+08 1.05e+01 3.62e-02 -1.0 1.10e+04 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8736431e+08 4.44e-01 3.62e-04 -1.0 2.27e+03 - 9.90e-01 1.00e+00f 1
11 1.8736431e+08 8.79e-07 2.07e-06 -1.0 3.37e+00 - 9.92e-01 1.00e+00h 1
12 1.8736415e+08 2.60e-02 1.91e-05 -1.7 5.49e+02 - 1.00e+00 1.00e+00f 1
13 1.8736411e+08 1.85e-03 1.25e-06 -3.8 1.47e+02 - 1.00e+00 1.00e+00f 1
14 1.8736411e+08 2.24e-07 1.04e-10 -5.7 1.60e+00 - 1.00e+00 1.00e+00f 1
15 1.8736411e+08 1.42e-07 5.00e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8736410669541315e+06 1.8736410669541314e+08
Dual infeasibility......: 4.9987222853477989e-11 4.9987222853477989e-09
Constraint violation....: 2.5611370801925659e-09 1.4156103134155273e-07
Complementarity.........: 2.5380061167868902e-09 2.5380061167868902e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5380061167868902e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.686
Total CPU secs in NLP function evaluations = 5.809
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4538 rows, 5666 columns and 13854 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4538 rows, 5666 columns and 13854 nonzeros
Model fingerprint: 0xab194f9a
INFO:gurobipy.gurobipy:Model fingerprint: 0xab194f9a
Variable types: 3970 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3970 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [8e-03, 2e+01]
INFO:gurobipy.gurobipy: RHS range [8e-03, 2e+01]
Found heuristic solution: objective 6.461774e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 6.461774e+09
Presolve removed 4245 rows and 4940 columns
INFO:gurobipy.gurobipy:Presolve removed 4245 rows and 4940 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 293 rows, 726 columns, 2274 nonzeros
INFO:gurobipy.gurobipy:Presolved: 293 rows, 726 columns, 2274 nonzeros
Found heuristic solution: objective 6.459274e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 6.459274e+09
Variable types: 726 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 726 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 2.854989e+05, 144 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 2.854989e+05, 144 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 285498.87948 285498.879 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 285498.87948 285498.879 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (144 simplex iterations) in 0.05 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (144 simplex iterations) in 0.05 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 285499 6.46177e+09
INFO:gurobipy.gurobipy:Solution count 2: 285499 6.46177e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.854988794824e+05, best bound 2.854988794804e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.854988794824e+05, best bound 2.854988794804e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4538 rows, 5666 columns and 13854 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4538 rows, 5666 columns and 13854 nonzeros
Model fingerprint: 0xd4adad52
INFO:gurobipy.gurobipy:Model fingerprint: 0xd4adad52
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [8e-03, 2e+01]
INFO:gurobipy.gurobipy: RHS range [8e-03, 2e+01]
Presolve removed 4233 rows and 4908 columns
INFO:gurobipy.gurobipy:Presolve removed 4233 rows and 4908 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 305 rows, 760 columns, 2507 nonzeros
INFO:gurobipy.gurobipy:Presolved: 305 rows, 760 columns, 2507 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 handle free variables 0s
INFO:gurobipy.gurobipy: 0 handle free variables 0s
190 2.8549888e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 190 2.8549888e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 190 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 190 iterations and 0.03 seconds (0.01 work units)
Optimal objective 2.854988795e+05
INFO:gurobipy.gurobipy:Optimal objective 2.854988795e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 60712.24
Variable costs: 42649.90
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 117.45
Simulating time_step 2020-07-10 22:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8563440e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8553448e+08 9.90e+04 9.65e+01 -1.0 2.95e+06 - 9.61e-01 1.02e-02f 1
2 1.8466365e+08 9.55e+04 9.45e+01 -1.0 6.66e+07 - 6.65e-01 4.14e-02f 1
3 1.8453260e+08 9.36e+04 8.60e+02 -1.0 6.26e+07 - 6.37e-01 2.17e-02f 1
4 1.8497033e+08 5.71e+04 7.38e+02 -1.0 4.20e+06 - 8.26e-01 3.92e-01h 1
5 1.8560641e+08 3.99e+03 9.47e+02 -1.0 2.56e+06 - 7.77e-01 9.36e-01h 1
6 1.8563898e+08 1.09e+02 2.29e+01 -1.0 1.28e+05 - 1.36e-01 1.00e+00h 1
7 1.8563547e+08 2.34e+01 6.38e+00 -1.0 1.65e+04 - 7.21e-01 1.00e+00f 1
8 1.8563372e+08 5.03e+01 3.12e+00 -1.0 2.42e+04 - 5.12e-01 1.00e+00f 1
9 1.8563325e+08 8.96e+00 3.13e-02 -1.0 1.02e+04 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8563318e+08 2.54e-01 3.12e-04 -1.0 1.71e+03 - 9.90e-01 1.00e+00f 1
11 1.8563318e+08 1.34e-07 1.69e-06 -1.0 1.05e+00 - 9.92e-01 1.00e+00h 1
12 1.8563302e+08 2.55e-02 1.89e-05 -1.7 5.44e+02 - 1.00e+00 1.00e+00f 1
13 1.8563298e+08 1.85e-03 1.25e-06 -3.8 1.46e+02 - 1.00e+00 1.00e+00f 1
14 1.8563298e+08 1.94e-07 1.02e-10 -5.7 1.60e+00 - 1.00e+00 1.00e+00f 1
15 1.8563298e+08 1.79e-07 7.61e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8563298177616787e+06 1.8563298177616787e+08
Dual infeasibility......: 7.6114204845783006e-11 7.6114204845783006e-09
Constraint violation....: 2.5611370801925659e-09 1.7881393432617188e-07
Complementarity.........: 2.5380061167868873e-09 2.5380061167868875e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5380061167868875e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.608
Total CPU secs in NLP function evaluations = 5.346
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4538 rows, 5669 columns and 13866 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4538 rows, 5669 columns and 13866 nonzeros
Model fingerprint: 0xeeaa908a
INFO:gurobipy.gurobipy:Model fingerprint: 0xeeaa908a
Variable types: 3973 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3973 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [1e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [1e-02, 2e+01]
Found heuristic solution: objective 5.136824e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 5.136824e+09
Presolve removed 4245 rows and 4937 columns
INFO:gurobipy.gurobipy:Presolve removed 4245 rows and 4937 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 293 rows, 732 columns, 2292 nonzeros
INFO:gurobipy.gurobipy:Presolved: 293 rows, 732 columns, 2292 nonzeros
Found heuristic solution: objective 5.134324e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 5.134324e+09
Variable types: 732 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 732 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 2.761235e+05, 119 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 2.761235e+05, 119 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 276123.52663 276123.527 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 276123.52663 276123.527 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (119 simplex iterations) in 0.05 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (119 simplex iterations) in 0.05 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 276124 5.13682e+09
INFO:gurobipy.gurobipy:Solution count 2: 276124 5.13682e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.761235266291e+05, best bound 2.761235266291e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.761235266291e+05, best bound 2.761235266291e+05, gap 0.0000%
[MIP phase] iteration 0, found 1 violation(s)
INFO:egret:[MIP phase] iteration 0, found 1 violation(s)
[MIP phase] iteration 0, added 1 flow constraint(s)
INFO:egret:[MIP phase] iteration 0, added 1 flow constraint(s)
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4539 rows, 5670 columns and 13939 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4539 rows, 5670 columns and 13939 nonzeros
Model fingerprint: 0x5bb4a176
INFO:gurobipy.gurobipy:Model fingerprint: 0x5bb4a176
Variable types: 3974 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3974 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [1e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [1e-02, 2e+01]
INFO:gurobipy.gurobipy:
User MIP start did not produce a new incumbent solution
INFO:gurobipy.gurobipy:User MIP start did not produce a new incumbent solution
User MIP start violates constraint TransmissionBlock(2)_ineq_pf_branch_thermal_bounds(A27) by 0.080233159
INFO:gurobipy.gurobipy:User MIP start violates constraint TransmissionBlock(2)_ineq_pf_branch_thermal_bounds(A27) by 0.080233159
MIP start from previous solve produced solution with objective 276124 (0.03s)
INFO:gurobipy.gurobipy:MIP start from previous solve produced solution with objective 276124 (0.03s)
Loaded MIP start from previous solve with objective 276124
INFO:gurobipy.gurobipy:Loaded MIP start from previous solve with objective 276124
INFO:gurobipy.gurobipy:
Presolve removed 4232 rows and 4924 columns
INFO:gurobipy.gurobipy:Presolve removed 4232 rows and 4924 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 307 rows, 746 columns, 2359 nonzeros
INFO:gurobipy.gurobipy:Presolved: 307 rows, 746 columns, 2359 nonzeros
Variable types: 746 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 746 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: cutoff, 148 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: cutoff, 148 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
0 0 cutoff 0 276123.527 276123.527 0.00% - 0s
INFO:gurobipy.gurobipy: 0 0 cutoff 0 276123.527 276123.527 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (148 simplex iterations) in 0.06 seconds (0.02 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (148 simplex iterations) in 0.06 seconds (0.02 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 1: 276124
INFO:gurobipy.gurobipy:Solution count 1: 276124
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.761235266291e+05, best bound 2.761235266291e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.761235266291e+05, best bound 2.761235266291e+05, gap 0.0000%
[MIP phase] iteration 1, found 0 violation(s)
INFO:egret:[MIP phase] iteration 1, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4539 rows, 5670 columns and 13939 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4539 rows, 5670 columns and 13939 nonzeros
Model fingerprint: 0xf4231b3b
INFO:gurobipy.gurobipy:Model fingerprint: 0xf4231b3b
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [3e-04, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [3e-04, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [1e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [1e-02, 2e+01]
Presolve removed 4233 rows and 4905 columns
INFO:gurobipy.gurobipy:Presolve removed 4233 rows and 4905 columns
Presolve time: 0.02s
INFO:gurobipy.gurobipy:Presolve time: 0.02s
Presolved: 306 rows, 767 columns, 2592 nonzeros
INFO:gurobipy.gurobipy:Presolved: 306 rows, 767 columns, 2592 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 handle free variables 0s
INFO:gurobipy.gurobipy: 0 handle free variables 0s
209 2.7612353e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 209 2.7612353e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 209 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 209 iterations and 0.03 seconds (0.01 work units)
Optimal objective 2.761235266e+05
INFO:gurobipy.gurobipy:Optimal objective 2.761235266e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 60712.24
Variable costs: 33274.55
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 351.37
Simulating time_step 2020-07-10 23:00
Solving SCED instance
Ipopt 3.13.2: max_iter=200
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6387
Number of nonzeros in inequality constraint Jacobian.: 56
Number of nonzeros in Lagrangian Hessian.............: 1980
Total number of variables............................: 2449
variables with only lower bounds: 28
variables with lower and upper bounds: 1673
variables with only upper bounds: 0
Total number of equality constraints.................: 2426
Total number of inequality constraints...............: 32
inequality constraints with only lower bounds: 4
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 28
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.8390247e+08 1.00e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.8384725e+08 9.90e+04 9.65e+01 -1.0 2.95e+06 - 9.61e-01 1.02e-02f 1
2 1.8294393e+08 9.55e+04 9.45e+01 -1.0 6.65e+07 - 6.65e-01 4.14e-02f 1
3 1.8282380e+08 9.36e+04 8.60e+02 -1.0 6.26e+07 - 6.37e-01 2.17e-02f 1
4 1.8325309e+08 5.71e+04 7.38e+02 -1.0 4.20e+06 - 8.26e-01 3.91e-01h 1
5 1.8387561e+08 3.99e+03 9.48e+02 -1.0 2.56e+06 - 7.78e-01 9.36e-01h 1
6 1.8390711e+08 1.08e+02 2.29e+01 -1.0 1.28e+05 - 1.35e-01 1.00e+00h 1
7 1.8390357e+08 2.35e+01 6.79e+00 -1.0 1.65e+04 - 7.03e-01 1.00e+00f 1
8 1.8390180e+08 5.00e+01 3.17e+00 -1.0 2.41e+04 - 5.34e-01 1.00e+00f 1
9 1.8390131e+08 9.82e+00 3.18e-02 -1.0 1.07e+04 - 9.90e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.8390125e+08 2.87e-01 3.18e-04 -1.0 1.82e+03 - 9.90e-01 1.00e+00f 1
11 1.8390125e+08 1.34e-07 1.72e-06 -1.0 1.07e+00 - 9.92e-01 1.00e+00h 1
12 1.8390109e+08 2.50e-02 1.87e-05 -1.7 5.39e+02 - 1.00e+00 1.00e+00f 1
13 1.8390105e+08 1.85e-03 1.25e-06 -3.8 1.46e+02 - 1.00e+00 1.00e+00f 1
14 1.8390105e+08 2.09e-07 1.04e-10 -5.7 1.60e+00 - 1.00e+00 1.00e+00f 1
15 1.8390105e+08 1.64e-07 8.48e-11 -8.6 1.29e-02 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.8390104743733611e+06 1.8390104743733612e+08
Dual infeasibility......: 8.4754687071382083e-11 8.4754687071382083e-09
Constraint violation....: 2.5611370801925659e-09 1.6391277313232422e-07
Complementarity.........: 2.5380061167868832e-09 2.5380061167868833e-07
Overall NLP error.......: 2.5611370801925659e-09 2.5380061167868833e-07
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total CPU secs in IPOPT (w/o function evaluations) = 0.631
Total CPU secs in NLP function evaluations = 5.350
EXIT: Optimal Solution Found.
------------------------------------------------------------------------------
Set parameter MIPGap to value 0.01
INFO:gurobipy.gurobipy:Set parameter MIPGap to value 0.01
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4538 rows, 5669 columns and 13875 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4538 rows, 5669 columns and 13875 nonzeros
Model fingerprint: 0x7a45e5f7
INFO:gurobipy.gurobipy:Model fingerprint: 0x7a45e5f7
Variable types: 3973 continuous, 1696 integer (1696 binary)
INFO:gurobipy.gurobipy:Variable types: 3973 continuous, 1696 integer (1696 binary)
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+08]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+08]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [1e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [1e-02, 2e+01]
Found heuristic solution: objective 5.073990e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 5.073990e+09
Presolve removed 4245 rows and 4937 columns
INFO:gurobipy.gurobipy:Presolve removed 4245 rows and 4937 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 293 rows, 732 columns, 2292 nonzeros
INFO:gurobipy.gurobipy:Presolved: 293 rows, 732 columns, 2292 nonzeros
Found heuristic solution: objective 5.071490e+09
INFO:gurobipy.gurobipy:Found heuristic solution: objective 5.071490e+09
Variable types: 732 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:Variable types: 732 continuous, 0 integer (0 binary)
INFO:gurobipy.gurobipy:
Root relaxation: objective 2.725386e+05, 122 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:Root relaxation: objective 2.725386e+05, 122 iterations, 0.00 seconds (0.00 work units)
INFO:gurobipy.gurobipy:
Nodes | Current Node | Objective Bounds | Work
INFO:gurobipy.gurobipy: Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy: Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
INFO:gurobipy.gurobipy:
* 0 0 0 272538.59507 272538.595 0.00% - 0s
INFO:gurobipy.gurobipy:* 0 0 0 272538.59507 272538.595 0.00% - 0s
INFO:gurobipy.gurobipy:
Explored 1 nodes (122 simplex iterations) in 0.05 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Explored 1 nodes (122 simplex iterations) in 0.05 seconds (0.01 work units)
Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:Thread count was 8 (of 8 available processors)
INFO:gurobipy.gurobipy:
Solution count 2: 272539 5.07399e+09
INFO:gurobipy.gurobipy:Solution count 2: 272539 5.07399e+09
INFO:gurobipy.gurobipy:
Optimal solution found (tolerance 1.00e-02)
INFO:gurobipy.gurobipy:Optimal solution found (tolerance 1.00e-02)
Best objective 2.725385950688e+05, best bound 2.725385950680e+05, gap 0.0000%
INFO:gurobipy.gurobipy:Best objective 2.725385950688e+05, best bound 2.725385950680e+05, gap 0.0000%
[MIP phase] iteration 0, found 0 violation(s)
INFO:egret:[MIP phase] iteration 0, found 0 violation(s)
Solving for LMPs
Set parameter QCPDual to value 1
INFO:gurobipy.gurobipy:Set parameter QCPDual to value 1
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
INFO:gurobipy.gurobipy:Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
INFO:gurobipy.gurobipy:Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 4538 rows, 5669 columns and 13875 nonzeros
INFO:gurobipy.gurobipy:Optimize a model with 4538 rows, 5669 columns and 13875 nonzeros
Model fingerprint: 0xadafae94
INFO:gurobipy.gurobipy:Model fingerprint: 0xadafae94
Coefficient statistics:
INFO:gurobipy.gurobipy:Coefficient statistics:
Matrix range [1e-03, 6e+04]
INFO:gurobipy.gurobipy: Matrix range [1e-03, 6e+04]
Objective range [1e+00, 1e+06]
INFO:gurobipy.gurobipy: Objective range [1e+00, 1e+06]
Bounds range [7e-03, 1e+01]
INFO:gurobipy.gurobipy: Bounds range [7e-03, 1e+01]
RHS range [1e-02, 2e+01]
INFO:gurobipy.gurobipy: RHS range [1e-02, 2e+01]
Presolve removed 4233 rows and 4905 columns
INFO:gurobipy.gurobipy:Presolve removed 4233 rows and 4905 columns
Presolve time: 0.01s
INFO:gurobipy.gurobipy:Presolve time: 0.01s
Presolved: 305 rows, 766 columns, 2525 nonzeros
INFO:gurobipy.gurobipy:Presolved: 305 rows, 766 columns, 2525 nonzeros
INFO:gurobipy.gurobipy:
Iteration Objective Primal Inf. Dual Inf. Time
INFO:gurobipy.gurobipy:Iteration Objective Primal Inf. Dual Inf. Time
0 handle free variables 0s
INFO:gurobipy.gurobipy: 0 handle free variables 0s
192 2.7253860e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy: 192 2.7253860e+05 0.000000e+00 0.000000e+00 0s
INFO:gurobipy.gurobipy:
Solved in 192 iterations and 0.03 seconds (0.01 work units)
INFO:gurobipy.gurobipy:Solved in 192 iterations and 0.03 seconds (0.01 work units)
Optimal objective 2.725385951e+05
INFO:gurobipy.gurobipy:Optimal objective 2.725385951e+05
[LP phase] iteration 0, found 0 violation(s)
INFO:egret:[LP phase] iteration 0, found 0 violation(s)
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Implemented Power (MPC) 287
Realized SOC (MPC) 76000
Fixed costs: 60712.24
Variable costs: 29689.62
Number on/offs: 0
Sum on/off ramps: 0.00
Sum nominal ramps: 143.91
Saving bidding results to disk...
Saving tracking results to disk...
Simulation Complete
Total simulation time: 953.56 seconds