Surrogate Models for Grid Outcomes

1. The Steady-State Co-Optimization with Market Interactions

We developed two surrogate model architectures to map market input parameters to market outputs. Each surrogate takes 8 inputs from the production cost model (PCM) Prescient outlined in table below. The data for training the surrogates is from the Prescient sensitivity analysis.

Input	Descriptions	Units
x₁	Maximum Designed Capacity (P_max)	MW
x₂	Minimum Operating Multiplier	–
x₃	Ramp Rate Multiplier	–
x₄	Minimum Up Time	hr
x₅	Minimum Down Multiplier	–
x₆	Marginal Cost	$/MWh
x₇	No Load Cost	$/hr@P_max
x₈	Representative Startup Cost	$/MW capacity

Output	Descriptions	Units
y₁	Annual Revenue	MM $
y₂	Annual Number of Startups	#
y_z	Annual Hours Dispatched in zone z	hr

Market revenue y₁ is a surrogate function of the bid parameters, x, which correspond to the data which each individual resource communicates to the wholesale electricity market. y₂ approximates the number of startups of the generator during the simulation time periods. y_z is the surrogate for frequency of each scenario, we use eleven total zones to represent generator power output scaled by the nameplate capacity (maximum power output). The zones consist of an ’off’ state and ten power outputs between the minimum and maximum output of the generator, i.e., 0-10%, 10-20%, …, 90-100%.

2. ALAMO Surrogate Models

We use ALAMO (version 2021.12.28) (https://idaes-pse.readthedocs.io/en/1.4.4/apps/alamopy.html) to train algebraic surrogates which consists of a linear combination of nonlinear basis functions x_j and regressed coefficients for coefficient $\beta$_j for index j in set B

\[y_alamo = \sum_{j \in \beta} \beta_j X_j(x)\]

For training, ALAMO considers monomial and binomial basis functions with up to 15 total terms with power values of 1, 2, and 3. We use Bayesian Information Criteria (BIC) implemented in ALAMO to select the best algebraic surrogate using enumeration mode. In total, we train a total of fourteen surrogate models using the ALAMO version accessible through the IDAES-PSE interface: revenue (one), number of startups (one), and surrogates for each zone (eleven).

Three ALAMO surrogate models are trained in ‘train_nstartups_idaes.py’, ‘train_revenue_idaes.py’ and ‘train_zones_idaes.py’. The input training data can be read in or simulated using available Python packages and 1/3 of the training data are withheld for testing the model. The data are normalized before fed to the trainer. There are no other arguments needed to specify the training. ALAMO solves ordinary least squares regression problems and generates the output results in the json files. (The ALAMO training options are default set in ‘train_nstartups/revenue/zones.py’) There will be three output json files. The ‘alamo_nstartups/revenue/zones.json’ stores the coefficients of the monomial and binomial basis functions. The ‘alamo_parameters_nstartups/revenue/zones.json’ saves scaling and training bounds for the input data. The ‘alamo_nstartups/revenue/zones_accuracy.json’ has the computed R² matrices.

3. Neural Network (NN) Surrogate Models

Feed-forward neural network (NN) surrogate models are trained.

\[x = z_0\]

\[z_k = \sigma(W_k z_{k-1} + b_k), k\in \{1,2,...,K-1\}\]

\[y_{nn} = W_k z_{k-1} + b_k\]

We use the ‘MLPRegressor’ package (Keras version v2.8.0, Scikit Learn version v0.24.2) with default settings to train three 2-layer neural networks.The revenue and startup surrogates contain two hidden layers with 100 nodes in the first hidden layer and 50 nodes in the second (for the annual zone output surrogate, 100 nodes in both layers).

Three NN surrogate models are trained in ‘train_nstartups.py’, ‘train_revenue.py’ and ‘train_zones.py’. The input training data can be read in or simulated using available python packages and 1/3 of the training data are split for testing the model. The data are normalized before fed to the trainer. There are no other arguments needed to specify the training. There are two output json files and one pickle file that save the results. The ‘scikit_nstartups/revenue/zones.pkl’ stores the coefficients of the neural networks. ‘The scikit_parameters_nstartups/revenue/zones.json’ saves scaling and training bounds for the input data. The ‘scikit_nstartups/revenue/zones_accuracy.json’ has the computed R² matrices.

The accuracy of the scikit NN surrogate models can be visualized by ‘plot_scikit_nstartups/revenue/zones.py’.

A Jupyter Notebook demonstration can be found in the following link: https://github.com/jalving/dispatches/blob/prescient_verify/dispatches/workflow/surrogate_design/rankine_cycle_case/grid_surrogate_design.ipynb

4. Optimization with Surrogate Models

We can implement the steady-state co-optimization with market interactions in part 1 using ‘run_surrogate_alamo.py’ and ‘run_surrogate_nn.py’. The scripts formulate the optimization using Pyomo and use Python packages to add the surrogate model coefficients and input data bounds from the json and pickle files. Optionally, some surrogate inputs may be fixed (removed as optimization degrees of freedom) in the scripts. The optimization solution is stored in ‘conceptual_design_solution_alamo/nn.json’s which can be read by the Prescient for further verification.