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start tests
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‎.github/actions/install/action.yml

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@@ -50,7 +50,7 @@ runs:
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if: inputs.variant == 'dev'
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shell: bash -l {0}
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run: |
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pip install --no-cache-dir -r requirements-dev.txt
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pip install -r requirements-dev.txt
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- name: Install using pip (${{ inputs.variant }})
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if: inputs.variant == 'site'

‎.github/workflows/checks.yml

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@@ -51,9 +51,8 @@ jobs:
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run: |
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_pip_install="pip install --progress-bar off"
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echo '::group::Output of "pip install" commands'
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pip install --upgrade pip
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$_pip_install --upgrade pip wheel setuptools
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$_pip_install --no-cache-dir -r requirements-dev.txt
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$_pip_install -r requirements-dev.txt
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echo '::endgroup::'
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echo '::group::Display installed packages'
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conda list

‎.github/workflows/teal-integration.yml

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@@ -46,7 +46,7 @@ jobs:
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run: |
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echo '::group::Output of "pip install" commands'
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pip install --upgrade wheel setuptools
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pip install --no-cache-dir -r requirements-teal.txt
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pip install -r requirements-teal.txt
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echo '::endgroup::'
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echo '::group::Display installed packages'
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pip list

‎dispatches/case_studies/renewables_case/RE_surrogate_optimization_steadystate.py

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@@ -19,7 +19,6 @@
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#the rankine cycle is a directory above this one, so modify path
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import json
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import time
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from functools import partial
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import numpy as np
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import pandas as pd
@@ -51,45 +50,49 @@
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# path for folder that has surrogate models
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re_nn_dir = Path("/Users/dguittet/Projects/Dispatches/NN_models/steady_state_new")
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# load scaling and bounds for NN surrogates (rev and # of startups)
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with open(re_nn_dir / "dispatch_frequency" / "static_clustering_wind_pmax.pkl", 'rb') as f:
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model = pickle.load(f)
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centers = model.cluster_centers_
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dispatch_clusters_mean = centers[:, 0]
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pem_clusters_mean = centers[:, 1]
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resource_clusters_mean = centers[:, 2]
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with open(re_nn_dir / "revenue" / "RE_revenue_params_3layers.json", 'rb') as f:
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rev_data = json.load(f)
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# load keras neural networks
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# Input variables are PEM bid price, PEM MW, Reserve Factor and Load Shed Price
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nn_rev = keras.models.load_model(re_nn_dir / "revenue" / "RE_revenue_3layers")
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nn_dispatch = keras.models.load_model(re_nn_dir / "dispatch_frequency" / "ss_surrogate_model_wind_pmax")
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with open(re_nn_dir / "dispatch_frequency" / "ss_surrogate_param_wind_pmax.json", 'r') as f:
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dispatch_data = json.load(f)
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# load keras models and create OMLT NetworkDefinition objects
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#Revenue model definition
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input_bounds_rev = {i:(rev_data['xmin'][i],rev_data['xmax'][i]) for i in range(len(rev_data['xmin']))}
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scaling_object_rev = omlt.OffsetScaling(offset_inputs=rev_data['xm_inputs'],
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factor_inputs=rev_data['xstd_inputs'],
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offset_outputs=[rev_data['y_mean']],
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factor_outputs=[rev_data['y_std']])
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net_rev_defn = load_keras_sequential(nn_rev,scaling_object_rev,input_bounds_rev)
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# the dispatch frequency surrogate
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input_bounds_dispatch = {i:(dispatch_data['xmin'][i],dispatch_data['xmax'][i]) for i in range(len(dispatch_data['xmin']))}
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scaling_object_dispatch = omlt.OffsetScaling(offset_inputs=dispatch_data['xm_inputs'],
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factor_inputs=dispatch_data['xstd_inputs'],
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offset_outputs=dispatch_data['ws_mean'],
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factor_outputs=dispatch_data['ws_std'])
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net_frequency_defn = load_keras_sequential(nn_dispatch,scaling_object_dispatch,input_bounds_dispatch)
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def conceptual_design_dynamic_RE(input_params, num_rep_days, PEM_bid=None, PEM_MW=None, verbose=False):
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def load_surrogate_model(re_nn_dir):
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# load scaling and bounds for NN surrogates (rev and # of startups)
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with open(re_nn_dir / "dispatch_frequency" / "static_clustering_wind_pmax.pkl", 'rb') as f:
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model = pickle.load(f)
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centers = model.cluster_centers_
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dispatch_clusters_mean = centers[:, 0]
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pem_clusters_mean = centers[:, 1]
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resource_clusters_mean = centers[:, 2]
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with open(re_nn_dir / "revenue" / "RE_revenue_params_3layers.json", 'rb') as f:
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rev_data = json.load(f)
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# load keras neural networks
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# Input variables are PEM bid price, PEM MW, Reserve Factor and Load Shed Price
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nn_rev = keras.models.load_model(re_nn_dir / "revenue" / "RE_revenue_3layers")
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nn_dispatch = keras.models.load_model(re_nn_dir / "dispatch_frequency" / "ss_surrogate_model_wind_pmax")
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with open(re_nn_dir / "dispatch_frequency" / "ss_surrogate_param_wind_pmax.json", 'r') as f:
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dispatch_data = json.load(f)
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# load keras models and create OMLT NetworkDefinition objects
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#Revenue model definition
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input_bounds_rev = {i:(rev_data['xmin'][i],rev_data['xmax'][i]) for i in range(len(rev_data['xmin']))}
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scaling_object_rev = omlt.OffsetScaling(offset_inputs=rev_data['xm_inputs'],
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factor_inputs=rev_data['xstd_inputs'],
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offset_outputs=[rev_data['y_mean']],
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factor_outputs=[rev_data['y_std']])
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net_rev_defn = load_keras_sequential(nn_rev,scaling_object_rev,input_bounds_rev)
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# the dispatch frequency surrogate
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input_bounds_dispatch = {i:(dispatch_data['xmin'][i],dispatch_data['xmax'][i]) for i in range(len(dispatch_data['xmin']))}
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scaling_object_dispatch = omlt.OffsetScaling(offset_inputs=dispatch_data['xm_inputs'],
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factor_inputs=dispatch_data['xstd_inputs'],
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offset_outputs=dispatch_data['ws_mean'],
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factor_outputs=dispatch_data['ws_std'])
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net_frequency_defn = load_keras_sequential(nn_dispatch,scaling_object_dispatch,input_bounds_dispatch)
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return net_rev_defn, net_frequency_defn, dispatch_clusters_mean, pem_clusters_mean, resource_clusters_mean
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def conceptual_design_dynamic_RE(input_params, PEM_bid=None, PEM_MW=None, verbose=False):
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net_rev_defn, net_frequency_defn, dispatch_clusters_mean, pem_clusters_mean, resource_clusters_mean = load_surrogate_model(re_nn_dir)
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num_rep_days = len(dispatch_clusters_mean)
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m = ConcreteModel(name = 'RE_Conceptual_Design_full_surrogates')
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@@ -275,7 +278,7 @@ def record_result(m, num_rep_days):
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def run_design(PEM_bid=None, PEM_size=None):
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model = conceptual_design_dynamic_RE(default_input_params, num_rep_days=n_rep_days, PEM_bid=PEM_bid, PEM_MW=PEM_size, verbose=False)
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model = conceptual_design_dynamic_RE(default_input_params, PEM_bid=PEM_bid, PEM_MW=PEM_size, verbose=False)
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nlp_solver = SolverFactory('ipopt')
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nlp_solver.options['max_iter'] = 8000
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nlp_solver.options['acceptable_tol'] = 1e-8
@@ -320,9 +323,6 @@ def run_design(PEM_bid=None, PEM_size=None):
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"pem_var_cost": pem_var_cost
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}
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323-
start_time = time.time()
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n_rep_days = centers.shape[0]
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if __name__ == "__main__":
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# result = run_design()
@@ -339,4 +339,4 @@ def run_design(PEM_bid=None, PEM_size=None):
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res = p.starmap(run_design, inputs)
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df = pd.DataFrame(res)
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df.to_csv("surrogate_results_ss.csv")
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# df.to_csv("surrogate_results_ss.csv")

‎dispatches/case_studies/renewables_case/tests/test_surrogate_opt.py

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#################################################################################
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# DISPATCHES was produced under the DOE Design Integration and Synthesis Platform
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# to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES), and is
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# copyright (c) 2020-2023 by the software owners: The Regents of the University
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# of California, through Lawrence Berkeley National Laboratory, National
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# Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable
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# Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et
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# al. All rights reserved.
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#
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# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
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# information, respectively. Both files are also available online at the URL:
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# "https://github.com/gmlc-dispatches/dispatches".
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#################################################################################
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import pytest
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# from dispatches.case_studies.renewables_case.RE_surrogate_optimization_steadystate import *

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