Fix RE Notebook for Issue 200 (#211)

* copy over to this branch * update setup.py * fix DoubleLoopOptimization.ipynb * fix DoubleLoopOptimization.ipynb * update * Update test_RE_flowsheet.py * add subscenario * add steadystate optimization * update results with steadystate surrogate * notebook order * notebook order * update results * update pysam version * update ymls * update ymls * update ymls * update ymls * update ymls * pull from git lfs * update setup.py * update notebooks * add tests * update test * start tests * add surrogate tests * update * update test_surrogate_opt * add to tests * fix DoubleLoopOptimization --------- Co-authored-by: Keith Beattie <ksbeattie@lbl.gov> Co-authored-by: Ludovico Bianchi <lbianchi@lbl.gov>
gmlc-dispatches · Jul 14, 2023 · 5f38880 · 5f38880
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diff --git a/dispatches/case_studies/renewables_case/DoubleLoopOptimization.ipynb b/dispatches/case_studies/renewables_case/DoubleLoopOptimization.ipynb
@@ -1909,7 +1909,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(2, 1, figsize=(24, 10), sharex=True)\n",
-    "ax[0].plot(time_index, np.append([0] * 24, da_bidder_df[da_bidder_df['Horizon [hr]'] >= 24][da_bid_powers].sum(axis=1))[time_slice], color='b', alpha=0.4, label=\"48-Hr Ahead DA Bid\")\n",
+    "ax[0].plot(time_index, np.append([0] * 24, da_bidder_df[da_bidder_df['Horizon [hr]'] < 24][da_bid_powers].sum(axis=1))[time_slice], color='b', alpha=0.4, label=\"48-Hr Ahead DA Bid\")\n",
     "ax[0].plot(time_index, da_bidder_df[da_bidder_df['Horizon [hr]'] < 24][da_bid_powers].sum(axis=1)[time_slice], color='b', alpha=1, label=\"24-Hr Ahead DA Bid\")\n",
     "ax[0].plot(time_index, np.append([0] * 3, rt_bidder_df[rt_bidder_df['Horizon [hr]'] == 3][rt_bid_powers].sum(axis=1))[time_slice], color='g', alpha=0.2, label=\"3-Hr Ahead RT Bid\")\n",
     "ax[0].plot(time_index, np.append([0] * 2, rt_bidder_df[rt_bidder_df['Horizon [hr]'] == 2][rt_bid_powers].sum(axis=1))[time_slice], color='g', alpha=0.3, label=\"2-Hr Ahead RT Bid\")\n",

diff --git a/dispatches/case_studies/renewables_case/RE_surrogate_optimization_steadystate.py b/dispatches/case_studies/renewables_case/RE_surrogate_optimization_steadystate.py
@@ -31,7 +31,6 @@
 from pyomo.util.infeasible import log_infeasible_constraints, log_infeasible_bounds, log_close_to_bounds
 import tensorflow as tf
 from tensorflow import keras
-from tensorflow.keras import layers
 import omlt
 from omlt.neuralnet import NetworkDefinition, FullSpaceNNFormulation
 from omlt.io import load_keras_sequential

diff --git a/dispatches/case_studies/renewables_case/tests/test_surrogate_opt.py b/dispatches/case_studies/renewables_case/tests/test_surrogate_opt.py
@@ -17,7 +17,6 @@
 
 from dispatches.case_studies.renewables_case.RE_surrogate_optimization_steadystate import *
 
-
 def load_model():
     net_rev_defn, net_frequency_defn, dispatch_clusters_mean, pem_clusters_mean, resource_clusters_mean = load_surrogate_model(re_nn_dir)
     assert len([l for l in net_rev_defn.layers]) == 4
@@ -26,14 +25,12 @@ def load_model():
     assert pem_clusters_mean.mean() == pytest.approx(0.2215, rel=1e-3)
     assert resource_clusters_mean.mean() == pytest.approx(0.5547, rel=1e-3)
 
-
 def test_RE_surrogate_steady_state_fixed():
     results = run_design(PEM_bid=30, PEM_size=200)
     assert results['e_revenue'] == pytest.approx(-5989492, rel=1e-3)
     assert results['h_revenue'] == pytest.approx(43656556, rel=1e-3)
     assert results['NPV'] == pytest.approx(-1353268767, rel=1e-3)
 
-
 def test_RE_surrogate_steady_state():
     results = run_design()
     assert results['pem_mw'] == pytest.approx(350.02, rel=1e-3)