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65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import numpy as np
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from scipy.stats import uniform, randint
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from miplearn.problems.uc import (
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UnitCommitmentData,
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build_uc_model_gurobipy,
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UnitCommitmentGenerator,
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)
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def test_generator() -> None:
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np.random.seed(42)
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gen = UnitCommitmentGenerator(
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n_units=randint(low=3, high=4),
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n_periods=randint(low=4, high=5),
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max_power=uniform(loc=50, scale=450),
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min_power=uniform(loc=0.25, scale=0.5),
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cost_startup=uniform(loc=1, scale=1),
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cost_prod=uniform(loc=1, scale=1),
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cost_prod_quad=uniform(loc=1, scale=1),
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cost_fixed=uniform(loc=1, scale=1),
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min_uptime=randint(low=1, high=8),
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min_downtime=randint(low=1, high=8),
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cost_jitter=uniform(loc=0.75, scale=0.5),
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demand_jitter=uniform(loc=0.9, scale=0.2),
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fix_units=True,
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)
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data = gen.generate(1)
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assert data[0].demand.tolist() == [430.3, 511.29, 484.91, 860.61]
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assert data[0].min_power.tolist() == [120.05, 156.73, 124.44]
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assert data[0].max_power.tolist() == [218.54, 477.82, 379.4]
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assert data[0].min_uptime.tolist() == [3, 3, 5]
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assert data[0].min_downtime.tolist() == [4, 3, 6]
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assert data[0].cost_startup.tolist() == [1.06, 1.72, 1.94]
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assert data[0].cost_prod.tolist() == [1.0, 1.99, 1.62]
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assert data[0].cost_prod_quad.tolist() == [1.6117, 1.0071, 1.0231]
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assert data[0].cost_fixed.tolist() == [1.52, 1.4, 1.05]
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def test_uc() -> None:
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data = UnitCommitmentData(
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demand=np.array([10, 12, 15, 10, 8, 5]),
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min_power=np.array([5, 5, 10]),
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max_power=np.array([10, 8, 20]),
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min_uptime=np.array([4, 3, 2]),
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min_downtime=np.array([4, 3, 2]),
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cost_startup=np.array([100, 120, 200]),
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cost_prod=np.array([1.0, 1.25, 1.5]),
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cost_fixed=np.array([10, 12, 9]),
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cost_prod_quad=np.array([0, 0, 0]),
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)
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model = build_uc_model_gurobipy(data)
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model.optimize()
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assert model.inner.objVal == 154.5
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if __name__ == "__main__":
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data = UnitCommitmentGenerator().generate(1)[0]
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model = build_uc_model_gurobipy(data)
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model.optimize()
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