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uc: Add quadratic terms

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This commit is contained in:
Alinson S. Xavier
2025-09-23 11:39:39 -05:00
parent 4eeb1c1ab3
commit ca05429203
2 changed files with 72 additions and 49 deletions
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7
miplearn/problems/uc.py
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@@ -25,6 +25,7 @@ class UnitCommitmentData:
min_downtime: np.ndarray
cost_startup: np.ndarray
cost_prod: np.ndarray
cost_prod_quad: np.ndarray
cost_fixed: np.ndarray
@@ -37,6 +38,7 @@ class UnitCommitmentGenerator:
min_power: rv_frozen = uniform(loc=0.5, scale=0.25),
cost_startup: rv_frozen = uniform(loc=0, scale=10_000),
cost_prod: rv_frozen = uniform(loc=0, scale=50),
cost_prod_quad: rv_frozen = uniform(loc=0, scale=0),
cost_fixed: rv_frozen = uniform(loc=0, scale=1_000),
min_uptime: rv_frozen = randint(low=2, high=8),
min_downtime: rv_frozen = randint(low=2, high=8),
@@ -50,6 +52,7 @@ class UnitCommitmentGenerator:
self.min_power = min_power
self.cost_startup = cost_startup
self.cost_prod = cost_prod
self.cost_prod_quad = cost_prod_quad
self.cost_fixed = cost_fixed
self.min_uptime = min_uptime
self.min_downtime = min_downtime
@@ -72,6 +75,7 @@ class UnitCommitmentGenerator:
min_downtime = self.min_downtime.rvs(G)
cost_startup = self.cost_startup.rvs(G)
cost_prod = self.cost_prod.rvs(G)
cost_prod_quad = self.cost_prod_quad.rvs(G)
cost_fixed = self.cost_fixed.rvs(G)
capacity = max_power.sum()
@@ -91,6 +95,7 @@ class UnitCommitmentGenerator:
min_downtime = self.ref_data.min_downtime
cost_startup = self.ref_data.cost_startup * self.cost_jitter.rvs(G)
cost_prod = self.ref_data.cost_prod * self.cost_jitter.rvs(G)
cost_prod_quad = self.ref_data.cost_prod_quad * self.cost_jitter.rvs(G)
cost_fixed = self.ref_data.cost_fixed * self.cost_jitter.rvs(G)
data = UnitCommitmentData(
@@ -101,6 +106,7 @@ class UnitCommitmentGenerator:
min_downtime,
cost_startup.round(2),
cost_prod.round(2),
cost_prod_quad.round(4),
cost_fixed.round(2),
)
@@ -143,6 +149,7 @@ def build_uc_model_gurobipy(data: Union[str, UnitCommitmentData]) -> GurobiModel
is_on[g, t] * data.cost_fixed[g]
+ switch_on[g, t] * data.cost_startup[g]
+ prod[g, t] * data.cost_prod[g]
+ prod[g, t] * prod[g, t] * data.cost_prod_quad[g]
for g in range(G)
for t in range(T)
)