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https://github.com/ANL-CEEESA/UnitCommitment.jl.git
synced 2025-12-06 00:08:52 -06:00
Updated randomize, validate and initial conditions
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@@ -17,24 +17,29 @@ function generate_initial_conditions!(
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)::Nothing
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G = sc.thermal_units
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B = sc.buses
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PU = sc.profiled_units
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t = 1
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mip = JuMP.Model(optimizer)
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# Decision variables
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@variable(mip, x[G], Bin)
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@variable(mip, p[G] >= 0)
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@variable(mip, pu[PU])
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# Constraint: Minimum power
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@constraint(mip, min_power[g in G], p[g] >= g.min_power[t] * x[g])
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@constraint(mip, pu_min_power[k in PU], pu[k] >= k.min_power[t])
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# Constraint: Maximum power
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@constraint(mip, max_power[g in G], p[g] <= g.max_power[t] * x[g])
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@constraint(mip, pu_max_power[k in PU], pu[k] <= k.capacity[t])
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# Constraint: Production equals demand
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@constraint(
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mip,
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power_balance,
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sum(b.load[t] for b in B) == sum(p[g] for g in G)
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sum(b.load[t] for b in B) ==
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sum(p[g] for g in G) + sum(pu[k] for k in PU)
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)
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# Constraint: Must run
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@@ -58,7 +63,12 @@ function generate_initial_conditions!(
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return c / mw
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end
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end
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@objective(mip, Min, sum(p[g] * cost_slope(g) for g in G))
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@objective(
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mip,
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Min,
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sum(p[g] * cost_slope(g) for g in G) +
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sum(pu[k] * k.cost[t] for k in PU)
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)
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JuMP.optimize!(mip)
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@@ -133,6 +133,10 @@ function _randomize_costs(
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s.cost *= α
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end
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end
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for pu in sc.profiled_units
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α = rand(rng, distribution)
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pu.cost *= α
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end
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return
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end
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@@ -305,6 +305,35 @@ function _validate_units(instance::UnitCommitmentInstance, solution; tol = 0.01)
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end
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end
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end
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for pu in sc.profiled_units
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production = solution[sc.name]["Profiled production (MW)"][pu.name]
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for t in 1:instance.time
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# Unit must produce at least its minimum power
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if production[t] < pu.min_power[t] - tol
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@error @sprintf(
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"Profiled unit %s produces below its minimum limit at time %d (%.2f < %.2f)",
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pu.name,
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t,
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production[t],
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pu.min_power[t]
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)
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err_count += 1
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end
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# Unit must produce at most its maximum power
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if production[t] > pu.capacity[t] + tol
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@error @sprintf(
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"Profiled unit %s produces above its maximum limit at time %d (%.2f > %.2f)",
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pu.name,
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t,
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production[t],
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pu.capacity[t]
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)
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err_count += 1
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end
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end
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end
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end
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return err_count
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end
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@@ -316,16 +345,25 @@ function _validate_reserve_and_demand(instance, solution, tol = 0.01)
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load_curtail = 0
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fixed_load = sum(b.load[t] for b in sc.buses)
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ps_load = 0
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production = 0
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if length(sc.price_sensitive_loads) > 0
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ps_load = sum(
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solution[sc.name]["Price-sensitive loads (MW)"][ps.name][t]
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for ps in sc.price_sensitive_loads
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)
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end
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production = sum(
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solution[sc.name]["Thermal production (MW)"][g.name][t] for
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g in sc.thermal_units
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)
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if length(sc.thermal_units) > 0
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production = sum(
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solution[sc.name]["Thermal production (MW)"][g.name][t]
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for g in sc.thermal_units
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)
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end
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if length(sc.profiled_units) > 0
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production += sum(
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solution[sc.name]["Profiled production (MW)"][pu.name][t]
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for pu in sc.profiled_units
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)
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end
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if "Load curtail (MW)" in keys(solution)
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load_curtail = sum(
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solution[sc.name]["Load curtail (MW)"][b.name][t] for
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@@ -62,4 +62,22 @@ test_approx(x, y) = @test isapprox(x, y, atol = 1e-3)
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@test round.(system_load(sc), digits = 1)[1:8] ≈
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[4854.7, 4849.2, 4732.7, 4848.2, 4948.4, 5231.1, 5874.8, 5934.8]
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end
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@testset "profiled unit cost" begin
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sc =
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UnitCommitment.read("$FIXTURES/case14-profiled.json.gz").scenarios[1]
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# Check original costs
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pu1 = sc.profiled_units[1]
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pu2 = sc.profiled_units[2]
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test_approx(pu1.cost[1], 100.0)
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test_approx(pu2.cost[1], 50.0)
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randomize!(
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sc,
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XavQiuAhm2021.Randomization(randomize_load_profile = false),
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rng = MersenneTwister(42),
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)
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# Check randomized costs
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test_approx(pu1.cost[1], 98.039)
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test_approx(pu2.cost[1], 48.385)
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end
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end
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