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# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
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# Copyright (C) 2020-2021, 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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module XavQiuAhm2021
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using Distributions
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import ..UnitCommitmentInstance
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import ..UnitCommitmentScenario
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"""
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struct Randomization
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cost = Uniform(0.95, 1.05)
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load_profile_mu = [...]
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load_profile_sigma = [...]
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load_share = Uniform(0.90, 1.10)
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peak_load = Uniform(0.6 * 0.925, 0.6 * 1.075)
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randomize_costs = true
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randomize_load_profile = true
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randomize_load_share = true
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end
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Randomization method that changes: (1) production and startup costs, (2)
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share of load coming from each bus, (3) peak system load, and (4) temporal
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load profile, as follows:
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1. **Production and startup costs:**
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For each unit `u`, the vectors `u.min_power_cost` and `u.cost_segments`
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are multiplied by a constant `α[u]` sampled from the provided `cost`
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distribution. If `randomize_costs` is false, skips this step.
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2. **Load share:**
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For each bus `b` and time `t`, the value `b.load[t]` is multiplied by
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`(β[b] * b.load[t]) / sum(β[b2] * b2.load[t] for b2 in buses)`, where
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`β[b]` is sampled from the provided `load_share` distribution. If
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`randomize_load_share` is false, skips this step.
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3. **Peak system load and temporal load profile:**
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Sets the peak load to `ρ * C`, where `ρ` is sampled from `peak_load` and `C`
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is the maximum system capacity, at any time. Also scales the loads of all
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buses, so that `system_load[t+1]` becomes equal to `system_load[t] * γ[t]`,
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where `γ[t]` is sampled from `Normal(load_profile_mu[t], load_profile_sigma[t])`.
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The system load for the first time period is set so that the peak load
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matches `ρ * C`. If `load_profile_sigma` and `load_profile_mu` have fewer
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elements than `instance.time`, wraps around. If `randomize_load_profile`
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is false, skips this step.
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The default parameters were obtained based on an analysis of publicly available
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bid and hourly data from PJM, corresponding to the month of January, 2017. For
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more details, see Section 4.2 of the paper.
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# References
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- **Xavier, Álinson S., Feng Qiu, and Shabbir Ahmed.** *"Learning to solve
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large-scale security-constrained unit commitment problems."* INFORMS Journal
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on Computing 33.2 (2021): 739-756. DOI: 10.1287/ijoc.2020.0976
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"""
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Base.@kwdef struct Randomization
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cost = Uniform(0.95, 1.05)
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load_profile_mu::Vector{Float64} = [
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1.0,
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0.978,
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0.98,
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1.004,
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1.02,
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1.078,
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1.132,
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1.018,
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0.999,
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1.006,
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0.999,
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0.987,
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0.975,
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0.984,
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0.995,
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1.005,
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1.045,
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1.106,
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0.981,
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0.981,
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0.978,
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0.948,
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0.928,
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0.953,
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]
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load_profile_sigma::Vector{Float64} = [
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0.0,
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0.011,
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0.015,
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0.01,
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0.012,
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0.029,
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0.055,
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0.027,
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0.026,
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0.023,
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0.013,
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0.012,
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0.014,
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0.011,
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0.008,
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0.008,
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0.02,
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0.02,
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0.016,
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0.012,
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0.014,
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0.015,
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0.017,
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0.024,
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]
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load_share = Uniform(0.90, 1.10)
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peak_load = Uniform(0.6 * 0.925, 0.6 * 1.075)
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randomize_load_profile::Bool = true
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randomize_costs::Bool = true
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randomize_load_share::Bool = true
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end
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function _randomize_costs(
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rng,
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sc::UnitCommitmentScenario,
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distribution,
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)::Nothing
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for unit in sc.units
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α = rand(rng, distribution)
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unit.min_power_cost *= α
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for k in unit.cost_segments
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k.cost *= α
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end
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for s in unit.startup_categories
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s.cost *= α
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end
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end
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return
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end
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function _randomize_load_share(
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rng,
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sc::UnitCommitmentScenario,
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distribution,
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)::Nothing
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α = rand(rng, distribution, length(sc.buses))
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for t in 1:sc.time
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total = sum(bus.load[t] for bus in sc.buses)
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den =
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sum(bus.load[t] / total * α[i] for (i, bus) in enumerate(sc.buses))
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for (i, bus) in enumerate(sc.buses)
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bus.load[t] *= α[i] / den
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end
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end
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return
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end
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function _randomize_load_profile(
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rng,
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sc::UnitCommitmentScenario,
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params::Randomization,
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)::Nothing
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# Generate new system load
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system_load = [1.0]
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for t in 2:sc.time
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idx = (t - 1) % length(params.load_profile_mu) + 1
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gamma = rand(
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rng,
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Normal(params.load_profile_mu[idx], params.load_profile_sigma[idx]),
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)
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push!(system_load, system_load[t-1] * gamma)
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end
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capacity = sum(maximum(u.max_power) for u in sc.units)
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peak_load = rand(rng, params.peak_load) * capacity
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system_load = system_load ./ maximum(system_load) .* peak_load
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# Scale bus loads to match the new system load
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prev_system_load = sum(b.load for b in sc.buses)
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for b in sc.buses
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for t in 1:sc.time
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b.load[t] *= system_load[t] / prev_system_load[t]
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end
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end
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return
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end
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end
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"""
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function randomize!(
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instance::UnitCommitment.UnitCommitmentInstance,
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method::XavQiuAhm2021.Randomization,
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rng = MersenneTwister(),
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)::Nothing
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Randomize costs and loads based on the method described in XavQiuAhm2021.
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"""
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function randomize!(
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instance::UnitCommitment.UnitCommitmentInstance,
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method::XavQiuAhm2021.Randomization;
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rng = MersenneTwister(),
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)::Nothing
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for sc in instance.scenarios
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randomize!(sc; method, rng)
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end
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return
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end
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function randomize!(
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sc::UnitCommitment.UnitCommitmentScenario;
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method::XavQiuAhm2021.Randomization,
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rng = MersenneTwister(),
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)::Nothing
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if method.randomize_costs
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XavQiuAhm2021._randomize_costs(rng, sc, method.cost)
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end
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if method.randomize_load_share
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XavQiuAhm2021._randomize_load_share(rng, sc, method.load_share)
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end
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if method.randomize_load_profile
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XavQiuAhm2021._randomize_load_profile(rng, sc, method)
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end
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return
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end
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"""
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function randomize!(
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instance::UnitCommitmentInstance;
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method = UnitCommitment.XavQiuAhm2021.Randomization();
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rng = MersenneTwister(),
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)::Nothing
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Randomizes instance parameters according to the provided randomization method.
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# Example
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```julia
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instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
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UnitCommitment.randomize!(instance)
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model = UnitCommitment.build_model(; instance)
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```
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"""
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function randomize!(
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instance::UnitCommitment.UnitCommitmentInstance;
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method = XavQiuAhm2021.Randomization(),
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rng = MersenneTwister(),
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)::Nothing
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randomize!(instance, method; rng)
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return
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end
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export randomize!
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