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319a787904
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[deps]
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[deps]
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
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Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
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Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
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UnitCommitment = "64606440-39ea-11e9-0f29-3303a1d3d877"
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UnitCommitment = "64606440-39ea-11e9-0f29-3303a1d3d877"
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# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
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# Copyright (C) 2020, 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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using JuMP
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"""
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function compute_lmp(
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model::JuMP.Model,
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method::AELMP;
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optimizer,
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)::OrderedDict{Tuple{String,Int},Float64}
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Calculates the approximate extended locational marginal prices of the given unit commitment instance.
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The AELPM does the following three things:
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1. It sets the minimum power output of each generator to zero
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2. It averages the start-up cost over the offer blocks for each generator
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3. It relaxes all integrality constraints
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Returns a dictionary mapping `(bus_name, time)` to the marginal price.
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WARNING: This approximation method is not fully developed. The implementation is based on MISO Phase I only.
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1. It only supports Fast Start resources. More specifically, the minimum up/down time has to be zero.
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2. The method does NOT support time-varying start-up costs.
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3. An asset is considered offline if it is never on throughout all time periods.
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4. The method does NOT support multiple scenarios.
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Arguments
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---------
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- `model`:
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the UnitCommitment model, must be solved before calling this function if offline participation is not allowed.
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- `method`:
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the AELMP method.
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- `optimizer`:
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the optimizer for solving the LP problem.
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Examples
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--------
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```julia
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using UnitCommitment
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using HiGHS
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import UnitCommitment: AELMP
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# Read benchmark instance
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instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
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# Build the model
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model = UnitCommitment.build_model(
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instance = instance,
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optimizer = HiGHS.Optimizer,
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)
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# Optimize the model
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UnitCommitment.optimize!(model)
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# Compute the AELMPs
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aelmp = UnitCommitment.compute_lmp(
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model,
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AELMP(
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allow_offline_participation = false,
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consider_startup_costs = true
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),
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optimizer = HiGHS.Optimizer
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)
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# Access the AELMPs
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# Example: "s1" is the scenario name, "b1" is the bus name, 1 is the first time slot
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# Note: although scenario is supported, the query still keeps the scenario keys for consistency.
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@show aelmp["s1", "b1", 1]
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```
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"""
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function compute_lmp(
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model::JuMP.Model,
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method::AELMP;
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optimizer,
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)::OrderedDict{Tuple{String,String,Int},Float64}
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@info "Building the approximation model..."
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instance = deepcopy(model[:instance])
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_aelmp_check_parameters(instance, model, method)
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_modify_scenario!(instance.scenarios[1], model, method)
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# prepare the result dictionary and solve the model
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elmp = OrderedDict()
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@info "Solving the approximation model."
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approx_model = build_model(instance = instance, variable_names = true)
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# relax the binary constraint, and relax integrality
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for v in all_variables(approx_model)
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if is_binary(v)
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unset_binary(v)
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end
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end
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relax_integrality(approx_model)
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set_optimizer(approx_model, optimizer)
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# solve the model
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set_silent(approx_model)
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optimize!(approx_model)
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# access the dual values
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@info "Getting dual values (AELMPs)."
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for (key, val) in approx_model[:eq_net_injection]
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elmp[key] = dual(val)
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end
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return elmp
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end
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function _aelmp_check_parameters(
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instance::UnitCommitmentInstance,
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model::JuMP.Model,
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method::AELMP,
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)
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# CHECK: model cannot have multiple scenarios
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if length(instance.scenarios) > 1
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error("The method does NOT support multiple scenarios.")
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end
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sc = instance.scenarios[1]
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# CHECK: model must be solved if allow_offline_participation=false
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if !method.allow_offline_participation
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if isnothing(model) || !has_values(model)
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error(
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"A solved UC model is required if allow_offline_participation=false.",
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)
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end
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end
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all_units = sc.thermal_units
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# CHECK: model cannot handle non-fast-starts (MISO Phase I: can ONLY solve fast-starts)
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if any(u -> u.min_uptime > 1 || u.min_downtime > 1, all_units)
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error(
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"The minimum up/down time of all generators must be 1. AELMP only supports fast-starts.",
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)
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end
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if any(u -> u.initial_power > 0, all_units)
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error("The initial power of all generators must be 0.")
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end
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if any(u -> u.initial_status >= 0, all_units)
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error("The initial status of all generators must be negative.")
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end
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# CHECK: model does not support startup costs (in time series)
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if any(u -> length(u.startup_categories) > 1, all_units)
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error("The method does NOT support time-varying start-up costs.")
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end
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end
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function _modify_scenario!(
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sc::UnitCommitmentScenario,
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model::JuMP.Model,
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method::AELMP,
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)
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# this function modifies the sc units (generators)
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if !method.allow_offline_participation
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# 1. remove (if NOT allowing) the offline generators
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units_to_remove = []
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for unit in sc.thermal_units
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# remove based on the solved UC model result
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# remove the unit if it is never on
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if all(t -> value(model[:is_on][unit.name, t]) == 0, sc.time)
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# unregister from the bus
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filter!(x -> x.name != unit.name, unit.bus.thermal_units)
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# unregister from the reserve
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for r in unit.reserves
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filter!(x -> x.name != unit.name, r.thermal_units)
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end
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# append the name to the remove list
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push!(units_to_remove, unit.name)
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end
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end
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# unregister the units from the remove list
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filter!(x -> !(x.name in units_to_remove), sc.thermal_units)
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end
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for unit in sc.thermal_units
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# 2. set min generation requirement to 0 by adding 0 to production curve and cost
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# min_power & min_costs are vectors with dimension T
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if unit.min_power[1] != 0
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first_cost_segment = unit.cost_segments[1]
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pushfirst!(
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unit.cost_segments,
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CostSegment(
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ones(size(first_cost_segment.mw)) * unit.min_power[1],
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ones(size(first_cost_segment.cost)) *
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unit.min_power_cost[1] / unit.min_power[1],
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),
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)
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unit.min_power = zeros(size(first_cost_segment.mw))
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unit.min_power_cost = zeros(size(first_cost_segment.cost))
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end
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# 3. average the start-up costs (if considering)
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# if consider_startup_costs = false, then use the current first_startup_cost
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first_startup_cost = unit.startup_categories[1].cost
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if method.consider_startup_costs
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additional_unit_cost = first_startup_cost / unit.max_power[1]
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for i in eachindex(unit.cost_segments)
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unit.cost_segments[i].cost .+= additional_unit_cost
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end
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first_startup_cost = 0.0 # zero out the start up cost
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end
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unit.startup_categories =
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StartupCategory[StartupCategory(0, first_startup_cost)]
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end
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return sc.thermal_units_by_name =
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Dict(g.name => g for g in sc.thermal_units)
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end
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@ -0,0 +1,92 @@
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# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
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# Copyright (C) 2020, 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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using JuMP
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"""
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function compute_lmp(
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model::JuMP.Model,
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method::ConventionalLMP;
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optimizer,
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)::OrderedDict{Tuple{String,String,Int},Float64}
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Calculates conventional locational marginal prices of the given unit commitment
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instance. Returns a dictionary mapping `(bus_name, time)` to the marginal price.
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Arguments
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---------
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- `model`:
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the UnitCommitment model, must be solved before calling this function.
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- `method`:
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the LMP method.
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- `optimizer`:
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the optimizer for solving the LP problem.
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Examples
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--------
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```julia
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using UnitCommitment
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using HiGHS
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import UnitCommitment: ConventionalLMP
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# Read benchmark instance
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instance = UnitCommitment.read_benchmark("matpower/case118/2018-01-01")
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# Build the model
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model = UnitCommitment.build_model(
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instance = instance,
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optimizer = HiGHS.Optimizer,
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)
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# Optimize the model
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UnitCommitment.optimize!(model)
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# Compute the LMPs using the conventional method
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lmp = UnitCommitment.compute_lmp(
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model,
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ConventionalLMP(),
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optimizer = HiGHS.Optimizer,
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)
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# Access the LMPs
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# Example: "s1" is the scenario name, "b1" is the bus name, 1 is the first time slot
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@show lmp["s1", "b1", 1]
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```
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"""
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function compute_lmp(
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model::JuMP.Model,
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::ConventionalLMP;
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optimizer,
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)::OrderedDict{Tuple{String,String,Int},Float64}
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if !has_values(model)
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error("The UC model must be solved before calculating the LMPs.")
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end
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lmp = OrderedDict()
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@info "Fixing binary variables and relaxing integrality..."
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vals = Dict(v => value(v) for v in all_variables(model))
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for v in all_variables(model)
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if is_binary(v)
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unset_binary(v)
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fix(v, vals[v])
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end
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end
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relax_integrality(model)
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set_optimizer(model, optimizer)
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@info "Solving the LP..."
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JuMP.optimize!(model)
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@info "Getting dual values (LMPs)..."
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for (key, val) in model[:eq_net_injection]
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lmp[key] = dual(val)
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end
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return lmp
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end
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@ -0,0 +1,28 @@
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# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
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# Copyright (C) 2020, 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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abstract type PricingMethod end
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struct ConventionalLMP <: PricingMethod end
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"""
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struct AELMP <: PricingMethod
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allow_offline_participation::Bool = true
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consider_startup_costs::Bool = true
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end
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|
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|
Approximate Extended LMPs.
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|
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|
Arguments
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||||||
|
---------
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|
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||||||
|
- `allow_offline_participation`:
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|
If true, offline assets are allowed to participate in pricing.
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- `consider_startup_costs`:
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|
If true, the start-up costs are averaged over each unit production; otherwise the production costs stay the same.
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"""
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Base.@kwdef struct AELMP <: PricingMethod
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allow_offline_participation::Bool = true
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consider_startup_costs::Bool = true
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end
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@ -0,0 +1,32 @@
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|
# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
|
||||||
|
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||||
|
# Released under the modified BSD license. See COPYING.md for more details.
|
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|
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|
function _add_profiled_unit!(
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|
model::JuMP.Model,
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|
pu::ProfiledUnit,
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|
sc::UnitCommitmentScenario,
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|
)::Nothing
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|
punits = _init(model, :prod_profiled)
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|
net_injection = _init(model, :expr_net_injection)
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|
for t in 1:model[:instance].time
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# Decision variable
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|
punits[sc.name, pu.name, t] =
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@variable(model, lower_bound = 0, upper_bound = pu.capacity[t])
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|
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|
# Objective function terms
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||||||
|
add_to_expression!(
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|
model[:obj],
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|
punits[sc.name, pu.name, t],
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|
pu.cost[t] * sc.probability,
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|
)
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|
|
||||||
|
# Net injection
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||||||
|
add_to_expression!(
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||||||
|
net_injection[sc.name, pu.bus.name, t],
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|
punits[sc.name, pu.name, t],
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||||||
|
1.0,
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||||||
|
)
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||||||
|
end
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|
return
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||||||
|
end
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@ -0,0 +1,35 @@
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|||||||
|
# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
|
||||||
|
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||||
|
# Released under the modified BSD license. See COPYING.md for more details.
|
||||||
|
|
||||||
|
using UnitCommitment, Cbc, HiGHS, JuMP
|
||||||
|
import UnitCommitment: AELMP
|
||||||
|
|
||||||
|
@testset "aelmp" begin
|
||||||
|
path = "$FIXTURES/aelmp_simple.json.gz"
|
||||||
|
# model has to be solved first
|
||||||
|
instance = UnitCommitment.read(path)
|
||||||
|
model = UnitCommitment.build_model(
|
||||||
|
instance = instance,
|
||||||
|
optimizer = Cbc.Optimizer,
|
||||||
|
variable_names = true,
|
||||||
|
)
|
||||||
|
JuMP.set_silent(model)
|
||||||
|
UnitCommitment.optimize!(model)
|
||||||
|
|
||||||
|
# policy 1: allow offlines; consider startups
|
||||||
|
aelmp_1 =
|
||||||
|
UnitCommitment.compute_lmp(model, AELMP(), optimizer = HiGHS.Optimizer)
|
||||||
|
@test aelmp_1["s1", "B1", 1] ≈ 231.7 atol = 0.1
|
||||||
|
|
||||||
|
# policy 2: do not allow offlines; but consider startups
|
||||||
|
aelmp_2 = UnitCommitment.compute_lmp(
|
||||||
|
model,
|
||||||
|
AELMP(
|
||||||
|
allow_offline_participation = false,
|
||||||
|
consider_startup_costs = true,
|
||||||
|
),
|
||||||
|
optimizer = HiGHS.Optimizer,
|
||||||
|
)
|
||||||
|
@test aelmp_2["s1", "B1", 1] ≈ 274.3 atol = 0.1
|
||||||
|
end
|
||||||
@ -0,0 +1,51 @@
|
|||||||
|
# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
|
||||||
|
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||||
|
# Released under the modified BSD license. See COPYING.md for more details.
|
||||||
|
|
||||||
|
using UnitCommitment, Cbc, HiGHS, JuMP
|
||||||
|
import UnitCommitment: ConventionalLMP
|
||||||
|
|
||||||
|
function solve_conventional_testcase(path::String)
|
||||||
|
instance = UnitCommitment.read(path)
|
||||||
|
model = UnitCommitment.build_model(
|
||||||
|
instance = instance,
|
||||||
|
optimizer = Cbc.Optimizer,
|
||||||
|
variable_names = true,
|
||||||
|
)
|
||||||
|
JuMP.set_silent(model)
|
||||||
|
UnitCommitment.optimize!(model)
|
||||||
|
lmp = UnitCommitment.compute_lmp(
|
||||||
|
model,
|
||||||
|
ConventionalLMP(),
|
||||||
|
optimizer = HiGHS.Optimizer,
|
||||||
|
)
|
||||||
|
return lmp
|
||||||
|
end
|
||||||
|
|
||||||
|
@testset "conventional" begin
|
||||||
|
# instance 1
|
||||||
|
path = "$FIXTURES/lmp_simple_test_1.json.gz"
|
||||||
|
lmp = solve_conventional_testcase(path)
|
||||||
|
@test lmp["s1", "A", 1] == 50.0
|
||||||
|
@test lmp["s1", "B", 1] == 50.0
|
||||||
|
|
||||||
|
# instance 2
|
||||||
|
path = "$FIXTURES/lmp_simple_test_2.json.gz"
|
||||||
|
lmp = solve_conventional_testcase(path)
|
||||||
|
@test lmp["s1", "A", 1] == 50.0
|
||||||
|
@test lmp["s1", "B", 1] == 60.0
|
||||||
|
|
||||||
|
# instance 3
|
||||||
|
path = "$FIXTURES/lmp_simple_test_3.json.gz"
|
||||||
|
lmp = solve_conventional_testcase(path)
|
||||||
|
@test lmp["s1", "A", 1] == 50.0
|
||||||
|
@test lmp["s1", "B", 1] == 70.0
|
||||||
|
@test lmp["s1", "C", 1] == 100.0
|
||||||
|
|
||||||
|
# instance 4
|
||||||
|
path = "$FIXTURES/lmp_simple_test_4.json.gz"
|
||||||
|
lmp = solve_conventional_testcase(path)
|
||||||
|
@test lmp["s1", "A", 1] == 50.0
|
||||||
|
@test lmp["s1", "B", 1] == 70.0
|
||||||
|
@test lmp["s1", "C", 1] == 90.0
|
||||||
|
end
|
||||||
Loading…
Reference in new issue