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@ -3,21 +3,26 @@
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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.Method;
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method::AELMP;
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optimizer = nothing,
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)
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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 removes the minimum generation requirement for each generator
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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 the binary constraints and integrality
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Returns a dictionary of AELMPs. Each key is usually a tuple of "Bus name" and time index.
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NOTE: this approximation method is not fully developed. The implementation is based on MISO Phase I only.
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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 series of start-up costs.
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3. The method can only calculate for the first time slot if allow_offline_participation=false.
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@ -29,7 +34,7 @@ Arguments
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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, must be specified.
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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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@ -38,60 +43,77 @@ Examples
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--------
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```julia
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using UnitCommitment
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using Cbc
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using HiGHS
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import UnitCommitment:
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AELMP
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import UnitCommitment: AELMP
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# Read benchmark instance
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instance = UnitCommitment.read("instance.json")
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instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
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# Construct model (using state-of-the-art defaults)
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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 = Cbc.Optimizer,
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variable_names = true,
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optimizer = HiGHS.Optimizer,
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)
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# Get the AELMP with the default policy:
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# 1. Offline generators are allowed to participate in pricing
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# 2. Start-up costs are considered.
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# DO NOT use Cbc as the optimizer here. Cbc does not support dual values.
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my_aelmp_default = UnitCommitment.compute_lmp(
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model, # pre-solving is optional if allowing offline participation
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AELMP.Method(),
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optimizer = HiGHS.Optimizer
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)
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# Get the AELMPs with an alternative policy
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# 1. Offline generators are NOT allowed to participate in pricing
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# 2. Start-up costs are considered.
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# UC model must be solved first if offline generators are NOT allowed
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# Optimize the model
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UnitCommitment.optimize!(model)
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# then call the AELMP method
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my_aelmp_alt = UnitCommitment.compute_lmp(
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model, # pre-solving is required here
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AELMP.Method(
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allow_offline_participation=false,
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consider_startup_costs=true
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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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# Accessing the 'my_aelmp_alt' dictionary
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# Access the AELMPs
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# Example: "b1" is the bus name, 1 is the first time slot
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@show my_aelmp_alt["b1", 1]
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@show aelmp["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,Int},Float64}
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@info "Calculating the AELMP..."
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@info "Building the approximation model..."
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instance = deepcopy(model[:instance])
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_preset_aelmp_parameters!(method, model)
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_modify_instance!(instance, 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 _preset_aelmp_parameters!(
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method::AELMP.Method,
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method::AELMP,
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model::JuMP.Model
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)
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# this function corrects the allow_offline_participation parameter to match the model status
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@ -125,7 +147,7 @@ end
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function _modify_instance!(
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instance::UnitCommitmentInstance,
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model::JuMP.Model,
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method::AELMP.Method
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method::AELMP
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)
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# this function modifies the instance units (generators)
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# 1. remove (if NOT allowing) the offline generators
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@ -183,49 +205,3 @@ function _modify_instance!(
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end
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instance.units_by_name = Dict(g.name => g for g in instance.units)
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end
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function compute_lmp(
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model::JuMP.Model,
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method::AELMP.Method;
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optimizer = nothing
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)
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# Error if a linear optimizer is not specified
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if isnothing(optimizer)
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@error "Please supply a linear optimizer."
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return nothing
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end
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@info "Calculating the AELMP..."
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@info "Building the approximation model..."
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# get the instance and make a deep copy
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instance = deepcopy(model[:instance])
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# preset the method to match the model status (solved, unsolved, not supplied)
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_preset_aelmp_parameters!(method, model)
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# modify the instance (generator)
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_modify_instance!(instance, 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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