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43c68a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | # 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 JuMP
"""
function compute_lmp(
model::JuMP.Model,
method::ConventionalLMP;
optimizer,
)::OrderedDict{Tuple{String,String,Int},Float64}
Calculates conventional locational marginal prices of the given unit commitment
instance. Returns a dictionary mapping `(bus_name, time)` to the marginal price.
Arguments
---------
- `model`:
the UnitCommitment model, must be solved before calling this function.
- `method`:
the LMP method.
- `optimizer`:
the optimizer for solving the LP problem.
Examples
--------
```julia
using UnitCommitment
using HiGHS
import UnitCommitment: ConventionalLMP
# Read benchmark instance
instance = UnitCommitment.read_benchmark("matpower/case118/2018-01-01")
# Build the model
model = UnitCommitment.build_model(
instance = instance,
optimizer = HiGHS.Optimizer,
)
# Optimize the model
UnitCommitment.optimize!(model)
# Compute the LMPs using the conventional method
lmp = UnitCommitment.compute_lmp(
model,
ConventionalLMP(),
optimizer = HiGHS.Optimizer,
)
# Access the LMPs
# Example: "s1" is the scenario name, "b1" is the bus name, 1 is the first time slot
@show lmp["s1", "b1", 1]
```
"""
function compute_lmp(
model::JuMP.Model,
::ConventionalLMP;
optimizer,
)::OrderedDict{Tuple{String,String,Int},Float64}
if !has_values(model)
error("The UC model must be solved before calculating the LMPs.")
end
lmp = OrderedDict()
@info "Fixing binary variables and relaxing integrality..."
vals = Dict(v => value(v) for v in all_variables(model))
for v in all_variables(model)
if is_binary(v)
unset_binary(v)
fix(v, vals[v])
end
end
relax_integrality(model)
set_optimizer(model, optimizer)
@info "Solving the LP..."
JuMP.optimize!(model)
@info "Getting dual values (LMPs)..."
for (key, val) in model[:eq_net_injection]
lmp[key] = dual(val)
end
return lmp
end
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