UnitCommitment.jl/test/src/market/market_test.jl

# 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: MarketSettings

function simple_market_test()
    @testset "da-to-rt simple market" begin
        da_path = fixture("market_da_simple.json.gz")
        rt_paths = [
            fixture("market_rt1_simple.json.gz"),
            fixture("market_rt2_simple.json.gz"),
            fixture("market_rt3_simple.json.gz"),
            fixture("market_rt4_simple.json.gz"),
        ]
        # solve market with default setting
        solution = UnitCommitment.solve_market(
            da_path,
            rt_paths,
            settings = MarketSettings(), # keep everything default
            optimizer = optimizer_with_attributes(
                Cbc.Optimizer,
                "logLevel" => 0,
            ),
            lp_optimizer = optimizer_with_attributes(
                HiGHS.Optimizer,
                "log_to_console" => false,
            ),
        )

        # the commitment status must agree with DA market
        da_solution = solution["DA"]
        @test da_solution["Is on"]["GenY"] == [0.0, 1.0]
        @test da_solution["LMP (\$/MW)"][("s1", "B1", 1)] == 50.0
        @test da_solution["LMP (\$/MW)"][("s1", "B1", 2)] == 56.0

        rt_solution = solution["RT"]
        @test length(rt_solution) == 4
        @test rt_solution[1]["Is on"]["GenY"] == [0.0, 0.0]
        @test rt_solution[2]["Is on"]["GenY"] == [0.0, 1.0]
        @test rt_solution[3]["Is on"]["GenY"] == [1.0, 1.0]
        @test rt_solution[4]["Is on"]["GenY"] == [1.0]
        @test length(rt_solution[1]["LMP (\$/MW)"]) == 2
        @test length(rt_solution[2]["LMP (\$/MW)"]) == 2
        @test length(rt_solution[3]["LMP (\$/MW)"]) == 2
        @test length(rt_solution[4]["LMP (\$/MW)"]) == 1

        # solve market with no lmp method
        solution_no_lmp = UnitCommitment.solve_market(
            da_path,
            rt_paths,
            settings = MarketSettings(lmp_method = nothing), # no lmp
            optimizer = optimizer_with_attributes(
                Cbc.Optimizer,
                "logLevel" => 0,
            ),
        )

        # the commitment status must agree with DA market
        da_solution = solution_no_lmp["DA"]
        @test haskey(da_solution, "LMP (\$/MW)") == false
        rt_solution = solution_no_lmp["RT"][1]
        @test haskey(rt_solution, "LMP (\$/MW)") == false
    end
end

function stochastic_market_test()
    @testset "da-to-rt stochastic market" begin
        da_path = [
            fixture("market_da_simple.json.gz"),
            fixture("market_da_scenario.json.gz"),
        ]
        rt_paths = [
            fixture("market_rt1_simple.json.gz"),
            fixture("market_rt2_simple.json.gz"),
            fixture("market_rt3_simple.json.gz"),
            fixture("market_rt4_simple.json.gz"),
        ]
        # after build and after optimize 
        function after_build(model, instance)
            @constraint(model, model[:is_on]["GenY", 1] == 1,)
        end

        lmps_da = []
        lmps_rt = []

        function after_optimize_da(solution, model, instance)
            lmp = UnitCommitment.compute_lmp(
                model,
                ConventionalLMP(),
                optimizer = optimizer_with_attributes(
                    HiGHS.Optimizer,
                    "log_to_console" => false,
                ),
            )
            return push!(lmps_da, lmp)
        end

        function after_optimize_rt(solution, model, instance)
            lmp = UnitCommitment.compute_lmp(
                model,
                ConventionalLMP(),
                optimizer = optimizer_with_attributes(
                    HiGHS.Optimizer,
                    "log_to_console" => false,
                ),
            )
            return push!(lmps_rt, lmp)
        end

        # solve the stochastic market with callbacks
        solution = UnitCommitment.solve_market(
            da_path,
            rt_paths,
            settings = MarketSettings(), # keep everything default
            optimizer = optimizer_with_attributes(
                Cbc.Optimizer,
                "logLevel" => 0,
            ),
            lp_optimizer = optimizer_with_attributes(
                HiGHS.Optimizer,
                "log_to_console" => false,
            ),
            after_build_da = after_build,
            after_optimize_da = after_optimize_da,
            after_optimize_rt = after_optimize_rt,
        )
        # the commitment status must agree with DA market
        da_solution_sp = solution["DA"]["market_da_simple"]
        da_solution_sc = solution["DA"]["market_da_scenario"]
        @test da_solution_sc["Is on"]["GenY"] == [1.0, 1.0]
        @test da_solution_sp["LMP (\$/MW)"][("market_da_simple", "B1", 1)] ==
              25.0
        @test da_solution_sc["LMP (\$/MW)"][("market_da_scenario", "B1", 2)] ==
              0.0

        rt_solution = solution["RT"]
        @test rt_solution[1]["Is on"]["GenY"] == [1.0, 1.0]
        @test rt_solution[2]["Is on"]["GenY"] == [1.0, 1.0]
        @test rt_solution[3]["Is on"]["GenY"] == [1.0, 1.0]
        @test rt_solution[4]["Is on"]["GenY"] == [1.0]
        @test length(lmps_rt) == 4
    end
end