da to rt market with tests

test/src/UnitCommitmentT.jl

@@ -22,6 +22,7 @@ include("transform/randomize/XavQiuAhm2021_test.jl")
 include("validation/repair_test.jl")
 include("lmp/conventional_test.jl")
 include("lmp/aelmp_test.jl")
+include("market/market_test.jl")
 
 basedir = dirname(@__FILE__)
 
@@ -51,6 +52,8 @@ function runtests()
         validation_repair_test()
         lmp_conventional_test()
         lmp_aelmp_test()
+        simple_market_test()
+        stochastic_market_test()
     end
     return
 end

test/src/market/market_test.jl

@@ -0,0 +1,151 @@
+# 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,
+            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["Day-ahead market"]
+        @test da_solution["Is on"]["GenY"] == [0.0, 1.0]
+        @test da_solution["Locational marginal price"][("s1", "B1", 1)] == 50.0
+        @test da_solution["Locational marginal price"][("s1", "B1", 2)] == 56.0
+
+        rt_solution = solution["Real-time markets"]
+        @test length(rt_solution) == 4
+        @test rt_solution[rt_paths[1]]["Is on"]["GenY"] == [0.0, 0.0]
+        @test rt_solution[rt_paths[2]]["Is on"]["GenY"] == [0.0, 1.0]
+        @test rt_solution[rt_paths[3]]["Is on"]["GenY"] == [1.0, 1.0]
+        @test rt_solution[rt_paths[4]]["Is on"]["GenY"] == [1.0]
+        @test length(rt_solution[rt_paths[1]]["Locational marginal price"]) == 2
+        @test length(rt_solution[rt_paths[2]]["Locational marginal price"]) == 2
+        @test length(rt_solution[rt_paths[3]]["Locational marginal price"]) == 2
+        @test length(rt_solution[rt_paths[4]]["Locational marginal price"]) == 1
+
+        # solve market with no lmp method
+        solution_no_lmp = UnitCommitment.solve_market(
+            da_path,
+            rt_paths,
+            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["Day-ahead market"]
+        @test haskey(da_solution, "Locational marginal price") == false
+        rt_solution = solution_no_lmp["Real-time markets"]
+        @test haskey(rt_solution, "Locational marginal price") == 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,
+            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["Day-ahead market"]["market_da_simple"]
+        da_solution_sc = solution["Day-ahead market"]["market_da_scenario"]
+        @test da_solution_sc["Is on"]["GenY"] == [1.0, 1.0]
+        @test da_solution_sp["Locational marginal price"][(
+            "market_da_simple",
+            "B1",
+            1,
+        )] == 25.0
+        @test da_solution_sc["Locational marginal price"][(
+            "market_da_scenario",
+            "B1",
+            2,
+        )] == 0.0
+
+        rt_solution = solution["Real-time markets"]
+        @test rt_solution[rt_paths[1]]["Is on"]["GenY"] == [1.0, 1.0]
+        @test rt_solution[rt_paths[2]]["Is on"]["GenY"] == [1.0, 1.0]
+        @test rt_solution[rt_paths[3]]["Is on"]["GenY"] == [1.0, 1.0]
+        @test rt_solution[rt_paths[4]]["Is on"]["GenY"] == [1.0]
+        @test length(lmps_rt) == 4
+    end
+end