Upload PGLIB_LOAD.jl

PGLib/Load/PGLIB_LOAD.jl

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+using JuMP
+using PowerModels
+using PGLib
+using Ipopt
+
+ipopt = Ipopt.Optimizer
+
+network_formulation = ACPPowerModel # ACPPowerModel SOCWRConicPowerModel DCPPowerModel
+
+matpower_case_name = "pglib_opf_case5_pjm"
+
+network_data = make_basic_network(pglib(matpower_case_name))
+
+# The problem to iterate over
+model = JuMP.Model()
+
+num_loads = length(network_data["load"])
+
+@variable(model, load_scaler[i = 1:num_loads] in MOI.Parameter.(1.0))
+
+for (str_i, l) in network_data["load"]
+    i = parse(Int, str_i)
+    l["pd"] = load_scaler[i] * l["pd"]
+    l["qd"] = load_scaler[i] * l["qd"]
+end
+
+pm = instantiate_model(
+    network_data,
+    network_formulation,
+    PowerModels.build_opf;
+    setting = Dict("output" => Dict("branch_flows" => true, "duals" => true)),
+    jump_model = model,
+)
+
+# Check it works
+JuMP.optimize!(model)
+JuMP.termination_status(model)
+JuMP.objective_value(model)
+
+# Save the model to a file
+write_to_file(model, "$(matpower_case_name)_$(network_formulation)_POI_load.mof.json")
+
+# Check if the file was written correctly
+model_test = read_from_file("$(matpower_case_name)_$(network_formulation)_POI_load.mof.json"; use_nlp_block = false)
+
+set_optimizer(model_test, optimizer_with_attributes(Ipopt.Optimizer, "print_level" => 0))
+
+JuMP.optimize!(model_test)