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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) |