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)