nlpmodels.jl

#!/usr/bin/env julia
###### AC-OPF using ADNLPModels ######
#
# implementation reference: https://juliasmoothoptimizers.github.io/ADNLPModels.jl/stable/tutorial/
# other AD libraries can be considered: https://juliasmoothoptimizers.github.io/ADNLPModels.jl/stable/
#
# The original rosetta-opf implementation can be found here,
# https://github.com/lanl-ansi/rosetta-opf/blob/bdc924c23694d09f6393575f6d732176aef7605d/nlpmodels.jl
#
# This implementation is an adaption of the original rosetta-opf implementation
# that uses ConcreteStructs based on the SciMLBenchmarks from 01/16/2024,
# https://github.com/SciML/SciMLBenchmarks.jl/blob/master/benchmarks/OptimizationFrameworks/optimal_powerflow.jmd
#

import PowerModels
import ADNLPModels
import ConcreteStructs
import NLPModelsIpopt

ConcreteStructs.@concrete struct DataRepresentation
    data
    ref
    var_lookup
    var_init
    var_lb
    var_ub
    ref_gen_idxs
    lookup_pg
    lookup_qg
    lookup_va
    lookup_vm
    lookup_lij
    lookup_p_lij
    lookup_q_lij
    cost_arrs
    f_bus
    t_bus
    ref_bus_idxs
    ref_buses_idxs
    ref_bus_gens
    ref_bus_arcs
    ref_branch_idxs
    ref_arcs_from
    ref_arcs_to
    p_idxmap
    q_idxmap
    bus_pd
    bus_qd
    bus_gs
    bus_bs
    br_g
    br_b
    br_tr
    br_ti
    br_ttm
    br_g_fr
    br_b_fr
    br_g_to
    br_b_to
end

function load_and_setup_data(file_name)
    data = PowerModels.parse_file(file_name)
    PowerModels.standardize_cost_terms!(data, order=2)
    PowerModels.calc_thermal_limits!(data)
    ref = PowerModels.build_ref(data)[:it][:pm][:nw][0]

    # Some data munging to type-stable forms

    var_lookup = Dict{String,Int}()

    var_init = Float64[]
    var_lb = Float64[]
    var_ub = Float64[]

    var_idx = 1
    for (i,bus) in ref[:bus]
        push!(var_init, 0.0) #va
        push!(var_lb, -Inf)
        push!(var_ub, Inf)
        var_lookup["va_$(i)"] = var_idx
        var_idx += 1

        push!(var_init, 1.0) #vm
        push!(var_lb, bus["vmin"])
        push!(var_ub, bus["vmax"])
        var_lookup["vm_$(i)"] = var_idx
        var_idx += 1
    end

    for (i,gen) in ref[:gen]
        push!(var_init, 0.0) #pg
        push!(var_lb, gen["pmin"])
        push!(var_ub, gen["pmax"])
        var_lookup["pg_$(i)"] = var_idx
        var_idx += 1

        push!(var_init, 0.0) #qg
        push!(var_lb, gen["qmin"])
        push!(var_ub, gen["qmax"])
        var_lookup["qg_$(i)"] = var_idx
        var_idx += 1
    end

    for (l,i,j) in ref[:arcs]
        branch = ref[:branch][l]

        push!(var_init, 0.0) #p
        push!(var_lb, -branch["rate_a"])
        push!(var_ub,  branch["rate_a"])
        var_lookup["p_$(l)_$(i)_$(j)"] = var_idx
        var_idx += 1

        push!(var_init, 0.0) #q
        push!(var_lb, -branch["rate_a"])
        push!(var_ub,  branch["rate_a"])
        var_lookup["q_$(l)_$(i)_$(j)"] = var_idx
        var_idx += 1
    end

    @assert var_idx == length(var_init)+1

    ref_gen_idxs = [i for i in keys(ref[:gen])]
    lookup_pg = Dict{Int,Int}()
    lookup_qg = Dict{Int,Int}()
    lookup_va = Dict{Int,Int}()
    lookup_vm = Dict{Int,Int}()
    lookup_lij = Tuple{Int,Int,Int}[]
    lookup_p_lij = Int[]
    lookup_q_lij = Int[]
    cost_arrs = Dict{Int,Vector{Float64}}()

    for (i,gen) in ref[:gen]
        lookup_pg[i] = var_lookup["pg_$(i)"]
        lookup_qg[i] = var_lookup["qg_$(i)"]
        cost_arrs[i] = gen["cost"]
    end

    for (i,bus) in ref[:bus]
        lookup_va[i] = var_lookup["va_$(i)"]
        lookup_vm[i] = var_lookup["vm_$(i)"]
    end

    for (l,i,j) in ref[:arcs]
        push!(lookup_lij, (l,i,j))
        push!(lookup_p_lij,var_lookup["p_$(l)_$(i)_$(j)"])
        push!(lookup_q_lij,var_lookup["q_$(l)_$(i)_$(j)"])
    end

    f_bus = Dict{Int,Int}()
    t_bus = Dict{Int,Int}()

    for (l,branch) in ref[:branch]
        f_bus[l] = branch["f_bus"]
        t_bus[l] = branch["t_bus"]
    end

    ref_bus_idxs = [i for i in keys(ref[:bus])]
    ref_buses_idxs = [i for i in keys(ref[:ref_buses])]
    ref_bus_gens = ref[:bus_gens]
    ref_bus_arcs = ref[:bus_arcs]
    ref_branch_idxs = [i for i in keys(ref[:branch])]
    ref_arcs_from = ref[:arcs_from]
    ref_arcs_to = ref[:arcs_to]

    p_idxmap = Dict(lookup_lij[i] => lookup_p_lij[i] for i in 1:length(lookup_lij))
    q_idxmap = Dict(lookup_lij[i] => lookup_q_lij[i] for i in 1:length(lookup_lij))

    bus_pd = Dict(i => 0.0 for (i,bus) in ref[:bus])
    bus_qd = Dict(i => 0.0 for (i,bus) in ref[:bus])

    bus_gs = Dict(i => 0.0 for (i,bus) in ref[:bus])
    bus_bs = Dict(i => 0.0 for (i,bus) in ref[:bus])

    for (i,bus) in ref[:bus]
        if length(ref[:bus_loads][i]) > 0
            bus_pd[i] = sum(ref[:load][l]["pd"] for l in ref[:bus_loads][i])
            bus_qd[i] = sum(ref[:load][l]["qd"] for l in ref[:bus_loads][i])
        end

        if length(ref[:bus_shunts][i]) > 0
            bus_gs[i] = sum(ref[:shunt][s]["gs"] for s in ref[:bus_shunts][i])
            bus_bs[i] = sum(ref[:shunt][s]["bs"] for s in ref[:bus_shunts][i])
        end
    end


    br_g = Dict(i => 0.0 for (i,branch) in ref[:branch])
    br_b = Dict(i => 0.0 for (i,branch) in ref[:branch])

    br_tr = Dict(i => 0.0 for (i,branch) in ref[:branch])
    br_ti = Dict(i => 0.0 for (i,branch) in ref[:branch])
    br_ttm = Dict(i => 0.0 for (i,branch) in ref[:branch])

    br_g_fr = Dict(i => 0.0 for (i,branch) in ref[:branch])
    br_b_fr = Dict(i => 0.0 for (i,branch) in ref[:branch])
    br_g_to = Dict(i => 0.0 for (i,branch) in ref[:branch])
    br_b_to = Dict(i => 0.0 for (i,branch) in ref[:branch])

    for (i,branch) in ref[:branch]
        g, b = PowerModels.calc_branch_y(branch)
        tr, ti = PowerModels.calc_branch_t(branch)

        br_g[i] = g
        br_b[i] = b

        br_tr[i] = tr
        br_ti[i] = ti
        br_ttm[i] = tr^2 + ti^2

        br_g_fr[i] = branch["g_fr"]
        br_b_fr[i] = branch["b_fr"]
        br_g_to[i] = branch["g_to"]
        br_b_to[i] = branch["b_to"]
    end

    return DataRepresentation(
        data,
        ref,
        var_lookup,
        var_init,
        var_lb,
        var_ub,
        ref_gen_idxs,
        lookup_pg,
        lookup_qg,
        lookup_va,
        lookup_vm,
        lookup_lij,
        lookup_p_lij,
        lookup_q_lij,
        cost_arrs,
        f_bus,
        t_bus,
        ref_bus_idxs,
        ref_buses_idxs,
        ref_bus_gens,
        ref_bus_arcs,
        ref_branch_idxs,
        ref_arcs_from,
        ref_arcs_to,
        p_idxmap,
        q_idxmap,
        bus_pd,
        bus_qd,
        bus_gs,
        bus_bs,
        br_g,
        br_b,
        br_tr,
        br_ti,
        br_ttm,
        br_g_fr,
        br_b_fr,
        br_g_to,
        br_b_to)
end

function build_opf_optimization_prob(dataset)
    (;data,
    ref,
    var_lookup,
    var_init,
    var_lb,
    var_ub,
    ref_gen_idxs,
    lookup_pg,
    lookup_qg,
    lookup_va,
    lookup_vm,
    lookup_lij,
    lookup_p_lij,
    lookup_q_lij,
    cost_arrs,
    f_bus,
    t_bus,
    ref_bus_idxs,
    ref_buses_idxs,
    ref_bus_gens,
    ref_bus_arcs,
    ref_branch_idxs,
    ref_arcs_from,
    ref_arcs_to,
    p_idxmap,
    q_idxmap,
    bus_pd,
    bus_qd,
    bus_gs,
    bus_bs,
    br_g,
    br_b,
    br_tr,
    br_ti,
    br_ttm,
    br_g_fr,
    br_b_fr,
    br_g_to,
    br_b_to) = dataset

    function opf_objective(x)
        cost = 0.0
        for i in ref_gen_idxs
            pg = x[lookup_pg[i]]
            _cost_arr = cost_arrs[i]
            cost += _cost_arr[1]*pg^2 + _cost_arr[2]*pg + _cost_arr[3]
        end
        return cost
    end

    function opf_constraints!(ret, x)
        offsetidx = 0

        # va_con
        for (reti,i) in enumerate(ref_buses_idxs)
            ret[reti + offsetidx] = x[lookup_va[i]]
        end

        offsetidx += length(ref_buses_idxs)

        #     @constraint(model,
        #         sum(p[a] for a in ref[:bus_arcs][i]) ==
        #         sum(pg[g] for g in ref_bus_gens[i]) -
        #         sum(load["pd"] for load in bus_loads) -
        #         sum(shunt["gs"] for shunt in bus_shunts)*x[lookup_vm[i]]^2
        #     )
        for (reti,i) in enumerate(ref_bus_idxs)
            ret[reti + offsetidx] = sum(x[lookup_pg[j]] for j in ref_bus_gens[i]; init=0.0) -
                bus_pd[i] -
                bus_gs[i]*x[lookup_vm[i]]^2 -
                sum(x[p_idxmap[a]] for a in ref_bus_arcs[i])
        end

        offsetidx += length(ref_bus_idxs)

        #     @constraint(model,
        #         sum(q[a] for a in ref[:bus_arcs][i]) ==
        #         sum(x[lookup_qg[g]] for g in ref_bus_gens[i]) -
        #         sum(load["qd"] for load in bus_loads) +
        #         sum(shunt["bs"] for shunt in bus_shunts)*x[lookup_vm[i]]^2
        #     )
        for (reti,i) in enumerate(ref_bus_idxs)
            ret[reti + offsetidx] = sum(x[lookup_qg[j]] for j in ref_bus_gens[i]; init=0.0) -
                bus_qd[i] +
                bus_bs[i]*x[lookup_vm[i]]^2 -
                sum(x[q_idxmap[a]] for a in ref_bus_arcs[i])
        end

        offsetidx += length(ref_bus_idxs)

        # @NLconstraint(model, p_fr ==  (g+g_fr)/ttm*vm_fr^2 + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-b*tr-g*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to)) )
        # power_flow_p_from_con =
        for (reti,(l,i,j)) in enumerate(ref_arcs_from)
            ret[reti + offsetidx] = (br_g[l]+br_g_fr[l])/br_ttm[l]*x[lookup_vm[f_bus[l]]]^2 +
                (-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[f_bus[l]]]*x[lookup_vm[t_bus[l]]]*cos(x[lookup_va[f_bus[l]]]-x[lookup_va[t_bus[l]]])) +
                (-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[f_bus[l]]]*x[lookup_vm[t_bus[l]]]*sin(x[lookup_va[f_bus[l]]]-x[lookup_va[t_bus[l]]])) -
                x[p_idxmap[(l,i,j)]]
        end

        offsetidx += length(ref_arcs_from)

        # @NLconstraint(model, p_to ==  (g+g_to)*vm_to^2 + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) + (-b*tr+g*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr)) )
        # power_flow_p_to_con
        for (reti,(l,i,j)) in enumerate(ref_arcs_to)
            ret[reti + offsetidx] = (br_g[l]+br_g_to[l])*x[lookup_vm[t_bus[l]]]^2 +
                (-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[t_bus[l]]]*x[lookup_vm[f_bus[l]]]*cos(x[lookup_va[t_bus[l]]]-x[lookup_va[f_bus[l]]])) +
                (-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[t_bus[l]]]*x[lookup_vm[f_bus[l]]]*sin(x[lookup_va[t_bus[l]]]-x[lookup_va[f_bus[l]]])) -
                x[p_idxmap[(l,i,j)]]
        end

        offsetidx += length(ref_arcs_to)

        # @NLconstraint(model, q_fr == -(b+b_fr)/ttm*vm_fr^2 - (-b*tr-g*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to)) )
        # power_flow_q_from_con
        for (reti,(l,i,j)) in enumerate(ref_arcs_from)
            ret[reti + offsetidx] = -(br_b[l]+br_b_fr[l])/br_ttm[l]*x[lookup_vm[f_bus[l]]]^2 -
                (-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[f_bus[l]]]*x[lookup_vm[t_bus[l]]]*cos(x[lookup_va[f_bus[l]]]-x[lookup_va[t_bus[l]]])) +
                (-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[f_bus[l]]]*x[lookup_vm[t_bus[l]]]*sin(x[lookup_va[f_bus[l]]]-x[lookup_va[t_bus[l]]])) -
                x[q_idxmap[(l,i,j)]]
        end

        offsetidx += length(ref_arcs_from)

        # @NLconstraint(model, q_to == -(b+b_to)*vm_to^2 - (-b*tr+g*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr)) )
        # power_flow_q_to_con
        for (reti,(l,i,j)) in enumerate(ref_arcs_to)
            ret[reti + offsetidx] = -(br_b[l]+br_b_to[l])*x[lookup_vm[t_bus[l]]]^2 -
                (-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[t_bus[l]]]*x[lookup_vm[f_bus[l]]]*cos(x[lookup_va[t_bus[l]]]-x[lookup_va[f_bus[l]]])) +
                (-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[t_bus[l]]]*x[lookup_vm[f_bus[l]]]*sin(x[lookup_va[t_bus[l]]]-x[lookup_va[f_bus[l]]])) -
                x[q_idxmap[(l,i,j)]]
        end

        offsetidx += length(ref_arcs_to)

        # @constraint(model, va_fr - va_to <= branch["angmax"])
        # @constraint(model, va_fr - va_to >= branch["angmin"])
        # power_flow_vad_con
        for (reti,(l,i,j)) in enumerate(ref_arcs_from)
            ret[reti + offsetidx] = x[lookup_va[f_bus[l]]] - x[lookup_va[t_bus[l]]]
        end

        offsetidx += length(ref_arcs_from)

        # @constraint(model, p_fr^2 + q_fr^2 <= branch["rate_a"]^2)
        # power_flow_mva_from_con
        for (reti,(l,i,j)) in enumerate(ref_arcs_from)
            ret[reti + offsetidx] = x[p_idxmap[(l,i,j)]]^2 + x[q_idxmap[(l,i,j)]]^2
        end

        offsetidx += length(ref_arcs_from)

        # @constraint(model, p_to^2 + q_to^2 <= branch["rate_a"]^2)
        # power_flow_mva_to_con
        for (reti,(l,i,j)) in enumerate(ref_arcs_to)
            ret[reti + offsetidx] = x[p_idxmap[(l,i,j)]]^2 + x[q_idxmap[(l,i,j)]]^2
        end

        offsetidx += length(ref_arcs_to)

        @assert offsetidx == length(ret)
        return ret
    end

    con_lbs = Float64[]
    con_ubs = Float64[]

    #@constraint(model, va[i] == 0)
    for (i,bus) in ref[:ref_buses]
        push!(con_lbs, 0.0)
        push!(con_ubs, 0.0)
    end

    #power_balance_p_con
    for (i,bus) in ref[:bus]
        push!(con_lbs, 0.0)
        push!(con_ubs, 0.0)
    end

    #power_balance_q_con
    for (i,bus) in ref[:bus]
        push!(con_lbs, 0.0)
        push!(con_ubs, 0.0)
    end

    #power_flow_p_from_con
    for (l,i,j) in ref[:arcs_from]
        push!(con_lbs, 0.0)
        push!(con_ubs, 0.0)
    end

    #power_flow_p_to_con
    for (l,i,j) in ref[:arcs_to]
        push!(con_lbs, 0.0)
        push!(con_ubs, 0.0)
    end

    #power_flow_q_from_con
    for (l,i,j) in ref[:arcs_from]
        push!(con_lbs, 0.0)
        push!(con_ubs, 0.0)
    end

    #power_flow_q_to_con
    for (l,i,j) in ref[:arcs_to]
        push!(con_lbs, 0.0)
        push!(con_ubs, 0.0)
    end

    #power_flow_vad_con
    for (l,i,j) in ref[:arcs_from]
        branch = ref[:branch][l]
        push!(con_lbs, branch["angmin"])
        push!(con_ubs, branch["angmax"])
    end

    #power_flow_mva_from_con
    for (l,i,j) in ref[:arcs_from]
        branch = ref[:branch][l]
        push!(con_lbs, -Inf)
        push!(con_ubs, branch["rate_a"]^2)
    end

    #power_flow_mva_to_con
    for (l,i,j) in ref[:arcs_to]
        branch = ref[:branch][l]
        push!(con_lbs, -Inf)
        push!(con_ubs, branch["rate_a"]^2)
    end

    nlp = ADNLPModels.ADNLPModel!(opf_objective, var_init, var_lb, var_ub, opf_constraints!, con_lbs, con_ubs, backend = :optimized)
    return (
        nlp = nlp,
        con_lbs = con_lbs,
        con_ubs = con_ubs,
    )
end

function solve_opf(file_name)
    start = time()
    dataset = load_and_setup_data(file_name);
    data_load_time = time() - start
    model = build_opf_optimization_prob(dataset)
    nlp = model.nlp
    model_build_time = time() - start - data_load_time
    output = NLPModelsIpopt.ipopt(nlp)
    cost = output.objective
    feasible = (output.primal_feas <= 1e-6)
    total_time = time() - start
    solve_time = total_time - model_build_time - data_load_time
    model_variables = length(dataset.var_init)
    model_constraints = length(model.con_lbs)
    println("variables: $(model_variables), $(length(dataset.var_lb)), $(length(dataset.var_ub))")
    println("constraints: $(model_constraints), $(length(model.con_lbs)), $(length(model.con_ubs))")
    println("")
    println("\033[1mSummary\033[0m")
    println("   case........: $(file_name)")
    println("   variables...: $(model_variables)")
    println("   constraints.: $(model_constraints)")
    println("   feasible....: $(feasible)")
    println("   cost........: $(round(Int, cost))")
    println("   total time..: $(total_time)")
    println("     data time.: $(data_load_time)")
    println("     build time: $(model_build_time)")
    println("     solve time: $(solve_time)")
    println("")

    return Dict(
        "case" => file_name,
        "variables" => model_variables,
        "constraints" => model_constraints,
        "feasible" => feasible,
        "cost" => cost,
        "time_total" => total_time,
        "time_data" => data_load_time,
        "time_build" => model_build_time,
        "time_solve" => solve_time,
        "solution" => Dict(k => output.solution[v] for (k, v) in dataset.var_lookup),
    )
end

if isinteractive() == false
    solve_opf("$(@__DIR__)/data/opf_warmup.m")
end