Optimal Powerflow Nonlinear Optimization Benchmark
Data Load and Setup Code
This is generic setup code usable for all solver setups. Basically removing some unnecessary untyped dictionaries before getting to the benchmarks.
PRINT_LEVEL = 0
# This is a soft upper limit to the time of each optimization.
# If times go above this, they will halt early
MAX_CPU_TIME = 100.0
# Maximum number of variables in an optimization problem for the benchmark
# Anything with more variables is rejected and not run
# This is for testing. 100 is a good size for running a test of changes
# Should be set to typemax(Int) to run the whole benchmark
SIZE_LIMIT = 10001000import PowerModels
import ConcreteStructs
using BenchmarkTools
using DataFrames
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
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
file_name = "../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m"
dataset = load_and_setup_data(file_name);Test Setup
Ensure that all objectives and constraints evaluate to the same value on a feasible point in the same dataset
test_u0 = [0.062436387733897314, 1.0711076238965598, 0.0, 1.066509799068872, -0.023231313776594726, 1.0879315976617783, -0.033094993289919016, 1.0999999581285527, 0.07121718642320936, 1.094374845084077, 0.4228458440068076, -3.7102746662566277, 1.8046846767604458e-8, -0.44810504067067086, 8.80063717152151, -0.0, 0.8709675496332583, 3.6803022758556523, -0.0, 4.618897246588245, -1.1691336178031877, 1.3748418519024668, 0.9623014391707738, -1.3174990482204871, -2.3850868109149004, 0.1445158405684026, 2.813869610747349, 0.8151138880859179, 1.9869253829584679, 3.768252275480421, 3.9998421778156934, 0.03553108302190666, 1.177155791026922, -1.3025310027752557, -0.9598988325635542, 1.3193604239530325, 2.399997991458022, -0.003103523654225171, -2.7920689620650667, -0.6047898784636468, -1.9771521474512397, -3.7071711426024025, -3.9623014391707136, 0.3313990482205271]
test_obj = 16236.704322376236
test_cons = [0.0, 2.5424107263916085e-14, -1.0835776720341528e-13, -6.039613253960852e-14, 0.0, 0.0, 0.0, -1.7075230118734908e-13, -3.9968028886505635e-14, 1.532107773982716e-13, 0.0, 6.661338147750939e-16, -1.7763568394002505e-15, 0.0, 8.881784197001252e-16, 4.440892098500626e-16, 0.0, 4.440892098500626e-16, -1.7763568394002505e-15, -8.881784197001252e-16, -4.440892098500626e-16, 2.220446049250313e-16, 0.0, 1.7763568394002505e-15, 0.0, 6.8833827526759706e-15, -8.992806499463768e-15, 3.9968028886505635e-14, -7.105427357601002e-15, 0.0, 0.0, 7.327471962526033e-15, -7.105427357601002e-15, 2.842170943040401e-14, -7.105427357601002e-15, -0.033094993289919016, 0.00986367951332429, -0.062436387733897314, 0.07121718642320936, 0.008780798689312044, 0.09444850019980408, 3.2570635340201743, 2.661827801892032, 5.709523923775402, 8.58227283683798, 18.147597689108025, 15.999999905294098, 3.0822827695389314, 2.6621176970504, 5.759999990861611, 8.161419886019171, 17.65224849471505, 15.80965802401578]53-element Vector{Float64}:
0.0
2.5424107263916085e-14
-1.0835776720341528e-13
-6.039613253960852e-14
0.0
0.0
0.0
-1.7075230118734908e-13
-3.9968028886505635e-14
1.532107773982716e-13
⋮
8.58227283683798
18.147597689108025
15.999999905294098
3.0822827695389314
2.6621176970504
5.759999990861611
8.161419886019171
17.65224849471505
15.80965802401578Setup and Validations
Now is the setup code for each optimization framework, along with the validation runs on the test case. Any test which fails the validation case, i.e. has x_test_res[1] !≈ test_obj or x_test_res[2] !≈ test_cons should be considered invalidated as this means that the model in that modeling platform does not evaluate to give the same results
Optimization.jl
Constraint optimization implementation reference: https://github.com/SciML/Optimization.jl/blob/master/lib/OptimizationMOI/test/runtests.jl Other AD libraries can be considered: https://docs.sciml.ai/dev/modules/Optimization/API/optimization_function/
import Optimization
import OptimizationMOI
import ModelingToolkit
import Ipopt
import Enzyme
import ReverseDiff
function build_opf_optimization_prob(dataset; adchoice = Optimization.AutoEnzyme())
(;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
#total_callback_time = 0.0
function opf_objective(x, param)
#start = time()
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
#total_callback_time += time() - start
return cost
end
function opf_constraints(ret, x, param)
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
# )
# power_balance_p_con
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
# )
# power_balance_q_con
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)
nothing
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
model_variables = length(var_init)
ret = Array{Float64}(undef, length(con_lbs))
model_constraints = length(con_lbs)
optf = Optimization.OptimizationFunction(opf_objective, adchoice; cons=opf_constraints)
prob = Optimization.OptimizationProblem(optf, var_init; lb=var_lb, ub=var_ub, lcons=con_lbs, ucons=con_ubs)
end
function solve_opf_optimization(dataset; adchoice = Optimization.AutoSparseReverseDiff(true))
model_build_time = @elapsed prob = build_opf_optimization_prob(dataset; adchoice)
# Correctness tests
ret = zeros(length(prob.lcons))
prob.f.cons(ret, prob.u0, nothing)
@allocated prob.f(prob.u0, nothing) == 0
@allocated prob.f.cons(ret, prob.u0, nothing) == 0
solve_time_with_compilation = @elapsed sol = Optimization.solve(prob, Ipopt.Optimizer(), print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME)
cost = sol.minimum
feasible = (sol.retcode == Optimization.SciMLBase.ReturnCode.Success)
#println(sol.u) # solution vector
solve_time_without_compilation = @elapsed sol = Optimization.solve(prob, Ipopt.Optimizer(), print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME)
return (prob,sol),Dict(
"case" => file_name,
"variables" => length(prob.u0),
"constraints" => length(prob.lcons),
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation,
)
end
function test_optimization_prob(dataset, test_u0)
prob = build_opf_optimization_prob(dataset)
ret = zeros(length(prob.lcons))
prob.f.cons(ret, test_u0, nothing)
obj = prob.f(test_u0, nothing)
obj, ret
endtest_optimization_prob (generic function with 1 method)optimization_test_res = test_optimization_prob(dataset, test_u0)(16236.704322376236, [0.0, 2.5424107263916085e-14, -1.0835776720341528e-13,
-6.039613253960852e-14, 0.0, 0.0, 0.0, -1.7075230118734908e-13, -3.9968028
886505635e-14, 1.532107773982716e-13 … 5.709523923775402, 8.5822728368379
8, 18.147597689108025, 15.999999905294098, 3.0822827695389314, 2.6621176970
504, 5.759999990861611, 8.161419886019171, 17.65224849471505, 15.8096580240
1578])@assert optimization_test_res[1] == test_obj@assert optimization_test_res[2] == test_consModelingToolkit.jl
Showcases symbolic interface to Optimization.jl, through ModelingToolkit.jl. The simplification process in ModelingToolkit.jl transforms the system to solve for a smaller subset of variables. As a result, while the optimization problem being solved is equivalent constraint function values don't match. The test for this system is thus modified. The test_u0 vector will be appropriately subsetted to match the reduced set of variables, and the resultant point is ensured to be feasible as per the modified constraints. The subsetted point will be used to re-generate the full point using the observed equations in the reduced system, and this point will be validated to match test_u0.
import PowerModels
import Ipopt
using ModelingToolkit, Optimization, OptimizationMOI
import ModelingToolkit: ≲, unknowns
import SymbolicIndexingInterface
using SymbolicIndexingInterface: variable_symbols, all_variable_symbols, getname
function build_opf_mtk_prob(dataset)
(;data, ref) = dataset
vars = Num[]
lb = Float64[]
ub = Float64[]
ModelingToolkit.@variables va[1:maximum(keys(ref[:bus]))]
for i in keys(ref[:bus])
push!(lb, -Inf)
push!(ub, Inf)
end
ModelingToolkit.@variables vm[1:maximum(keys(ref[:bus]))]
for i in keys(ref[:bus])
push!(lb, ref[:bus][i]["vmin"])
push!(ub, ref[:bus][i]["vmax"])
end
vars = vcat(vars, [va[i] for i in keys(ref[:bus])], [vm[i] for i in keys(ref[:bus])])
ModelingToolkit.@variables pg[1:maximum(keys(ref[:gen]))]
for i in keys(ref[:gen])
push!(lb, ref[:gen][i]["pmin"])
push!(ub, ref[:gen][i]["pmax"])
end
ModelingToolkit.@variables qg[1:maximum(keys(ref[:gen]))]
for i in keys(ref[:gen])
push!(lb, ref[:gen][i]["qmin"])
push!(ub, ref[:gen][i]["qmax"])
end
vars = vcat(vars, [pg[i] for i in keys(ref[:gen])], [qg[i] for i in keys(ref[:gen])])
i_inds, j_inds, l_inds = maximum(first.(ref[:arcs])), maximum(getindex.(ref[:arcs], Ref(2))), maximum(last.(ref[:arcs]))
ModelingToolkit.@variables p[1:i_inds, 1:j_inds, 1:l_inds]
ModelingToolkit.@variables q[1:i_inds, 1:j_inds, 1:l_inds]
for (l, i, j) in ref[:arcs]
push!(vars, p[l, i, j])
push!(lb, -ref[:branch][l]["rate_a"])
push!(ub, ref[:branch][l]["rate_a"])
end
for (l, i, j) in ref[:arcs]
push!(vars, q[l, i, j])
push!(lb, -ref[:branch][l]["rate_a"])
push!(ub, ref[:branch][l]["rate_a"])
end
loss = sum(gen["cost"][1] * pg[i]^2 + gen["cost"][2] * pg[i] + gen["cost"][3] for (i, gen) in ref[:gen])
cons = Array{Union{ModelingToolkit.Equation,ModelingToolkit.Inequality}}([])
for (i, bus) in ref[:ref_buses]
push!(cons, va[i] ~ 0)
end
for (i, bus) in ref[:bus]
bus_loads = [ref[:load][l] for l in ref[:bus_loads][i]]
bus_shunts = [ref[:shunt][s] for s in ref[:bus_shunts][i]]
push!(cons,
sum(p[a...] for a in ref[:bus_arcs][i]) ~
(sum(pg[g] for g in ref[:bus_gens][i]; init = 0.0)) -
(sum(load["pd"] for load in bus_loads; init = 0.0)) -
sum(shunt["gs"] for shunt in bus_shunts; init = 0.0)*vm[i]^2
)
push!(cons,
sum(q[a...] for a in ref[:bus_arcs][i]) ~
(sum(qg[g] for g in ref[:bus_gens][i]; init = 0.0)) -
(sum(load["qd"] for load in bus_loads; init = 0.0))
+ sum(shunt["bs"] for shunt in bus_shunts; init = 0.0)*vm[i]^2
)
end
# Branch power flow physics and limit constraints
for (i, branch) in ref[:branch]
f_idx = (i, branch["f_bus"], branch["t_bus"])
t_idx = (i, branch["t_bus"], branch["f_bus"])
p_fr = p[f_idx...]
q_fr = q[f_idx...]
p_to = p[t_idx...]
q_to = q[t_idx...]
vm_fr = vm[branch["f_bus"]]
vm_to = vm[branch["t_bus"]]
va_fr = va[branch["f_bus"]]
va_to = va[branch["t_bus"]]
g, b = PowerModels.calc_branch_y(branch)
tr, ti = PowerModels.calc_branch_t(branch)
ttm = tr^2 + ti^2
g_fr = branch["g_fr"]
b_fr = branch["b_fr"]
g_to = branch["g_to"]
b_to = branch["b_to"]
# From side of the branch flow
push!(cons, 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)))
push!(cons, 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)))
# To side of the branch flow
push!(cons, 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)))
push!(cons, 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)))
# Voltage angle difference limit
push!(cons, va_fr - va_to ≲ branch["angmax"])
push!(cons, branch["angmin"] ≲ va_fr - va_to)
# Apparent power limit, from side and to side
push!(cons, p_fr^2 + q_fr^2 ≲ branch["rate_a"]^2)
push!(cons, p_to^2 + q_to^2 ≲ branch["rate_a"]^2)
end
optsys = ModelingToolkit.OptimizationSystem(loss, vars, [], constraints=cons, name=:rosetta)
optsys = ModelingToolkit.complete(optsys)
u0map = [Num(k) => 0.0 for k in collect(unknowns(optsys))]
ks = collect(Num.(unknowns(optsys)))
for key in keys(ref[:bus])
ind = findfirst(x -> isequal(x, vm[key]), ks)
if ind !== nothing
u0map[ind] = vm[key] => 1.0
end
end
inds = Int[]
for k in collect(unknowns(optsys))
push!(inds, findall(x -> isequal(x, k), vars)[1])
end
prob = Optimization.OptimizationProblem(optsys, Dict(u0map), lb = lb[inds], ub = ub[inds], grad=true, hess=true, cons_j=true, cons_h=true, cons_sparse=true, sparse=true)
end
function solve_opf_mtk(dataset)
model_build_time = @elapsed prob = build_opf_mtk_prob(dataset)
# @assert prob.f(prob.u0, nothing) == 0.0 #MTK with simplification doesn't evaluate the same
ret = zeros(length(prob.lcons))
prob.f.cons(ret, prob.u0, nothing, )
@allocated prob.f(prob.u0, nothing) == 0
@allocated prob.f.cons(ret, prob.u0, nothing) == 0
solve_time_with_compilation = @elapsed sol = OptimizationMOI.solve(prob, Ipopt.Optimizer())
solve_time_without_compilation = @elapsed sol = OptimizationMOI.solve(prob, Ipopt.Optimizer())
cost = sol.minimum
feasible = (sol.retcode == Optimization.SciMLBase.ReturnCode.Success)
return (prob,sol),Dict(
"case" => file_name,
"variables" => length(prob.u0),
"constraints" => length(prob.lcons),
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation,
)
end
# Given a ModelingToolkit.jl variable, translate it to the corresponding
# name it has in the dataset
function mtk_sym_to_name(sym)
sym = ModelingToolkit.unwrap(sym)
return string(getname(sym)) * "_" * join(ModelingToolkit.arguments(sym)[2:end], "_")
end
function test_mtk_prob(dataset, test_u0)
prob = build_opf_mtk_prob(dataset)
syms_subset = variable_symbols(prob)
syms_names = map(mtk_sym_to_name, syms_subset)
subset_idxs = [dataset.var_lookup[name] for name in syms_names]
sub_u0 = test_u0[subset_idxs]
objective = prob.f(sub_u0, prob.p)
cons_buffer = zeros(length(prob.lcons))
prob.f.cons(cons_buffer, sub_u0)
all_syms = all_variable_symbols(prob)
all_syms_names = map(mtk_sym_to_name, all_syms)
all_syms_idxs = [dataset.var_lookup[name] for name in all_syms_names]
reordered_syms = similar(all_syms)
for (sym, idx) in zip(all_syms, all_syms_idxs)
reordered_syms[idx] = sym
end
reconstructed_u0 = SymbolicIndexingInterface.observed(prob, reordered_syms)(sub_u0, prob.p)
return objective, reconstructed_u0, cons_buffer, prob.lcons, prob.ucons
endtest_mtk_prob (generic function with 1 method)objective, reconstructed_u0, cons_vals, lcons, ucons = test_mtk_prob(dataset, test_u0)(16236.704322376236, [0.062436387733897314, 1.0711076238965598, 0.0, 1.0665
09799068872, -0.023231313776594726, 1.0879315976617783, -0.0330949932899190
16, 1.0999999581285527, 0.07121718642320936, 1.094374845084077 … -0.95989
88325635542, 1.3193604239530325, 2.399997991458022, -0.003103523654225171,
-2.7920689620650667, -0.6047898784636468, -1.9771521474512397, -3.707171142
6024025, -3.9623014391707136, 0.3313990482205271], [0.0, -2.542410726391608
5e-14, 0.0, 1.0782979045774244e-13, 1.7091883464104285e-13, 6.0174087934683
48e-14, 3.9968028886505635e-14, 0.0, -1.5276668818842154e-13, 0.0 … -2.31
08919702252706e-6, -0.49535150528494754, 0.0, 1.5543122344752192e-15, 4.440
892098500626e-16, -1.5543122344752192e-15, -0.42915027539849476, -0.6180472
757981029, -9.470590205053371e-8, -0.19034197598422065], [0.0, 0.0, 0.0, 0.
0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … -Inf, -Inf, 0.0, 0.0, 0.0, 0.0, -Inf, -
Inf, -Inf, -Inf], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0
, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])@assert isapprox(objective, test_obj)# Some pairs of variables have interchangeable values.
# Either one could be specified in terms of the other, and the choice
# is arbitrary during simplification of the symbolic system.
swappable_pairs = [
[dataset.var_lookup["pg_1"], dataset.var_lookup["pg_2"]],
[dataset.var_lookup["qg_1"], dataset.var_lookup["qg_2"]],
]
# Indexes that must match
non_swappable_idxs = setdiff(eachindex(reconstructed_u0), reduce(vcat, swappable_pairs))
@assert isapprox(reconstructed_u0[non_swappable_idxs], test_u0[non_swappable_idxs])
for (i, j) in swappable_pairs
@assert isapprox(reconstructed_u0[[i, j]], test_u0[[i, j]]) || isapprox(reconstructed_u0[[j, i]], test_u0[[i, j]])
end# Test all constraint values
@assert all(isapprox.(lcons, cons_vals, atol=1e-12) .|| (lcons .<= cons_vals .<= ucons) .|| isapprox.(cons_vals, ucons, atol=1e-12))JuMP.jl
Implementation reference: https://github.com/lanl-ansi/PowerModelsAnnex.jl/blob/master/src/model/ac-opf.jl Only the built-in AD library is supported
import PowerModels
import Ipopt
import JuMP
function build_opf_jump_prob(dataset)
(;data, ref) = dataset
constraints = Any[]
model = JuMP.Model(Ipopt.Optimizer)
vars = [JuMP.@variable(model, va[i in keys(ref[:bus])]),
JuMP.@variable(model, ref[:bus][i]["vmin"] <= vm[i in keys(ref[:bus])] <= ref[:bus][i]["vmax"], start=1.0),
JuMP.@variable(model, ref[:gen][i]["pmin"] <= pg[i in keys(ref[:gen])] <= ref[:gen][i]["pmax"]),
JuMP.@variable(model, ref[:gen][i]["qmin"] <= qg[i in keys(ref[:gen])] <= ref[:gen][i]["qmax"]),
JuMP.@variable(model, -ref[:branch][l]["rate_a"] <= p[(l,i,j) in ref[:arcs]] <= ref[:branch][l]["rate_a"]),
JuMP.@variable(model, -ref[:branch][l]["rate_a"] <= q[(l,i,j) in ref[:arcs]] <= ref[:branch][l]["rate_a"])]
JuMP.@objective(model, Min, sum(gen["cost"][1]*pg[i]^2 + gen["cost"][2]*pg[i] + gen["cost"][3] for (i,gen) in ref[:gen]))
for (i,bus) in ref[:ref_buses]
push!(constraints,JuMP.@constraint(model, va[i] == 0))
end
for (i,bus) in ref[:bus]
bus_loads = [ref[:load][l] for l in ref[:bus_loads][i]]
bus_shunts = [ref[:shunt][s] for s in ref[:bus_shunts][i]]
push!(constraints,JuMP.@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)*vm[i]^2
))
push!(constraints,JuMP.@constraint(model,
sum(q[a] for a in ref[:bus_arcs][i]) ==
sum(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)*vm[i]^2
))
end
# Branch power flow physics and limit constraints
for (i,branch) in ref[:branch]
f_idx = (i, branch["f_bus"], branch["t_bus"])
t_idx = (i, branch["t_bus"], branch["f_bus"])
p_fr = p[f_idx]
q_fr = q[f_idx]
p_to = p[t_idx]
q_to = q[t_idx]
vm_fr = vm[branch["f_bus"]]
vm_to = vm[branch["t_bus"]]
va_fr = va[branch["f_bus"]]
va_to = va[branch["t_bus"]]
g, b = PowerModels.calc_branch_y(branch)
tr, ti = PowerModels.calc_branch_t(branch)
ttm = tr^2 + ti^2
g_fr = branch["g_fr"]
b_fr = branch["b_fr"]
g_to = branch["g_to"]
b_to = branch["b_to"]
# From side of the branch flow
push!(constraints,JuMP.@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)) ))
push!(constraints,JuMP.@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)) ))
# To side of the branch flow
push!(constraints,JuMP.@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)) ))
push!(constraints,JuMP.@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)) ))
# Voltage angle difference limit
push!(constraints,JuMP.@constraint(model, branch["angmin"] <= va_fr - va_to <= branch["angmax"]))
# Apparent power limit, from side and to side
push!(constraints,JuMP.@constraint(model, p_fr^2 + q_fr^2 <= branch["rate_a"]^2))
push!(constraints,JuMP.@constraint(model, p_to^2 + q_to^2 <= branch["rate_a"]^2))
end
model_variables = JuMP.num_variables(model)
# for consistency with other solvers, skip the variable bounds in the constraint count
non_nl_constraints = sum(JuMP.num_constraints(model, ft, st) for (ft, st) in JuMP.list_of_constraint_types(model) if ft != JuMP.VariableRef)
model_constraints = JuMP.num_nonlinear_constraints(model) + non_nl_constraints
model, vars, constraints
end
function solve_opf_jump(dataset)
model_build_time = @elapsed model = build_opf_jump_prob(dataset)[1]
JuMP.set_attribute(model, "max_cpu_time", MAX_CPU_TIME)
JuMP.set_attribute(model, "print_level", PRINT_LEVEL)
solve_time_with_compilation = @elapsed JuMP.optimize!(model)
solve_time_without_compilation = @elapsed JuMP.optimize!(model)
cost = JuMP.objective_value(model)
feasible = (JuMP.termination_status(model) == JuMP.LOCALLY_SOLVED)
nlp_block = JuMP.MOI.get(model, JuMP.MOI.NLPBlock())
total_callback_time =
nlp_block.evaluator.eval_objective_timer +
nlp_block.evaluator.eval_objective_gradient_timer +
nlp_block.evaluator.eval_constraint_timer +
nlp_block.evaluator.eval_constraint_jacobian_timer +
nlp_block.evaluator.eval_hessian_lagrangian_timer
model_variables = JuMP.num_variables(model)
non_nl_constraints = sum(JuMP.num_constraints(model, ft, st) for (ft, st) in JuMP.list_of_constraint_types(model) if ft != JuMP.VariableRef)
model_constraints = JuMP.num_nonlinear_constraints(model) + non_nl_constraints
return model, Dict(
"case" => file_name,
"variables" => model_variables,
"constraints" => model_constraints,
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation,
)
end
function test_jump_prob(dataset, test_u0)
model, vars, constraints = build_opf_jump_prob(dataset)
(;
lookup_pg,
lookup_qg,
lookup_va,
lookup_vm,
lookup_lij,
lookup_p_lij,
lookup_q_lij) = dataset
f = JuMP.objective_function(model)
flatvars = reduce(vcat,[reduce(vcat,vars[i]) for i in 1:length(vars)])
point = Dict()
for v in flatvars
varname, varint = split(JuMP.name(v), "[")
idx = if varint[1] == '('
varint = (parse(Int, varint[2]), parse(Int, varint[5]), parse(Int, varint[8]))
if varname == "p"
lookup_p_lij[findfirst(x->x==varint,lookup_lij)]
elseif varname == "q"
lookup_q_lij[findfirst(x->x==varint,lookup_lij)]
else
error("Invalid $varname, $varint")
end
else
varint = parse(Int, varint[1:end-1])
if varname == "va"
lookup_va[varint]
elseif varname == "pg"
lookup_pg[varint]
elseif varname == "qg"
lookup_qg[varint]
elseif varname == "vm"
lookup_vm[varint]
else
error("Invalid $varname, $varint")
end
end
point[v] = test_u0[idx]
end
obj = JuMP.value(x->point[x], f)
# The JuMP assertion error is because JuMP and optimization.jl build different problems. JuMP builds f(x) == a and optimization.jl builds f(x) - a == 0
# Workaround this for consistent evaluation
# It's not a general purpose approach because only some of the Optimization.jl constraints are written as f(x) - a = 0 .
# Others are written as f(x) <= a, like the p_fr^2 + q_fr^2 <= branch["rate_a"]^2 constraints
primal_value(set::JuMP.MOI.EqualTo) = JuMP.MOI.constant(set)
primal_value(set) = 0.0
function primal_value(f, constraint)
object = JuMP.constraint_object(constraint)
fx = JuMP.value(f, object.func)
return fx - primal_value(object.set)
end
function primal_value(f, constraint::JuMP.NonlinearConstraintRef)
return JuMP.value(f, constraint)
end
obj = JuMP.value(x->point[x], f)
cons = [primal_value(x->point[x], c) for c in constraints]
obj, cons
endtest_jump_prob (generic function with 1 method)jump_test_res = test_jump_prob(dataset, test_u0)(16236.704322376236, [0.0, -2.55351295663786e-14, 0.0, 1.0835776720341528e-
13, 1.7075230118734908e-13, 6.039613253960852e-14, 3.9968028886505635e-14,
0.0, -1.5298873279334657e-13, 4.440892098500626e-16 … 0.00878079868931204
4, 18.147597689108025, 17.65224849471505, 0.0, 7.105427357601002e-15, 0.0,
7.105427357601002e-15, 0.09444850019980408, 15.999999905294098, 15.80965802
401578])@assert jump_test_res[1] ≈ test_obj@assert sort(abs.(jump_test_res[2])) ≈ sort(abs.(test_cons))NLPModels.jl
Implementation reference: https://juliasmoothoptimizers.github.io/ADNLPModels.jl/stable/tutorial/ Other AD libraries can be considered: https://juliasmoothoptimizers.github.io/ADNLPModels.jl/stable/
import ADNLPModels
import NLPModelsIpopt
function build_opf_nlpmodels_prob(dataset)
(;data, ref) = dataset
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
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)_$(j)"] = var_idx
var_idx += 1
end
@assert var_idx == length(var_init)+1
#total_callback_time = 0.0
function opf_objective(x)
#start = time()
cost = 0.0
for (i,gen) in ref[:gen]
pg = x[var_lookup["pg_$(i)"]]
cost += gen["cost"][1]*pg^2 + gen["cost"][2]*pg + gen["cost"][3]
end
#total_callback_time += time() - start
return cost
end
function opf_constraints!(cx, x)
#start = time()
va = Dict(i => x[var_lookup["va_$(i)"]] for (i,bus) in ref[:bus])
vm = Dict(i => x[var_lookup["vm_$(i)"]] for (i,bus) in ref[:bus])
pg = Dict(i => x[var_lookup["pg_$(i)"]] for (i,gen) in ref[:gen])
qg = Dict(i => x[var_lookup["qg_$(i)"]] for (i,gen) in ref[:gen])
p = Dict((l,i,j) => x[var_lookup["p_$(l)_$(i)_$(j)"]] for (l,i,j) in ref[:arcs])
q = Dict((l,i,j) => x[var_lookup["q_$(l)_$(i)_$(j)"]] for (l,i,j) in ref[:arcs])
vm_fr = Dict(l => vm[branch["f_bus"]] for (l,branch) in ref[:branch])
vm_to = Dict(l => vm[branch["t_bus"]] for (l,branch) in ref[:branch])
va_fr = Dict(l => va[branch["f_bus"]] for (l,branch) in ref[:branch])
va_to = Dict(l => va[branch["t_bus"]] for (l,branch) in ref[:branch])
# va_con = [va[i] for (i,bus) in ref[:ref_buses]]
k = 0
for (i,bus) in ref[:ref_buses]
k += 1
cx[k] = va[i]
end
# @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)*vm[i]^2
# )
for (i, bus) in ref[:bus]
k += 1
cx[k] = sum(pg[j] for j in ref[:bus_gens][i]; init=0.0) - bus_pd[i] - bus_gs[i]*vm[i]^2 - sum(p[a] for a in ref[:bus_arcs][i])
end
# @constraint(model,
# sum(q[a] for a in ref[:bus_arcs][i]) ==
# sum(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)*vm[i]^2
# )
for (i, bus) in ref[:bus]
k += 1
cx[k] = sum(qg[j] for j in ref[:bus_gens][i]; init=0.0) - bus_qd[i] + bus_bs[i]*vm[i]^2 - sum(q[a] for a in ref[:bus_arcs][i])
end
# @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)) )
for (l,i,j) in ref[:arcs_from]
k += 1
cx[k] = (br_g[l]+br_g_fr[l])/br_ttm[l]*vm_fr[l]^2 +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*cos(va_fr[l]-va_to[l])) +
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*sin(va_fr[l]-va_to[l])) -
p[(l,i,j)]
end
# @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)) )
for (l,i,j) in ref[:arcs_to]
k += 1
cx[k] = (br_g[l]+br_g_to[l])*vm_to[l]^2 +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*cos(va_to[l]-va_fr[l])) +
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*sin(va_to[l]-va_fr[l])) -
p[(l,i,j)]
end
# @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)) )
for (l,i,j) in ref[:arcs_from]
k += 1
cx[k] = -(br_b[l]+br_b_fr[l])/br_ttm[l]*vm_fr[l]^2 -
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*cos(va_fr[l]-va_to[l])) +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*sin(va_fr[l]-va_to[l])) -
q[(l,i,j)]
end
# @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)) )
for (l,i,j) in ref[:arcs_to]
k += 1
cx[k] = -(br_b[l]+br_b_to[l])*vm_to[l]^2 -
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*cos(va_to[l]-va_fr[l])) +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*sin(va_to[l]-va_fr[l])) -
q[(l,i,j)]
end
# @constraint(model, va_fr - va_to <= branch["angmax"])
# @constraint(model, va_fr - va_to >= branch["angmin"])
for (l,i,j) in ref[:arcs_from]
k += 1
cx[k] = va_fr[l] - va_to[l]
end
# @constraint(model, p_fr^2 + q_fr^2 <= branch["rate_a"]^2)
for (l,i,j) in ref[:arcs_from]
k += 1
cx[k] = p[(l,i,j)]^2 + q[(l,i,j)]^2
end
# @constraint(model, p_to^2 + q_to^2 <= branch["rate_a"]^2)
for (l,i,j) in ref[:arcs_to]
k += 1
cx[k] = p[(l,i,j)]^2 + q[(l,i,j)]^2
end
#total_callback_time += time() - start
return cx
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
model_variables = length(var_init)
model_constraints = length(opf_constraints!(similar(con_lbs), var_init))
#=
backend = ADNLPModels.ADModelBackend(model_variables, opf_objective, model_constraints, opf_constraints!;
gradient_backend = ADNLPModels.ReverseDiffADGradient,
hprod_backend = ADNLPModels.SDTForwardDiffADHvprod,
jprod_backend = ADNLPModels.ForwardDiffADJprod,
jtprod_backend = ADNLPModels.ReverseDiffADJtprod,
jacobian_backend = ADNLPModels.ForwardDiffADJacobian, # SDTSparseADJacobian,
hessian_backend = ADNLPModels.ForwardDiffADHessian, # SparseADJacobian,
ghjvprod_backend = ADNLPModels.ForwardDiffADGHjvprod,
hprod_residual_backend = ADNLPModels.ReverseDiffADHvprod,
jprod_residual_backend = ADNLPModels.ForwardDiffADJprod,
jtprod_residual_backend = ADNLPModels.ReverseDiffADJtprod,
jacobian_residual_backend = ADNLPModels.ForwardDiffADHessian, # SparseADJacobian,
hessian_residual_backend = ADNLPModels.ForwardDiffADHessian
)
=#
nlp = ADNLPModels.ADNLPModel!(opf_objective, var_init, var_lb, var_ub, opf_constraints!, con_lbs, con_ubs, backend = :optimized)
end
function solve_opf_nlpmodels(dataset)
model_build_time = @elapsed nlp = build_opf_nlpmodels_prob(dataset)
solve_time_with_compilation = @elapsed output = NLPModelsIpopt.ipopt(nlp, print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME)
solve_time_without_compilation = @elapsed output = NLPModelsIpopt.ipopt(nlp, print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME)
cost = output.objective
feasible = (output.primal_feas <= 1e-6)
model_variables = nlp.meta.nvar
model_constraints = nlp.meta.ncon
return (nlp, output), Dict(
"case" => file_name,
"variables" => model_variables,
"constraints" => model_constraints,
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation,
)
end
function test_nlpmodels_prob(dataset, test_u0)
nlp = build_opf_nlpmodels_prob(dataset)
ret = zeros(nlp.meta.ncon)
nlp.c!(ret, test_u0)
obj = nlp.f(test_u0)
obj, ret
endtest_nlpmodels_prob (generic function with 1 method)nlpmodels_test_res = test_nlpmodels_prob(dataset, test_u0)(16236.704322376236, [0.0, 2.5424107263916085e-14, -1.0835776720341528e-13,
-6.039613253960852e-14, 0.0, 0.0, 0.0, -1.7075230118734908e-13, -3.9968028
886505635e-14, 1.532107773982716e-13 … 5.709523923775402, 8.5822728368379
8, 18.147597689108025, 15.999999905294098, 3.0822827695389314, 2.6621176970
504, 5.759999990861611, 8.161419886019171, 17.65224849471505, 15.8096580240
1578])@assert nlpmodels_test_res[1] == test_obj@assert nlpmodels_test_res[2] == test_consNonconvex
Implementation reference: https://julianonconvex.github.io/Nonconvex.jl/stable/problem/ Currently does not converge due to an upstream issue with the AD backend Zygote: https://github.com/JuliaNonconvex/Nonconvex.jl/issues/130
import Nonconvex
Nonconvex.@load Ipopt
function build_opf_nonconvex_prob(dataset)
(;data, ref) = dataset
time_model_start = time()
model = Nonconvex.DictModel()
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])
br_rate_a = Dict(i => 0.0 for (i,branch) in ref[:branch])
br_angmin = Dict(i => 0.0 for (i,branch) in ref[:branch])
br_angmax = 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"]
br_rate_a[i] = branch["rate_a"]
br_angmin[i] = branch["angmin"]
br_angmax[i] = branch["angmax"]
end
for (i,bus) in ref[:bus]
addvar!(model, "va_$(i)", -Inf, Inf, init=0.0) #va
addvar!(model, "vm_$(i)", bus["vmin"], bus["vmax"], init=1.0) #vm
end
for (i,gen) in ref[:gen]
addvar!(model, "pg_$(i)", gen["pmin"], gen["pmax"], init=0.0) #pg
addvar!(model, "qg_$(i)", gen["qmin"], gen["qmax"], init=0.0) #qg
end
for (l,i,j) in ref[:arcs]
branch = ref[:branch][l]
addvar!(model, "p_$(l)_$(i)_$(j)", -branch["rate_a"], branch["rate_a"], init=0.0) #p
addvar!(model, "q_$(l)_$(i)_$(j)", -branch["rate_a"], branch["rate_a"], init=0.0) #q
end
# JuMP.@objective(model, Min, sum(gen["cost"][1]*pg[i]^2 + gen["cost"][2]*pg[i] + gen["cost"][3] for (i,gen) in ref[:gen]))
function opf_objective(x::OrderedDict)
cost = 0.0
for (i,gen) in ref[:gen]
pg = x["pg_$(i)"]
cost += gen["cost"][1]*pg^2 + gen["cost"][2]*pg + gen["cost"][3]
end
return cost
end
Nonconvex.set_objective!(model, opf_objective)
# JuMP.@constraint(model, va[i] == 0)
function const_ref_bus(x::OrderedDict, i)
return x["va_$(i)"]
end
for (i,bus) in ref[:ref_buses]
add_eq_constraint!(model, x -> const_ref_bus(x,i))
end
# @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)*vm[i]^2
# )
function const_power_balance_p(x::OrderedDict, b)
balance = - bus_pd[b] - bus_gs[b]*x["vm_$(b)"]^2
for (l,i,j) in ref[:bus_arcs][b]
balance -= x["p_$(l)_$(i)_$(j)"]
end
for j in ref[:bus_gens][b]
balance += x["pg_$(j)"]
end
return balance
end
# @constraint(model,
# sum(q[a] for a in ref[:bus_arcs][i]) ==
# sum(qg[g] for g in ref[:bus_gens][i]) -
# sum(load["qd"] for load in bus_loads) +
# sum(shunt["bs"] for shunt in bus_shunts)*vm[i]^2
# )
function const_power_balance_q(x::OrderedDict, b)
balance = - bus_qd[b] + bus_bs[b]*x["vm_$(b)"]^2
for (l,i,j) in ref[:bus_arcs][b]
balance -= x["q_$(l)_$(i)_$(j)"]
end
for j in ref[:bus_gens][b]
balance += x["qg_$(j)"]
end
return balance
end
for (i,bus) in ref[:bus]
add_eq_constraint!(model, x -> const_power_balance_p(x,i))
add_eq_constraint!(model, x -> const_power_balance_q(x,i))
end
# @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)) )
function const_flow_p_from(x::OrderedDict,l,i,j)
return (br_g[l]+br_g_fr[l])/br_ttm[l]*x["vm_$(i)"]^2 +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(x["vm_$(i)"]*x["vm_$(j)"]*cos(x["va_$(i)"]-x["va_$(j)"])) +
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(x["vm_$(i)"]*x["vm_$(j)"]*sin(x["va_$(i)"]-x["va_$(j)"])) -
x["p_$(l)_$(i)_$(j)"]
end
# @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)) )
function const_flow_q_from(x::OrderedDict,l,i,j)
return -(br_b[l]+br_b_fr[l])/br_ttm[l]*x["vm_$(i)"]^2 -
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(x["vm_$(i)"]*x["vm_$(j)"]*cos(x["va_$(i)"]-x["va_$(j)"])) +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(x["vm_$(i)"]*x["vm_$(j)"]*sin(x["va_$(i)"]-x["va_$(j)"])) -
x["q_$(l)_$(i)_$(j)"]
end
# @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)) )
function const_flow_p_to(x::OrderedDict,l,i,j)
return (br_g[l]+br_g_to[l])*x["vm_$(j)"]^2 +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(x["vm_$(j)"]*x["vm_$(i)"]*cos(x["va_$(j)"]-x["va_$(i)"])) +
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(x["vm_$(j)"]*x["vm_$(i)"]*sin(x["va_$(j)"]-x["va_$(i)"])) -
x["p_$(l)_$(j)_$(i)"]
end
# @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)) )
function const_flow_q_to(x::OrderedDict,l,i,j)
return -(br_b[l]+br_b_to[l])*x["vm_$(j)"]^2 -
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(x["vm_$(j)"]*x["vm_$(i)"]*cos(x["va_$(j)"]-x["va_$(i)"])) +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(x["vm_$(j)"]*x["vm_$(i)"]*sin(x["va_$(j)"]-x["va_$(i)"])) -
x["q_$(l)_$(j)_$(i)"]
end
function const_thermal_limit(x::OrderedDict,l,i,j)
return x["p_$(l)_$(i)_$(j)"]^2 + x["q_$(l)_$(i)_$(j)"]^2 - br_rate_a[l]^2
end
function const_voltage_angle_difference_lb(x::OrderedDict,l,i,j)
return br_angmin[l] - x["va_$(i)"] + x["va_$(j)"]
end
function const_voltage_angle_difference_ub(x::OrderedDict,l,i,j)
return x["va_$(i)"] - x["va_$(j)"] - br_angmax[l]
end
for (l,i,j) in ref[:arcs_from]
add_eq_constraint!(model, x -> const_flow_p_from(x,l,i,j))
add_eq_constraint!(model, x -> const_flow_q_from(x,l,i,j))
add_eq_constraint!(model, x -> const_flow_p_to(x,l,i,j))
add_eq_constraint!(model, x -> const_flow_q_to(x,l,i,j))
add_ineq_constraint!(model, x -> const_thermal_limit(x,l,i,j))
add_ineq_constraint!(model, x -> const_thermal_limit(x,l,j,i))
add_ineq_constraint!(model, x -> const_voltage_angle_difference_lb(x,l,i,j))
add_ineq_constraint!(model, x -> const_voltage_angle_difference_ub(x,l,i,j))
end
model
end
function solve_opf_nonconvex(dataset)
model_build_time = @elapsed model = build_opf_nonconvex_prob(dataset)
solve_time_with_compilation = @elapsed result = Nonconvex.optimize(
model,
IpoptAlg(),
NonconvexCore.getinit(model);
options = IpoptOptions(; first_order=false, symbolic=false, sparse=true, print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME),
)
solve_time_without_compilation = @elapsed result = Nonconvex.optimize(
model,
IpoptAlg(),
NonconvexCore.getinit(model);
options = IpoptOptions(; first_order=false, symbolic=false, sparse=true, print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME),
)
cost = result.minimum
feasible = result.status == 0 # just guessing this is correct for Ipopt
model_variables = Nonconvex.NonconvexCore.getnvars(model)
model_constraints = Nonconvex.NonconvexCore.getnconstraints(model)
return (model, result), Dict(
"case" => file_name,
"variables" => model_variables,
"constraints" => model_constraints,
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation,
)
end
function test_nonconvex_prob(dataset, test_u0)
model = build_opf_nonconvex_prob(dataset)
(;
lookup_pg,
lookup_qg,
lookup_va,
lookup_vm,
lookup_lij,
lookup_p_lij,
lookup_q_lij) = dataset
point = Dict()
for v in keys(model.init)
varsplit = split(v, "_")
varname = varsplit[1]
varint = parse.(Int, varsplit[2:end])
idx = if varname == "p"
lookup_p_lij[findfirst(x->x==Tuple(varint),lookup_lij)]
elseif varname == "q"
lookup_q_lij[findfirst(x->x==Tuple(varint),lookup_lij)]
elseif varname == "va"
lookup_va[varint[1]]
elseif varname == "pg"
lookup_pg[varint[1]]
elseif varname == "qg"
lookup_qg[varint[1]]
elseif varname == "vm"
lookup_vm[varint[1]]
else
error("Invalid $varname, $varint")
end
point[v] = test_u0[idx]
end
u0 = OrderedDict(keys(model.init) .=> getindex.((point,),keys(model.init)))
obj = model.objective(u0)
cons = vcat(model.eq_constraints(u0), model.ineq_constraints(u0))
obj, cons
endtest_nonconvex_prob (generic function with 1 method)nonconvex_test_res = test_nonconvex_prob(dataset, test_u0)(16236.704322376236, [0.0, 2.5424107263916085e-14, 0.0, -1.0782979045774244
e-13, -1.7091883464104285e-13, -5.995204332975845e-14, -3.9968028886505635e
-14, 2.220446049250313e-16, 1.5276668818842154e-13, 0.0 … -0.594815962021
5082, -0.45238158917508947, -2.3108919720016274e-6, -0.49535150528494754, -
0.532379574287611, -0.5148179769089868, -9.470590178750626e-8, -0.190341975
98421976, -0.6180472757981029, -0.4291502753984947])@assert nonconvex_test_res[1] ≈ test_obj@assert sort(abs.(nonconvex_test_res[2])) ≈ sort(abs.(test_cons))Error: DimensionMismatch: dimensions must match: a has dims (Base.OneTo(59)
,), b has dims (Base.OneTo(53),), mismatch at 1println(sort(abs.(nonconvex_test_res[2])))[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.220446049250313e-16, 2
.220446049250313e-16, 4.440892098500626e-16, 4.440892098500626e-16, 4.44089
2098500626e-16, 6.661338147750939e-16, 8.881784197001252e-16, 8.88178419700
1252e-16, 1.7763568394002505e-15, 1.7763568394002505e-15, 1.776356839400250
5e-15, 6.8833827526759706e-15, 7.105427357601002e-15, 7.105427357601002e-15
, 7.105427357601002e-15, 7.327471962526033e-15, 8.992806499463768e-15, 2.54
24107263916085e-14, 2.842170943040401e-14, 3.9968028886505635e-14, 3.996802
8886505635e-14, 5.995204332975845e-14, 1.0782979045774244e-13, 1.5276668818
842154e-13, 1.7091883464104285e-13, 9.138388712415235e-9, 9.470590178750626
e-8, 2.3108919720016274e-6, 0.05047607622459793, 0.19034197598421976, 0.429
1502753984947, 0.45238158917508947, 0.4611623878644015, 0.4905037823083798,
0.49535150528494754, 0.5137350960849745, 0.5148179769089868, 0.53237957428
7611, 0.5334624551116232, 0.5566937688882179, 0.5860351633321961, 0.5948159
620215082, 0.6180472757981029, 9.565327163162017, 9.986180113980826, 14.890
536465979823, 15.065317230461066, 15.485482302949597, 15.485772198107965]println(sort(abs.(test_cons)))[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.220446049250313e-
16, 4.440892098500626e-16, 4.440892098500626e-16, 4.440892098500626e-16, 6.
661338147750939e-16, 8.881784197001252e-16, 8.881784197001252e-16, 1.776356
8394002505e-15, 1.7763568394002505e-15, 1.7763568394002505e-15, 6.883382752
6759706e-15, 7.105427357601002e-15, 7.105427357601002e-15, 7.10542735760100
2e-15, 7.327471962526033e-15, 8.992806499463768e-15, 2.5424107263916085e-14
, 2.842170943040401e-14, 3.9968028886505635e-14, 3.9968028886505635e-14, 6.
039613253960852e-14, 1.0835776720341528e-13, 1.532107773982716e-13, 1.70752
30118734908e-13, 0.008780798689312044, 0.00986367951332429, 0.0330949932899
19016, 0.062436387733897314, 0.07121718642320936, 0.09444850019980408, 2.66
1827801892032, 2.6621176970504, 3.0822827695389314, 3.2570635340201743, 5.7
09523923775402, 5.759999990861611, 8.161419886019171, 8.58227283683798, 15.
80965802401578, 15.999999905294098, 17.65224849471505, 18.147597689108025]Optim.jl
Implementation reference: https://julianlsolvers.github.io/Optim.jl/stable/#examples/generated/ipnewton_basics/ Currently does not converge to a feasible point, root cause in unclear debug/optim-debug.jl can be used to confirm it will converge if given a suitable starting point
import Optim
function build_opf_optim_prob(dataset)
(;data, ref) = dataset
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
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_$(i)"] = var_idx
var_idx += 1
end
for (i,gen) in ref[:gen]
#push!(var_init, 0.0) #pg
push!(var_init, (gen["pmax"]+gen["pmin"])/2) # non-standard start
push!(var_lb, gen["pmin"])
push!(var_ub, gen["pmax"])
var_lookup["pg_$(i)"] = var_idx
var_idx += 1
#push!(var_init, 0.0) #qg
push!(var_init, (gen["qmax"]+gen["qmin"])/2) # non-standard start
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)_$(j)"] = var_idx
var_idx += 1
end
@assert var_idx == length(var_init)+1
#total_callback_time = 0.0
function opf_objective(x)
#start = time()
cost = 0.0
for (i,gen) in ref[:gen]
pg = x[var_lookup["pg_$(i)"]]
cost += gen["cost"][1]*pg^2 + gen["cost"][2]*pg + gen["cost"][3]
end
#total_callback_time += time() - start
return cost
end
function opf_constraints(c,x)
#start = time()
va = Dict(i => x[var_lookup["va_$(i)"]] for (i,bus) in ref[:bus])
vm = Dict(i => x[var_lookup["vm_$(i)"]] for (i,bus) in ref[:bus])
pg = Dict(i => x[var_lookup["pg_$(i)"]] for (i,gen) in ref[:gen])
qg = Dict(i => x[var_lookup["qg_$(i)"]] for (i,gen) in ref[:gen])
p = Dict((l,i,j) => x[var_lookup["p_$(l)_$(i)_$(j)"]] for (l,i,j) in ref[:arcs])
q = Dict((l,i,j) => x[var_lookup["q_$(l)_$(i)_$(j)"]] for (l,i,j) in ref[:arcs])
vm_fr = Dict(l => vm[branch["f_bus"]] for (l,branch) in ref[:branch])
vm_to = Dict(l => vm[branch["t_bus"]] for (l,branch) in ref[:branch])
va_fr = Dict(l => va[branch["f_bus"]] for (l,branch) in ref[:branch])
va_to = Dict(l => va[branch["t_bus"]] for (l,branch) in ref[:branch])
va_con = [va[i] for (i,bus) in ref[:ref_buses]]
# @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)*vm[i]^2
# )
power_balance_p_con = [
sum(pg[j] for j in ref[:bus_gens][i]; init=0.0) -
bus_pd[i] -
bus_gs[i]*vm[i]^2 -
sum(p[a] for a in ref[:bus_arcs][i])
for (i,bus) in ref[:bus]
]
# @constraint(model,
# sum(q[a] for a in ref[:bus_arcs][i]) ==
# sum(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)*vm[i]^2
# )
power_balance_q_con = [
sum(qg[j] for j in ref[:bus_gens][i]; init=0.0) -
bus_qd[i] +
bus_bs[i]*vm[i]^2 -
sum(q[a] for a in ref[:bus_arcs][i])
for (i,bus) in ref[:bus]
]
# @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 = [
(br_g[l]+br_g_fr[l])/br_ttm[l]*vm_fr[l]^2 +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*cos(va_fr[l]-va_to[l])) +
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*sin(va_fr[l]-va_to[l])) -
p[(l,i,j)]
for (l,i,j) in 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 = [
(br_g[l]+br_g_to[l])*vm_to[l]^2 +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*cos(va_to[l]-va_fr[l])) +
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*sin(va_to[l]-va_fr[l])) -
p[(l,i,j)]
for (l,i,j) in 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 = [
-(br_b[l]+br_b_fr[l])/br_ttm[l]*vm_fr[l]^2 -
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*cos(va_fr[l]-va_to[l])) +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*sin(va_fr[l]-va_to[l])) -
q[(l,i,j)]
for (l,i,j) in 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 = [
-(br_b[l]+br_b_to[l])*vm_to[l]^2 -
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*cos(va_to[l]-va_fr[l])) +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*sin(va_to[l]-va_fr[l])) -
q[(l,i,j)]
for (l,i,j) in ref[:arcs_to]
]
# @constraint(model, va_fr - va_to <= branch["angmax"])
# @constraint(model, va_fr - va_to >= branch["angmin"])
power_flow_vad_con = [
va_fr[l] - va_to[l]
for (l,i,j) in ref[:arcs_from]
]
# @constraint(model, p_fr^2 + q_fr^2 <= branch["rate_a"]^2)
power_flow_mva_from_con = [
p[(l,i,j)]^2 + q[(l,i,j)]^2
for (l,i,j) in ref[:arcs_from]
]
# @constraint(model, p_to^2 + q_to^2 <= branch["rate_a"]^2)
power_flow_mva_to_con = [
p[(l,i,j)]^2 + q[(l,i,j)]^2
for (l,i,j) in ref[:arcs_to]
]
c .= [
va_con...,
power_balance_p_con...,
power_balance_q_con...,
power_flow_p_from_con...,
power_flow_p_to_con...,
power_flow_q_from_con...,
power_flow_q_to_con...,
power_flow_vad_con...,
power_flow_mva_from_con...,
power_flow_mva_to_con...,
]
#total_callback_time += time() - start
return c
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)
#push!(con_lbs, -Inf)
#push!(con_ubs, Inf)
end
#power_balance_q_con
for (i,bus) in ref[:bus]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
#push!(con_lbs, -Inf)
#push!(con_ubs, Inf)
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
df = Optim.TwiceDifferentiable(opf_objective, var_init)
dfc = Optim.TwiceDifferentiableConstraints(opf_constraints, var_lb, var_ub, con_lbs, con_ubs)
df, dfc, var_init, con_lbs, con_ubs
end
function solve_opf_optim(dataset)
model_build_time = @elapsed df, dfc, var_init, con_lbs, con_ubs = build_opf_optim_prob(dataset)
options = Optim.Options(show_trace=PRINT_LEVEL != 0,time_limit=MAX_CPU_TIME)
solve_time_with_compilation = @elapsed res = Optim.optimize(df, dfc, var_init, Optim.IPNewton(), options)
solve_time_without_compilation = @elapsed res = Optim.optimize(df, dfc, var_init, Optim.IPNewton(), options)
sol = res.minimizer
cost = res.minimum
# NOTE: confirmed these constraint violations can be eliminated
# if a better starting point is used
sol_eval = dfc.c!(zeros(dfc.bounds.nc), sol)
vio_lb = [max(v,0) for v in (con_lbs .- sol_eval)]
vio_ub = [max(v,0) for v in (sol_eval .- con_ubs)]
const_vio = vio_lb .+ vio_ub
constraint_tol = 1e-6
feasible = (sum(const_vio) <= constraint_tol)
return (res,), Dict(
"case" => file_name,
"variables" => length(var_init),
"constraints" => dfc.bounds.nc,
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation,
)
end
function test_optim_prob(dataset, test_u0)
df, dfc, var_init, con_lbs, con_ubs = build_opf_optim_prob(dataset)
obj = df.f(test_u0)
cons = dfc.c!(zeros(dfc.bounds.nc), test_u0)
obj, cons
endtest_optim_prob (generic function with 1 method)optim_test_res = test_optim_prob(dataset, test_u0)(16236.704322376236, [0.0, 2.5424107263916085e-14, -1.0835776720341528e-13,
-6.039613253960852e-14, 0.0, 0.0, 0.0, -1.7075230118734908e-13, -3.9968028
886505635e-14, 1.532107773982716e-13 … 5.709523923775402, 8.5822728368379
8, 18.147597689108025, 15.999999905294098, 3.0822827695389314, 2.6621176970
504, 5.759999990861611, 8.161419886019171, 17.65224849471505, 15.8096580240
1578])@assert optim_test_res[1] == test_obj@assert optim_test_res[2] == test_consCASADI
Implementation reference: https://github.com/lanl-ansi/PowerModelsAnnex.jl/blob/master/src/model/ac-opf.jl
CASADI Segfaults so removed for now.
import PowerModels
import PythonCall
import CondaPkg
CondaPkg.add("casadi")
function solve_opf_casadi(dataset)
(;data, ref) = dataset
time_model_start = time()
casadi = PythonCall.pyimport("casadi")
x, x0, lbx, ubx, cons, lbg, ubg = [], [], [], [], [], [], []
va, vm = Dict{Int,Any}(), Dict{Int,Any}()
for (k, _) in ref[:bus]
va[k] = casadi.SX.sym("va$k")
push!(x, va[k])
push!(x0, 0.0)
push!(lbx, -casadi.inf)
push!(ubx, casadi.inf)
vm[k] = casadi.SX.sym("vm$k")
push!(x, vm[k])
push!(x0, 1.0)
push!(lbx, ref[:bus][k]["vmin"])
push!(ubx, ref[:bus][k]["vmax"])
end
pg, qg = Dict{Int,Any}(), Dict{Int,Any}()
for (k, ) in ref[:gen]
pg[k] = casadi.SX.sym("pg$k")
push!(x, pg[k])
push!(x0, 0.0)
push!(lbx, ref[:gen][k]["pmin"])
push!(ubx, ref[:gen][k]["pmax"])
qg[k] = casadi.SX.sym("qg$k")
push!(x, qg[k])
push!(x0, 0.0)
push!(lbx, ref[:gen][k]["qmin"])
push!(ubx, ref[:gen][k]["qmax"])
end
p, q = Dict{NTuple{3,Int},Any}(), Dict{NTuple{3,Int},Any}()
for k in ref[:arcs]
a = ref[:branch][k[1]]["rate_a"]
p[k] = casadi.SX.sym("p$k")
push!(x, p[k])
push!(x0, 0.0)
push!(lbx, -a)
push!(ubx, a)
q[k] = casadi.SX.sym("q$k")
push!(x, q[k])
push!(x0, 0.0)
push!(lbx, -a)
push!(ubx, a)
end
f = sum(
cost["cost"][1] * pg[k]^2 +
cost["cost"][2] * pg[k] +
cost["cost"][3] for (k, cost) in ref[:gen]
)
for (k, _) in ref[:ref_buses]
push!(cons, va[k])
push!(lbg, 0)
push!(ubg, 0)
end
for (i, _) in ref[:bus]
bus_loads = [ref[:load][l] for l in ref[:bus_loads][i]]
bus_shunts = [ref[:shunt][s] for s in ref[:bus_shunts][i]]
push!(
cons,
sum(p[k] for k in ref[:bus_arcs][i]) -
sum(pg[g] for g in ref[:bus_gens][i]; init = 0) +
sum(load["pd"] for load in bus_loads; init = 0) +
sum(shunt["gs"] for shunt in bus_shunts; init = 0) * vm[i]^2
)
push!(lbg, 0)
push!(ubg, 0)
push!(
cons,
sum(q[k] for k in ref[:bus_arcs][i]) -
sum(qg[g] for g in ref[:bus_gens][i]; init = 0) +
sum(load["qd"] for load in bus_loads; init = 0) -
sum(shunt["bs"] for shunt in bus_shunts; init = 0) * vm[i]^2
)
push!(lbg, 0)
push!(ubg, 0)
end
for (i, branch) in ref[:branch]
f_idx = (i, branch["f_bus"], branch["t_bus"])
t_idx = (i, branch["t_bus"], branch["f_bus"])
p_fr = p[f_idx]
q_fr = q[f_idx]
p_to = p[t_idx]
q_to = q[t_idx]
vm_fr = vm[branch["f_bus"]]
vm_to = vm[branch["t_bus"]]
va_fr = va[branch["f_bus"]]
va_to = va[branch["t_bus"]]
g, b = PowerModels.calc_branch_y(branch)
tr, ti = PowerModels.calc_branch_t(branch)
ttm = tr^2 + ti^2
g_fr = branch["g_fr"]
b_fr = branch["b_fr"]
g_to = branch["g_to"]
b_to = branch["b_to"]
push!(
cons,
(g+g_fr)/ttm*vm_fr^2 + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*casadi.cos(va_fr-va_to)) + (-b*tr-g*ti)/ttm*(vm_fr*vm_to*casadi.sin(va_fr-va_to)) - p_fr
)
push!(
cons,
-(b+b_fr)/ttm*vm_fr^2 - (-b*tr-g*ti)/ttm*(vm_fr*vm_to*casadi.cos(va_fr-va_to)) + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*casadi.sin(va_fr-va_to)) - q_fr
)
push!(
cons,
(g+g_to)*vm_to^2 + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*casadi.cos(va_to-va_fr)) + (-b*tr+g*ti)/ttm*(vm_to*vm_fr*casadi.sin(va_to-va_fr)) - p_to
)
push!(
cons,
-(b+b_to)*vm_to^2 - (-b*tr+g*ti)/ttm*(vm_to*vm_fr*casadi.cos(va_to-va_fr)) + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*casadi.sin(va_to-va_fr)) - q_to
)
for i in 1:4
push!(lbg, 0)
push!(ubg, 0)
end
push!(cons, va_fr - va_to)
push!(lbg, branch["angmin"])
push!(ubg, branch["angmax"])
push!(cons, p_fr^2 + q_fr^2)
push!(lbg, -casadi.inf)
push!(ubg, branch["rate_a"]^2)
push!(cons, p_to^2 + q_to^2)
push!(lbg, -casadi.inf)
push!(ubg, branch["rate_a"]^2)
end
nlp = Dict("x" => casadi.vcat(x), "f" => f, "g" => casadi.vcat(cons))
options = PythonCall.pydict(Dict("error_on_fail" => true))
model = casadi.nlpsol("model", "ipopt", PythonCall.pydict(nlp), options)
model_variables = length(x)
model_constraints = length(lbg)
model_build_time = time() - time_model_start
time_solve_start = time()
solution = model(; lbx = lbx, ubx = ubx, lbg = lbg, ubg = ubg, x0 = x0)
cost = PythonCall.pyconvert(Float64, (PythonCall.pyfloat(solution["f"])))
feasible = true # error if not feasible
solve_time = time() - time_solve_start
total_time = time() - time_data_start
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(" callbacks: $(total_callback_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,
#"time_callbacks" => TBD,
)
end
solve_opf_casadi("../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m")Test the Benchmarking
file_name = "../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m"
dataset = load_and_setup_data(file_name);model, res = solve_opf_optimization(dataset);
res***************************************************************************
***
This program contains Ipopt, a library for large-scale nonlinear optimizati
on.
Ipopt is released as open source code under the Eclipse Public License (EP
L).
For more information visit https://github.com/coin-or/Ipopt
***************************************************************************
***
Dict{String, Any} with 8 entries:
"cost" => 17551.9
"variables" => 44
"constraints" => 53
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.000189458
"time_solve_compilation" => 16.5577
"time_solve" => 0.459221
"feasible" => truemodel, res = solve_opf_jump(dataset);
resDict{String, Any} with 8 entries:
"cost" => 17551.9
"variables" => 44
"constraints" => 53
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.00215803
"time_solve_compilation" => 1.08609
"time_solve" => 0.012472
"feasible" => truemodel, res = solve_opf_nlpmodels(dataset);
resDict{String, Any} with 8 entries:
"cost" => 17551.9
"variables" => 44
"constraints" => 53
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.621379
"time_solve_compilation" => 2.6468
"time_solve" => 0.0346682
"feasible" => truemodel, res = solve_opf_nonconvex(dataset);
resError: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDi
ff.Tag{NonconvexUtils.var"#101#108"{NonconvexUtils.var"#100#107"{NonconvexU
tils.var"#97#104"{NonconvexCore.VectorOfFunctions{Tuple{NonconvexCore.IneqC
onstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.var"
##WeaveSandBox#225".var"#221#263"{Main.var"##WeaveSandBox#225".var"#const_t
hermal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, Differentia
bleFlatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Float64}}
, DifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int
64}, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity
), OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64}, Nonconve
xCore.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrappe
r{Main.var"##WeaveSandBox#225".var"#222#264"{Main.var"##WeaveSandBox#225".v
ar"#const_thermal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}},
DifferentiableFlatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String
, Float64}}, DifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}
, Tuple{Int64}, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{type
of(identity), OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64
}, NonconvexCore.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.Fun
ctionWrapper{Main.var"##WeaveSandBox#225".var"#223#265"{Main.var"##WeaveSan
dBox#225".var"#const_voltage_angle_difference_lb#257"{Dict{Int64, Float64}}
, Int64, Int64, Int64}}, DifferentiableFlatten.Unflatten{Tuple{OrderedColle
ctions.OrderedDict{String, Float64}}, DifferentiableFlatten.var"#unflatten_
to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{DifferentiableFlatten.var"#u
nflatten_to_Dict#16"{typeof(identity), OrderedCollections.OrderedDict{Strin
g, Float64}}}}}}, Float64}, NonconvexCore.IneqConstraint{NonconvexCore.var"
#80#82"{NonconvexCore.FunctionWrapper{Main.var"##WeaveSandBox#225".var"#224
#266"{Main.var"##WeaveSandBox#225".var"#const_voltage_angle_difference_ub#2
58"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFlatten.Unf
latten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, Differentiab
leFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{Dif
ferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), OrderedColl
ections.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCore.IneqCons
traint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.var"##W
eaveSandBox#225".var"#221#263"{Main.var"##WeaveSandBox#225".var"#const_ther
mal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, Differentiable
Flatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, D
ifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}
, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity),
OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCo
re.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{M
ain.var"##WeaveSandBox#225".var"#222#264"{Main.var"##WeaveSandBox#225".var"
#const_thermal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, Dif
ferentiableFlatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, F
loat64}}, DifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, T
uple{Int64}, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(
identity), OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64},
NonconvexCore.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.Functi
onWrapper{Main.var"##WeaveSandBox#225".var"#223#265"{Main.var"##WeaveSandBo
x#225".var"#const_voltage_angle_difference_lb#257"{Dict{Int64, Float64}}, I
nt64, Int64, Int64}}, DifferentiableFlatten.Unflatten{Tuple{OrderedCollecti
ons.OrderedDict{String, Float64}}, DifferentiableFlatten.var"#unflatten_to_
Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{DifferentiableFlatten.var"#unfl
atten_to_Dict#16"{typeof(identity), OrderedCollections.OrderedDict{String,
Float64}}}}}}, Float64}, NonconvexCore.IneqConstraint{NonconvexCore.var"#80
#82"{NonconvexCore.FunctionWrapper{Main.var"##WeaveSandBox#225".var"#224#26
6"{Main.var"##WeaveSandBox#225".var"#const_voltage_angle_difference_ub#258"
{Dict{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFlatten.Unflat
ten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, DifferentiableF
latten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{Differ
entiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), OrderedCollect
ions.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCore.IneqConstra
int{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.var"##Weav
eSandBox#225".var"#221#263"{Main.var"##WeaveSandBox#225".var"#const_thermal
_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFla
tten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, Diff
erentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, T
uple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), Ord
eredCollections.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCore.
IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main
.var"##WeaveSandBox#225".var"#222#264"{Main.var"##WeaveSandBox#225".var"#co
nst_thermal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, Differ
entiableFlatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Floa
t64}}, DifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tupl
e{Int64}, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(ide
ntity), OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64}, Non
convexCore.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionW
rapper{Main.var"##WeaveSandBox#225".var"#223#265"{Main.var"##WeaveSandBox#2
25".var"#const_voltage_angle_difference_lb#257"{Dict{Int64, Float64}}, Int6
4, Int64, Int64}}, DifferentiableFlatten.Unflatten{Tuple{OrderedCollections
.OrderedDict{String, Float64}}, DifferentiableFlatten.var"#unflatten_to_Tup
le#11"{Tuple{Int64}, Tuple{Int64}, Tuple{DifferentiableFlatten.var"#unflatt
en_to_Dict#16"{typeof(identity), OrderedCollections.OrderedDict{String, Flo
at64}}}}}}, Float64}, NonconvexCore.IneqConstraint{NonconvexCore.var"#80#82
"{NonconvexCore.FunctionWrapper{Main.var"##WeaveSandBox#225".var"#224#266"{
Main.var"##WeaveSandBox#225".var"#const_voltage_angle_difference_ub#258"{Di
ct{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFlatten.Unflatten
{Tuple{OrderedCollections.OrderedDict{String, Float64}}, DifferentiableFlat
ten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{Different
iableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), OrderedCollection
s.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCore.IneqConstraint
{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.var"##WeaveSa
ndBox#225".var"#221#263"{Main.var"##WeaveSandBox#225".var"#const_thermal_li
mit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFlatte
n.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, Differe
ntiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tupl
e{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), Ordere
dCollections.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCore.Ine
qConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.va
r"##WeaveSandBox#225".var"#222#264"{Main.var"##WeaveSandBox#225".var"#const
_thermal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, Different
iableFlatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Float64
}}, DifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{I
nt64}, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identi
ty), OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64}, Noncon
vexCore.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrap
per{Main.var"##WeaveSandBox#225".var"#223#265"{Main.var"##WeaveSandBox#225"
.var"#const_voltage_angle_difference_lb#257"{Dict{Int64, Float64}}, Int64,
Int64, Int64}}, DifferentiableFlatten.Unflatten{Tuple{OrderedCollections.Or
deredDict{String, Float64}}, DifferentiableFlatten.var"#unflatten_to_Tuple#
11"{Tuple{Int64}, Tuple{Int64}, Tuple{DifferentiableFlatten.var"#unflatten_
to_Dict#16"{typeof(identity), OrderedCollections.OrderedDict{String, Float6
4}}}}}}, Float64}, NonconvexCore.IneqConstraint{NonconvexCore.var"#80#82"{N
onconvexCore.FunctionWrapper{Main.var"##WeaveSandBox#225".var"#224#266"{Mai
n.var"##WeaveSandBox#225".var"#const_voltage_angle_difference_ub#258"{Dict{
Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFlatten.Unflatten{Tu
ple{OrderedCollections.OrderedDict{String, Float64}}, DifferentiableFlatten
.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{Differentiab
leFlatten.var"#unflatten_to_Dict#16"{typeof(identity), OrderedCollections.O
rderedDict{String, Float64}}}}}}, Float64}, NonconvexCore.IneqConstraint{No
nconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.var"##WeaveSandB
ox#225".var"#221#263"{Main.var"##WeaveSandBox#225".var"#const_thermal_limit
#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFlatten.U
nflatten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, Differenti
ableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{D
ifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), OrderedCo
llections.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCore.IneqCo
nstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.var"#
#WeaveSandBox#225".var"#222#264"{Main.var"##WeaveSandBox#225".var"#const_th
ermal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, Differentiab
leFlatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Float64}},
DifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int6
4}, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity)
, OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64}, Nonconvex
Core.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper
{Main.var"##WeaveSandBox#225".var"#223#265"{Main.var"##WeaveSandBox#225".va
r"#const_voltage_angle_difference_lb#257"{Dict{Int64, Float64}}, Int64, Int
64, Int64}}, DifferentiableFlatten.Unflatten{Tuple{OrderedCollections.Order
edDict{String, Float64}}, DifferentiableFlatten.var"#unflatten_to_Tuple#11"
{Tuple{Int64}, Tuple{Int64}, Tuple{DifferentiableFlatten.var"#unflatten_to_
Dict#16"{typeof(identity), OrderedCollections.OrderedDict{String, Float64}}
}}}}, Float64}, NonconvexCore.IneqConstraint{NonconvexCore.var"#80#82"{Nonc
onvexCore.FunctionWrapper{Main.var"##WeaveSandBox#225".var"#224#266"{Main.v
ar"##WeaveSandBox#225".var"#const_voltage_angle_difference_ub#258"{Dict{Int
64, Float64}}, Int64, Int64, Int64}}, DifferentiableFlatten.Unflatten{Tuple
{OrderedCollections.OrderedDict{String, Float64}}, DifferentiableFlatten.va
r"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{DifferentiableF
latten.var"#unflatten_to_Dict#16"{typeof(identity), OrderedCollections.Orde
redDict{String, Float64}}}}}}, Float64}, NonconvexCore.IneqConstraint{Nonco
nvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.var"##WeaveSandBox#
225".var"#221#263"{Main.var"##WeaveSandBox#225".var"#const_thermal_limit#25
6"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFlatten.Unfl
atten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, Differentiabl
eFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{Diff
erentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), OrderedColle
ctions.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCore.IneqConst
raint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.var"##We
aveSandBox#225".var"#222#264"{Main.var"##WeaveSandBox#225".var"#const_therm
al_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableF
latten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, Di
fferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64},
Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), O
rderedCollections.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCor
e.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Ma
in.var"##WeaveSandBox#225".var"#223#265"{Main.var"##WeaveSandBox#225".var"#
const_voltage_angle_difference_lb#257"{Dict{Int64, Float64}}, Int64, Int64,
Int64}}, DifferentiableFlatten.Unflatten{Tuple{OrderedCollections.OrderedD
ict{String, Float64}}, DifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tu
ple{Int64}, Tuple{Int64}, Tuple{DifferentiableFlatten.var"#unflatten_to_Dic
t#16"{typeof(identity), OrderedCollections.OrderedDict{String, Float64}}}}}
}, Float64}, NonconvexCore.IneqConstraint{NonconvexCore.var"#80#82"{Nonconv
exCore.FunctionWrapper{Main.var"##WeaveSandBox#225".var"#224#266"{Main.var"
##WeaveSandBox#225".var"#const_voltage_angle_difference_ub#258"{Dict{Int64,
Float64}}, Int64, Int64, Int64}}, DifferentiableFlatten.Unflatten{Tuple{Or
deredCollections.OrderedDict{String, Float64}}, DifferentiableFlatten.var"#
unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{DifferentiableFlat
ten.var"#unflatten_to_Dict#16"{typeof(identity), OrderedCollections.Ordered
Dict{String, Float64}}}}}}, Float64}}}}}}, Float64}, Float64, 1})
Closest candidates are:
(::Type{T})(::Real, !Matched::RoundingMode) where T<:AbstractFloat
@ Base rounding.jl:207
(::Type{T})(::T) where T<:Number
@ Core boot.jl:792
Float64(!Matched::IrrationalConstants.Sqrt3)
@ IrrationalConstants /cache/julia-buildkite-plugin/depots/5b300254-1738
-4989-ae0a-f4d2d937f953/packages/IrrationalConstants/vp5v4/src/macro.jl:112
...model, res = solve_opf_optim(dataset);
resDict{String, Any} with 8 entries:
"cost" => 77.9548
"variables" => 44
"constraints" => 53
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.000570855
"time_solve_compilation" => 21.4562
"time_solve" => 16.304
"feasible" => falsefile_name = "../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case3_lmbd.m"
dataset = load_and_setup_data(file_name);model, res = solve_opf_optimization(dataset);
resDict{String, Any} with 8 entries:
"cost" => 5812.64
"variables" => 24
"constraints" => 28
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 8.7959e-5
"time_solve_compilation" => 0.0758728
"time_solve" => 0.0818315
"feasible" => truemodel, res = solve_opf_jump(dataset);
resDict{String, Any} with 8 entries:
"cost" => 5812.64
"variables" => 24
"constraints" => 28
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.00333447
"time_solve_compilation" => 0.00856609
"time_solve" => 0.00782539
"feasible" => truemodel, res = solve_opf_nlpmodels(dataset);
resDict{String, Any} with 8 entries:
"cost" => 5812.64
"variables" => 24
"constraints" => 28
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.0159399
"time_solve_compilation" => 0.0181383
"time_solve" => 0.0169084
"feasible" => truemodel, res = solve_opf_nonconvex(dataset);
resError: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDi
ff.Tag{NonconvexUtils.var"#101#108"{NonconvexUtils.var"#100#107"{NonconvexU
tils.var"#97#104"{NonconvexCore.VectorOfFunctions{Tuple{NonconvexCore.IneqC
onstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.var"
##WeaveSandBox#225".var"#221#263"{Main.var"##WeaveSandBox#225".var"#const_t
hermal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, Differentia
bleFlatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Float64}}
, DifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int
64}, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity
), OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64}, Nonconve
xCore.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrappe
r{Main.var"##WeaveSandBox#225".var"#222#264"{Main.var"##WeaveSandBox#225".v
ar"#const_thermal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}},
DifferentiableFlatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String
, Float64}}, DifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}
, Tuple{Int64}, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{type
of(identity), OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64
}, NonconvexCore.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.Fun
ctionWrapper{Main.var"##WeaveSandBox#225".var"#223#265"{Main.var"##WeaveSan
dBox#225".var"#const_voltage_angle_difference_lb#257"{Dict{Int64, Float64}}
, Int64, Int64, Int64}}, DifferentiableFlatten.Unflatten{Tuple{OrderedColle
ctions.OrderedDict{String, Float64}}, DifferentiableFlatten.var"#unflatten_
to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{DifferentiableFlatten.var"#u
nflatten_to_Dict#16"{typeof(identity), OrderedCollections.OrderedDict{Strin
g, Float64}}}}}}, Float64}, NonconvexCore.IneqConstraint{NonconvexCore.var"
#80#82"{NonconvexCore.FunctionWrapper{Main.var"##WeaveSandBox#225".var"#224
#266"{Main.var"##WeaveSandBox#225".var"#const_voltage_angle_difference_ub#2
58"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFlatten.Unf
latten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, Differentiab
leFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{Dif
ferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), OrderedColl
ections.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCore.IneqCons
traint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.var"##W
eaveSandBox#225".var"#221#263"{Main.var"##WeaveSandBox#225".var"#const_ther
mal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, Differentiable
Flatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, D
ifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}
, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity),
OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCo
re.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{M
ain.var"##WeaveSandBox#225".var"#222#264"{Main.var"##WeaveSandBox#225".var"
#const_thermal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, Dif
ferentiableFlatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, F
loat64}}, DifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, T
uple{Int64}, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(
identity), OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64},
NonconvexCore.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.Functi
onWrapper{Main.var"##WeaveSandBox#225".var"#223#265"{Main.var"##WeaveSandBo
x#225".var"#const_voltage_angle_difference_lb#257"{Dict{Int64, Float64}}, I
nt64, Int64, Int64}}, DifferentiableFlatten.Unflatten{Tuple{OrderedCollecti
ons.OrderedDict{String, Float64}}, DifferentiableFlatten.var"#unflatten_to_
Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{DifferentiableFlatten.var"#unfl
atten_to_Dict#16"{typeof(identity), OrderedCollections.OrderedDict{String,
Float64}}}}}}, Float64}, NonconvexCore.IneqConstraint{NonconvexCore.var"#80
#82"{NonconvexCore.FunctionWrapper{Main.var"##WeaveSandBox#225".var"#224#26
6"{Main.var"##WeaveSandBox#225".var"#const_voltage_angle_difference_ub#258"
{Dict{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFlatten.Unflat
ten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, DifferentiableF
latten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{Differ
entiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), OrderedCollect
ions.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCore.IneqConstra
int{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main.var"##Weav
eSandBox#225".var"#221#263"{Main.var"##WeaveSandBox#225".var"#const_thermal
_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFla
tten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Float64}}, Diff
erentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, T
uple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), Ord
eredCollections.OrderedDict{String, Float64}}}}}}, Float64}, NonconvexCore.
IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionWrapper{Main
.var"##WeaveSandBox#225".var"#222#264"{Main.var"##WeaveSandBox#225".var"#co
nst_thermal_limit#256"{Dict{Int64, Float64}}, Int64, Int64, Int64}}, Differ
entiableFlatten.Unflatten{Tuple{OrderedCollections.OrderedDict{String, Floa
t64}}, DifferentiableFlatten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tupl
e{Int64}, Tuple{DifferentiableFlatten.var"#unflatten_to_Dict#16"{typeof(ide
ntity), OrderedCollections.OrderedDict{String, Float64}}}}}}, Float64}, Non
convexCore.IneqConstraint{NonconvexCore.var"#80#82"{NonconvexCore.FunctionW
rapper{Main.var"##WeaveSandBox#225".var"#223#265"{Main.var"##WeaveSandBox#2
25".var"#const_voltage_angle_difference_lb#257"{Dict{Int64, Float64}}, Int6
4, Int64, Int64}}, DifferentiableFlatten.Unflatten{Tuple{OrderedCollections
.OrderedDict{String, Float64}}, DifferentiableFlatten.var"#unflatten_to_Tup
le#11"{Tuple{Int64}, Tuple{Int64}, Tuple{DifferentiableFlatten.var"#unflatt
en_to_Dict#16"{typeof(identity), OrderedCollections.OrderedDict{String, Flo
at64}}}}}}, Float64}, NonconvexCore.IneqConstraint{NonconvexCore.var"#80#82
"{NonconvexCore.FunctionWrapper{Main.var"##WeaveSandBox#225".var"#224#266"{
Main.var"##WeaveSandBox#225".var"#const_voltage_angle_difference_ub#258"{Di
ct{Int64, Float64}}, Int64, Int64, Int64}}, DifferentiableFlatten.Unflatten
{Tuple{OrderedCollections.OrderedDict{String, Float64}}, DifferentiableFlat
ten.var"#unflatten_to_Tuple#11"{Tuple{Int64}, Tuple{Int64}, Tuple{Different
iableFlatten.var"#unflatten_to_Dict#16"{typeof(identity), OrderedCollection
s.OrderedDict{String, Float64}}}}}}, Float64}}}}}}, Float64}, Float64, 1})
Closest candidates are:
(::Type{T})(::Real, !Matched::RoundingMode) where T<:AbstractFloat
@ Base rounding.jl:207
(::Type{T})(::T) where T<:Number
@ Core boot.jl:792
Float64(!Matched::IrrationalConstants.Sqrt3)
@ IrrationalConstants /cache/julia-buildkite-plugin/depots/5b300254-1738
-4989-ae0a-f4d2d937f953/packages/IrrationalConstants/vp5v4/src/macro.jl:112
...model, res = solve_opf_optim(dataset);
resDict{String, Any} with 8 entries:
"cost" => 6273.63
"variables" => 24
"constraints" => 28
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.0517723
"time_solve_compilation" => 2.09519
"time_solve" => 2.06204
"feasible" => falseusing DataFrames, PrettyTables
function multidata_multisolver_benchmark(dataset_files; sizelimit = SIZE_LIMIT)
cases = String[]
vars = Int[]
cons = Int[]
optimization_time = Float64[]
mtk_time = Float64[]
jump_time = Float64[]
nlpmodels_time = Float64[]
nonconvex_time = Float64[]
optim_time = Float64[]
optimization_time_modelbuild = Float64[]
mtk_time_modelbuild = Float64[]
jump_time_modelbuild = Float64[]
nlpmodels_time_modelbuild = Float64[]
nonconvex_time_modelbuild = Float64[]
optim_time_modelbuild = Float64[]
optimization_time_compilation = Float64[]
mtk_time_compilation = Float64[]
jump_time_compilation = Float64[]
nlpmodels_time_compilation = Float64[]
nonconvex_time_compilation = Float64[]
optim_time_compilation = Float64[]
optimization_cost = Float64[]
mtk_cost = Float64[]
jump_cost = Float64[]
nlpmodels_cost = Float64[]
nonconvex_cost = Float64[]
optim_cost = Float64[]
for file in dataset_files
@show file
dataset = load_and_setup_data(file)
prob = build_opf_optimization_prob(dataset)
@info "Number of Variables: $(length(prob.u0))"
@info "Number of Constraints: $(length(prob.lcons))"
if length(prob.u0) > sizelimit
@info "Variable size over global limit. Skipping for now"
continue
end
@info "Running Optimization.jl"
model, res = solve_opf_optimization(dataset)
push!(cases, split(file,"/")[end])
push!(vars, res["variables"])
push!(cons, res["constraints"])
push!(optimization_time, res["time_solve"])
push!(optimization_time_modelbuild, res["time_build"])
push!(optimization_time_compilation, res["time_solve_compilation"])
push!(optimization_cost, res["cost"])
@info "Running ModelingToolkit.jl"
model, res = solve_opf_mtk(dataset)
push!(mtk_time, res["time_solve"])
push!(mtk_time_modelbuild, res["time_build"])
push!(mtk_time_compilation, res["time_solve_compilation"])
push!(mtk_cost, res["cost"])
@info "Running JuMP.jl"
model, res = solve_opf_jump(dataset)
push!(jump_time, res["time_solve"])
push!(jump_time_modelbuild, res["time_build"])
push!(jump_time_compilation, res["time_solve_compilation"])
push!(jump_cost, res["cost"])
@info "Running NLPModels.jl"
model, res = solve_opf_nlpmodels(dataset)
push!(nlpmodels_time, res["time_solve"])
push!(nlpmodels_time_modelbuild, res["time_build"])
push!(nlpmodels_time_compilation, res["time_solve_compilation"])
push!(nlpmodels_cost, res["cost"])
#=
@info "Running Nonconvex.jl"
model, res = solve_opf_nonconvex(dataset)
push!(nonconvex_time, res["time_solve"])
push!(nonconvex_time_modelbuild, res["time_build"])
push!(nonconvex_time_compilation, res["time_solve_compilation"])
push!(nonconvex_cost, res["cost"])
=#
if length(prob.u0) > 400
@info "Running Optim.jl"
model, res = solve_opf_optim(dataset)
push!(optim_time, NaN)
push!(optim_time_modelbuild, NaN)
push!(optim_time_compilation, NaN)
push!(optim_cost, NaN)
else
@info "Running Optim.jl"
model, res = solve_opf_optim(dataset)
push!(optim_time, res["time_solve"])
push!(optim_time_modelbuild, res["time_build"])
push!(optim_time_compilation, res["time_solve_compilation"])
push!(optim_cost, res["cost"])
end
end
DataFrame(:case => cases, :vars => vars, :cons => cons,
:optimization => optimization_time, :optimization_modelbuild => optimization_time_modelbuild, :optimization_wcompilation => optimization_time_compilation, :optimization_cost => optimization_cost,
:mtk => mtk_time, :mtk_time_modelbuild => mtk_time_modelbuild, :mtk_time_wcompilation => mtk_time_compilation, :mtk_cost => mtk_cost,
:jump => jump_time, :jump_modelbuild => jump_time_modelbuild, :jump_wcompilation => jump_time_compilation, :jump_cost => jump_cost,
:nlpmodels => nlpmodels_time, :nlpmodels_modelbuild => nlpmodels_time_modelbuild, :nlpmodels_wcompilation => nlpmodels_time_compilation, :nlpmodels_cost => nlpmodels_cost,
#:nonconvex => nonconvex_time, :nonconvex_modelbuild => nonconvex_time_modelbuild, :nonconvex_wcompilation => nonconvex_time_compilation, :nonconvex_cost => nonconvex_cost,
:optim => optim_time, :optim_modelbuild => optim_time_modelbuild, :optim_wcompilation => optim_time_compilation, :optim_cost => optim_cost)
end
test_datasets = [
"../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case3_lmbd.m",
"../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m"
]2-element Vector{String}:
"../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case3_lmbd.m"
"../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m"timing_data = multidata_multisolver_benchmark(test_datasets)file = "../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case3_lmbd.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 78
Number of nonzeros in inequality constraint Jacobian.: 24
Number of nonzeros in Lagrangian Hessian.............: 122
Total number of variables............................: 23
variables with only lower bounds: 0
variables with lower and upper bounds: 20
variables with only upper bounds: 0
Total number of equality constraints.................: 19
Total number of inequality constraints...............: 12
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 12
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.3949934e+00 1.09e+00 1.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.1048421e+03 4.62e-01 1.04e+02 -1.0 1.81e+00 - 5.77e-03 5.76e-01h 1
2 4.4503068e+03 1.36e-01 3.09e+01 -1.0 8.29e-01 - 8.15e-01 7.05e-01h 1
3 4.6140270e+03 1.18e-01 2.67e+01 -1.0 2.99e-01 - 4.13e-01 1.37e-01h 1
4 4.9343698e+03 8.25e-02 3.85e+01 -1.0 4.36e-01 - 4.94e-01 2.98e-01h 1
5 5.4019378e+03 3.46e-02 2.51e+01 -1.0 3.62e-01 - 9.90e-01 5.81e-01h 1
6 5.4116196e+03 3.37e-02 1.26e+02 -1.0 1.45e-01 - 1.84e-01 2.70e-02h 1
7 5.6094774e+03 1.60e-02 5.13e+01 -1.0 2.53e-01 - 2.76e-01 5.25e-01h 1
8 5.7031738e+03 8.53e-03 8.21e+01 -1.0 1.43e-01 - 7.00e-01 4.67e-01h 1
9 5.8145457e+03 6.10e-04 2.19e+00 -1.0 5.77e-02 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 5.8146316e+03 2.55e-05 7.83e-03 -1.0 1.23e-02 - 1.00e+00 1.00e+00h 1
11 5.8127612e+03 1.60e-05 1.36e-02 -2.5 8.27e-03 - 1.00e+00 1.00e+00f 1
12 5.8126464e+03 2.60e-07 1.15e-04 -3.8 1.05e-03 - 1.00e+00 1.00e+00f 1
13 5.8126430e+03 1.32e-10 8.31e-08 -5.7 2.50e-05 - 1.00e+00 1.00e+00h 1
14 5.8126429e+03 7.55e-15 6.25e-12 -8.6 1.78e-07 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 14
(scaled) (unscaled)
Objective...............: 1.1625285870072360e+03 5.8126429350361796e+03
Dual infeasibility......: 6.2541517268298295e-12 3.1270758634149147e-11
Constraint violation....: 7.5495165674510645e-15 7.5495165674510645e-15
Variable bound violation: 1.0911841874516881e-08 1.0911841874516881e-08
Complementarity.........: 2.5102170848799089e-09 1.2551085424399544e-08
Overall NLP error.......: 2.5102170848799089e-09 1.2551085424399544e-08
Number of objective function evaluations = 15
Number of objective gradient evaluations = 15
Number of equality constraint evaluations = 15
Number of inequality constraint evaluations = 15
Number of equality constraint Jacobian evaluations = 15
Number of inequality constraint Jacobian evaluations = 15
Number of Lagrangian Hessian evaluations = 14
Total seconds in IPOPT = 0.799
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 78
Number of nonzeros in inequality constraint Jacobian.: 24
Number of nonzeros in Lagrangian Hessian.............: 122
Total number of variables............................: 23
variables with only lower bounds: 0
variables with lower and upper bounds: 20
variables with only upper bounds: 0
Total number of equality constraints.................: 19
Total number of inequality constraints...............: 12
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 12
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.3949934e+00 1.09e+00 1.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.1048421e+03 4.62e-01 1.04e+02 -1.0 1.81e+00 - 5.77e-03 5.76e-01h 1
2 4.4503068e+03 1.36e-01 3.09e+01 -1.0 8.29e-01 - 8.15e-01 7.05e-01h 1
3 4.6140270e+03 1.18e-01 2.67e+01 -1.0 2.99e-01 - 4.13e-01 1.37e-01h 1
4 4.9343698e+03 8.25e-02 3.85e+01 -1.0 4.36e-01 - 4.94e-01 2.98e-01h 1
5 5.4019378e+03 3.46e-02 2.51e+01 -1.0 3.62e-01 - 9.90e-01 5.81e-01h 1
6 5.4116196e+03 3.37e-02 1.26e+02 -1.0 1.45e-01 - 1.84e-01 2.70e-02h 1
7 5.6094774e+03 1.60e-02 5.13e+01 -1.0 2.53e-01 - 2.76e-01 5.25e-01h 1
8 5.7031738e+03 8.53e-03 8.21e+01 -1.0 1.43e-01 - 7.00e-01 4.67e-01h 1
9 5.8145457e+03 6.10e-04 2.19e+00 -1.0 5.77e-02 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 5.8146316e+03 2.55e-05 7.83e-03 -1.0 1.23e-02 - 1.00e+00 1.00e+00h 1
11 5.8127612e+03 1.60e-05 1.36e-02 -2.5 8.27e-03 - 1.00e+00 1.00e+00f 1
12 5.8126464e+03 2.60e-07 1.15e-04 -3.8 1.05e-03 - 1.00e+00 1.00e+00f 1
13 5.8126430e+03 1.32e-10 8.31e-08 -5.7 2.50e-05 - 1.00e+00 1.00e+00h 1
14 5.8126429e+03 7.55e-15 6.25e-12 -8.6 1.78e-07 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 14
(scaled) (unscaled)
Objective...............: 1.1625285870072360e+03 5.8126429350361796e+03
Dual infeasibility......: 6.2541517268298295e-12 3.1270758634149147e-11
Constraint violation....: 7.5495165674510645e-15 7.5495165674510645e-15
Variable bound violation: 1.0911841874516881e-08 1.0911841874516881e-08
Complementarity.........: 2.5102170848799089e-09 1.2551085424399544e-08
Overall NLP error.......: 2.5102170848799089e-09 1.2551085424399544e-08
Number of objective function evaluations = 15
Number of objective gradient evaluations = 15
Number of equality constraint evaluations = 15
Number of inequality constraint evaluations = 15
Number of equality constraint Jacobian evaluations = 15
Number of inequality constraint Jacobian evaluations = 15
Number of Lagrangian Hessian evaluations = 14
Total seconds in IPOPT = 0.008
EXIT: Optimal Solution Found.
file = "../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 155
Number of nonzeros in inequality constraint Jacobian.: 48
Number of nonzeros in Lagrangian Hessian.............: 240
Total number of variables............................: 44
variables with only lower bounds: 0
variables with lower and upper bounds: 39
variables with only upper bounds: 0
Total number of equality constraints.................: 35
Total number of inequality constraints...............: 24
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 24
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0059989e+02 3.99e+00 2.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 8.3066305e+03 2.47e+00 1.01e+02 -1.0 2.78e+00 - 4.11e-03 3.82e-01h 1
2 6.7181372e+03 2.36e+00 9.62e+01 -1.0 1.60e+01 - 7.37e-02 4.44e-02f 1
3 6.6689587e+03 2.30e+00 9.34e+01 -1.0 1.30e+01 - 4.94e-01 2.40e-02f 1
4 6.5741805e+03 2.04e+00 8.25e+01 -1.0 1.29e+01 - 3.67e-01 1.12e-01f 2
5 6.8264259e+03 1.80e+00 7.10e+01 -1.0 1.23e+01 - 8.72e-01 1.20e-01h 2
6 8.8540136e+03 1.08e+00 4.20e+01 -1.0 9.14e+00 - 5.92e-01 4.00e-01h 1
7 1.0572806e+04 8.62e-01 3.58e+01 -1.0 2.94e+00 - 4.93e-01 2.00e-01h 1
8 1.7308577e+04 3.63e-02 1.46e+01 -1.0 2.41e+00 - 7.65e-01 9.58e-01h 1
9 1.7572869e+04 1.33e-02 1.10e+00 -1.0 2.11e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7590631e+04 1.68e-03 1.61e-01 -1.0 5.04e-01 - 1.00e+00 1.00e+00h 1
11 1.7558724e+04 5.24e-03 5.03e-01 -2.5 6.03e-01 - 8.35e-01 9.36e-01f 1
12 1.7553111e+04 3.34e-03 4.12e+00 -2.5 2.84e-01 - 1.00e+00 8.20e-01h 1
13 1.7552956e+04 3.24e-05 1.26e-02 -2.5 6.35e-02 - 1.00e+00 1.00e+00h 1
14 1.7551990e+04 1.35e-05 1.09e+00 -3.8 2.53e-02 - 1.00e+00 9.25e-01h 1
15 1.7551938e+04 4.46e-08 1.22e-02 -3.8 7.00e-03 - 1.00e+00 1.00e+00f 1
16 1.7551940e+04 2.35e-10 2.06e-04 -3.8 3.83e-04 - 1.00e+00 1.00e+00h 1
17 1.7551893e+04 1.75e-07 2.10e-01 -5.7 2.49e-03 - 1.00e+00 9.68e-01f 1
18 1.7551891e+04 6.80e-11 3.09e-05 -5.7 2.38e-04 - 1.00e+00 1.00e+00f 1
19 1.7551891e+04 5.68e-14 6.47e-10 -5.7 5.17e-07 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.7551891e+04 6.26e-12 3.03e-07 -8.6 3.52e-05 - 1.00e+00 1.00e+00f 1
21 1.7551891e+04 5.68e-14 3.38e-12 -8.6 3.33e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 21
(scaled) (unscaled)
Objective...............: 4.3879727248486898e+02 1.7551890899394759e+04
Dual infeasibility......: 3.3822003142280486e-12 1.3528801256912194e-10
Constraint violation....: 3.6743585951626306e-14 5.6843418860808015e-14
Variable bound violation: 2.9463905093507492e-08 2.9463905093507492e-08
Complementarity.........: 2.5059076126917168e-09 1.0023630450766867e-07
Overall NLP error.......: 2.5059076126917168e-09 1.0023630450766867e-07
Number of objective function evaluations = 28
Number of objective gradient evaluations = 22
Number of equality constraint evaluations = 28
Number of inequality constraint evaluations = 28
Number of equality constraint Jacobian evaluations = 22
Number of inequality constraint Jacobian evaluations = 22
Number of Lagrangian Hessian evaluations = 21
Total seconds in IPOPT = 1.356
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 155
Number of nonzeros in inequality constraint Jacobian.: 48
Number of nonzeros in Lagrangian Hessian.............: 240
Total number of variables............................: 44
variables with only lower bounds: 0
variables with lower and upper bounds: 39
variables with only upper bounds: 0
Total number of equality constraints.................: 35
Total number of inequality constraints...............: 24
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 24
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0059989e+02 3.99e+00 2.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 8.3066305e+03 2.47e+00 1.01e+02 -1.0 2.78e+00 - 4.11e-03 3.82e-01h 1
2 6.7181372e+03 2.36e+00 9.62e+01 -1.0 1.60e+01 - 7.37e-02 4.44e-02f 1
3 6.6689587e+03 2.30e+00 9.34e+01 -1.0 1.30e+01 - 4.94e-01 2.40e-02f 1
4 6.5741805e+03 2.04e+00 8.25e+01 -1.0 1.29e+01 - 3.67e-01 1.12e-01f 2
5 6.8264259e+03 1.80e+00 7.10e+01 -1.0 1.23e+01 - 8.72e-01 1.20e-01h 2
6 8.8540136e+03 1.08e+00 4.20e+01 -1.0 9.14e+00 - 5.92e-01 4.00e-01h 1
7 1.0572806e+04 8.62e-01 3.58e+01 -1.0 2.94e+00 - 4.93e-01 2.00e-01h 1
8 1.7308577e+04 3.63e-02 1.46e+01 -1.0 2.41e+00 - 7.65e-01 9.58e-01h 1
9 1.7572869e+04 1.33e-02 1.10e+00 -1.0 2.11e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7590631e+04 1.68e-03 1.61e-01 -1.0 5.04e-01 - 1.00e+00 1.00e+00h 1
11 1.7558724e+04 5.24e-03 5.03e-01 -2.5 6.03e-01 - 8.35e-01 9.36e-01f 1
12 1.7553111e+04 3.34e-03 4.12e+00 -2.5 2.84e-01 - 1.00e+00 8.20e-01h 1
13 1.7552956e+04 3.24e-05 1.26e-02 -2.5 6.35e-02 - 1.00e+00 1.00e+00h 1
14 1.7551990e+04 1.35e-05 1.09e+00 -3.8 2.53e-02 - 1.00e+00 9.25e-01h 1
15 1.7551938e+04 4.46e-08 1.22e-02 -3.8 7.00e-03 - 1.00e+00 1.00e+00f 1
16 1.7551940e+04 2.35e-10 2.06e-04 -3.8 3.83e-04 - 1.00e+00 1.00e+00h 1
17 1.7551893e+04 1.75e-07 2.10e-01 -5.7 2.49e-03 - 1.00e+00 9.68e-01f 1
18 1.7551891e+04 6.80e-11 3.09e-05 -5.7 2.38e-04 - 1.00e+00 1.00e+00f 1
19 1.7551891e+04 5.68e-14 6.47e-10 -5.7 5.17e-07 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.7551891e+04 6.26e-12 3.03e-07 -8.6 3.52e-05 - 1.00e+00 1.00e+00f 1
21 1.7551891e+04 5.68e-14 3.38e-12 -8.6 3.33e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 21
(scaled) (unscaled)
Objective...............: 4.3879727248486898e+02 1.7551890899394759e+04
Dual infeasibility......: 3.3822003142280486e-12 1.3528801256912194e-10
Constraint violation....: 3.6743585951626306e-14 5.6843418860808015e-14
Variable bound violation: 2.9463905093507492e-08 2.9463905093507492e-08
Complementarity.........: 2.5059076126917168e-09 1.0023630450766867e-07
Overall NLP error.......: 2.5059076126917168e-09 1.0023630450766867e-07
Number of objective function evaluations = 28
Number of objective gradient evaluations = 22
Number of equality constraint evaluations = 28
Number of inequality constraint evaluations = 28
Number of equality constraint Jacobian evaluations = 22
Number of inequality constraint Jacobian evaluations = 22
Number of Lagrangian Hessian evaluations = 21
Total seconds in IPOPT = 0.016
EXIT: Optimal Solution Found.
2×23 DataFrame
Row │ case vars cons optimization optimization_modelb ⋯
│ String Int64 Int64 Float64 Float64 ⋯
─────┼──────────────────────────────────────────────────────────────────────────
1 │ pglib_opf_case3_lmbd.m 24 28 0.0761628 4.997 ⋯
2 │ pglib_opf_case5_pjm.m 44 53 0.395627 4.678
19 columns omittedio = IOBuffer()
println(io, "```@raw html")
pretty_table(io, timing_data; backend = Val(:html))
# show(io, "text/html", pretty_table(timing_data; backend = Val(:html)))
println(io, "```")
Text(String(take!(io)))| case | vars | cons | optimization | optimization_modelbuild | optimization_wcompilation | optimization_cost | mtk | mtk_time_modelbuild | mtk_time_wcompilation | mtk_cost | jump | jump_modelbuild | jump_wcompilation | jump_cost | nlpmodels | nlpmodels_modelbuild | nlpmodels_wcompilation | nlpmodels_cost | optim | optim_modelbuild | optim_wcompilation | optim_cost |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| String | Int64 | Int64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 |
| pglib_opf_case3_lmbd.m | 24 | 28 | 0.0761628 | 4.9979e-5 | 0.0745601 | 5812.64 | 0.0169464 | 1.25872 | 3.08062 | 5812.64 | 0.00765398 | 0.00178746 | 0.00805817 | 5812.64 | 0.0170089 | 0.0149911 | 0.0178768 | 5812.64 | 2.07783 | 0.000258597 | 2.10584 | 6273.63 |
| pglib_opf_case5_pjm.m | 44 | 53 | 0.395627 | 4.6789e-5 | 0.426829 | 17551.9 | 0.0319069 | 0.36729 | 2.70288 | 17551.9 | 0.0121948 | 0.00211386 | 0.0125961 | 17551.9 | 0.0349266 | 0.0261484 | 0.0355382 | 17551.9 | 17.1599 | 0.000399686 | 17.1126 | 77.9548 |
Run the Full Benchmark
using LibGit2
tmpdir = Base.Filesystem.mktempdir()
LibGit2.clone("https://github.com/power-grid-lib/pglib-opf", tmpdir)
benchmarkfiles = readdir(tmpdir)
benchmarkfiles = benchmarkfiles[endswith(".m").(benchmarkfiles)]
benchmark_datasets = joinpath.((tmpdir,),benchmarkfiles)66-element Vector{String}:
"/tmp/jl_XPUwWW/pglib_opf_case10000_goc.m"
"/tmp/jl_XPUwWW/pglib_opf_case10192_epigrids.m"
"/tmp/jl_XPUwWW/pglib_opf_case10480_goc.m"
"/tmp/jl_XPUwWW/pglib_opf_case118_ieee.m"
"/tmp/jl_XPUwWW/pglib_opf_case1354_pegase.m"
"/tmp/jl_XPUwWW/pglib_opf_case13659_pegase.m"
"/tmp/jl_XPUwWW/pglib_opf_case14_ieee.m"
"/tmp/jl_XPUwWW/pglib_opf_case162_ieee_dtc.m"
"/tmp/jl_XPUwWW/pglib_opf_case179_goc.m"
"/tmp/jl_XPUwWW/pglib_opf_case1803_snem.m"
⋮
"/tmp/jl_XPUwWW/pglib_opf_case6515_rte.m"
"/tmp/jl_XPUwWW/pglib_opf_case7336_epigrids.m"
"/tmp/jl_XPUwWW/pglib_opf_case73_ieee_rts.m"
"/tmp/jl_XPUwWW/pglib_opf_case78484_epigrids.m"
"/tmp/jl_XPUwWW/pglib_opf_case793_goc.m"
"/tmp/jl_XPUwWW/pglib_opf_case8387_pegase.m"
"/tmp/jl_XPUwWW/pglib_opf_case89_pegase.m"
"/tmp/jl_XPUwWW/pglib_opf_case9241_pegase.m"
"/tmp/jl_XPUwWW/pglib_opf_case9591_goc.m"timing_data = multidata_multisolver_benchmark(benchmark_datasets)file = "/tmp/jl_XPUwWW/pglib_opf_case10000_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case10192_epigrids.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case10480_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case118_ieee.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case1354_pegase.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case13659_pegase.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case14_ieee.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 489
Number of nonzeros in inequality constraint Jacobian.: 160
Number of nonzeros in Lagrangian Hessian.............: 791
Total number of variables............................: 115
variables with only lower bounds: 0
variables with lower and upper bounds: 101
variables with only upper bounds: 0
Total number of equality constraints.................: 109
Total number of inequality constraints...............: 80
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 80
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.1649922e+01 9.42e-01 1.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.4464214e+03 2.50e-01 1.33e+02 -1.0 1.81e+00 - 5.61e-03 7.34e-01H 1
2 2.3967630e+03 4.55e-02 8.94e+01 -1.0 1.75e+00 - 1.74e-02 9.97e-01f 1
3 2.1633361e+03 4.42e-02 8.75e+01 -1.0 2.61e+01 - 2.18e-01 1.95e-02f 1
4 2.1823081e+03 8.47e-04 1.41e+01 -1.0 8.82e-01 - 7.57e-01 1.00e+00h 1
5 2.1890842e+03 3.76e-03 7.25e-01 -1.0 6.67e-02 - 1.00e+00 1.00e+00f 1
6 2.1849633e+03 4.30e-04 5.24e-01 -1.7 1.38e-02 - 9.64e-01 1.00e+00h 1
7 2.1794160e+03 2.31e-03 2.33e-02 -1.7 4.13e-02 - 1.00e+00 1.00e+00f 1
8 2.1786673e+03 6.12e-04 2.89e-01 -3.8 7.90e-03 - 8.46e-01 7.40e-01h 1
9 2.1780966e+03 1.45e-04 3.05e-01 -3.8 4.14e-02 - 4.43e-01 9.69e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 2.1780936e+03 7.00e-06 6.66e-04 -3.8 1.35e-02 - 1.00e+00 1.00e+00h 1
11 2.1780803e+03 1.65e-06 9.68e-04 -5.7 4.57e-03 - 9.56e-01 9.45e-01h 1
12 2.1780805e+03 1.82e-07 1.04e-05 -5.7 1.67e-03 - 1.00e+00 1.00e+00h 1
13 2.1780804e+03 2.19e-09 2.91e-06 -8.6 1.68e-04 - 1.00e+00 9.99e-01h 1
14 2.1780804e+03 1.10e-09 1.44e-05 -8.6 2.01e-06 - 1.00e+00 5.00e-01f 2
15 2.1780804e+03 6.66e-14 3.85e-12 -8.6 1.01e-06 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 9.3602396804143993e+01 2.1780804108196480e+03
Dual infeasibility......: 3.8475889141409425e-12 8.9531447152069183e-11
Constraint violation....: 6.6613381477509392e-14 6.6613381477509392e-14
Variable bound violation: 1.0340993394919451e-08 1.0340993394919451e-08
Complementarity.........: 2.5059040485519189e-09 5.8311119222354586e-08
Overall NLP error.......: 2.5059040485519189e-09 5.8311119222354586e-08
Number of objective function evaluations = 18
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 18
Number of inequality constraint evaluations = 18
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total seconds in IPOPT = 4.395
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 489
Number of nonzeros in inequality constraint Jacobian.: 160
Number of nonzeros in Lagrangian Hessian.............: 791
Total number of variables............................: 115
variables with only lower bounds: 0
variables with lower and upper bounds: 101
variables with only upper bounds: 0
Total number of equality constraints.................: 109
Total number of inequality constraints...............: 80
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 80
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.1649922e+01 9.42e-01 1.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.4464214e+03 2.50e-01 1.33e+02 -1.0 1.81e+00 - 5.61e-03 7.34e-01H 1
2 2.3967630e+03 4.55e-02 8.94e+01 -1.0 1.75e+00 - 1.74e-02 9.97e-01f 1
3 2.1633361e+03 4.42e-02 8.75e+01 -1.0 2.61e+01 - 2.18e-01 1.95e-02f 1
4 2.1823081e+03 8.47e-04 1.41e+01 -1.0 8.82e-01 - 7.57e-01 1.00e+00h 1
5 2.1890842e+03 3.76e-03 7.25e-01 -1.0 6.67e-02 - 1.00e+00 1.00e+00f 1
6 2.1849633e+03 4.30e-04 5.24e-01 -1.7 1.38e-02 - 9.64e-01 1.00e+00h 1
7 2.1794160e+03 2.31e-03 2.33e-02 -1.7 4.13e-02 - 1.00e+00 1.00e+00f 1
8 2.1786673e+03 6.12e-04 2.89e-01 -3.8 7.90e-03 - 8.46e-01 7.40e-01h 1
9 2.1780966e+03 1.45e-04 3.05e-01 -3.8 4.14e-02 - 4.43e-01 9.69e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 2.1780936e+03 7.00e-06 6.66e-04 -3.8 1.35e-02 - 1.00e+00 1.00e+00h 1
11 2.1780803e+03 1.65e-06 9.68e-04 -5.7 4.57e-03 - 9.56e-01 9.45e-01h 1
12 2.1780805e+03 1.82e-07 1.04e-05 -5.7 1.67e-03 - 1.00e+00 1.00e+00h 1
13 2.1780804e+03 2.19e-09 2.91e-06 -8.6 1.68e-04 - 1.00e+00 9.99e-01h 1
14 2.1780804e+03 1.10e-09 1.44e-05 -8.6 2.01e-06 - 1.00e+00 5.00e-01f 2
15 2.1780804e+03 6.66e-14 3.85e-12 -8.6 1.01e-06 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 9.3602396804143993e+01 2.1780804108196480e+03
Dual infeasibility......: 3.8475889141409425e-12 8.9531447152069183e-11
Constraint violation....: 6.6613381477509392e-14 6.6613381477509392e-14
Variable bound violation: 1.0340993394919451e-08 1.0340993394919451e-08
Complementarity.........: 2.5059040485519189e-09 5.8311119222354586e-08
Overall NLP error.......: 2.5059040485519189e-09 5.8311119222354586e-08
Number of objective function evaluations = 18
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 18
Number of inequality constraint evaluations = 18
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total seconds in IPOPT = 0.023
EXIT: Optimal Solution Found.
file = "/tmp/jl_XPUwWW/pglib_opf_case162_ieee_dtc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case179_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case1803_snem.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case1888_rte.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case19402_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case1951_rte.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case197_snem.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case2000_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case200_activ.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case20758_epigrids.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case2312_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case2383wp_k.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case240_pserc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case24464_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case24_ieee_rts.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 979
Number of nonzeros in inequality constraint Jacobian.: 304
Number of nonzeros in Lagrangian Hessian.............: 1543
Total number of variables............................: 265
variables with only lower bounds: 0
variables with lower and upper bounds: 241
variables with only upper bounds: 0
Total number of equality constraints.................: 201
Total number of inequality constraints...............: 152
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 152
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 4.0097983e+04 2.52e+00 4.56e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 5.6173148e+04 1.56e+00 6.27e+01 -1.0 2.38e+00 - 1.00e-02 3.79e-01h 1
2 5.3163376e+04 1.35e+00 5.43e+01 -1.0 6.42e+00 - 1.34e-02 1.39e-01f 1
3 5.2196944e+04 1.33e+00 5.02e+01 -1.0 8.86e+00 - 3.43e-01 1.34e-02f 1
4 5.3182043e+04 1.06e+00 3.73e+01 -1.0 7.58e+00 - 7.33e-01 2.03e-01h 1
5 5.5137462e+04 3.87e-01 1.31e+01 -1.0 9.48e+00 - 9.74e-01 6.34e-01H 1
6 5.4942906e+04 3.18e-01 1.19e+01 -1.0 1.03e+01 - 4.67e-01 1.79e-01f 1
7 5.5363333e+04 2.73e-01 1.12e+01 -1.0 7.18e+00 - 1.00e+00 1.42e-01h 1
8 5.5935463e+04 2.33e-01 6.62e+00 -1.0 3.08e+00 - 2.59e-01 1.44e-01h 1
9 6.2388698e+04 6.41e-02 5.12e+00 -1.0 1.40e+00 - 6.92e-01 8.82e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 6.3791569e+04 2.81e-03 8.67e-01 -1.0 1.80e+00 - 7.56e-01 1.00e+00h 1
11 6.3501031e+04 8.45e-04 6.21e-01 -1.7 1.69e-01 - 8.51e-01 1.00e+00f 1
12 6.3454928e+04 1.01e-03 1.25e-02 -1.7 1.02e-01 - 1.00e+00 1.00e+00h 1
13 6.3378078e+04 8.53e-04 1.25e-01 -3.8 1.49e-01 - 8.03e-01 7.30e-01f 1
14 6.3364846e+04 4.37e-04 2.13e+00 -3.8 2.00e-01 - 7.95e-01 5.13e-01h 1
15 6.3352718e+04 1.22e-04 8.47e-02 -3.8 2.47e-01 - 1.00e+00 9.79e-01h 1
16 6.3352941e+04 1.86e-06 2.89e-05 -3.8 9.06e-03 - 1.00e+00 1.00e+00h 1
17 6.3352216e+04 3.36e-07 5.07e-04 -5.7 9.37e-03 - 9.90e-01 9.93e-01h 1
18 6.3352210e+04 1.17e-09 2.51e-08 -5.7 1.81e-04 - 1.00e+00 1.00e+00h 1
19 6.3352201e+04 5.65e-11 8.23e-10 -8.6 1.04e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 19
(scaled) (unscaled)
Objective...............: 4.8732462373895481e+02 6.3352201086064124e+04
Dual infeasibility......: 8.2331781542267553e-10 1.0703131600494782e-07
Constraint violation....: 5.6523674629715970e-11 5.6523674629715970e-11
Variable bound violation: 3.9922449346363464e-08 3.9922449346363464e-08
Complementarity.........: 3.7502813809752752e-09 4.8753657952678579e-07
Overall NLP error.......: 3.7502813809752752e-09 4.8753657952678579e-07
Number of objective function evaluations = 21
Number of objective gradient evaluations = 20
Number of equality constraint evaluations = 21
Number of inequality constraint evaluations = 21
Number of equality constraint Jacobian evaluations = 20
Number of inequality constraint Jacobian evaluations = 20
Number of Lagrangian Hessian evaluations = 19
Total seconds in IPOPT = 11.453
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 979
Number of nonzeros in inequality constraint Jacobian.: 304
Number of nonzeros in Lagrangian Hessian.............: 1543
Total number of variables............................: 265
variables with only lower bounds: 0
variables with lower and upper bounds: 241
variables with only upper bounds: 0
Total number of equality constraints.................: 201
Total number of inequality constraints...............: 152
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 152
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 4.0097983e+04 2.52e+00 4.56e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 5.6173148e+04 1.56e+00 6.27e+01 -1.0 2.38e+00 - 1.00e-02 3.79e-01h 1
2 5.3163376e+04 1.35e+00 5.43e+01 -1.0 6.42e+00 - 1.34e-02 1.39e-01f 1
3 5.2196944e+04 1.33e+00 5.02e+01 -1.0 8.86e+00 - 3.43e-01 1.34e-02f 1
4 5.3182043e+04 1.06e+00 3.73e+01 -1.0 7.58e+00 - 7.33e-01 2.03e-01h 1
5 5.5137462e+04 3.87e-01 1.31e+01 -1.0 9.48e+00 - 9.74e-01 6.34e-01H 1
6 5.4942906e+04 3.18e-01 1.19e+01 -1.0 1.03e+01 - 4.67e-01 1.79e-01f 1
7 5.5363333e+04 2.73e-01 1.12e+01 -1.0 7.18e+00 - 1.00e+00 1.42e-01h 1
8 5.5935463e+04 2.33e-01 6.62e+00 -1.0 3.08e+00 - 2.59e-01 1.44e-01h 1
9 6.2388698e+04 6.41e-02 5.12e+00 -1.0 1.40e+00 - 6.92e-01 8.82e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 6.3791569e+04 2.81e-03 8.67e-01 -1.0 1.80e+00 - 7.56e-01 1.00e+00h 1
11 6.3501031e+04 8.45e-04 6.21e-01 -1.7 1.69e-01 - 8.51e-01 1.00e+00f 1
12 6.3454928e+04 1.01e-03 1.25e-02 -1.7 1.02e-01 - 1.00e+00 1.00e+00h 1
13 6.3378078e+04 8.53e-04 1.25e-01 -3.8 1.49e-01 - 8.03e-01 7.30e-01f 1
14 6.3364846e+04 4.37e-04 2.13e+00 -3.8 2.00e-01 - 7.95e-01 5.13e-01h 1
15 6.3352718e+04 1.22e-04 8.47e-02 -3.8 2.47e-01 - 1.00e+00 9.79e-01h 1
16 6.3352941e+04 1.86e-06 2.89e-05 -3.8 9.06e-03 - 1.00e+00 1.00e+00h 1
17 6.3352216e+04 3.36e-07 5.07e-04 -5.7 9.37e-03 - 9.90e-01 9.93e-01h 1
18 6.3352210e+04 1.17e-09 2.51e-08 -5.7 1.81e-04 - 1.00e+00 1.00e+00h 1
19 6.3352201e+04 5.65e-11 8.23e-10 -8.6 1.04e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 19
(scaled) (unscaled)
Objective...............: 4.8732462373895481e+02 6.3352201086064124e+04
Dual infeasibility......: 8.2331781542267553e-10 1.0703131600494782e-07
Constraint violation....: 5.6523674629715970e-11 5.6523674629715970e-11
Variable bound violation: 3.9922449346363464e-08 3.9922449346363464e-08
Complementarity.........: 3.7502813809752752e-09 4.8753657952678579e-07
Overall NLP error.......: 3.7502813809752752e-09 4.8753657952678579e-07
Number of objective function evaluations = 21
Number of objective gradient evaluations = 20
Number of equality constraint evaluations = 21
Number of inequality constraint evaluations = 21
Number of equality constraint Jacobian evaluations = 20
Number of inequality constraint Jacobian evaluations = 20
Number of Lagrangian Hessian evaluations = 19
Total seconds in IPOPT = 0.051
EXIT: Optimal Solution Found.
file = "/tmp/jl_XPUwWW/pglib_opf_case2736sp_k.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case2737sop_k.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case2742_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case2746wop_k.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case2746wp_k.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case2848_rte.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case2853_sdet.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case2868_rte.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case2869_pegase.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case30000_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case300_ieee.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case3012wp_k.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case3022_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case30_as.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 999
Number of nonzeros in inequality constraint Jacobian.: 328
Number of nonzeros in Lagrangian Hessian.............: 1634
Total number of variables............................: 236
variables with only lower bounds: 0
variables with lower and upper bounds: 206
variables with only upper bounds: 0
Total number of equality constraints.................: 225
Total number of inequality constraints...............: 164
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 164
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.9330612e+02 7.89e-01 6.98e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 7.3680988e+02 1.06e-01 4.80e+01 -1.0 7.66e-01 - 1.76e-02 8.65e-01h 1
2 7.6515454e+02 5.53e-02 2.51e+01 -1.0 9.70e-01 - 7.06e-01 4.79e-01h 1
3 8.0390995e+02 9.81e-03 3.83e+00 -1.0 6.95e-01 - 1.00e+00 1.00e+00h 1
4 8.0419681e+02 2.85e-04 1.48e-01 -1.0 1.06e-01 - 1.00e+00 1.00e+00h 1
5 8.0355508e+02 1.29e-04 2.65e-02 -1.7 7.56e-02 - 1.00e+00 1.00e+00f 1
6 8.0322260e+02 1.45e-04 2.45e-02 -2.5 4.50e-02 - 9.81e-01 1.00e+00f 1
7 8.0312215e+02 2.25e-04 7.52e-02 -3.8 4.76e-02 - 8.87e-01 9.26e-01h 1
8 8.0312831e+02 3.92e-05 5.43e-04 -3.8 1.60e-02 - 1.00e+00 1.00e+00h 1
9 8.0312738e+02 1.51e-06 2.40e-04 -5.7 4.36e-03 - 1.00e+00 9.98e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.0312733e+02 3.89e-09 4.27e-07 -5.7 3.29e-04 - 1.00e+00 1.00e+00f 1
11 8.0312731e+02 7.14e-11 1.82e-09 -8.6 2.73e-05 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 11
(scaled) (unscaled)
Objective...............: 2.4711609550316729e+02 8.0312731038529364e+02
Dual infeasibility......: 1.8152057634779339e-09 5.8994187313032853e-09
Constraint violation....: 7.1401551338112768e-11 7.1401551338112768e-11
Variable bound violation: 1.0369035186030828e-08 1.0369035186030828e-08
Complementarity.........: 3.8105847373601750e-09 1.2384400396420568e-08
Overall NLP error.......: 3.8105847373601750e-09 1.2384400396420568e-08
Number of objective function evaluations = 12
Number of objective gradient evaluations = 12
Number of equality constraint evaluations = 12
Number of inequality constraint evaluations = 12
Number of equality constraint Jacobian evaluations = 12
Number of inequality constraint Jacobian evaluations = 12
Number of Lagrangian Hessian evaluations = 11
Total seconds in IPOPT = 11.386
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 999
Number of nonzeros in inequality constraint Jacobian.: 328
Number of nonzeros in Lagrangian Hessian.............: 1634
Total number of variables............................: 236
variables with only lower bounds: 0
variables with lower and upper bounds: 206
variables with only upper bounds: 0
Total number of equality constraints.................: 225
Total number of inequality constraints...............: 164
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 164
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.9330612e+02 7.89e-01 6.98e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 7.3680988e+02 1.06e-01 4.80e+01 -1.0 7.66e-01 - 1.76e-02 8.65e-01h 1
2 7.6515454e+02 5.53e-02 2.51e+01 -1.0 9.70e-01 - 7.06e-01 4.79e-01h 1
3 8.0390995e+02 9.81e-03 3.83e+00 -1.0 6.95e-01 - 1.00e+00 1.00e+00h 1
4 8.0419681e+02 2.85e-04 1.48e-01 -1.0 1.06e-01 - 1.00e+00 1.00e+00h 1
5 8.0355508e+02 1.29e-04 2.65e-02 -1.7 7.56e-02 - 1.00e+00 1.00e+00f 1
6 8.0322260e+02 1.45e-04 2.45e-02 -2.5 4.50e-02 - 9.81e-01 1.00e+00f 1
7 8.0312215e+02 2.25e-04 7.52e-02 -3.8 4.76e-02 - 8.87e-01 9.26e-01h 1
8 8.0312831e+02 3.92e-05 5.43e-04 -3.8 1.60e-02 - 1.00e+00 1.00e+00h 1
9 8.0312738e+02 1.51e-06 2.40e-04 -5.7 4.36e-03 - 1.00e+00 9.98e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.0312733e+02 3.89e-09 4.27e-07 -5.7 3.29e-04 - 1.00e+00 1.00e+00f 1
11 8.0312731e+02 7.14e-11 1.82e-09 -8.6 2.73e-05 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 11
(scaled) (unscaled)
Objective...............: 2.4711609550316729e+02 8.0312731038529364e+02
Dual infeasibility......: 1.8152057634779339e-09 5.8994187313032853e-09
Constraint violation....: 7.1401551338112768e-11 7.1401551338112768e-11
Variable bound violation: 1.0369035186030828e-08 1.0369035186030828e-08
Complementarity.........: 3.8105847373601750e-09 1.2384400396420568e-08
Overall NLP error.......: 3.8105847373601750e-09 1.2384400396420568e-08
Number of objective function evaluations = 12
Number of objective gradient evaluations = 12
Number of equality constraint evaluations = 12
Number of inequality constraint evaluations = 12
Number of equality constraint Jacobian evaluations = 12
Number of inequality constraint Jacobian evaluations = 12
Number of Lagrangian Hessian evaluations = 11
Total seconds in IPOPT = 0.033
EXIT: Optimal Solution Found.
file = "/tmp/jl_XPUwWW/pglib_opf_case30_ieee.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 995
Number of nonzeros in inequality constraint Jacobian.: 328
Number of nonzeros in Lagrangian Hessian.............: 1628
Total number of variables............................: 232
variables with only lower bounds: 0
variables with lower and upper bounds: 202
variables with only upper bounds: 0
Total number of equality constraints.................: 225
Total number of inequality constraints...............: 164
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 164
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.6429132e+01 9.42e-01 1.81e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 7.3805974e+03 1.92e-01 1.37e+02 -1.0 1.73e+00 - 5.86e-03 7.96e-01H 1
2 7.0362117e+03 7.70e-02 5.47e+01 -1.0 1.62e+00 - 2.07e-02 6.00e-01f 1
3 7.0544217e+03 7.45e-02 5.34e+01 -1.0 7.99e-01 - 9.68e-01 3.19e-02h 1
4 8.2288089e+03 5.15e-04 1.49e+00 -1.0 3.21e-01 - 1.00e+00 1.00e+00h 1
5 8.2390074e+03 3.30e-05 1.34e-01 -1.0 2.16e-02 - 1.00e+00 1.00e+00h 1
6 8.2178583e+03 7.40e-05 4.92e-01 -2.5 2.35e-02 - 8.48e-01 9.57e-01f 1
7 8.2101534e+03 7.44e-05 6.45e+00 -2.5 4.92e-02 - 1.00e+00 7.43e-01f 1
8 8.2093612e+03 5.58e-06 6.10e-02 -2.5 2.33e-02 - 1.00e+00 1.00e+00f 1
9 8.2094471e+03 1.15e-07 8.09e-03 -2.5 1.99e-03 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.2085399e+03 5.53e-06 8.48e-04 -3.8 8.62e-03 - 1.00e+00 1.00e+00f 1
11 8.2085221e+03 8.56e-06 1.69e+01 -5.7 1.49e-03 - 8.58e-01 4.60e-01h 1
12 8.2085142e+03 1.44e-06 1.47e-02 -5.7 1.50e-03 - 1.00e+00 1.00e+00h 1
13 8.2085158e+03 5.61e-08 7.72e-05 -5.7 4.49e-04 - 1.00e+00 1.00e+00h 1
14 8.2085159e+03 2.07e-09 5.33e-06 -5.7 2.37e-04 - 1.00e+00 1.00e+00h 1
15 8.2085154e+03 5.30e-09 2.50e-03 -8.6 8.83e-05 - 9.80e-01 1.00e+00h 1
16 8.2085154e+03 4.62e-09 2.33e-03 -8.6 1.11e-05 - 1.00e+00 1.25e-01f 4
17 8.2085154e+03 3.63e-12 1.37e-08 -8.6 9.90e-06 - 1.00e+00 1.00e+00h 1
18 8.2085154e+03 1.42e-14 2.54e-12 -9.0 4.10e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 18
(scaled) (unscaled)
Objective...............: 1.5730473122733963e+02 8.2085154403067681e+03
Dual infeasibility......: 2.5374700336157655e-12 1.3241090581152641e-10
Constraint violation....: 1.4210854715202004e-14 1.4210854715202004e-14
Variable bound violation: 1.0549795703695963e-08 1.0549795703695963e-08
Complementarity.........: 9.0917662089346910e-10 4.7442885362324712e-08
Overall NLP error.......: 9.0917662089346910e-10 4.7442885362324712e-08
Number of objective function evaluations = 23
Number of objective gradient evaluations = 19
Number of equality constraint evaluations = 23
Number of inequality constraint evaluations = 23
Number of equality constraint Jacobian evaluations = 19
Number of inequality constraint Jacobian evaluations = 19
Number of Lagrangian Hessian evaluations = 18
Total seconds in IPOPT = 11.588
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 995
Number of nonzeros in inequality constraint Jacobian.: 328
Number of nonzeros in Lagrangian Hessian.............: 1628
Total number of variables............................: 232
variables with only lower bounds: 0
variables with lower and upper bounds: 202
variables with only upper bounds: 0
Total number of equality constraints.................: 225
Total number of inequality constraints...............: 164
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 164
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.6429132e+01 9.42e-01 1.81e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 7.3805974e+03 1.92e-01 1.37e+02 -1.0 1.73e+00 - 5.86e-03 7.96e-01H 1
2 7.0362117e+03 7.70e-02 5.47e+01 -1.0 1.62e+00 - 2.07e-02 6.00e-01f 1
3 7.0544217e+03 7.45e-02 5.34e+01 -1.0 7.99e-01 - 9.68e-01 3.19e-02h 1
4 8.2288089e+03 5.15e-04 1.49e+00 -1.0 3.21e-01 - 1.00e+00 1.00e+00h 1
5 8.2390074e+03 3.30e-05 1.34e-01 -1.0 2.16e-02 - 1.00e+00 1.00e+00h 1
6 8.2178583e+03 7.40e-05 4.92e-01 -2.5 2.35e-02 - 8.48e-01 9.57e-01f 1
7 8.2101534e+03 7.44e-05 6.45e+00 -2.5 4.92e-02 - 1.00e+00 7.43e-01f 1
8 8.2093612e+03 5.58e-06 6.10e-02 -2.5 2.33e-02 - 1.00e+00 1.00e+00f 1
9 8.2094471e+03 1.15e-07 8.09e-03 -2.5 1.99e-03 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.2085399e+03 5.53e-06 8.48e-04 -3.8 8.62e-03 - 1.00e+00 1.00e+00f 1
11 8.2085221e+03 8.56e-06 1.69e+01 -5.7 1.49e-03 - 8.58e-01 4.60e-01h 1
12 8.2085142e+03 1.44e-06 1.47e-02 -5.7 1.50e-03 - 1.00e+00 1.00e+00h 1
13 8.2085158e+03 5.61e-08 7.72e-05 -5.7 4.49e-04 - 1.00e+00 1.00e+00h 1
14 8.2085159e+03 2.07e-09 5.33e-06 -5.7 2.37e-04 - 1.00e+00 1.00e+00h 1
15 8.2085154e+03 5.30e-09 2.50e-03 -8.6 8.83e-05 - 9.80e-01 1.00e+00h 1
16 8.2085154e+03 4.62e-09 2.33e-03 -8.6 1.11e-05 - 1.00e+00 1.25e-01f 4
17 8.2085154e+03 3.63e-12 1.37e-08 -8.6 9.90e-06 - 1.00e+00 1.00e+00h 1
18 8.2085154e+03 1.42e-14 2.54e-12 -9.0 4.10e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 18
(scaled) (unscaled)
Objective...............: 1.5730473122733963e+02 8.2085154403067681e+03
Dual infeasibility......: 2.5374700336157655e-12 1.3241090581152641e-10
Constraint violation....: 1.4210854715202004e-14 1.4210854715202004e-14
Variable bound violation: 1.0549795703695963e-08 1.0549795703695963e-08
Complementarity.........: 9.0917662089346910e-10 4.7442885362324712e-08
Overall NLP error.......: 9.0917662089346910e-10 4.7442885362324712e-08
Number of objective function evaluations = 23
Number of objective gradient evaluations = 19
Number of equality constraint evaluations = 23
Number of inequality constraint evaluations = 23
Number of equality constraint Jacobian evaluations = 19
Number of inequality constraint Jacobian evaluations = 19
Number of Lagrangian Hessian evaluations = 18
Total seconds in IPOPT = 0.052
EXIT: Optimal Solution Found.
file = "/tmp/jl_XPUwWW/pglib_opf_case3120sp_k.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case3375wp_k.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case3970_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case39_epri.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 1125
Number of nonzeros in inequality constraint Jacobian.: 368
Number of nonzeros in Lagrangian Hessian.............: 1832
Total number of variables............................: 282
variables with only lower bounds: 0
variables with lower and upper bounds: 243
variables with only upper bounds: 0
Total number of equality constraints.................: 263
Total number of inequality constraints...............: 184
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 184
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.3768629e+02 1.10e+01 1.25e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.1096016e+04 1.02e+01 6.03e+01 -1.0 6.92e+00 - 1.45e-03 7.43e-02h 2
2 2.0586599e+04 9.35e+00 5.53e+01 -1.0 1.21e+01 - 5.88e-02 8.41e-02h 4
3 2.8045737e+04 8.65e+00 5.11e+01 -1.0 3.53e+01 - 1.19e-01 7.49e-02h 4
4 3.4162191e+04 8.06e+00 4.76e+01 -1.0 5.29e+01 - 2.03e-01 6.79e-02h 4
5 4.4158466e+04 7.09e+00 4.18e+01 -1.0 6.46e+01 - 2.50e-01 1.21e-01h 3
6 5.4914496e+04 6.03e+00 3.55e+01 -1.0 7.52e+01 - 2.65e-01 1.50e-01h 2
7 6.6043801e+04 4.95e+00 2.90e+01 -1.0 7.38e+01 - 5.73e-01 1.79e-01h 1
8 7.6876929e+04 4.05e+00 2.38e+01 -1.0 5.15e+01 - 5.38e-01 1.80e-01h 1
9 9.6582595e+04 2.65e+00 1.55e+01 -1.0 5.05e+01 - 3.04e-01 3.46e-01H 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.1317794e+05 1.47e+00 8.54e+00 -1.0 6.29e+01 - 3.50e-01 4.46e-01H 1
11 1.1373986e+05 1.43e+00 1.46e+01 -1.0 3.33e+01 - 8.90e-01 2.45e-02h 1
12 1.2662465e+05 6.62e-01 9.96e+00 -1.0 2.83e+01 - 1.00e+00 5.38e-01h 1
13 1.3081352e+05 4.07e-01 4.65e+00 -1.0 5.24e+01 - 8.69e-01 3.86e-01h 1
14 1.3171708e+05 3.56e-01 9.76e+00 -1.0 1.01e+01 - 3.50e-01 1.25e-01h 1
15 1.3630115e+05 1.14e-01 2.18e+00 -1.0 6.05e+00 - 1.00e+00 6.81e-01h 1
16 1.3815859e+05 2.30e-02 7.46e-01 -1.0 3.59e+00 - 8.11e-01 8.46e-01h 1
17 1.3844499e+05 4.46e-03 1.00e-01 -1.7 1.19e+00 - 9.76e-01 1.00e+00h 1
18 1.3842260e+05 3.88e-03 1.28e+00 -2.5 5.82e-01 - 1.00e+00 6.00e-01h 1
19 1.3841745e+05 2.48e-03 9.52e+00 -2.5 5.85e-01 - 6.99e-01 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.3841745e+05 3.85e-05 7.32e-04 -2.5 5.45e-02 - 1.00e+00 1.00e+00h 1
21 1.3841575e+05 4.57e-05 5.16e-02 -3.8 1.08e-01 - 9.11e-01 9.17e-01h 1
22 1.3841566e+05 7.70e-06 2.95e-04 -3.8 1.89e-02 - 1.00e+00 1.00e+00h 1
23 1.3841557e+05 3.47e-06 1.17e-01 -5.7 9.00e-03 - 9.92e-01 9.04e-01h 1
24 1.3841556e+05 1.69e-07 2.44e-06 -5.7 1.56e-03 - 1.00e+00 1.00e+00h 1
25 1.3841556e+05 1.86e-10 4.07e-06 -8.6 7.90e-05 - 1.00e+00 1.00e+00h 1
26 1.3841556e+05 1.14e-13 1.36e-11 -8.6 5.30e-08 - 1.00e+00 1.00e+00f 1
Number of Iterations....: 26
(scaled) (unscaled)
Objective...............: 3.9723627718148396e+03 1.3841556265037853e+05
Dual infeasibility......: 1.3577390042013769e-11 4.7309930888572472e-10
Constraint violation....: 4.9308361011817179e-14 1.1368683772161603e-13
Variable bound violation: 1.0982981990537155e-07 1.0982981990537155e-07
Complementarity.........: 2.5059244715281850e-09 8.7318043595363258e-08
Overall NLP error.......: 2.5059244715281850e-09 8.7318043595363258e-08
Number of objective function evaluations = 55
Number of objective gradient evaluations = 27
Number of equality constraint evaluations = 55
Number of inequality constraint evaluations = 55
Number of equality constraint Jacobian evaluations = 27
Number of inequality constraint Jacobian evaluations = 27
Number of Lagrangian Hessian evaluations = 26
Total seconds in IPOPT = 14.936
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 1125
Number of nonzeros in inequality constraint Jacobian.: 368
Number of nonzeros in Lagrangian Hessian.............: 1832
Total number of variables............................: 282
variables with only lower bounds: 0
variables with lower and upper bounds: 243
variables with only upper bounds: 0
Total number of equality constraints.................: 263
Total number of inequality constraints...............: 184
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 184
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.3768629e+02 1.10e+01 1.25e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.1096016e+04 1.02e+01 6.03e+01 -1.0 6.92e+00 - 1.45e-03 7.43e-02h 2
2 2.0586599e+04 9.35e+00 5.53e+01 -1.0 1.21e+01 - 5.88e-02 8.41e-02h 4
3 2.8045737e+04 8.65e+00 5.11e+01 -1.0 3.53e+01 - 1.19e-01 7.49e-02h 4
4 3.4162191e+04 8.06e+00 4.76e+01 -1.0 5.29e+01 - 2.03e-01 6.79e-02h 4
5 4.4158466e+04 7.09e+00 4.18e+01 -1.0 6.46e+01 - 2.50e-01 1.21e-01h 3
6 5.4914496e+04 6.03e+00 3.55e+01 -1.0 7.52e+01 - 2.65e-01 1.50e-01h 2
7 6.6043801e+04 4.95e+00 2.90e+01 -1.0 7.38e+01 - 5.73e-01 1.79e-01h 1
8 7.6876929e+04 4.05e+00 2.38e+01 -1.0 5.15e+01 - 5.38e-01 1.80e-01h 1
9 9.6582595e+04 2.65e+00 1.55e+01 -1.0 5.05e+01 - 3.04e-01 3.46e-01H 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.1317794e+05 1.47e+00 8.54e+00 -1.0 6.29e+01 - 3.50e-01 4.46e-01H 1
11 1.1373986e+05 1.43e+00 1.46e+01 -1.0 3.33e+01 - 8.90e-01 2.45e-02h 1
12 1.2662465e+05 6.62e-01 9.96e+00 -1.0 2.83e+01 - 1.00e+00 5.38e-01h 1
13 1.3081352e+05 4.07e-01 4.65e+00 -1.0 5.24e+01 - 8.69e-01 3.86e-01h 1
14 1.3171708e+05 3.56e-01 9.76e+00 -1.0 1.01e+01 - 3.50e-01 1.25e-01h 1
15 1.3630115e+05 1.14e-01 2.18e+00 -1.0 6.05e+00 - 1.00e+00 6.81e-01h 1
16 1.3815859e+05 2.30e-02 7.46e-01 -1.0 3.59e+00 - 8.11e-01 8.46e-01h 1
17 1.3844499e+05 4.46e-03 1.00e-01 -1.7 1.19e+00 - 9.76e-01 1.00e+00h 1
18 1.3842260e+05 3.88e-03 1.28e+00 -2.5 5.82e-01 - 1.00e+00 6.00e-01h 1
19 1.3841745e+05 2.48e-03 9.52e+00 -2.5 5.85e-01 - 6.99e-01 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.3841745e+05 3.85e-05 7.32e-04 -2.5 5.45e-02 - 1.00e+00 1.00e+00h 1
21 1.3841575e+05 4.57e-05 5.16e-02 -3.8 1.08e-01 - 9.11e-01 9.17e-01h 1
22 1.3841566e+05 7.70e-06 2.95e-04 -3.8 1.89e-02 - 1.00e+00 1.00e+00h 1
23 1.3841557e+05 3.47e-06 1.17e-01 -5.7 9.00e-03 - 9.92e-01 9.04e-01h 1
24 1.3841556e+05 1.69e-07 2.44e-06 -5.7 1.56e-03 - 1.00e+00 1.00e+00h 1
25 1.3841556e+05 1.86e-10 4.07e-06 -8.6 7.90e-05 - 1.00e+00 1.00e+00h 1
26 1.3841556e+05 1.14e-13 1.36e-11 -8.6 5.30e-08 - 1.00e+00 1.00e+00f 1
Number of Iterations....: 26
(scaled) (unscaled)
Objective...............: 3.9723627718148396e+03 1.3841556265037853e+05
Dual infeasibility......: 1.3577390042013769e-11 4.7309930888572472e-10
Constraint violation....: 4.9308361011817179e-14 1.1368683772161603e-13
Variable bound violation: 1.0982981990537155e-07 1.0982981990537155e-07
Complementarity.........: 2.5059244715281850e-09 8.7318043595363258e-08
Overall NLP error.......: 2.5059244715281850e-09 8.7318043595363258e-08
Number of objective function evaluations = 55
Number of objective gradient evaluations = 27
Number of equality constraint evaluations = 55
Number of inequality constraint evaluations = 55
Number of equality constraint Jacobian evaluations = 27
Number of inequality constraint Jacobian evaluations = 27
Number of Lagrangian Hessian evaluations = 26
Total seconds in IPOPT = 0.088
EXIT: Optimal Solution Found.
file = "/tmp/jl_XPUwWW/pglib_opf_case3_lmbd.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 78
Number of nonzeros in inequality constraint Jacobian.: 24
Number of nonzeros in Lagrangian Hessian.............: 122
Total number of variables............................: 23
variables with only lower bounds: 0
variables with lower and upper bounds: 20
variables with only upper bounds: 0
Total number of equality constraints.................: 19
Total number of inequality constraints...............: 12
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 12
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.3949934e+00 1.09e+00 1.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.1048421e+03 4.62e-01 1.04e+02 -1.0 1.81e+00 - 5.77e-03 5.76e-01h 1
2 4.4503068e+03 1.36e-01 3.09e+01 -1.0 8.29e-01 - 8.15e-01 7.05e-01h 1
3 4.6140270e+03 1.18e-01 2.67e+01 -1.0 2.99e-01 - 4.13e-01 1.37e-01h 1
4 4.9343698e+03 8.25e-02 3.85e+01 -1.0 4.36e-01 - 4.94e-01 2.98e-01h 1
5 5.4019378e+03 3.46e-02 2.51e+01 -1.0 3.62e-01 - 9.90e-01 5.81e-01h 1
6 5.4116196e+03 3.37e-02 1.26e+02 -1.0 1.45e-01 - 1.84e-01 2.70e-02h 1
7 5.6094774e+03 1.60e-02 5.13e+01 -1.0 2.53e-01 - 2.76e-01 5.25e-01h 1
8 5.7031738e+03 8.53e-03 8.21e+01 -1.0 1.43e-01 - 7.00e-01 4.67e-01h 1
9 5.8145457e+03 6.10e-04 2.19e+00 -1.0 5.77e-02 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 5.8146316e+03 2.55e-05 7.83e-03 -1.0 1.23e-02 - 1.00e+00 1.00e+00h 1
11 5.8127612e+03 1.60e-05 1.36e-02 -2.5 8.27e-03 - 1.00e+00 1.00e+00f 1
12 5.8126464e+03 2.60e-07 1.15e-04 -3.8 1.05e-03 - 1.00e+00 1.00e+00f 1
13 5.8126430e+03 1.32e-10 8.31e-08 -5.7 2.50e-05 - 1.00e+00 1.00e+00h 1
14 5.8126429e+03 7.55e-15 6.25e-12 -8.6 1.78e-07 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 14
(scaled) (unscaled)
Objective...............: 1.1625285870072360e+03 5.8126429350361796e+03
Dual infeasibility......: 6.2541517268298295e-12 3.1270758634149147e-11
Constraint violation....: 7.5495165674510645e-15 7.5495165674510645e-15
Variable bound violation: 1.0911841874516881e-08 1.0911841874516881e-08
Complementarity.........: 2.5102170848799089e-09 1.2551085424399544e-08
Overall NLP error.......: 2.5102170848799089e-09 1.2551085424399544e-08
Number of objective function evaluations = 15
Number of objective gradient evaluations = 15
Number of equality constraint evaluations = 15
Number of inequality constraint evaluations = 15
Number of equality constraint Jacobian evaluations = 15
Number of inequality constraint Jacobian evaluations = 15
Number of Lagrangian Hessian evaluations = 14
Total seconds in IPOPT = 0.010
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 78
Number of nonzeros in inequality constraint Jacobian.: 24
Number of nonzeros in Lagrangian Hessian.............: 122
Total number of variables............................: 23
variables with only lower bounds: 0
variables with lower and upper bounds: 20
variables with only upper bounds: 0
Total number of equality constraints.................: 19
Total number of inequality constraints...............: 12
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 12
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.3949934e+00 1.09e+00 1.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.1048421e+03 4.62e-01 1.04e+02 -1.0 1.81e+00 - 5.77e-03 5.76e-01h 1
2 4.4503068e+03 1.36e-01 3.09e+01 -1.0 8.29e-01 - 8.15e-01 7.05e-01h 1
3 4.6140270e+03 1.18e-01 2.67e+01 -1.0 2.99e-01 - 4.13e-01 1.37e-01h 1
4 4.9343698e+03 8.25e-02 3.85e+01 -1.0 4.36e-01 - 4.94e-01 2.98e-01h 1
5 5.4019378e+03 3.46e-02 2.51e+01 -1.0 3.62e-01 - 9.90e-01 5.81e-01h 1
6 5.4116196e+03 3.37e-02 1.26e+02 -1.0 1.45e-01 - 1.84e-01 2.70e-02h 1
7 5.6094774e+03 1.60e-02 5.13e+01 -1.0 2.53e-01 - 2.76e-01 5.25e-01h 1
8 5.7031738e+03 8.53e-03 8.21e+01 -1.0 1.43e-01 - 7.00e-01 4.67e-01h 1
9 5.8145457e+03 6.10e-04 2.19e+00 -1.0 5.77e-02 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 5.8146316e+03 2.55e-05 7.83e-03 -1.0 1.23e-02 - 1.00e+00 1.00e+00h 1
11 5.8127612e+03 1.60e-05 1.36e-02 -2.5 8.27e-03 - 1.00e+00 1.00e+00f 1
12 5.8126464e+03 2.60e-07 1.15e-04 -3.8 1.05e-03 - 1.00e+00 1.00e+00f 1
13 5.8126430e+03 1.32e-10 8.31e-08 -5.7 2.50e-05 - 1.00e+00 1.00e+00h 1
14 5.8126429e+03 7.55e-15 6.25e-12 -8.6 1.78e-07 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 14
(scaled) (unscaled)
Objective...............: 1.1625285870072360e+03 5.8126429350361796e+03
Dual infeasibility......: 6.2541517268298295e-12 3.1270758634149147e-11
Constraint violation....: 7.5495165674510645e-15 7.5495165674510645e-15
Variable bound violation: 1.0911841874516881e-08 1.0911841874516881e-08
Complementarity.........: 2.5102170848799089e-09 1.2551085424399544e-08
Overall NLP error.......: 2.5102170848799089e-09 1.2551085424399544e-08
Number of objective function evaluations = 15
Number of objective gradient evaluations = 15
Number of equality constraint evaluations = 15
Number of inequality constraint evaluations = 15
Number of equality constraint Jacobian evaluations = 15
Number of inequality constraint Jacobian evaluations = 15
Number of Lagrangian Hessian evaluations = 14
Total seconds in IPOPT = 0.009
EXIT: Optimal Solution Found.
file = "/tmp/jl_XPUwWW/pglib_opf_case4020_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case4601_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case4619_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case4661_sdet.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case4837_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case4917_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case500_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case5658_epigrids.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case57_ieee.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 1935
Number of nonzeros in inequality constraint Jacobian.: 640
Number of nonzeros in Lagrangian Hessian.............: 3167
Total number of variables............................: 445
variables with only lower bounds: 0
variables with lower and upper bounds: 388
variables with only upper bounds: 0
Total number of equality constraints.................: 435
Total number of inequality constraints...............: 320
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 320
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0503586e+02 3.76e+00 1.91e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.0716549e+04 2.65e+00 1.19e+02 -1.0 3.70e+00 - 2.74e-03 2.95e-01h 1
2 1.3984057e+04 2.15e+00 9.71e+01 -1.0 7.02e+00 - 5.91e-02 1.90e-01h 1
3 1.4673527e+04 2.08e+00 7.81e+01 -1.0 3.90e+00 - 3.67e-01 2.99e-02h 1
4 2.0340384e+04 1.57e+00 5.90e+01 -1.0 4.22e+00 - 7.79e-01 2.46e-01h 1
5 3.5210269e+04 2.14e-01 4.79e+01 -1.0 4.88e+00 - 7.60e-01 8.64e-01h 1
6 3.6360055e+04 1.01e-01 2.37e+01 -1.0 1.06e+01 - 7.33e-01 5.27e-01h 1
7 3.7555239e+04 1.23e-02 1.55e+00 -1.0 6.75e+00 - 1.00e+00 1.00e+00h 1
8 3.7614438e+04 3.64e-04 1.91e-02 -1.0 1.25e+00 - 1.00e+00 1.00e+00h 1
9 3.7591308e+04 4.39e-04 3.47e+00 -2.5 1.26e+00 - 8.64e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 3.7590571e+04 8.45e-05 2.31e+00 -2.5 1.37e-01 - 9.45e-01 8.36e-01h 1
11 3.7590149e+04 1.43e-05 1.36e-03 -2.5 2.81e-02 - 1.00e+00 1.00e+00f 1
12 3.7589396e+04 2.20e-06 1.90e-02 -3.8 4.24e-02 - 9.89e-01 1.00e+00h 1
13 3.7589383e+04 8.07e-09 2.05e-06 -3.8 5.69e-04 - 1.00e+00 1.00e+00h 1
14 3.7589339e+04 7.59e-09 1.23e-06 -5.7 2.32e-03 - 1.00e+00 1.00e+00h 1
15 3.7589338e+04 1.86e-12 2.72e-10 -8.6 2.92e-05 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.0107655336327775e+03 3.7589338204193162e+04
Dual infeasibility......: 2.7159293080629876e-10 1.0100263800303899e-08
Constraint violation....: 1.8649361568923162e-12 1.8649361568923162e-12
Variable bound violation: 2.4448082225347889e-08 2.4448082225347889e-08
Complementarity.........: 2.6288227592950274e-09 9.7763234390144846e-08
Overall NLP error.......: 2.6288227592950274e-09 9.7763234390144846e-08
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total seconds in IPOPT = 35.324
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 1935
Number of nonzeros in inequality constraint Jacobian.: 640
Number of nonzeros in Lagrangian Hessian.............: 3167
Total number of variables............................: 445
variables with only lower bounds: 0
variables with lower and upper bounds: 388
variables with only upper bounds: 0
Total number of equality constraints.................: 435
Total number of inequality constraints...............: 320
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 320
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0503586e+02 3.76e+00 1.91e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.0716549e+04 2.65e+00 1.19e+02 -1.0 3.70e+00 - 2.74e-03 2.95e-01h 1
2 1.3984057e+04 2.15e+00 9.71e+01 -1.0 7.02e+00 - 5.91e-02 1.90e-01h 1
3 1.4673527e+04 2.08e+00 7.81e+01 -1.0 3.90e+00 - 3.67e-01 2.99e-02h 1
4 2.0340384e+04 1.57e+00 5.90e+01 -1.0 4.22e+00 - 7.79e-01 2.46e-01h 1
5 3.5210269e+04 2.14e-01 4.79e+01 -1.0 4.88e+00 - 7.60e-01 8.64e-01h 1
6 3.6360055e+04 1.01e-01 2.37e+01 -1.0 1.06e+01 - 7.33e-01 5.27e-01h 1
7 3.7555239e+04 1.23e-02 1.55e+00 -1.0 6.75e+00 - 1.00e+00 1.00e+00h 1
8 3.7614438e+04 3.64e-04 1.91e-02 -1.0 1.25e+00 - 1.00e+00 1.00e+00h 1
9 3.7591308e+04 4.39e-04 3.47e+00 -2.5 1.26e+00 - 8.64e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 3.7590571e+04 8.45e-05 2.31e+00 -2.5 1.37e-01 - 9.45e-01 8.36e-01h 1
11 3.7590149e+04 1.43e-05 1.36e-03 -2.5 2.81e-02 - 1.00e+00 1.00e+00f 1
12 3.7589396e+04 2.20e-06 1.90e-02 -3.8 4.24e-02 - 9.89e-01 1.00e+00h 1
13 3.7589383e+04 8.07e-09 2.05e-06 -3.8 5.69e-04 - 1.00e+00 1.00e+00h 1
14 3.7589339e+04 7.59e-09 1.23e-06 -5.7 2.32e-03 - 1.00e+00 1.00e+00h 1
15 3.7589338e+04 1.86e-12 2.72e-10 -8.6 2.92e-05 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.0107655336327775e+03 3.7589338204193162e+04
Dual infeasibility......: 2.7159293080629876e-10 1.0100263800303899e-08
Constraint violation....: 1.8649361568923162e-12 1.8649361568923162e-12
Variable bound violation: 2.4448082225347889e-08 2.4448082225347889e-08
Complementarity.........: 2.6288227592950274e-09 9.7763234390144846e-08
Overall NLP error.......: 2.6288227592950274e-09 9.7763234390144846e-08
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total seconds in IPOPT = 0.084
EXIT: Optimal Solution Found.
file = "/tmp/jl_XPUwWW/pglib_opf_case588_sdet.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case5_pjm.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 155
Number of nonzeros in inequality constraint Jacobian.: 48
Number of nonzeros in Lagrangian Hessian.............: 240
Total number of variables............................: 44
variables with only lower bounds: 0
variables with lower and upper bounds: 39
variables with only upper bounds: 0
Total number of equality constraints.................: 35
Total number of inequality constraints...............: 24
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 24
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0059989e+02 3.99e+00 2.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 8.3066305e+03 2.47e+00 1.01e+02 -1.0 2.78e+00 - 4.11e-03 3.82e-01h 1
2 6.7181372e+03 2.36e+00 9.62e+01 -1.0 1.60e+01 - 7.37e-02 4.44e-02f 1
3 6.6689587e+03 2.30e+00 9.34e+01 -1.0 1.30e+01 - 4.94e-01 2.40e-02f 1
4 6.5741805e+03 2.04e+00 8.25e+01 -1.0 1.29e+01 - 3.67e-01 1.12e-01f 2
5 6.8264259e+03 1.80e+00 7.10e+01 -1.0 1.23e+01 - 8.72e-01 1.20e-01h 2
6 8.8540136e+03 1.08e+00 4.20e+01 -1.0 9.14e+00 - 5.92e-01 4.00e-01h 1
7 1.0572806e+04 8.62e-01 3.58e+01 -1.0 2.94e+00 - 4.93e-01 2.00e-01h 1
8 1.7308577e+04 3.63e-02 1.46e+01 -1.0 2.41e+00 - 7.65e-01 9.58e-01h 1
9 1.7572869e+04 1.33e-02 1.10e+00 -1.0 2.11e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7590631e+04 1.68e-03 1.61e-01 -1.0 5.04e-01 - 1.00e+00 1.00e+00h 1
11 1.7558724e+04 5.24e-03 5.03e-01 -2.5 6.03e-01 - 8.35e-01 9.36e-01f 1
12 1.7553111e+04 3.34e-03 4.12e+00 -2.5 2.84e-01 - 1.00e+00 8.20e-01h 1
13 1.7552956e+04 3.24e-05 1.26e-02 -2.5 6.35e-02 - 1.00e+00 1.00e+00h 1
14 1.7551990e+04 1.35e-05 1.09e+00 -3.8 2.53e-02 - 1.00e+00 9.25e-01h 1
15 1.7551938e+04 4.46e-08 1.22e-02 -3.8 7.00e-03 - 1.00e+00 1.00e+00f 1
16 1.7551940e+04 2.35e-10 2.06e-04 -3.8 3.83e-04 - 1.00e+00 1.00e+00h 1
17 1.7551893e+04 1.75e-07 2.10e-01 -5.7 2.49e-03 - 1.00e+00 9.68e-01f 1
18 1.7551891e+04 6.80e-11 3.09e-05 -5.7 2.38e-04 - 1.00e+00 1.00e+00f 1
19 1.7551891e+04 5.68e-14 6.47e-10 -5.7 5.17e-07 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.7551891e+04 6.26e-12 3.03e-07 -8.6 3.52e-05 - 1.00e+00 1.00e+00f 1
21 1.7551891e+04 5.68e-14 3.38e-12 -8.6 3.33e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 21
(scaled) (unscaled)
Objective...............: 4.3879727248486898e+02 1.7551890899394759e+04
Dual infeasibility......: 3.3822003142280486e-12 1.3528801256912194e-10
Constraint violation....: 3.6743585951626306e-14 5.6843418860808015e-14
Variable bound violation: 2.9463905093507492e-08 2.9463905093507492e-08
Complementarity.........: 2.5059076126917168e-09 1.0023630450766867e-07
Overall NLP error.......: 2.5059076126917168e-09 1.0023630450766867e-07
Number of objective function evaluations = 28
Number of objective gradient evaluations = 22
Number of equality constraint evaluations = 28
Number of inequality constraint evaluations = 28
Number of equality constraint Jacobian evaluations = 22
Number of inequality constraint Jacobian evaluations = 22
Number of Lagrangian Hessian evaluations = 21
Total seconds in IPOPT = 0.019
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 155
Number of nonzeros in inequality constraint Jacobian.: 48
Number of nonzeros in Lagrangian Hessian.............: 240
Total number of variables............................: 44
variables with only lower bounds: 0
variables with lower and upper bounds: 39
variables with only upper bounds: 0
Total number of equality constraints.................: 35
Total number of inequality constraints...............: 24
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 24
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0059989e+02 3.99e+00 2.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 8.3066305e+03 2.47e+00 1.01e+02 -1.0 2.78e+00 - 4.11e-03 3.82e-01h 1
2 6.7181372e+03 2.36e+00 9.62e+01 -1.0 1.60e+01 - 7.37e-02 4.44e-02f 1
3 6.6689587e+03 2.30e+00 9.34e+01 -1.0 1.30e+01 - 4.94e-01 2.40e-02f 1
4 6.5741805e+03 2.04e+00 8.25e+01 -1.0 1.29e+01 - 3.67e-01 1.12e-01f 2
5 6.8264259e+03 1.80e+00 7.10e+01 -1.0 1.23e+01 - 8.72e-01 1.20e-01h 2
6 8.8540136e+03 1.08e+00 4.20e+01 -1.0 9.14e+00 - 5.92e-01 4.00e-01h 1
7 1.0572806e+04 8.62e-01 3.58e+01 -1.0 2.94e+00 - 4.93e-01 2.00e-01h 1
8 1.7308577e+04 3.63e-02 1.46e+01 -1.0 2.41e+00 - 7.65e-01 9.58e-01h 1
9 1.7572869e+04 1.33e-02 1.10e+00 -1.0 2.11e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7590631e+04 1.68e-03 1.61e-01 -1.0 5.04e-01 - 1.00e+00 1.00e+00h 1
11 1.7558724e+04 5.24e-03 5.03e-01 -2.5 6.03e-01 - 8.35e-01 9.36e-01f 1
12 1.7553111e+04 3.34e-03 4.12e+00 -2.5 2.84e-01 - 1.00e+00 8.20e-01h 1
13 1.7552956e+04 3.24e-05 1.26e-02 -2.5 6.35e-02 - 1.00e+00 1.00e+00h 1
14 1.7551990e+04 1.35e-05 1.09e+00 -3.8 2.53e-02 - 1.00e+00 9.25e-01h 1
15 1.7551938e+04 4.46e-08 1.22e-02 -3.8 7.00e-03 - 1.00e+00 1.00e+00f 1
16 1.7551940e+04 2.35e-10 2.06e-04 -3.8 3.83e-04 - 1.00e+00 1.00e+00h 1
17 1.7551893e+04 1.75e-07 2.10e-01 -5.7 2.49e-03 - 1.00e+00 9.68e-01f 1
18 1.7551891e+04 6.80e-11 3.09e-05 -5.7 2.38e-04 - 1.00e+00 1.00e+00f 1
19 1.7551891e+04 5.68e-14 6.47e-10 -5.7 5.17e-07 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.7551891e+04 6.26e-12 3.03e-07 -8.6 3.52e-05 - 1.00e+00 1.00e+00f 1
21 1.7551891e+04 5.68e-14 3.38e-12 -8.6 3.33e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 21
(scaled) (unscaled)
Objective...............: 4.3879727248486898e+02 1.7551890899394759e+04
Dual infeasibility......: 3.3822003142280486e-12 1.3528801256912194e-10
Constraint violation....: 3.6743585951626306e-14 5.6843418860808015e-14
Variable bound violation: 2.9463905093507492e-08 2.9463905093507492e-08
Complementarity.........: 2.5059076126917168e-09 1.0023630450766867e-07
Overall NLP error.......: 2.5059076126917168e-09 1.0023630450766867e-07
Number of objective function evaluations = 28
Number of objective gradient evaluations = 22
Number of equality constraint evaluations = 28
Number of inequality constraint evaluations = 28
Number of equality constraint Jacobian evaluations = 22
Number of inequality constraint Jacobian evaluations = 22
Number of Lagrangian Hessian evaluations = 21
Total seconds in IPOPT = 0.018
EXIT: Optimal Solution Found.
file = "/tmp/jl_XPUwWW/pglib_opf_case60_c.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 2170
Number of nonzeros in inequality constraint Jacobian.: 704
Number of nonzeros in Lagrangian Hessian.............: 3460
Total number of variables............................: 517
variables with only lower bounds: 0
variables with lower and upper bounds: 457
variables with only upper bounds: 0
Total number of equality constraints.................: 473
Total number of inequality constraints...............: 352
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 352
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.8499996e+03 2.00e+01 1.41e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.3245588e+04 1.86e+01 3.73e+01 -1.0 7.57e+00 - 2.13e-03 7.03e-02h 3
2 1.7309771e+04 1.73e+01 3.47e+01 -1.0 1.53e+01 - 3.49e-02 6.78e-02h 1
3 1.8342944e+04 1.71e+01 3.43e+01 -1.0 4.18e+01 - 1.55e-01 1.62e-02h 1
4 2.4426939e+04 1.55e+01 3.37e+01 -1.0 4.49e+01 - 5.05e-01 8.84e-02h 1
5 3.5654049e+04 1.29e+01 3.04e+01 -1.0 7.04e+01 - 2.66e-01 1.69e-01H 1
6 4.5981864e+04 1.06e+01 3.76e+01 -1.0 1.07e+02 - 7.81e-01 1.83e-01h 1
7 6.4990845e+04 6.28e+00 2.46e+01 -1.0 1.29e+02 - 7.62e-01 4.05e-01H 1
8 7.2941782e+04 4.45e+00 1.98e+01 -1.0 1.46e+02 - 6.50e-01 2.92e-01h 1
9 7.8657212e+04 3.11e+00 1.34e+01 -1.0 1.50e+02 - 2.32e-01 3.01e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.2721881e+04 2.11e+00 8.87e+00 -1.0 1.56e+02 - 2.37e-01 3.22e-01h 1
11 8.6494258e+04 1.31e+00 8.94e+00 -1.0 1.47e+02 - 8.66e-01 3.79e-01h 1
12 9.1978730e+04 1.81e-01 3.49e+00 -1.0 1.42e+02 - 6.80e-01 8.76e-01h 1
13 9.2499709e+04 1.79e-01 6.09e+00 -1.0 1.18e+02 - 8.21e-01 6.38e-01h 1
14 9.2809697e+04 7.56e-03 5.99e-01 -1.0 8.99e+01 - 1.00e+00 1.00e+00H 1
15 9.2748777e+04 2.86e-03 2.52e+00 -1.7 2.65e+01 - 9.39e-01 7.19e-01h 1
16 9.2718202e+04 4.67e-03 8.70e-02 -1.7 1.35e+01 - 1.00e+00 1.00e+00h 1
17 9.2701386e+04 3.06e-03 1.06e+00 -2.5 1.16e+01 - 8.99e-01 7.12e-01h 1
18 9.2698201e+04 1.55e-03 2.09e+00 -2.5 5.16e+00 - 1.00e+00 5.76e-01h 1
19 9.2696531e+04 5.10e-04 3.57e-03 -2.5 2.53e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 9.2694394e+04 2.74e-04 1.07e+00 -3.8 2.72e+00 - 9.84e-01 7.42e-01h 1
21 9.2693778e+04 1.15e-04 6.40e-04 -3.8 1.13e+00 - 1.00e+00 1.00e+00h 1
22 9.2693813e+04 4.96e-06 2.97e-05 -3.8 9.20e-02 - 1.00e+00 1.00e+00h 1
23 9.2693672e+04 7.42e-06 1.07e-02 -5.7 2.34e-01 - 9.98e-01 9.80e-01h 1
24 9.2693671e+04 8.19e-07 9.01e-06 -5.7 1.39e-02 - 1.00e+00 1.00e+00h 1
25 9.2693670e+04 1.48e-07 8.95e-04 -8.6 3.09e-03 - 1.00e+00 9.48e-01h 1
26 9.2693670e+04 5.13e-09 5.45e-08 -8.6 3.72e-04 - 1.00e+00 1.00e+00f 1
27 9.2693670e+04 6.00e-10 3.72e-11 -8.6 2.53e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 27
(scaled) (unscaled)
Objective...............: 3.0897889867007520e+03 9.2693669601022557e+04
Dual infeasibility......: 3.7179050873715082e-11 1.1153715262114524e-09
Constraint violation....: 6.0020255432391423e-10 6.0020255432391423e-10
Variable bound violation: 7.6218864109023343e-08 7.6218864109023343e-08
Complementarity.........: 2.5423138023358464e-09 7.6269414070075392e-08
Overall NLP error.......: 2.5423138023358464e-09 7.6269414070075392e-08
Number of objective function evaluations = 35
Number of objective gradient evaluations = 28
Number of equality constraint evaluations = 35
Number of inequality constraint evaluations = 35
Number of equality constraint Jacobian evaluations = 28
Number of inequality constraint Jacobian evaluations = 28
Number of Lagrangian Hessian evaluations = 27
Total seconds in IPOPT = 38.884
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 2170
Number of nonzeros in inequality constraint Jacobian.: 704
Number of nonzeros in Lagrangian Hessian.............: 3460
Total number of variables............................: 517
variables with only lower bounds: 0
variables with lower and upper bounds: 457
variables with only upper bounds: 0
Total number of equality constraints.................: 473
Total number of inequality constraints...............: 352
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 352
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.8499996e+03 2.00e+01 1.41e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.3245588e+04 1.86e+01 3.73e+01 -1.0 7.57e+00 - 2.13e-03 7.03e-02h 3
2 1.7309771e+04 1.73e+01 3.47e+01 -1.0 1.53e+01 - 3.49e-02 6.78e-02h 1
3 1.8342944e+04 1.71e+01 3.43e+01 -1.0 4.18e+01 - 1.55e-01 1.62e-02h 1
4 2.4426939e+04 1.55e+01 3.37e+01 -1.0 4.49e+01 - 5.05e-01 8.84e-02h 1
5 3.5654049e+04 1.29e+01 3.04e+01 -1.0 7.04e+01 - 2.66e-01 1.69e-01H 1
6 4.5981864e+04 1.06e+01 3.76e+01 -1.0 1.07e+02 - 7.81e-01 1.83e-01h 1
7 6.4990845e+04 6.28e+00 2.46e+01 -1.0 1.29e+02 - 7.62e-01 4.05e-01H 1
8 7.2941782e+04 4.45e+00 1.98e+01 -1.0 1.46e+02 - 6.50e-01 2.92e-01h 1
9 7.8657212e+04 3.11e+00 1.34e+01 -1.0 1.50e+02 - 2.32e-01 3.01e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.2721881e+04 2.11e+00 8.87e+00 -1.0 1.56e+02 - 2.37e-01 3.22e-01h 1
11 8.6494258e+04 1.31e+00 8.94e+00 -1.0 1.47e+02 - 8.66e-01 3.79e-01h 1
12 9.1978730e+04 1.81e-01 3.49e+00 -1.0 1.42e+02 - 6.80e-01 8.76e-01h 1
13 9.2499709e+04 1.79e-01 6.09e+00 -1.0 1.18e+02 - 8.21e-01 6.38e-01h 1
14 9.2809697e+04 7.56e-03 5.99e-01 -1.0 8.99e+01 - 1.00e+00 1.00e+00H 1
15 9.2748777e+04 2.86e-03 2.52e+00 -1.7 2.65e+01 - 9.39e-01 7.19e-01h 1
16 9.2718202e+04 4.67e-03 8.70e-02 -1.7 1.35e+01 - 1.00e+00 1.00e+00h 1
17 9.2701386e+04 3.06e-03 1.06e+00 -2.5 1.16e+01 - 8.99e-01 7.12e-01h 1
18 9.2698201e+04 1.55e-03 2.09e+00 -2.5 5.16e+00 - 1.00e+00 5.76e-01h 1
19 9.2696531e+04 5.10e-04 3.57e-03 -2.5 2.53e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 9.2694394e+04 2.74e-04 1.07e+00 -3.8 2.72e+00 - 9.84e-01 7.42e-01h 1
21 9.2693778e+04 1.15e-04 6.40e-04 -3.8 1.13e+00 - 1.00e+00 1.00e+00h 1
22 9.2693813e+04 4.96e-06 2.97e-05 -3.8 9.20e-02 - 1.00e+00 1.00e+00h 1
23 9.2693672e+04 7.42e-06 1.07e-02 -5.7 2.34e-01 - 9.98e-01 9.80e-01h 1
24 9.2693671e+04 8.19e-07 9.01e-06 -5.7 1.39e-02 - 1.00e+00 1.00e+00h 1
25 9.2693670e+04 1.48e-07 8.95e-04 -8.6 3.09e-03 - 1.00e+00 9.48e-01h 1
26 9.2693670e+04 5.13e-09 5.45e-08 -8.6 3.72e-04 - 1.00e+00 1.00e+00f 1
27 9.2693670e+04 6.00e-10 3.72e-11 -8.6 2.53e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 27
(scaled) (unscaled)
Objective...............: 3.0897889867007520e+03 9.2693669601022557e+04
Dual infeasibility......: 3.7179050873715082e-11 1.1153715262114524e-09
Constraint violation....: 6.0020255432391423e-10 6.0020255432391423e-10
Variable bound violation: 7.6218864109023343e-08 7.6218864109023343e-08
Complementarity.........: 2.5423138023358464e-09 7.6269414070075392e-08
Overall NLP error.......: 2.5423138023358464e-09 7.6269414070075392e-08
Number of objective function evaluations = 35
Number of objective gradient evaluations = 28
Number of equality constraint evaluations = 35
Number of inequality constraint evaluations = 35
Number of equality constraint Jacobian evaluations = 28
Number of inequality constraint Jacobian evaluations = 28
Number of Lagrangian Hessian evaluations = 27
Total seconds in IPOPT = 0.165
EXIT: Optimal Solution Found.
file = "/tmp/jl_XPUwWW/pglib_opf_case6468_rte.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case6470_rte.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case6495_rte.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case6515_rte.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case7336_epigrids.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case73_ieee_rts.m"
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 3079
Number of nonzeros in inequality constraint Jacobian.: 960
Number of nonzeros in Lagrangian Hessian.............: 4867
Total number of variables............................: 821
variables with only lower bounds: 0
variables with lower and upper bounds: 748
variables with only upper bounds: 0
Total number of equality constraints.................: 627
Total number of inequality constraints...............: 480
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 480
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.2029395e+05 2.52e+00 4.58e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.6840177e+05 1.57e+00 6.28e+01 -1.0 2.15e+00 - 9.96e-03 3.79e-01h 1
2 1.5863973e+05 1.38e+00 5.55e+01 -1.0 5.37e+00 - 1.33e-02 1.21e-01f 1
3 1.5548642e+05 1.36e+00 5.17e+01 -1.0 7.22e+00 - 3.52e-01 1.30e-02f 1
4 1.5695662e+05 1.11e+00 4.06e+01 -1.0 6.22e+00 - 7.22e-01 1.83e-01h 1
5 1.5887394e+05 6.95e-01 2.54e+01 -1.0 7.76e+00 - 3.89e-01 3.74e-01h 1
6 1.5957285e+05 4.99e-01 2.10e+01 -1.0 9.38e+00 - 1.00e+00 2.82e-01h 1
7 1.6281289e+05 3.69e-01 1.55e+01 -1.0 6.50e+00 - 3.44e-01 2.60e-01h 1
8 1.6421420e+05 3.25e-01 1.22e+01 -1.0 5.49e+00 - 1.00e+00 1.19e-01h 1
9 1.6632915e+05 2.66e-01 7.11e+00 -1.0 3.63e+00 - 5.01e-01 1.83e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7504296e+05 1.51e-01 1.06e+01 -1.0 2.44e+00 - 6.58e-02 4.32e-01h 1
11 1.8861399e+05 6.24e-02 1.08e+01 -1.0 2.53e+00 - 2.74e-01 8.66e-01h 1
12 1.9092060e+05 2.17e-02 3.46e+00 -1.0 1.11e+00 - 7.53e-01 1.00e+00h 1
13 1.9108513e+05 3.84e-03 3.77e-01 -1.0 8.49e-01 - 1.00e+00 1.00e+00h 1
14 1.9022312e+05 1.55e-03 4.99e-01 -1.7 2.53e-01 - 8.65e-01 1.00e+00f 1
15 1.9008605e+05 1.06e-03 1.15e-02 -1.7 1.07e-01 - 1.00e+00 1.00e+00f 1
16 1.8987249e+05 8.00e-04 3.18e-01 -3.8 1.55e-01 - 8.03e-01 6.68e-01f 1
17 1.8982422e+05 5.39e-04 1.68e+00 -3.8 1.76e-01 - 8.21e-01 3.99e-01f 1
18 1.8979502e+05 3.18e-04 2.71e+00 -3.8 2.56e-01 - 8.80e-01 4.71e-01h 1
19 1.8976750e+05 9.31e-05 3.39e-01 -3.8 1.97e-01 - 1.00e+00 9.20e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.8976634e+05 3.16e-06 4.97e-05 -3.8 1.13e-02 - 1.00e+00 1.00e+00h 1
21 1.8976428e+05 8.44e-07 4.71e-02 -5.7 1.03e-02 - 9.81e-01 9.21e-01h 1
22 1.8976410e+05 4.22e-08 9.88e-07 -5.7 9.91e-04 - 1.00e+00 1.00e+00h 1
23 1.8976408e+05 3.98e-10 4.49e-07 -8.6 1.35e-04 - 1.00e+00 1.00e+00h 1
24 1.8976408e+05 2.84e-14 1.42e-12 -8.6 6.22e-05 - 1.00e+00 1.00e+00f 1
Number of Iterations....: 24
(scaled) (unscaled)
Objective...............: 1.4597236705182570e+03 1.8976407716737341e+05
Dual infeasibility......: 1.4208530936394057e-12 1.8471090217312274e-10
Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14
Variable bound violation: 3.9923579997491743e-08 3.9923579997491743e-08
Complementarity.........: 2.5067203665655705e-09 3.2587364765352413e-07
Overall NLP error.......: 2.5067203665655705e-09 3.2587364765352413e-07
Number of objective function evaluations = 25
Number of objective gradient evaluations = 25
Number of equality constraint evaluations = 25
Number of inequality constraint evaluations = 25
Number of equality constraint Jacobian evaluations = 25
Number of inequality constraint Jacobian evaluations = 25
Number of Lagrangian Hessian evaluations = 24
Total seconds in IPOPT = 94.252
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.
Number of nonzeros in equality constraint Jacobian...: 3079
Number of nonzeros in inequality constraint Jacobian.: 960
Number of nonzeros in Lagrangian Hessian.............: 4867
Total number of variables............................: 821
variables with only lower bounds: 0
variables with lower and upper bounds: 748
variables with only upper bounds: 0
Total number of equality constraints.................: 627
Total number of inequality constraints...............: 480
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 480
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.2029395e+05 2.52e+00 4.58e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.6840177e+05 1.57e+00 6.28e+01 -1.0 2.15e+00 - 9.96e-03 3.79e-01h 1
2 1.5863973e+05 1.38e+00 5.55e+01 -1.0 5.37e+00 - 1.33e-02 1.21e-01f 1
3 1.5548642e+05 1.36e+00 5.17e+01 -1.0 7.22e+00 - 3.52e-01 1.30e-02f 1
4 1.5695662e+05 1.11e+00 4.06e+01 -1.0 6.22e+00 - 7.22e-01 1.83e-01h 1
5 1.5887394e+05 6.95e-01 2.54e+01 -1.0 7.76e+00 - 3.89e-01 3.74e-01h 1
6 1.5957285e+05 4.99e-01 2.10e+01 -1.0 9.38e+00 - 1.00e+00 2.82e-01h 1
7 1.6281289e+05 3.69e-01 1.55e+01 -1.0 6.50e+00 - 3.44e-01 2.60e-01h 1
8 1.6421420e+05 3.25e-01 1.22e+01 -1.0 5.49e+00 - 1.00e+00 1.19e-01h 1
9 1.6632915e+05 2.66e-01 7.11e+00 -1.0 3.63e+00 - 5.01e-01 1.83e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7504296e+05 1.51e-01 1.06e+01 -1.0 2.44e+00 - 6.58e-02 4.32e-01h 1
11 1.8861399e+05 6.24e-02 1.08e+01 -1.0 2.53e+00 - 2.74e-01 8.66e-01h 1
12 1.9092060e+05 2.17e-02 3.46e+00 -1.0 1.11e+00 - 7.53e-01 1.00e+00h 1
13 1.9108513e+05 3.84e-03 3.77e-01 -1.0 8.49e-01 - 1.00e+00 1.00e+00h 1
14 1.9022312e+05 1.55e-03 4.99e-01 -1.7 2.53e-01 - 8.65e-01 1.00e+00f 1
15 1.9008605e+05 1.06e-03 1.15e-02 -1.7 1.07e-01 - 1.00e+00 1.00e+00f 1
16 1.8987249e+05 8.00e-04 3.18e-01 -3.8 1.55e-01 - 8.03e-01 6.68e-01f 1
17 1.8982422e+05 5.39e-04 1.68e+00 -3.8 1.76e-01 - 8.21e-01 3.99e-01f 1
18 1.8979502e+05 3.18e-04 2.71e+00 -3.8 2.56e-01 - 8.80e-01 4.71e-01h 1
19 1.8976750e+05 9.31e-05 3.39e-01 -3.8 1.97e-01 - 1.00e+00 9.20e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.8976634e+05 3.16e-06 4.97e-05 -3.8 1.13e-02 - 1.00e+00 1.00e+00h 1
21 1.8976428e+05 8.44e-07 4.71e-02 -5.7 1.03e-02 - 9.81e-01 9.21e-01h 1
22 1.8976410e+05 4.22e-08 9.88e-07 -5.7 9.91e-04 - 1.00e+00 1.00e+00h 1
23 1.8976408e+05 3.98e-10 4.49e-07 -8.6 1.35e-04 - 1.00e+00 1.00e+00h 1
24 1.8976408e+05 2.84e-14 1.42e-12 -8.6 6.22e-05 - 1.00e+00 1.00e+00f 1
Number of Iterations....: 24
(scaled) (unscaled)
Objective...............: 1.4597236705182570e+03 1.8976407716737341e+05
Dual infeasibility......: 1.4208530936394057e-12 1.8471090217312274e-10
Constraint violation....: 2.8421709430404007e-14 2.8421709430404007e-14
Variable bound violation: 3.9923579997491743e-08 3.9923579997491743e-08
Complementarity.........: 2.5067203665655705e-09 3.2587364765352413e-07
Overall NLP error.......: 2.5067203665655705e-09 3.2587364765352413e-07
Number of objective function evaluations = 25
Number of objective gradient evaluations = 25
Number of equality constraint evaluations = 25
Number of inequality constraint evaluations = 25
Number of equality constraint Jacobian evaluations = 25
Number of inequality constraint Jacobian evaluations = 25
Number of Lagrangian Hessian evaluations = 24
Total seconds in IPOPT = 0.233
EXIT: Optimal Solution Found.
file = "/tmp/jl_XPUwWW/pglib_opf_case78484_epigrids.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case793_goc.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case8387_pegase.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case89_pegase.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case9241_pegase.m"
file = "/tmp/jl_XPUwWW/pglib_opf_case9591_goc.m"
10×23 DataFrame
Row │ case vars cons optimization optimization_m ⋯
│ String Int64 Int64 Float64 Float64 ⋯
─────┼──────────────────────────────────────────────────────────────────────────
1 │ pglib_opf_case14_ieee.m 118 169 5.74188 0. ⋯
2 │ pglib_opf_case24_ieee_rts.m 266 315 48.4005 0.
3 │ pglib_opf_case30_as.m 236 348 28.6904 0.
4 │ pglib_opf_case30_ieee.m 236 348 43.4783 0.
5 │ pglib_opf_case39_epri.m 282 401 96.5703 0. ⋯
6 │ pglib_opf_case3_lmbd.m 24 28 0.0779732 4.
7 │ pglib_opf_case57_ieee.m 448 675 156.559 0.
8 │ pglib_opf_case5_pjm.m 44 53 0.427148 5.
9 │ pglib_opf_case60_c.m 518 737 159.878 0. ⋯
10 │ pglib_opf_case73_ieee_rts.m 824 987 326.898 0.
19 columns omittedio = IOBuffer()
println(io, "```@raw html")
pretty_table(io, timing_data; backend = Val(:html))
# show(io, "text/html", pretty_table(timing_data; backend = Val(:html)))
println(io, "```")
Text(String(take!(io)))| case | vars | cons | optimization | optimization_modelbuild | optimization_wcompilation | optimization_cost | mtk | mtk_time_modelbuild | mtk_time_wcompilation | mtk_cost | jump | jump_modelbuild | jump_wcompilation | jump_cost | nlpmodels | nlpmodels_modelbuild | nlpmodels_wcompilation | nlpmodels_cost | optim | optim_modelbuild | optim_wcompilation | optim_cost |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| String | Int64 | Int64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 |
| pglib_opf_case14_ieee.m | 118 | 169 | 5.74188 | 0.000104979 | 5.63988 | 2178.08 | 0.07549 | 1.64429 | 5.79364 | 2178.08 | 0.015382 | 0.244083 | 0.0488776 | 2178.08 | 0.0784789 | 0.13727 | 0.0779019 | 2178.08 | 126.724 | 0.0651057 | 125.78 | 1658.7 |
| pglib_opf_case24_ieee_rts.m | 266 | 315 | 48.4005 | 0.000140389 | 47.9654 | 63352.2 | 0.183388 | 3.8984 | 12.9557 | 63352.2 | 0.0302247 | 0.030845 | 0.0311659 | 63352.2 | 0.216706 | 0.160922 | 0.205827 | 63352.2 | 384.308 | 0.159995 | 385.39 | 63741.2 |
| pglib_opf_case30_as.m | 236 | 348 | 28.6904 | 0.000171728 | 27.4957 | 803.127 | 0.172819 | 3.34265 | 12.8841 | 803.127 | 0.0210139 | 0.00510361 | 0.0219404 | 803.127 | 0.139637 | 0.173657 | 0.140331 | 803.127 | 294.567 | 0.0401112 | 293.27 | 772.093 |
| pglib_opf_case30_ieee.m | 236 | 348 | 43.4783 | 0.000190889 | 43.1774 | 8208.52 | 0.187162 | 3.27919 | 13.1114 | 8208.52 | 0.0299482 | 0.00495531 | 0.0307215 | 8208.52 | 0.212489 | 0.304633 | 0.214642 | 8208.52 | 293.066 | 0.0445731 | 292.631 | 4244.05 |
| pglib_opf_case39_epri.m | 282 | 401 | 96.5703 | 0.000178499 | 93.867 | 1.38416e5 | 0.25493 | 4.23422 | 16.4849 | 1.38416e5 | 0.0507258 | 0.00515369 | 0.0513258 | 1.38416e5 | 0.310656 | 0.188602 | 0.288098 | 1.38416e5 | 363.644 | 0.0661765 | 363.456 | 78346.0 |
| pglib_opf_case3_lmbd.m | 24 | 28 | 0.0779732 | 4.618e-5 | 0.105845 | 5812.64 | 0.0178909 | 0.175923 | 0.0195717 | 5812.64 | 0.0091549 | 0.00478162 | 0.00972831 | 5812.64 | 0.0186829 | 0.0160607 | 0.0197536 | 5812.64 | 2.29765 | 0.000448636 | 2.29684 | 6273.63 |
| pglib_opf_case57_ieee.m | 448 | 675 | 156.559 | 0.000274488 | 152.2 | 37555.3 | 0.42469 | 12.1244 | 37.0462 | 37589.3 | 0.0442181 | 0.00775402 | 0.0450932 | 37589.3 | 0.306254 | 0.477642 | 0.30959 | 37589.3 | NaN | NaN | NaN | NaN |
| pglib_opf_case5_pjm.m | 44 | 53 | 0.427148 | 5.5219e-5 | 0.371297 | 17551.9 | 0.124084 | 0.359544 | 0.0360806 | 17551.9 | 0.01432 | 0.0046479 | 0.014778 | 17551.9 | 0.0367502 | 0.0267777 | 0.0378028 | 17551.9 | 19.1386 | 0.000638655 | 18.8175 | 77.9548 |
| pglib_opf_case60_c.m | 518 | 737 | 159.878 | 0.000257608 | 139.885 | 35648.5 | 0.560815 | 14.2151 | 40.6548 | 92693.7 | 0.0796966 | 0.0286964 | 0.0813441 | 92693.7 | 0.649169 | 0.484268 | 0.575294 | 92693.7 | NaN | NaN | NaN | NaN |
| pglib_opf_case73_ieee_rts.m | 824 | 987 | 326.898 | 0.000343018 | 319.089 | 1.5864e5 | 1.02645 | 27.3773 | 96.5115 | 1.89764e5 | 0.0997913 | 0.0136383 | 0.102478 | 1.89764e5 | 0.944354 | 0.851503 | 0.81381 | 1.89764e5 | NaN | NaN | NaN | NaN |
Appendix
Error: ArgumentError: Package SciMLBenchmarks not found in current path, ma
ybe you meant `import/using ..SciMLBenchmarks`.
- Otherwise, run `import Pkg; Pkg.add("SciMLBenchmarks")` to install the Sc
iMLBenchmarks package.