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Add Optim via JuMP to Debugging Models (#61)
* add jump model with optim solver for debugging * update variants manifest
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| time_start = time() | ||
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| using PowerModels | ||
| using JuMP | ||
| using Optim | ||
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| pkg_load_time = time() - time_start | ||
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| time_start = time() | ||
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| file_name = "../data/pglib_opf_case5_pjm.m" | ||
| #file_name = "../data/pglib_opf_case118_ieee.m" | ||
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| 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] | ||
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| data_load_time = time() - time_start | ||
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| time_start = time() | ||
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| model = JuMP.Model() | ||
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| @variable(model, va[i in keys(ref[:bus])]) | ||
| @variable(model, ref[:bus][i]["vmin"] <= vm[i in keys(ref[:bus])] <= ref[:bus][i]["vmax"], start=1.0) | ||
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| @variable(model, ref[:gen][i]["pmin"] <= pg[i in keys(ref[:gen])] <= ref[:gen][i]["pmax"], start=(ref[:gen][i]["pmin"] + ref[:gen][i]["pmax"])/2) | ||
| @variable(model, ref[:gen][i]["qmin"] <= qg[i in keys(ref[:gen])] <= ref[:gen][i]["qmax"], start=(ref[:gen][i]["qmin"] + ref[:gen][i]["qmax"])/2) | ||
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| @variable(model, -ref[:branch][l]["rate_a"] <= p[(l,i,j) in ref[:arcs]] <= ref[:branch][l]["rate_a"]) | ||
| @variable(model, -ref[:branch][l]["rate_a"] <= q[(l,i,j) in ref[:arcs]] <= ref[:branch][l]["rate_a"]) | ||
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| @objective(model, Min, sum(gen["cost"][1]*pg[i]^2 + gen["cost"][2]*pg[i] + gen["cost"][3] for (i,gen) in ref[:gen])) | ||
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| for (i,bus) in ref[:ref_buses] | ||
| @constraint(model, va[i] == 0) | ||
| end | ||
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| 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]] | ||
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| @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 | ||
| ) | ||
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| @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 | ||
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| # 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"]) | ||
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| p_fr = p[f_idx] # p_fr is a reference to the optimization variable p[f_idx] | ||
| q_fr = q[f_idx] # q_fr is a reference to the optimization variable q[f_idx] | ||
| p_to = p[t_idx] # p_to is a reference to the optimization variable p[t_idx] | ||
| q_to = q[t_idx] # q_to is a reference to the optimization variable q[t_idx] | ||
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| vm_fr = vm[branch["f_bus"]] # vm_fr is a reference to the optimization variable vm on the from side of the branch | ||
| vm_to = vm[branch["t_bus"]] # vm_to is a reference to the optimization variable vm on the to side of the branch | ||
| va_fr = va[branch["f_bus"]] # va_fr is a reference to the optimization variable va on the from side of the branch | ||
| va_to = va[branch["t_bus"]] # va_fr is a reference to the optimization variable va on the to side of the branch | ||
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| g, b = PowerModels.calc_branch_y(branch) | ||
| tr, ti = PowerModels.calc_branch_t(branch) | ||
| tm = tr^2 + ti^2 | ||
| g_fr = branch["g_fr"] | ||
| b_fr = branch["b_fr"] | ||
| g_to = branch["g_to"] | ||
| b_to = branch["b_to"] | ||
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| # From side of the branch flow | ||
| @constraint(model, p_fr == (g+g_fr)/tm*vm_fr^2 + (-g*tr+b*ti)/tm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-b*tr-g*ti)/tm*(vm_fr*vm_to*sin(va_fr-va_to)) ) | ||
| @constraint(model, q_fr == -(b+b_fr)/tm*vm_fr^2 - (-b*tr-g*ti)/tm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-g*tr+b*ti)/tm*(vm_fr*vm_to*sin(va_fr-va_to)) ) | ||
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| # To side of the branch flow | ||
| @constraint(model, p_to == (g+g_to)*vm_to^2 + (-g*tr-b*ti)/tm*(vm_to*vm_fr*cos(va_to-va_fr)) + (-b*tr+g*ti)/tm*(vm_to*vm_fr*sin(va_to-va_fr)) ) | ||
| @constraint(model, q_to == -(b+b_to)*vm_to^2 - (-b*tr+g*ti)/tm*(vm_to*vm_fr*cos(va_fr-va_to)) + (-g*tr-b*ti)/tm*(vm_to*vm_fr*sin(va_to-va_fr)) ) | ||
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| # Voltage angle difference limit | ||
| @constraint(model, branch["angmin"] <= va_fr - va_to <= branch["angmax"]) | ||
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| # Apparent power limit, from side and to side | ||
| @constraint(model, p_fr^2 + q_fr^2 <= branch["rate_a"]^2) | ||
| @constraint(model, p_to^2 + q_to^2 <= branch["rate_a"]^2) | ||
| end | ||
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| set_optimizer(model, Optim.Optimizer) | ||
| set_optimizer_attribute(model, "method", IPNewton()) | ||
| set_optimizer_attribute(model, "show_trace", true) | ||
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| model_build_time = time() - time_start | ||
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| time_start = time() | ||
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| JuMP.optimize!(model) | ||
| cost = JuMP.objective_value(model) | ||
| feasible = JuMP.termination_status(model) | ||
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| solve_time = time() - time_start | ||
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| println("") | ||
| println("\033[1mSummary\033[0m") | ||
| println(" case..........: $(file_name)") | ||
| println(" cost..........: $(round(Int, cost))") | ||
| println(" feasible......: $(feasible)") | ||
| println(" primal status.: $(JuMP.primal_status(model))") | ||
| println(" dual status...: $(JuMP.dual_status(model))") | ||
| println(" pkg time......: $(pkg_load_time)") | ||
| println(" data time.....: $(data_load_time)") | ||
| println(" build time....: $(model_build_time)") | ||
| println(" solve time....: $(solve_time)") | ||
| println("") | ||
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