Description of ktropf

0001 function [results, success, raw] = ktropf_solver(om, mpopt)
0002 %KTROPF_SOLVER  Solves AC optimal power flow using KNITRO.
0003 %
0004 %   [RESULTS, SUCCESS, RAW] = KTROPF_SOLVER(OM, MPOPT)
0005 %
0006 %   Inputs are an OPF model object and a MATPOWER options struct.
0007 %
0008 %   Outputs are a RESULTS struct, SUCCESS flag and RAW output struct.
0009 %
0010 %   RESULTS is a MATPOWER case struct (mpc) with the usual baseMVA, bus
0011 %   branch, gen, gencost fields, along with the following additional
0012 %   fields:
0013 %       .order      see 'help ext2int' for details of this field
0014 %       .x          final value of optimization variables (internal order)
0015 %       .f          final objective function value
0016 %       .mu         shadow prices on ...
0017 %           .var
0018 %               .l  lower bounds on variables
0019 %               .u  upper bounds on variables
0020 %           .nln
0021 %               .l  lower bounds on nonlinear constraints
0022 %               .u  upper bounds on nonlinear constraints
0023 %           .lin
0024 %               .l  lower bounds on linear constraints
0025 %               .u  upper bounds on linear constraints
0026 %
0027 %   SUCCESS     1 if solver converged successfully, 0 otherwise
0028 %
0029 %   RAW         raw output in form returned by MINOS
0030 %       .xr     final value of optimization variables
0031 %       .pimul  constraint multipliers
0032 %       .info   solver specific termination code
0033 %       .output solver specific output information
0034 %
0035 %   See also OPF, KTRLINK.
0036 
0037 %   MATPOWER
0038 %   Copyright (c) 2000-2015 by Power System Engineering Research Center (PSERC)
0039 %   by Ray Zimmerman, PSERC Cornell
0040 %   and Carlos E. Murillo-Sanchez, PSERC Cornell & Universidad Autonoma de Manizales
0041 %
0042 %   $Id: ktropf_solver.m 2644 2015-03-11 19:34:22Z ray $
0043 %
0044 %   This file is part of MATPOWER.
0045 %   Covered by the 3-clause BSD License (see LICENSE file for details).
0046 %   See http://www.pserc.cornell.edu/matpower/ for more info.
0047 
0048 %%----- initialization -----
0049 %% define named indices into data matrices
0050 [PQ, PV, REF, NONE, BUS_I, BUS_TYPE, PD, QD, GS, BS, BUS_AREA, VM, ...
0051     VA, BASE_KV, ZONE, VMAX, VMIN, LAM_P, LAM_Q, MU_VMAX, MU_VMIN] = idx_bus;
0052 [GEN_BUS, PG, QG, QMAX, QMIN, VG, MBASE, GEN_STATUS, PMAX, PMIN, ...
0053     MU_PMAX, MU_PMIN, MU_QMAX, MU_QMIN, PC1, PC2, QC1MIN, QC1MAX, ...
0054     QC2MIN, QC2MAX, RAMP_AGC, RAMP_10, RAMP_30, RAMP_Q, APF] = idx_gen;
0055 [F_BUS, T_BUS, BR_R, BR_X, BR_B, RATE_A, RATE_B, RATE_C, ...
0056     TAP, SHIFT, BR_STATUS, PF, QF, PT, QT, MU_SF, MU_ST, ...
0057     ANGMIN, ANGMAX, MU_ANGMIN, MU_ANGMAX] = idx_brch;
0058 [PW_LINEAR, POLYNOMIAL, MODEL, STARTUP, SHUTDOWN, NCOST, COST] = idx_cost;
0059 
0060 %% options
0061 use_ktropts_file = 1;       %% use a KNITRO options file to pass options
0062 create_ktropts_file = 0;    %% generate a KNITRO options file on the fly
0063 
0064 %% unpack data
0065 mpc = get_mpc(om);
0066 [baseMVA, bus, gen, branch, gencost] = ...
0067     deal(mpc.baseMVA, mpc.bus, mpc.gen, mpc.branch, mpc.gencost);
0068 [vv, ll, nn] = get_idx(om);
0069 
0070 %% problem dimensions
0071 nb = size(bus, 1);          %% number of buses
0072 ng = size(gen, 1);          %% number of gens
0073 nl = size(branch, 1);       %% number of branches
0074 ny = getN(om, 'var', 'y');  %% number of piece-wise linear costs
0075 
0076 %% bounds on optimization vars
0077 [x0, xmin, xmax] = getv(om);
0078 
0079 %% linear constraints
0080 [A, l, u] = linear_constraints(om);
0081 
0082 %% split l <= A*x <= u into less than, equal to, greater than, and
0083 %% doubly-bounded sets
0084 ieq = find( abs(u-l) <= eps );          %% equality
0085 igt = find( u >=  1e10 & l > -1e10 );   %% greater than, unbounded above
0086 ilt = find( l <= -1e10 & u <  1e10 );   %% less than, unbounded below
0087 ibx = find( (abs(u-l) > eps) & (u < 1e10) & (l > -1e10) );
0088 Af  = [ A(ilt, :); -A(igt, :); A(ibx, :); -A(ibx, :) ];
0089 bf  = [ u(ilt);   -l(igt);     u(ibx);    -l(ibx)];
0090 Afeq = A(ieq, :);
0091 bfeq = u(ieq);
0092 
0093 %% build admittance matrices
0094 [Ybus, Yf, Yt] = makeYbus(baseMVA, bus, branch);
0095 
0096 %% try to select an interior initial point
0097 if mpopt.opf.init_from_mpc ~= 1
0098     ll = xmin; uu = xmax;
0099     ll(xmin == -Inf) = -1e10;   %% replace Inf with numerical proxies
0100     uu(xmax ==  Inf) =  1e10;
0101     x0 = (ll + uu) / 2;         %% set x0 mid-way between bounds
0102     k = find(xmin == -Inf & xmax < Inf);    %% if only bounded above
0103     x0(k) = xmax(k) - 1;                    %% set just below upper bound
0104     k = find(xmin > -Inf & xmax == Inf);    %% if only bounded below
0105     x0(k) = xmin(k) + 1;                    %% set just above lower bound
0106     Varefs = bus(bus(:, BUS_TYPE) == REF, VA) * (pi/180);
0107     x0(vv.i1.Va:vv.iN.Va) = Varefs(1);  %% angles set to first reference angle
0108     if ny > 0
0109         ipwl = find(gencost(:, MODEL) == PW_LINEAR);
0110     %     PQ = [gen(:, PMAX); gen(:, QMAX)];
0111     %     c = totcost(gencost(ipwl, :), PQ(ipwl));
0112         c = gencost(sub2ind(size(gencost), ipwl, NCOST+2*gencost(ipwl, NCOST)));    %% largest y-value in CCV data
0113         x0(vv.i1.y:vv.iN.y) = max(c) + 0.1 * abs(max(c));
0114     %     x0(vv.i1.y:vv.iN.y) = c + 0.1 * abs(c);
0115     end
0116 end
0117 
0118 %% find branches with flow limits
0119 il = find(branch(:, RATE_A) ~= 0 & branch(:, RATE_A) < 1e10);
0120 nl2 = length(il);           %% number of constrained lines
0121 
0122 %% build Jacobian and Hessian structure
0123 nA = size(A, 1);                %% number of original linear constraints
0124 nx = length(x0);
0125 f = branch(:, F_BUS);                           %% list of "from" buses
0126 t = branch(:, T_BUS);                           %% list of "to" buses
0127 Cf = sparse(1:nl, f, ones(nl, 1), nl, nb);      %% connection matrix for line & from buses
0128 Ct = sparse(1:nl, t, ones(nl, 1), nl, nb);      %% connection matrix for line & to buses
0129 Cl = Cf + Ct;
0130 Cb = Cl' * Cl + speye(nb);
0131 Cl2 = Cl(il, :);
0132 Cg = sparse(gen(:, GEN_BUS), (1:ng)', 1, nb, ng);
0133 nz = nx - 2*(nb+ng);
0134 nxtra = nx - 2*nb;
0135 Js = [
0136     Cl2     Cl2     sparse(nl2, 2*ng)               sparse(nl2,nz);
0137     Cl2     Cl2     sparse(nl2, 2*ng)               sparse(nl2,nz);
0138     Cb      Cb      Cg              sparse(nb,ng)   sparse(nb,nz);
0139     Cb      Cb      sparse(nb,ng)   Cg              sparse(nb,nz);
0140 ];
0141 [f, df, d2f] = opf_costfcn(x0, om);
0142 Hs = d2f + [
0143     Cb  Cb  sparse(nb,nxtra);
0144     Cb  Cb  sparse(nb,nxtra);
0145     sparse(nxtra,nx);
0146 ];
0147 
0148 %% basic optimset options needed for ktrlink
0149 hess_fcn = @(x, lambda)opf_hessfcn(x, lambda, 1, om, Ybus, Yf(il,:), Yt(il,:), mpopt, il);
0150 fmoptions = optimset('GradObj', 'on', 'GradConstr', 'on', ...
0151                 'Hessian', 'user-supplied', 'HessFcn', hess_fcn, ...
0152                 'JacobPattern', Js, 'HessPattern', Hs );
0153 if use_ktropts_file
0154     if ~isempty(mpopt.knitro.opt_fname)
0155         opt_fname = mpopt.knitro.opt_fname;
0156     elseif mpopt.knitro.opt
0157         opt_fname = sprintf('knitro_user_options_%d.txt', mpopt.knitro.opt);
0158     else
0159         %% create ktropts file
0160         ktropts.algorithm           = 1;
0161         ktropts.outlev              = mpopt.verbose;
0162         ktropts.feastol             = mpopt.opf.violation;
0163         ktropts.xtol                = mpopt.knitro.tol_x;
0164         ktropts.opttol              = mpopt.knitro.tol_f;
0165         if mpopt.fmincon.max_it ~= 0
0166             ktropts.maxit           = mpopt.fmincon.max_it;
0167         end
0168         ktropts.bar_directinterval  = 0;
0169         opt_fname = write_ktropts(ktropts);
0170         create_ktropts_file = 1;    %% make a note that I created it
0171     end
0172 else
0173     fmoptions = optimset(fmoptions, 'Algorithm', 'interior-point', ...
0174         'TolCon', mpopt.opf.violation, 'TolX', mpopt.knitro.tol_x, ...
0175         'TolFun', mpopt.knitro.tol_f );
0176     if mpopt.fmincon.max_it ~= 0
0177         fmoptions = optimset(fmoptions, 'MaxIter', mpopt.fmincon.max_it, ...
0178                     'MaxFunEvals', 4 * mpopt.fmincon.max_it);
0179     end
0180     if mpopt.verbose == 0,
0181       fmoptions.Display = 'off';
0182     elseif mpopt.verbose == 1
0183       fmoptions.Display = 'iter';
0184     else
0185       fmoptions.Display = 'testing';
0186     end
0187     opt_fname = [];
0188 end
0189 
0190 %%-----  run opf  -----
0191 f_fcn = @(x)opf_costfcn(x, om);
0192 gh_fcn = @(x)opf_consfcn(x, om, Ybus, Yf(il,:), Yt(il,:), mpopt, il);
0193 if have_fcn('knitromatlab')
0194     [x, f, info, Output, Lambda] = knitromatlab(f_fcn, x0, Af, bf, Afeq, bfeq, ...
0195                                     xmin, xmax, gh_fcn, [], fmoptions, opt_fname);
0196 else
0197     [x, f, info, Output, Lambda] = ktrlink(f_fcn, x0, Af, bf, Afeq, bfeq, ...
0198                                     xmin, xmax, gh_fcn, fmoptions, opt_fname);
0199 end
0200 success = (info == 0);
0201 
0202 %% delete ktropts file
0203 if create_ktropts_file  %% ... but only if I created it
0204     delete(opt_fname);
0205 end
0206 
0207 %% update solution data
0208 Va = x(vv.i1.Va:vv.iN.Va);
0209 Vm = x(vv.i1.Vm:vv.iN.Vm);
0210 Pg = x(vv.i1.Pg:vv.iN.Pg);
0211 Qg = x(vv.i1.Qg:vv.iN.Qg);
0212 V = Vm .* exp(1j*Va);
0213 
0214 %%-----  calculate return values  -----
0215 %% update voltages & generator outputs
0216 bus(:, VA) = Va * 180/pi;
0217 bus(:, VM) = Vm;
0218 gen(:, PG) = Pg * baseMVA;
0219 gen(:, QG) = Qg * baseMVA;
0220 gen(:, VG) = Vm(gen(:, GEN_BUS));
0221 
0222 %% compute branch flows
0223 Sf = V(branch(:, F_BUS)) .* conj(Yf * V);  %% cplx pwr at "from" bus, p.u.
0224 St = V(branch(:, T_BUS)) .* conj(Yt * V);  %% cplx pwr at "to" bus, p.u.
0225 branch(:, PF) = real(Sf) * baseMVA;
0226 branch(:, QF) = imag(Sf) * baseMVA;
0227 branch(:, PT) = real(St) * baseMVA;
0228 branch(:, QT) = imag(St) * baseMVA;
0229 
0230 %% line constraint is actually on square of limit
0231 %% so we must fix multipliers
0232 muSf = zeros(nl, 1);
0233 muSt = zeros(nl, 1);
0234 if ~isempty(il)
0235     muSf(il) = 2 * Lambda.ineqnonlin(1:nl2)       .* branch(il, RATE_A) / baseMVA;
0236     muSt(il) = 2 * Lambda.ineqnonlin((1:nl2)+nl2) .* branch(il, RATE_A) / baseMVA;
0237 end
0238 
0239 %% update Lagrange multipliers
0240 bus(:, MU_VMAX)  = Lambda.upper(vv.i1.Vm:vv.iN.Vm);
0241 bus(:, MU_VMIN)  = -Lambda.lower(vv.i1.Vm:vv.iN.Vm);
0242 gen(:, MU_PMAX)  = Lambda.upper(vv.i1.Pg:vv.iN.Pg) / baseMVA;
0243 gen(:, MU_PMIN)  = -Lambda.lower(vv.i1.Pg:vv.iN.Pg) / baseMVA;
0244 gen(:, MU_QMAX)  = Lambda.upper(vv.i1.Qg:vv.iN.Qg) / baseMVA;
0245 gen(:, MU_QMIN)  = -Lambda.lower(vv.i1.Qg:vv.iN.Qg) / baseMVA;
0246 bus(:, LAM_P)    = Lambda.eqnonlin(nn.i1.Pmis:nn.iN.Pmis) / baseMVA;
0247 bus(:, LAM_Q)    = Lambda.eqnonlin(nn.i1.Qmis:nn.iN.Qmis) / baseMVA;
0248 branch(:, MU_SF) = muSf / baseMVA;
0249 branch(:, MU_ST) = muSt / baseMVA;
0250 
0251 %% package up results
0252 nlnN = getN(om, 'nln');
0253 nlt = length(ilt);
0254 ngt = length(igt);
0255 nbx = length(ibx);
0256 
0257 %% extract multipliers for nonlinear constraints
0258 kl = find(Lambda.eqnonlin < 0);
0259 ku = find(Lambda.eqnonlin > 0);
0260 nl_mu_l = zeros(nlnN, 1);
0261 nl_mu_u = [zeros(2*nb, 1); muSf; muSt];
0262 nl_mu_l(kl) = -Lambda.eqnonlin(kl);
0263 nl_mu_u(ku) =  Lambda.eqnonlin(ku);
0264 
0265 %% extract multipliers for linear constraints
0266 kl = find(Lambda.eqlin < 0);
0267 ku = find(Lambda.eqlin > 0);
0268 
0269 mu_l = zeros(size(u));
0270 mu_l(ieq(kl)) = -Lambda.eqlin(kl);
0271 mu_l(igt) = Lambda.ineqlin(nlt+(1:ngt));
0272 mu_l(ibx) = Lambda.ineqlin(nlt+ngt+nbx+(1:nbx));
0273 
0274 mu_u = zeros(size(u));
0275 mu_u(ieq(ku)) = Lambda.eqlin(ku);
0276 mu_u(ilt) = Lambda.ineqlin(1:nlt);
0277 mu_u(ibx) = Lambda.ineqlin(nlt+ngt+(1:nbx));
0278 
0279 mu = struct( ...
0280   'var', struct('l', -Lambda.lower, 'u', Lambda.upper), ...
0281   'nln', struct('l', nl_mu_l, 'u', nl_mu_u), ...
0282   'lin', struct('l', mu_l, 'u', mu_u) );
0283 
0284 results = mpc;
0285 [results.bus, results.branch, results.gen, ...
0286     results.om, results.x, results.mu, results.f] = ...
0287         deal(bus, branch, gen, om, x, mu, f);
0288 
0289 pimul = [ ...
0290   results.mu.nln.l - results.mu.nln.u;
0291   results.mu.lin.l - results.mu.lin.u;
0292   -ones(ny>0, 1);
0293   results.mu.var.l - results.mu.var.u;
0294 ];
0295 raw = struct('xr', x, 'pimul', pimul, 'info', info, 'output', Output);
0296 
0297 
0298 %%-----  write_ktropts  -----
0299 function fname = write_ktropts(ktropts)
0300 
0301 %% generate file name
0302 fname = sprintf('ktropts_%06d.txt', fix(1e6*rand));
0303 
0304 %% open file
0305 [fd, msg] = fopen(fname, 'wt');     %% write options file
0306 if fd == -1
0307     error('could not create %d : %s', fname, msg);
0308 end
0309 
0310 %% write options
0311 fields = fieldnames(ktropts);
0312 for k = 1:length(fields)
0313     fprintf(fd, '%s %g\n', fields{k}, ktropts.(fields{k}));
0314 end
0315 
0316 %% close file
0317 if fd ~= 1
0318     fclose(fd);
0319 end
ktropf_solver

PURPOSE

SYNOPSIS

DESCRIPTION

CROSS-REFERENCE INFORMATION

SUBFUNCTIONS

SOURCE CODE
