Description of mipsopf

0001 function [results, success, raw] = mipsopf_solver(om, mpopt)
0002 %MIPSOPF_SOLVER  Solves AC optimal power flow using MIPS.
0003 %
0004 %   [RESULTS, SUCCESS, RAW] = MIPSOPF_SOLVER(OM, MPOPT)
0005 %
0006 %   Inputs are an OPF model object and a MATPOWER options vector.
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, MIPS.
0036 
0037 %   MATPOWER
0038 %   $Id: mipsopf_solver.m,v 1.13 2010/06/09 14:56:58 ray Exp $
0039 %   by Ray Zimmerman, PSERC Cornell
0040 %   and Carlos E. Murillo-Sanchez, PSERC Cornell & Universidad Autonoma de Manizales
0041 %   Copyright (c) 2000-2010 by Power System Engineering Research Center (PSERC)
0042 %
0043 %   This file is part of MATPOWER.
0044 %   See http://www.pserc.cornell.edu/matpower/ for more info.
0045 %
0046 %   MATPOWER is free software: you can redistribute it and/or modify
0047 %   it under the terms of the GNU General Public License as published
0048 %   by the Free Software Foundation, either version 3 of the License,
0049 %   or (at your option) any later version.
0050 %
0051 %   MATPOWER is distributed in the hope that it will be useful,
0052 %   but WITHOUT ANY WARRANTY; without even the implied warranty of
0053 %   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
0054 %   GNU General Public License for more details.
0055 %
0056 %   You should have received a copy of the GNU General Public License
0057 %   along with MATPOWER. If not, see <http://www.gnu.org/licenses/>.
0058 %
0059 %   Additional permission under GNU GPL version 3 section 7
0060 %
0061 %   If you modify MATPOWER, or any covered work, to interface with
0062 %   other modules (such as MATLAB code and MEX-files) available in a
0063 %   MATLAB(R) or comparable environment containing parts covered
0064 %   under other licensing terms, the licensors of MATPOWER grant
0065 %   you additional permission to convey the resulting work.
0066 
0067 %%----- initialization -----
0068 %% define named indices into data matrices
0069 [PQ, PV, REF, NONE, BUS_I, BUS_TYPE, PD, QD, GS, BS, BUS_AREA, VM, ...
0070     VA, BASE_KV, ZONE, VMAX, VMIN, LAM_P, LAM_Q, MU_VMAX, MU_VMIN] = idx_bus;
0071 [GEN_BUS, PG, QG, QMAX, QMIN, VG, MBASE, GEN_STATUS, PMAX, PMIN, ...
0072     MU_PMAX, MU_PMIN, MU_QMAX, MU_QMIN, PC1, PC2, QC1MIN, QC1MAX, ...
0073     QC2MIN, QC2MAX, RAMP_AGC, RAMP_10, RAMP_30, RAMP_Q, APF] = idx_gen;
0074 [F_BUS, T_BUS, BR_R, BR_X, BR_B, RATE_A, RATE_B, RATE_C, ...
0075     TAP, SHIFT, BR_STATUS, PF, QF, PT, QT, MU_SF, MU_ST, ...
0076     ANGMIN, ANGMAX, MU_ANGMIN, MU_ANGMAX] = idx_brch;
0077 [PW_LINEAR, POLYNOMIAL, MODEL, STARTUP, SHUTDOWN, NCOST, COST] = idx_cost;
0078 
0079 %% options
0080 verbose = mpopt(31);    %% VERBOSE
0081 feastol = mpopt(81);    %% PDIPM_FEASTOL
0082 gradtol = mpopt(82);    %% PDIPM_GRADTOL
0083 comptol = mpopt(83);    %% PDIPM_COMPTOL
0084 costtol = mpopt(84);    %% PDIPM_COSTTOL
0085 max_it  = mpopt(85);    %% PDIPM_MAX_IT
0086 max_red = mpopt(86);    %% SCPDIPM_RED_IT
0087 step_control = (mpopt(11) == 565);  %% OPF_ALG == 565, MIPS-sc
0088 if feastol == 0
0089     feastol = mpopt(16);    %% = OPF_VIOLATION by default
0090 end
0091 opt = struct(   'feastol', feastol, ...
0092                 'gradtol', gradtol, ...
0093                 'comptol', comptol, ...
0094                 'costtol', costtol, ...
0095                 'max_it', max_it, ...
0096                 'max_red', max_red, ...
0097                 'step_control', step_control, ...
0098                 'cost_mult', 1e-4, ...
0099                 'verbose', verbose  );
0100 
0101 %% unpack data
0102 mpc = get_mpc(om);
0103 [baseMVA, bus, gen, branch, gencost] = ...
0104     deal(mpc.baseMVA, mpc.bus, mpc.gen, mpc.branch, mpc.gencost);
0105 [vv, ll, nn] = get_idx(om);
0106 
0107 %% problem dimensions
0108 nb = size(bus, 1);          %% number of buses
0109 nl = size(branch, 1);       %% number of branches
0110 ny = getN(om, 'var', 'y');  %% number of piece-wise linear costs
0111 
0112 %% linear constraints
0113 [A, l, u] = linear_constraints(om);
0114 
0115 %% bounds on optimization vars
0116 [x0, xmin, xmax] = getv(om);
0117 
0118 %% build admittance matrices
0119 [Ybus, Yf, Yt] = makeYbus(baseMVA, bus, branch);
0120 
0121 %% try to select an interior initial point
0122 ll = xmin; uu = xmax;
0123 ll(xmin == -Inf) = -1e10;   %% replace Inf with numerical proxies
0124 uu(xmax ==  Inf) =  1e10;
0125 x0 = (ll + uu) / 2;
0126 Varefs = bus(bus(:, BUS_TYPE) == REF, VA) * (pi/180);
0127 x0(vv.i1.Va:vv.iN.Va) = Varefs(1);  %% angles set to first reference angle
0128 if ny > 0
0129     ipwl = find(gencost(:, MODEL) == PW_LINEAR);
0130 %     PQ = [gen(:, PMAX); gen(:, QMAX)];
0131 %     c = totcost(gencost(ipwl, :), PQ(ipwl));
0132     c = gencost(sub2ind(size(gencost), ipwl, NCOST+2*gencost(ipwl, NCOST)));    %% largest y-value in CCV data
0133     x0(vv.i1.y:vv.iN.y) = max(c) + 0.1 * abs(max(c));
0134 %     x0(vv.i1.y:vv.iN.y) = c + 0.1 * abs(c);
0135 end
0136 
0137 %% find branches with flow limits
0138 il = find(branch(:, RATE_A) ~= 0 & branch(:, RATE_A) < 1e10);
0139 nl2 = length(il);           %% number of constrained lines
0140 
0141 %%-----  run opf  -----
0142 f_fcn = @(x)opf_costfcn(x, om);
0143 gh_fcn = @(x)opf_consfcn(x, om, Ybus, Yf(il,:), Yt(il,:), mpopt, il);
0144 hess_fcn = @(x, lambda, cost_mult)opf_hessfcn(x, lambda, cost_mult, om, Ybus, Yf(il,:), Yt(il,:), mpopt, il);
0145 [x, f, info, Output, Lambda] = ...
0146   mips(f_fcn, x0, A, l, u, xmin, xmax, gh_fcn, hess_fcn, opt);
0147 success = (info > 0);
0148 
0149 %% update solution data
0150 Va = x(vv.i1.Va:vv.iN.Va);
0151 Vm = x(vv.i1.Vm:vv.iN.Vm);
0152 Pg = x(vv.i1.Pg:vv.iN.Pg);
0153 Qg = x(vv.i1.Qg:vv.iN.Qg);
0154 V = Vm .* exp(1j*Va);
0155 
0156 %%-----  calculate return values  -----
0157 %% update voltages & generator outputs
0158 bus(:, VA) = Va * 180/pi;
0159 bus(:, VM) = Vm;
0160 gen(:, PG) = Pg * baseMVA;
0161 gen(:, QG) = Qg * baseMVA;
0162 gen(:, VG) = Vm(gen(:, GEN_BUS));
0163 
0164 %% compute branch flows
0165 Sf = V(branch(:, F_BUS)) .* conj(Yf * V);  %% cplx pwr at "from" bus, p.u.
0166 St = V(branch(:, T_BUS)) .* conj(Yt * V);  %% cplx pwr at "to" bus, p.u.
0167 branch(:, PF) = real(Sf) * baseMVA;
0168 branch(:, QF) = imag(Sf) * baseMVA;
0169 branch(:, PT) = real(St) * baseMVA;
0170 branch(:, QT) = imag(St) * baseMVA;
0171 
0172 %% line constraint is actually on square of limit
0173 %% so we must fix multipliers
0174 muSf = zeros(nl, 1);
0175 muSt = zeros(nl, 1);
0176 if ~isempty(il)
0177     muSf(il) = 2 * Lambda.ineqnonlin(1:nl2)       .* branch(il, RATE_A) / baseMVA;
0178     muSt(il) = 2 * Lambda.ineqnonlin((1:nl2)+nl2) .* branch(il, RATE_A) / baseMVA;
0179 end
0180 
0181 %% update Lagrange multipliers
0182 bus(:, MU_VMAX)  = Lambda.upper(vv.i1.Vm:vv.iN.Vm);
0183 bus(:, MU_VMIN)  = Lambda.lower(vv.i1.Vm:vv.iN.Vm);
0184 gen(:, MU_PMAX)  = Lambda.upper(vv.i1.Pg:vv.iN.Pg) / baseMVA;
0185 gen(:, MU_PMIN)  = Lambda.lower(vv.i1.Pg:vv.iN.Pg) / baseMVA;
0186 gen(:, MU_QMAX)  = Lambda.upper(vv.i1.Qg:vv.iN.Qg) / baseMVA;
0187 gen(:, MU_QMIN)  = Lambda.lower(vv.i1.Qg:vv.iN.Qg) / baseMVA;
0188 bus(:, LAM_P)    = Lambda.eqnonlin(nn.i1.Pmis:nn.iN.Pmis) / baseMVA;
0189 bus(:, LAM_Q)    = Lambda.eqnonlin(nn.i1.Qmis:nn.iN.Qmis) / baseMVA;
0190 branch(:, MU_SF) = muSf / baseMVA;
0191 branch(:, MU_ST) = muSt / baseMVA;
0192 
0193 %% package up results
0194 nlnN = getN(om, 'nln');
0195 
0196 %% extract multipliers for nonlinear constraints
0197 kl = find(Lambda.eqnonlin < 0);
0198 ku = find(Lambda.eqnonlin > 0);
0199 nl_mu_l = zeros(nlnN, 1);
0200 nl_mu_u = [zeros(2*nb, 1); muSf; muSt];
0201 nl_mu_l(kl) = -Lambda.eqnonlin(kl);
0202 nl_mu_u(ku) =  Lambda.eqnonlin(ku);
0203 
0204 mu = struct( ...
0205   'var', struct('l', Lambda.lower, 'u', Lambda.upper), ...
0206   'nln', struct('l', nl_mu_l, 'u', nl_mu_u), ...
0207   'lin', struct('l', Lambda.mu_l, 'u', Lambda.mu_u) );
0208 
0209 results = mpc;
0210 [results.bus, results.branch, results.gen, ...
0211     results.om, results.x, results.mu, results.f] = ...
0212         deal(bus, branch, gen, om, x, mu, f);
0213 
0214 pimul = [ ...
0215   results.mu.nln.l - results.mu.nln.u;
0216   results.mu.lin.l - results.mu.lin.u;
0217   -ones(ny>0, 1);
0218   results.mu.var.l - results.mu.var.u;
0219 ];
0220 raw = struct('xr', x, 'pimul', pimul, 'info', info, 'output', Output);
mipsopf_solver

PURPOSE

SYNOPSIS

DESCRIPTION

CROSS-REFERENCE INFORMATION

SOURCE CODE
