www.gusucode.com > 基于lingo求所以解，对潮流计算求出所有解 > matpower4.1/mips6opf_solver.m
function [results, success, raw] = mips6opf_solver(om, mpopt)
%------------------------------ deprecated ------------------------------
% MATLAB 6.x support to be removed in a future version.
%--------------------------------------------------------------------------
%MIPS6OPF_SOLVER Solves AC optimal power flow using MIPS (for MATLAB 6.x).
%
% [RESULTS, SUCCESS, RAW] = MIPS6OPF_SOLVER(OM, MPOPT)
%
% Inputs are an OPF model object and a MATPOWER options vector.
%
% Outputs are a RESULTS struct, SUCCESS flag and RAW output struct.
%
% RESULTS is a MATPOWER case struct (mpc) with the usual baseMVA, bus
% branch, gen, gencost fields, along with the following additional
% fields:
% .order see 'help ext2int' for details of this field
% .x final value of optimization variables (internal order)
% .f final objective function value
% .mu shadow prices on ...
% .var
% .l lower bounds on variables
% .u upper bounds on variables
% .nln
% .l lower bounds on nonlinear constraints
% .u upper bounds on nonlinear constraints
% .lin
% .l lower bounds on linear constraints
% .u upper bounds on linear constraints
%
% SUCCESS 1 if solver converged successfully, 0 otherwise
%
% RAW raw output in form returned by MINOS
% .xr final value of optimization variables
% .pimul constraint multipliers
% .info solver specific termination code
% .output solver specific output information
%
% See also OPF, MIPS6.
% MATPOWER
% $Id: mips6opf_solver.m,v 1.16 2010/06/09 14:56:58 ray Exp $
% by Ray Zimmerman, PSERC Cornell
% and Carlos E. Murillo-Sanchez, PSERC Cornell & Universidad Autonoma de Manizales
% Copyright (c) 2000-2010 by Power System Engineering Research Center (PSERC)
%
% This file is part of MATPOWER.
% See http://www.pserc.cornell.edu/matpower/ for more info.
%
% MATPOWER is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published
% by the Free Software Foundation, either version 3 of the License,
% or (at your option) any later version.
%
% MATPOWER is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with MATPOWER. If not, see <http://www.gnu.org/licenses/>.
%
% Additional permission under GNU GPL version 3 section 7
%
% If you modify MATPOWER, or any covered work, to interface with
% other modules (such as MATLAB code and MEX-files) available in a
% MATLAB(R) or comparable environment containing parts covered
% under other licensing terms, the licensors of MATPOWER grant
% you additional permission to convey the resulting work.
%%----- initialization -----
%% define named indices into data matrices
[PQ, PV, REF, NONE, BUS_I, BUS_TYPE, PD, QD, GS, BS, BUS_AREA, VM, ...
VA, BASE_KV, ZONE, VMAX, VMIN, LAM_P, LAM_Q, MU_VMAX, MU_VMIN] = idx_bus;
[GEN_BUS, PG, QG, QMAX, QMIN, VG, MBASE, GEN_STATUS, PMAX, PMIN, ...
MU_PMAX, MU_PMIN, MU_QMAX, MU_QMIN, PC1, PC2, QC1MIN, QC1MAX, ...
QC2MIN, QC2MAX, RAMP_AGC, RAMP_10, RAMP_30, RAMP_Q, APF] = idx_gen;
[F_BUS, T_BUS, BR_R, BR_X, BR_B, RATE_A, RATE_B, RATE_C, ...
TAP, SHIFT, BR_STATUS, PF, QF, PT, QT, MU_SF, MU_ST, ...
ANGMIN, ANGMAX, MU_ANGMIN, MU_ANGMAX] = idx_brch;
[PW_LINEAR, POLYNOMIAL, MODEL, STARTUP, SHUTDOWN, NCOST, COST] = idx_cost;
%% options
verbose = mpopt(31); %% VERBOSE
feastol = mpopt(81); %% PDIPM_FEASTOL
gradtol = mpopt(82); %% PDIPM_GRADTOL
comptol = mpopt(83); %% PDIPM_COMPTOL
costtol = mpopt(84); %% PDIPM_COSTTOL
max_it = mpopt(85); %% PDIPM_MAX_IT
max_red = mpopt(86); %% SCPDIPM_RED_IT
step_control = (mpopt(11) == 565); %% OPF_ALG == 565, MIPS-sc
if feastol == 0
feastol = mpopt(16); %% = OPF_VIOLATION by default
end
opt = struct( 'feastol', feastol, ...
'gradtol', gradtol, ...
'comptol', comptol, ...
'costtol', costtol, ...
'max_it', max_it, ...
'max_red', max_red, ...
'step_control', step_control, ...
'cost_mult', 1e-4, ...
'verbose', verbose );
%% unpack data
mpc = get_mpc(om);
[baseMVA, bus, gen, branch, gencost] = ...
deal(mpc.baseMVA, mpc.bus, mpc.gen, mpc.branch, mpc.gencost);
[vv, ll, nn] = get_idx(om);
%% problem dimensions
nb = size(bus, 1); %% number of buses
nl = size(branch, 1); %% number of branches
ny = getN(om, 'var', 'y'); %% number of piece-wise linear costs
%% linear constraints
[A, l, u] = linear_constraints(om);
%% bounds on optimization vars
[x0, xmin, xmax] = getv(om);
%% build admittance matrices
[Ybus, Yf, Yt] = makeYbus(baseMVA, bus, branch);
%% try to select an interior initial point
ll = xmin; uu = xmax;
ll(xmin == -Inf) = -1e10; %% replace Inf with numerical proxies
uu(xmax == Inf) = 1e10;
x0 = (ll + uu) / 2;
Varefs = bus(bus(:, BUS_TYPE) == REF, VA) * (pi/180);
x0(vv.i1.Va:vv.iN.Va) = Varefs(1); %% angles set to first reference angle
if ny > 0
ipwl = find(gencost(:, MODEL) == PW_LINEAR);
% PQ = [gen(:, PMAX); gen(:, QMAX)];
% c = totcost(gencost(ipwl, :), PQ(ipwl));
c = gencost(sub2ind(size(gencost), ipwl, NCOST+2*gencost(ipwl, NCOST))); %% largest y-value in CCV data
x0(vv.i1.y:vv.iN.y) = max(c) + 0.1 * abs(max(c));
% x0(vv.i1.y:vv.iN.y) = c + 0.1 * abs(c);
end
%% find branches with flow limits
il = find(branch(:, RATE_A) ~= 0 & branch(:, RATE_A) < 1e10);
nl2 = length(il); %% number of constrained lines
%%----- run opf -----
f_fcn = @opf_costfcn;
gh_fcn = @opf_consfcn;
ipm_hessian = @opf_hessfcn;
[x, f, info, Output, Lambda] = ...
mips6(f_fcn, x0, A, l, u, xmin, xmax, gh_fcn, ipm_hessian, opt, om, Ybus, Yf(il,:), Yt(il,:), mpopt, il);
success = (info > 0);
%% update solution data
Va = x(vv.i1.Va:vv.iN.Va);
Vm = x(vv.i1.Vm:vv.iN.Vm);
Pg = x(vv.i1.Pg:vv.iN.Pg);
Qg = x(vv.i1.Qg:vv.iN.Qg);
V = Vm .* exp(1j*Va);
%%----- calculate return values -----
%% update voltages & generator outputs
bus(:, VA) = Va * 180/pi;
bus(:, VM) = Vm;
gen(:, PG) = Pg * baseMVA;
gen(:, QG) = Qg * baseMVA;
gen(:, VG) = Vm(gen(:, GEN_BUS));
%% compute branch flows
Sf = V(branch(:, F_BUS)) .* conj(Yf * V); %% cplx pwr at "from" bus, p.u.
St = V(branch(:, T_BUS)) .* conj(Yt * V); %% cplx pwr at "to" bus, p.u.
branch(:, PF) = real(Sf) * baseMVA;
branch(:, QF) = imag(Sf) * baseMVA;
branch(:, PT) = real(St) * baseMVA;
branch(:, QT) = imag(St) * baseMVA;
%% line constraint is actually on square of limit
%% so we must fix multipliers
muSf = zeros(nl, 1);
muSt = zeros(nl, 1);
if ~isempty(il)
muSf(il) = 2 * Lambda.ineqnonlin(1:nl2) .* branch(il, RATE_A) / baseMVA;
muSt(il) = 2 * Lambda.ineqnonlin((1:nl2)+nl2) .* branch(il, RATE_A) / baseMVA;
end
%% update Lagrange multipliers
bus(:, MU_VMAX) = Lambda.upper(vv.i1.Vm:vv.iN.Vm);
bus(:, MU_VMIN) = Lambda.lower(vv.i1.Vm:vv.iN.Vm);
gen(:, MU_PMAX) = Lambda.upper(vv.i1.Pg:vv.iN.Pg) / baseMVA;
gen(:, MU_PMIN) = Lambda.lower(vv.i1.Pg:vv.iN.Pg) / baseMVA;
gen(:, MU_QMAX) = Lambda.upper(vv.i1.Qg:vv.iN.Qg) / baseMVA;
gen(:, MU_QMIN) = Lambda.lower(vv.i1.Qg:vv.iN.Qg) / baseMVA;
bus(:, LAM_P) = Lambda.eqnonlin(nn.i1.Pmis:nn.iN.Pmis) / baseMVA;
bus(:, LAM_Q) = Lambda.eqnonlin(nn.i1.Qmis:nn.iN.Qmis) / baseMVA;
branch(:, MU_SF) = muSf / baseMVA;
branch(:, MU_ST) = muSt / baseMVA;
%% package up results
nlnN = getN(om, 'nln');
%% extract multipliers for nonlinear constraints
kl = find(Lambda.eqnonlin < 0);
ku = find(Lambda.eqnonlin > 0);
nl_mu_l = zeros(nlnN, 1);
nl_mu_u = [zeros(2*nb, 1); muSf; muSt];
nl_mu_l(kl) = -Lambda.eqnonlin(kl);
nl_mu_u(ku) = Lambda.eqnonlin(ku);
mu = struct( ...
'var', struct('l', Lambda.lower, 'u', Lambda.upper), ...
'nln', struct('l', nl_mu_l, 'u', nl_mu_u), ...
'lin', struct('l', Lambda.mu_l, 'u', Lambda.mu_u) );
results = mpc;
[results.bus, results.branch, results.gen, ...
results.om, results.x, results.mu, results.f] = ...
deal(bus, branch, gen, om, x, mu, f);
pimul = [ ...
results.mu.nln.l - results.mu.nln.u;
results.mu.lin.l - results.mu.lin.u;
-ones(ny>0, 1);
results.mu.var.l - results.mu.var.u;
];
raw = struct('xr', x, 'pimul', pimul, 'info', info, 'output', Output);