【布局优化】基于帝国企鹅算法求解潮流计算的电力系统总线优化问题附matlab代码

1 内容介绍

电力系统最优潮流问题（ＯＰＦ）可描述为在满足电力系统各种运行约束限制前提条件下，通过优化得到电力网络中各控制变量取值，使系统的某个性能指标达到最优，因此ＯＰＦ问题是一个典型的约束非线性规划问题。以简化梯度法、牛顿法、内点法、解耦法为代表的传统优化方法已被运用于解决ＯＰＦ问题上，但简化梯度法会出现锯齿现象，收敛性较差，且计算量大耗时多；要求目标函数具有光滑、连续可微的特性限制了牛顿法在解决ＯＰＦ问题中的应用；内点法计算速度慢、易收敛至局部极值。针对经典优化算法存在的不足，帝企鹅启发式智能优化算法，为解决ＯＰＦ问题的一种新途径［１］

2 仿真代码

function om = opf_model(mpc)
%OPF_MODEL  Constructor for OPF model class.
%   OM = OPF_MODEL(MPC)
%
%   This class implements the OPF model object used to encapsulate
%   a given OPF problem formulation. It allows for access to optimization
%   variables, constraints and costs in named blocks, keeping track of the
%   ordering and indexing of the blocks as variables, constraints and costs
%   are added to the problem.
%
%   This class is a sub-class of OPT_MODEL and simply adds the 'mpc'
%   field for storing the MATPOWER case struct used to build the object
%   along with the get_mpc() method.
%
%   The following is the structure of the data in the OPF model object.
%   Each field of .idx or .data is a struct whose field names are the names
%   of the corresponding blocks of vars, constraints or costs (found in
%   order in the corresponding .order field). The description next to these
%   fields gives the meaning of the value for each named sub-field.
%   E.g. om.var.data.v0.Pg contains a vector of initial values for the 'Pg'
%   block of variables.
%
%   om
%       .opt_model  - the corresponding OPT_MODEL object
%       .mpc        - MATPOWER case struct used to create this model object
%           .baseMVA
%           .bus
%           .branch
%           .gen
%           .gencost
%           .A  (if present, must have l, u)
%           .l
%           .u
%           .N  (if present, must have fparm, H, Cw)
%           .fparm
%           .H
%           .Cw
%
%   See also OPT_MODEL.

    es = struct();
    s = struct('mpc', es);
    om = opt_model;
    om = class(s, 'opf_model', om);
else
    if isa(mpc,'opf_model') 
        om = mpc;
    else
        if isfield(mpc, 'om')   %% avoid nesting
            s = struct('mpc', rmfield(mpc, 'om'));
        else
            s = struct('mpc', mpc);
        end
        om = opt_model;
        om = class(s, 'opf_model', om);
    end
end

function results = maxloadlim(mpc,dir_mll,varargin)
% MAXLOADLIM computes the maximum loadability limit in one direction. It
% uses dispatchable loads in MATPOWER
%   RESULTS = MAXLOADLIM(MPC,DIR_MLL) returns the results from the
%   optimization problem looking for the maximum loadability limit in
%   the direction of load increase DIR_MLL. DIR_MLL defines the directions
%   of load increases for all buses. For buses with zero loads, the
%   direction of load increases must be zero. RESULTS contains all fields
%   returned from the runopf MATPOWER function. It also contains the 
%   following additional fields:
%   * dir_mll: the direction of load increase used as input.
%   * stab_marg: the stability margin to the maximum loadability point from
%   the base case defined in the input MPC.
%   * bif: information about the bifurcation at the MLL point.
%   
%   RESULTS = MAXLOADLIM(MPC,DIR_MLL,NAME,VALUE) uses the options defined
%   by the pair NAME,VALUE. The currently supported options are 
%     * 'verbose': 1 or 0 (Default). If set to 1, a summary of the results
%     at the maximum loadability limit is printed. 
%     * 'use_qlim': 1 (Default) or 0. Enforces or not the reactive power
%     limits of the generators.
%     * 'Vlims_bus_nb': [] (Default) or array of integers. By default, the
%     bus voltage limits are not enforced. This option allows for defining
%     a set of buses at which the voltage limits are enforced.
%
%   See also PREPARE_MAXLOADLIM, POSTPROC_MAXLOADLIM, PRINT_MAXLOADLIM, 
%   RUNOPF.

%   MATPOWER
%   Copyright (c) 2015-2016, Power Systems Engineering Research Center (PSERC)
%   by Camille Hamon
%
%   This file is part of MATPOWER.
%   Covered by the 3-clause BSD License (see LICENSE file for details).
%   See http://www.pserc.cornell.edu/matpower/ for more info.

define_constants;

%% Checking the options, if any
input_checker = inputParser;

default_verbose = 0;
verbose_levels = [0;1];
check_verbose = @(x)(isnumeric(x) && isscalar(x) && any(x == verbose_levels));
addParameter(input_checker,'verbose',default_verbose,check_verbose);

input_checker.KeepUnmatched = true;
parse(input_checker,varargin{:});

options = input_checker.Results;

%% Prepare the matpower case for the maximum loadability limit problem
mpc_vl = prepare_maxloadlim(mpc,dir_mll,varargin{:});

%% Run opf
% Turning off the printing and initializing from the base case
mpopt = mpoption('verbose',options.verbose,'opf.init_from_mpc',1);
mpopt = mpoption(mpopt,'out.all',0);
% Decreasing the threshold for the relative complementarity constraints
mpopt = mpoption(mpopt,'mips.comptol',1e-8);
% Change solver
mpopt = mpoption(mpopt, 'opf.ac.solver', 'MIPS');
% Execute opf
results = runopf(mpc_vl,mpopt);

%% Post-processing
results = postproc_maxloadlim(results,dir_mll);

%% Printing
if options.verbose
    print_maxloadlim(mpc,results);
end
 

3 运行结果

4 参考文献

[1]李英. 基于并行计算和粒子群优化算法的电力系统无功优化问题研究[D]. 浙江大学, 2010.

[2]张东寅, 王澎涛, 袁艳斌,等. 基于改进布谷鸟算法的电力系统最优潮流计算[J]. 水电能源科学, 2017, 35(1):5.

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部分理论引用网络文献，若有侵权联系博主删除。