1 主要内容
该程序是完全复现《Switch Opening and Exchange Method for Stochastic Distribution Network Reconfiguration》，也是一个开源代码，网上有些人卖的还挺贵，本次免费分享给大家，代码主要做的是一个通过配电网重构获取最优网络拓扑的问题，从而有效降低网损，提高经济效益，同时考虑了光伏和负荷的随机性，构建了多时段随机配电网重构模型，考虑到大型网络中计算较为耗时，采用一种基于开断和交换的SOE方法，已获得良好的径向拓扑，采用IEEE多个标准算例进行了测试，更加创新，而且求解的效果更好，结果和论文基本是一致，代码质量非常高，但是子程序比较多，适合有编程经验的同学学习！
2 部分程序
% core programme in decrese_reconfig_33.m already obtain optimal solution, no need to execute tabu clear all, clc, close all addpath('./code') %% basic setting tic fprintf('decrease_reconfig_33_tabu.m \n') warning('off') addpath(pathdef) mpopt = mpoption; mpopt.out.all = 0; % do not print anything mpopt.verbose = 0; version_LODF = 0 % 1: use decrease_reconfig_algo_LODF.m % 0: use decrease_reconfig_algo.m candi_brch_bus = []; % candidate branch i added to bus j % mpc0 = case33; casei=4 d33zhu_v2 substation_node = 1; n_bus = 33; n1 = 3 n2 = 5 n1_down_substation = n1+1; n2_up_ending = n2; Branch0 = Branch; brch_idx_in_loop0 = unique(brch_idx_in_loop(:)); %% original network's power flow (not radial) % show_biograph(Branch, Bus) from_to = show_biograph_not_sorted(Branch, substation_node, 0); mpc = generate_mpc(Bus, Branch, n_bus); res_orig = runpf(mpc, mpopt); losses = get_losses(res_orig.baseMVA, res_orig.bus, res_orig.branch); loss0 = sum(real(losses)); fprintf('case33_tabu: original loop network''s loss is %.5f \n\n', loss0) % for each branch in a loop, % if open that branch does not cause isolation, check the two ending buses % of that branch for connectivity, realized by shortestpath or conncomp % calculate the lowest loss increase, print out the sorted loss increase % open the branch with lowest loss increase % stop criterion: number of buses - number of branches = 1 %% ------------------------ Core algorithm ------------------------%% ff0 = Branch(:, 1); ff = ff0; tt0 = Branch(:, 2); tt = tt0; t1 = toc; if version_LODF [Branch] = decrease_reconfig_algo_LODF(Bus, Branch, brch_idx_in_loop, ... ff0, tt0, substation_node, n_bus, loss0); %%% core algorithm else [Branch] = decrease_reconfig_algo(Bus, Branch, brch_idx_in_loop, ff0, tt0, ... substation_node, n_bus, loss0); %%% core algorithm end t2 = toc; time_consumption.core = t2 - t1 % output of core algorithm show_biograph = 0; from_to = show_biograph_not_sorted(Branch(:, [1 2]), substation_node, ... 0); from_to0 = from_to; mpc = generate_mpc(Bus, Branch, n_bus); res_pf_dec = runpf(mpc, mpopt); losses = get_losses(res_pf_dec.baseMVA, res_pf_dec.bus, res_pf_dec.branch); loss0_dec = sum(real(losses)); % fprintf('case33_tabu: radial network obtained by my core algorithm''s loss is %.5f \n\n', loss0_dec) Branch_loss_record = []; % record Branch and loss Branch_loss_record.core.Branch = Branch; Branch_loss_record.core.loss = loss0_dec; %% prepare force open branches for tabu: branch_idx_focused [branch_idx_focused] = get_branch_idx_focused_for_tabu( ... from_to, Branch0, Branch, substation_node, brch_idx_in_loop0, n_bus, ... n1_down_substation, n2_up_ending); %% ------------------------ Tabu algorithm ------------------------%% % run the core program for each upstream branch connected to the idx_force_open % idx_considered = [35 69] % for iter = idx_considered for iter = 1:length(branch_idx_focused) fprintf('iter=%d/%d\n', iter, length(branch_idx_focused)); Branch = Branch0; Branch(branch_idx_focused(iter), :) = []; ff0 = Branch(:, 1); ff = ff0; tt0 = Branch(:, 2); tt = tt0; brch_idx_in_loop = brch_idx_in_loop0; idx_tmp = find(brch_idx_in_loop == branch_idx_focused(iter)); if isempty(idx_tmp) else brch_idx_in_loop(idx_tmp) = []; brch_idx_in_loop(idx_tmp:end) = brch_idx_in_loop(idx_tmp:end)-1; end t1 = toc; %%------------------- core algorithm in Tabu loop--------------------%% if version_LODF [Branch] = decrease_reconfig_algo_LODF(Bus, Branch, brch_idx_in_loop, ... ff0, tt0, substation_node, n_bus, loss0); %%% core algorithm else [Branch] = decrease_reconfig_algo(Bus, Branch, brch_idx_in_loop, ff0, tt0, ... substation_node, n_bus, loss0); %%% core algorithm end t2 = toc; time_consumption.tabu(iter) = t2-t1; from_to = show_biograph_not_sorted(Branch(:, [1 2]), substation_node, ... show_biograph); %%% show figure, take time mpc = generate_mpc(Bus, Branch, n_bus); t1 = toc; res_pf = runpf(mpc, mpopt); t2 = toc; losses = get_losses(res_pf.baseMVA, res_pf.bus, res_pf.branch); lossi = sum(real(losses)) % loss = 0.5364 loss_tabu(iter,1) = lossi; yij_dec = generate_yij_from_Branch(Branch, Branch0); % record Branch and loss Branch_loss_record.tabu(iter,1).Branch = Branch; Branch_loss_record.tabu(iter,1).loss = lossi; [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; % Vm = res_pf.bus(:, VM)'; % Va = res_pf.bus(:, VA)'; % ending_bus = find_ending_node(Branch, substation_node); % [ending_bus'; Vm(ending_bus)]; %% ---------------------one open and one close---------------------%% % prepare nodes_focused for one_open_one_close t1 = toc; [nodes_focused] = get_nodes_focused_o1c1( ... from_to, Branch, Branch0, substation_node, brch_idx_in_loop, ... n1_down_substation, n2_up_ending); loss_before_switch0 = lossi; [record_o1c1_loss_dec, loss_after_switch_combine_two_o1c1, Branch_loss] = ... one_open_one_close(nodes_focused, Bus, Branch0, Branch, from_to, ... substation_node, n_bus, loss_before_switch0); t2 = toc; time_consumption.tabu_o1c1(iter) = t2-t1; % record Branch and loss Branch_loss_record.tabu_o1c1_dec{iter}.Branch = Branch_loss.Branch_o1c1_dec; % Branch_loss_record.tabu_o1c1_dec(iter,1).Branch = Branch_loss.Branch_o1c1_dec; Branch_loss_record.tabu_o1c1_dec{iter}.loss = Branch_loss.loss_o1c1_dec; Branch_loss_record.tabu_combine_2_o1c1_dec{iter}.Branch = ... Branch_loss.Branch_after_switch_combine_two_o1c1; Branch_loss_record.tabu_combine_2_o1c1_dec{iter}.loss = ... Branch_loss.loss_after_switch_combine_two_o1c1; min_loss_o1c1 = min(record_o1c1_loss_dec(:,1)); fprintf('case33_tabu: minimum loss obtained after ''one open and one close'': %.5f\n', ... min_loss_o1c1); min_loss_combine_two_o1c1 = 1e9; fprintf('case33_tabu: loss obtained after combine two ''one open and one close'': \n') for i = 1:length(loss_after_switch_combine_two_o1c1) temp = min(loss_after_switch_combine_two_o1c1{i}); if temp %.5f \n', temp); end fprintf('case33_tabu: minimum loss obtained after combine two ''one open and one close'': %.5f \n', ... min_loss_combine_two_o1c1) %% ---------------------two open and two close---------------------%% flag_2o2c = 0 if flag_2o2c == 1 t1 = toc; loss_before_switch0 = lossi; [record_o2c2_loss_dec, loss_after_switch_combine_two_o2c2] = ... two_open_two_close(nodes_focused, Bus, Branch0, Branch, from_to, ... substation_node, n_bus, loss_before_switch0); t2 = toc; time_consumption.tabu_o2c2(iter) = t2-t1; min_loss_o2c2 = min(record_o2c2_loss_dec(:,1)); fprintf('case33_tabu: minimum loss obtained after ''two open and two close'': %.5f\n', ... min_loss_o2c2); min_loss_combine_two_o2c2 = 1e9; fprintf('case33_tabu: loss obtained after combine two ''two open and two close'': \n') for i = 1:length(loss_after_switch_combine_two_o2c2) temp = min(loss_after_switch_combine_two_o2c2{i}); if temp %.5f \n', temp); end fprintf('case33_tabu: minimum loss obtained after combine two ''two open and two close'': %.5f \n', ... min_loss_combine_two_o2c2) res_save{iter}.min_loss_o2c2 = min_loss_o2c2; res_save{iter}.min_loss_combine_two_o2c2 = min_loss_combine_two_o2c2; end res_save{iter}.yij_dec = yij_dec; res_save{iter}.Branch = Branch; res_save{iter}.lossi = lossi; res_save{iter}.record_o1c1_loss_dec = record_o1c1_loss_dec; res_save{iter}.min_loss_o1c1 = min_loss_o1c1; res_save{iter}.min_loss_combine_two_o1c1 = min_loss_combine_two_o1c1; % file_name = ['case33_yij_Branch_', num2str(idx_force_open(iter)), '.mat']; % save(file_name, 'yij_dec', 'Branch', 'lossi'); file_name = ['id1_case33_yij_Branch', '.mat']; save(file_name, 'res_save', 'branch_idx_focused', 'Branch_loss_record', ... 'time_consumption'); end file_name = ['id1_case33_yij_Branch', '.mat']; save(file_name, 'res_save', 'branch_idx_focused', 'Branch_loss_record', ... 'time_consumption'); % find_all_losses(Branch_loss_record); fprintf('case33_tabu: losses obtained after applying tabu strategy: \n') % 0.28343 zjp 2018-1-18 fprintf('%.5f \n', loss_tabu) fprintf('----- min: %.5f -----\n', min(loss_tabu)) min_loss = 1e9; for i = 1:length(res_save) if min_loss>res_save{i}.min_loss_o1c1 min_loss = res_save{i}.min_loss_o1c1 ; end if min_loss>res_save{i}.min_loss_combine_two_o1c1 min_loss = res_save{i}.min_loss_combine_two_o1c1 ; end end min_loss_o1c1 = min_loss if flag_2o2c == 1 min_loss = 1e9; for i = 1:length(res_save) if min_loss>res_save{i}.min_loss_o2c2 min_loss = res_save{i}.min_loss_o2c2 ; end if min_loss>res_save{i}.min_loss_combine_two_o2c2 min_loss = res_save{i}.min_loss_combine_two_o2c2 ; end end min_loss_o2c2 = min_loss end
3 部分模型级文献结果
