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
