一句很NB的代码

direction: rtl;
这句代码很NB,可以让你的浏览器body滚动条在左边.

转载于:https://www.cnblogs.com/c9log/archive/2009/12/11/1621513.html

<think>我们注意到原始程序中有一些函数(如BFS、dfs_shortest_paths)被洪水算法替代,同时需要确保所有变量都被正确定义。我们将按照以下步骤重写: 1. 使用洪水算法(flood fill)替代原来的BFS和DFS,用于形成孤岛和连接优先负荷。 2. 修正变量`island_num`未定义的问题:在孤岛融合后,重新计算孤岛数量。 3. 保持程序结构,但将原BFS和DFS部分替换为洪水算法。 洪水算法思路: - 从每个DG节点开始,像洪水蔓延一样访问相邻节点(广度优先),并累加负荷,直到总负荷超过DG容量。 - 构建网络拓扑图时排除故障支路。 步骤: a. 构建网络拓扑图(邻接表),排除故障支路。 b. 对每个DG节点执行洪水算法,形成初始孤岛。 c. 融合交叉的孤岛(即多个DG的孤岛有重叠,则合并成一个孤岛,包含这些DG)。 d. 检查优先恢复负荷是否被包含,若没有,则使用洪水算法(或最短路径)将其连接到最近的孤岛(这里我们使用洪水算法寻找路径,但为了效率,我们采用最短路径搜索,因为洪水算法在寻找路径时可能效率较低,所以我们用BFS找最短路径)。 e. 负荷削减:按照可控负荷、重要度顺序削减。 注意:在洪水算法中,我们只访问与DG连通的节点,并且不超过容量限制。 由于原程序较长,我们将重写整个程序,确保完整输出。以下是完整代码:</think>### 完整洪水算法实现配电网孤岛划分程序 ```matlab %% 洪水算法实现的孤电网孤岛划分(完整版) function islanding_solution() %% 步骤1:相关参数初始化 mpc1 = IEEE33(); % 配电网数据 Nb = 33; % 节点数 Nt = 24; % 时段数 % 光储系统接入位置 Y_DG = [6, 8, 12, 23, 30]; % 负荷等级系数 wf = 0.01*ones(1,Nb); wf([6, 12, 23, 30]) = 1; % 一级负荷 wf([1,5,8,14,18,21,22,27,29,32]) = 0.1; % 二级负荷 % 负荷时变需求系数 Ct = zeros(Nb, Nt); Ct([6, 12, 23, 30], :) = 10; % 一级负荷的需求度恒为10 time_coeff = [7.7796,5.3248,5.0429,5.1984,2.9881,3.4293,7.2042,10.6853,... 12.5477,15.5212,24.8697,33.2302,24.2863,24.7989,36.3317,... 27.4948,17.8808,14.0970,18.9046,20.4888,22.2151,19.7628,... 21.4368,17.6810]/36.3317; Ct(setdiff(1:Nb, [6, 12, 23, 30]), :) = ones(Nb - 4 , 1)*10*time_coeff; % 优先恢复系数 Ft = Ct.*(wf'*ones(1, Nt)); % 光伏出力数据 P_PV = [425, 200, 480, 120, 600; 700, 500, 720, 620, 720]; % 负荷总量 PL = [3305 , 4870]; % 各节点负荷 P_load = mpc1.bus(:,3)/3.715*PL; % 负荷削减系数 a_cut = zeros(1,Nb); C_load1 = [3,13,20,24]; % 完全可控的负荷集合 a_cut(C_load1) = 1; % 100%可控 C_load2 = [2,7,10]; % 50%可控的负荷集合 a_cut(C_load2) = 0.5; % 50%可控 %% 步骤2:读取故障支路 fault_branch = [5, 25, 20]; % 故障支路索引 %% 步骤3:确定故障时刻(假设index=1) index = 1; % 选择故障时刻索引 time0 = [7 , 15]; % 故障时刻 time = time0(index); % 当前时刻 if index ~= 1 && index ~= 2 error('输入有误请重新输入!!!'); end %% 步骤4:确定优先恢复集合 F_Rload = {[6, 12, 15, 23, 28, 30], [6, 12, 23, 30, 32]}; %% 步骤5:洪水算法确定孤岛 % 构建拓扑图(排除故障支路) graph = build_graph(mpc1, fault_branch); % 初始化孤岛信息 island_node = cell(1, length(Y_DG)); % 各孤岛包含的节点 island_power = zeros(1, length(Y_DG)); % 各孤岛总功率 island_load = zeros(1, length(Y_DG)); % 各孤岛总负荷 % 洪水算法形成初始孤岛 for k = 1:length(Y_DG) [island_node{k}, island_load(k)] = flood_fill(... Y_DG(k), graph, P_PV(index, k), P_load, time); island_power(k) = P_PV(index, k); end % 收集所有已恢复节点 island_node_all = []; for k = 1:length(Y_DG) island_node_all = union(island_node_all, island_node{k}); end %% 步骤5.1:孤岛融合(处理交叉孤岛) % 初始化融合后的孤岛 island_node_cross = island_node; % 初始化为原始孤岛 island_cross_DG = num2cell(1:length(Y_DG)); % 每个DG对应一个孤岛 island_num = length(Y_DG); % 初始孤岛数等于DG数 % 检测孤岛交叉并融合 i = 1; while i <= island_num j = i + 1; while j <= island_num if ~isempty(intersect(island_node_cross{i}, island_node_cross{j})) % 融合交叉孤岛 island_node_cross{i} = union(island_node_cross{i}, island_node_cross{j}); island_cross_DG{i} = [island_cross_DG{i}, island_cross_DG{j}]; % 更新孤岛功率 island_power(i) = island_power(i) + island_power(j); island_load(i) = island_load(i) + island_load(j); % 移除被融合的孤岛 island_node_cross(j) = []; island_cross_DG(j) = []; island_power(j) = []; island_load(j) = []; island_num = island_num - 1; % 更新孤岛数量 else j = j + 1; end end i = i + 1; end %% 步骤6:连接优先恢复负荷 for k = 1:length(F_Rload{index}) current_load = F_Rload{index}(k); % 检查是否已恢复 if ~ismember(current_load, island_node_all) % 寻找最近DG和最短路径 [nearest_dg, min_path] = find_nearest_dg(current_load, Y_DG, graph, island_node_all); % 加入对应孤岛 for kk = 1:island_num if ismember(nearest_dg, island_cross_DG{kk}) % 添加路径节点到孤岛 island_node_cross{kk} = union(island_node_cross{kk}, min_path); % 更新孤岛负荷 for node = min_path if ~ismember(node, island_node_cross{kk}) % 避免重复计算 island_load(kk) = island_load(kk) + P_load(node, index); end end % 更新全局恢复节点 island_node_all = union(island_node_all, min_path); break; end end end end %% 步骤7:负荷削减 for k = 1:island_num % 获取孤岛信息 current_nodes = island_node_cross{k}; current_power = sum(P_PV(index, island_cross_DG{k})); current_load = island_load(k); % 检查功率是否不足 if current_power < current_load % 第一步:削减完全可控负荷 for node_idx = 1:length(current_nodes) node = current_nodes(node_idx); if ismember(node, C_load1) % 完全可控负荷 current_load = current_load - P_load(node, index); island_node_cross{k} = setdiff(island_node_cross{k}, node); if current_power >= current_load break; end end end % 第二步:削减50%可控负荷 if current_power < current_load for node_idx = 1:length(current_nodes) node = current_nodes(node_idx); if ismember(node, C_load2) % 50%可控负荷 current_load = current_load - 0.5 * P_load(node, index); if current_power >= current_load break; end end end end % 第三步:按重要度等级切负荷 if current_power < current_load % 获取孤岛内节点的重要度排序 [~, idx] = sort(Ft(current_nodes, time), 'descend'); sorted_nodes = current_nodes(idx); for node_idx = 1:length(sorted_nodes) node = sorted_nodes(node_idx); current_load = current_load - P_load(node, index); island_node_cross{k} = setdiff(island_node_cross{k}, node); if current_power >= current_load break; end end end % 更新孤岛负荷 island_load(k) = current_load; end end %% 步骤8:输出孤岛划分方案 disp('=================== 孤岛划分最终方案 ==================='); for k = 1:island_num disp(['*************** 第', num2str(k), '个孤岛 ***************']) disp(['包含光储系统: ' , num2str(island_cross_DG{k})]) disp(['光储系统总功率: ' , num2str(sum(P_PV(index, island_cross_DG{k}))) , ' kW']) disp(['包含节点: ' , num2str(sort(island_node_cross{k}))]) disp(['孤岛内负荷总需求: ' , num2str(island_load(k)) , ' kW']) disp(' '); end end %% 洪水算法核心函数 function [island, total_load] = flood_fill(start, graph, capacity, P_load, time) n = length(graph); visited = false(1, n); % 访问标记 queue = start; % 初始化队列 island = []; % 孤岛节点集合 total_load = 0; % 孤岛总负荷 while ~isempty(queue) current = queue(1); % 获取队列首节点 queue(1) = []; % 移除首节点 % 跳过已访问节点 if visited(current) continue; end % 检查功率约束(考虑5%裕度) if total_load + P_load(current) > capacity * 1.05 continue; end % 加入孤岛 visited(current) = true; island(end+1) = current; total_load = total_load + P_load(current); % 获取邻居节点(按重要度排序) neighbors = graph{current}; if ~isempty(neighbors) % 按重要度排序(重要度高的优先) [~, idx] = sort(P_load(neighbors, time), 'descend'); neighbors = neighbors(idx); end % 添加未访问邻居到队列 for i = 1:length(neighbors) neighbor = neighbors(i); if ~visited(neighbor) queue(end+1) = neighbor; end end end % 确保包含起始节点 if ~ismember(start, island) island = [start, island]; total_load = total_load + P_load(start); end end %% 构建拓扑图(排除故障支路) function graph = build_graph(mpc, fault_branches) Nb = size(mpc.bus, 1); graph = cell(Nb, 1); % 创建空邻接表 % 遍历所有支路 for i = 1:size(mpc.branch, 1) % 跳过故障支路 if ismember(i, fault_branches) continue; end from = mpc.branch(i, 1); to = mpc.branch(i, 2); % 添加双向连接(无向图) graph{from} = [graph{from}, to]; graph{to} = [graph{to}, from]; end % 确保所有节点都有定义(即使没有连接) for i = 1:Nb if isempty(graph{i}) graph{i} = []; end end end %% 寻找最近DG的路径 function [nearest_dg, min_path] = find_nearest_dg(start, dg_nodes, graph, island_node_all) n = length(graph); visited = false(1, n); % 访问标记 queue = {start}; % 队列(存储路径) min_dist = Inf; % 最小距离 nearest_dg = -1; % 最近的DG min_path = []; % 最短路径 % BFS搜索最短路径 while ~isempty(queue) current_path = queue{1}; % 获取当前路径 current_node = current_path(end); % 当前节点 queue(1) = []; % 移除队列首项 % 跳过已访问节点 if visited(current_node) continue; end visited(current_node) = true; % 检查是否到达DG节点 if ismember(current_node, dg_nodes) dist = length(current_path) - 1; if dist < min_dist min_dist = dist; nearest_dg = current_node; min_path = current_path; end continue; end % 遍历邻居节点 neighbors = graph{current_node}; for i = 1:length(neighbors) neighbor = neighbors(i); % 跳过已恢复节点(除DG外) if ~ismember(neighbor, island_node_all) || ismember(neighbor, dg_nodes) if ~visited(neighbor) new_path = [current_path, neighbor]; queue{end+1} = new_path; end end end end % 如果未找到路径,返回最近DG(直接连接) if isempty(min_path) && ~isempty(dg_nodes) nearest_dg = dg_nodes(1); min_path = [start, nearest_dg]; end end %% IEEE33节点配电网数据 function mpc1 = IEEE33() %% MATPOWER Case Format : Version 2 mpc1.version = '2'; %%----- Power Flow Data -----%% %% system MVA base mpc1.baseMVA = 10; %% bus data % bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin mpc1.bus = [ %% (Pd and Qd are specified in kW & kVAr here, converted to MW & MVAr below) 1 3 0 0 0 0 1 1 0 12.66 1 1 1; 2 1 100 60 0 0 1 1 0 12.66 1 1.1 0.9; 3 1 90 40 0 0 1 1 0 12.66 1 1.1 0.9; 4 1 120 80 0 0 1 1 0 12.66 1 1.1 0.9; 5 1 60 30 0 0 1 1 0 12.66 1 1.1 0.9; 6 1 60 20 0 0 1 1 0 12.66 1 1.1 0.9; 7 1 200 100 0 0 1 1 0 12.66 1 1.1 0.9; 8 1 200 100 0 0 1 1 0 12.66 1 1.1 0.9; 9 1 60 20 0 0 1 1 0 12.66 1 1.1 0.9; 10 1 60 20 0 0 1 1 0 12.66 1 1.1 0.9; 11 1 45 30 0 0 1 1 0 12.66 1 1.1 0.9; 12 1 60 35 0 0 1 1 0 12.66 1 1.1 0.9; 13 1 60 35 0 0 1 1 0 12.66 1 1.1 0.9; 14 1 120 80 0 0 1 1 0 12.66 1 1.1 0.9; 15 1 60 10 0 0 1 1 0 12.66 1 1.1 0.9; 16 1 60 20 0 0 1 1 0 12.66 1 1.1 0.9; 17 1 60 20 0 0 1 1 0 12.66 1 1.1 0.9; 18 1 90 40 0 0 1 1 0 12.66 1 1.1 0.9; 19 1 90 40 0 0 1 1 0 12.66 1 1.1 0.9; 20 1 90 40 0 0 1 1 0 12.66 1 1.1 0.9; 21 1 90 40 0 0 1 1 0 12.66 1 1.1 0.9; 22 1 90 40 0 0 1 1 0 12.66 1 1.1 0.9; 23 1 90 50 0 0 1 1 0 12.66 1 1.1 0.9; 24 1 420 200 0 0 1 1 0 12.66 1 1.1 0.9; 25 1 420 200 0 0 1 1 0 12.66 1 1.1 0.9; 26 1 60 25 0 0 1 1 0 12.66 1 1.1 0.9; 27 1 60 25 0 0 1 1 0 12.66 1 1.1 0.9; 28 1 60 20 0 0 1 1 0 12.66 1 1.1 0.9; 29 1 120 70 0 0 1 1 0 12.66 1 1.1 0.9; 30 1 200 600 0 0 1 1 0 12.66 1 1.1 0.9; 31 1 150 70 0 0 1 1 0 12.66 1 1.1 0.9; 32 1 210 100 0 0 1 1 0 12.66 1 1.1 0.9; 33 1 60 40 0 0 1 1 0 12.66 1 1.1 0.9; ]; %% generator data % bus Pg Qg Qmax Qmin Vg mBase status Pmax Pmin Pc1 Pc2 Qc1min Qc1max Qc2min Qc2max ramp_agc ramp_10 ramp_30 ramp_q apf mpc1.gen = [ 1 0 0 10 -10 1 100 1 10 0 0 0 0 0 0 0 0 0 0 0 0; ]; %% branch data % fbus tbus r x b rateA rateB rateC ratio angle status angmin angmax mpc1.branch = [ %% (r and x specified in ohms here, converted to p.u. below) 1 2 0.0922 0.0470 0 0 0 0 0 0 1 -360 360; 2 3 0.4930 0.2511 0 0 0 0 0 0 1 -360 360; 3 4 0.3660 0.1864 0 0 0 0 0 0 1 -360 360; 4 5 0.3811 0.1941 0 0 0 0 0 0 1 -360 360; 5 6 0.8190 0.7070 0 0 0 0 0 0 1 -360 360; 6 7 0.1872 0.6188 0 0 0 0 0 0 1 -360 360; 7 8 0.7114 0.2351 0 0 0 0 0 0 1 -360 360; 8 9 1.0300 0.7400 0 0 0 0 0 0 1 -360 360; 9 10 1.0440 0.7400 0 0 0 0 0 0 1 -360 360; 10 11 0.1966 0.0650 0 0 0 0 0 0 1 -360 360; 11 12 0.3744 0.1238 0 0 0 0 0 0 1 -360 360; 12 13 1.4680 1.1550 0 0 0 0 0 0 1 -360 360; 13 14 0.5416 0.7129 0 0 0 0 0 0 1 -360 360; 14 15 0.5910 0.5260 0 0 0 0 0 0 1 -360 360; 15 16 0.7463 0.5450 0 0 0 0 0 0 1 -360 360; 16 17 1.2890 1.7210 0 0 0 0 0 0 1 -360 360; 17 18 0.7320 0.5740 0 0 0 0 0 0 1 -360 360; 2 19 0.1640 0.1565 0 0 0 0 0 0 1 -360 360; 19 20 1.5042 1.3554 0 0 0 0 0 0 1 -360 360; 20 21 0.4095 0.4784 0 0 0 0 0 0 1 -360 360; 21 22 0.7089 0.9373 0 0 0 0 0 0 1 -360 360; 3 23 0.4512 0.3083 0 0 0 0 0 0 1 -360 360; 23 24 0.8980 0.7091 0 0 0 0 0 0 1 -360 360; 24 25 0.8960 0.7011 0 0 0 0 0 0 1 -360 360; 6 26 0.2030 0.1034 0 0 0 0 0 0 1 -360 360; 26 27 0.2842 0.1447 0 0 0 0 0 0 1 -360 360; 27 28 1.0590 0.9337 0 0 0 0 0 0 1 -360 360; 28 29 0.8042 0.7006 0 0 0 0 0 0 1 -360 360; 29 30 0.5075 0.2585 0 0 0 0 0 0 1 -360 360; 30 31 0.9744 0.9630 0 0 0 0 0 0 1 -360 360; 31 32 0.3105 0.3619 0 0 0 0 0 0 1 -360 360; 32 33 0.3410 0.5302 0 0 0 0 0 0 1 -360 360; 21 8 2.0000 2.0000 0 0 0 0 0 0 0 -360 360; 9 15 2.0000 2.0000 0 0 0 0 0 0 0 -360 360; 12 22 2.0000 2.0000 0 0 0 0 0 0 0 -360 360; 18 33 0.5000 0.5000 0 0 0 0 0 0 0 -360 360; 25 29 0.5000 0.5000 0 0 0 0 0 0 0 -360 360; ]; %%----- OPF Data -----%% %% generator cost data % 1 startup shutdown n x1 y1 ... xn yn % 2 startup shutdown n c(n-1) ... c0 mpc1.gencost = [ 2 0 0 3 0 20 0; ]; %% convert branch impedances from Ohms to p.u. [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; [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; Vbase = mpc1.bus(1, BASE_KV) * 1e3; %% in Volts Sbase = mpc1.baseMVA * 1e6; %% in VA mpc1.branch(:, [BR_R BR_X]) = mpc1.branch(:, [BR_R BR_X]) / (Vbase^2 / Sbase); %% convert loads from kW to MW mpc1.bus(:, [PD, QD]) = mpc1.bus(:, [PD, QD]) / 1e3; end %% 辅助函数:总线索引 function [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 PQ = 1; PV = 2; REF = 3; NONE = 4; BUS_I = 1; BUS_TYPE = 2; PD = 3; QD = 4; GS = 5; BS = 6; BUS_AREA = 7; VM = 8; VA = 9; BASE_KV = 10; ZONE = 11; VMAX = 12; VMIN = 13; LAM_P = 14; LAM_Q = 15; MU_VMAX = 16; MU_VMIN = 17; end %% 辅助函数:支路索引 function [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 F_BUS = 1; T_BUS = 2; BR_R = 3; BR_X = 4; BR_B = 5; RATE_A = 6; RATE_B = 7; RATE_C = 8; TAP = 9; SHIFT = 10; BR_STATUS = 11; ANGMIN = 12; ANGMAX = 13; PF = 14; QF = 15; PT = 16; QT = 17; MU_SF = 18; MU_ST = 19; MU_ANGMIN = 20; MU_ANGMAX = 21; end ``` ### 洪水算法实现关键点说明 1. **洪水算法核心逻辑**: ```matlab function [island, total_load] = flood_fill(start, graph, capacity, P_load, time) % 初始化 visited = false(1, n); queue = start; while ~isempty(queue) current = queue(1); queue(1) = []; % 功率约束检查 if total_load + P_load(current) > capacity * 1.05 continue; end % 加入孤岛 visited(current) = true; island(end+1) = current; total_load = total_load + P_load(current); % 邻居节点按重要度排序 neighbors = graph{current}; [~, idx] = sort(P_load(neighbors, time), 'descend'); neighbors = neighbors(idx); % 添加邻居到队列 for neighbor = neighbors if ~visited(neighbor) queue(end+1) = neighbor; end end end end ``` 2. **孤岛融合算法**: ```matlab % 动态融合交叉孤岛 i = 1; while i <= island_num j = i + 1; while j <= island_num if ~isempty(intersect(island_node_cross{i}, island_node_cross{j})) % 合并孤岛 island_node_cross{i} = union(..., ...); island_cross_DG{i} = [..., ...]; % 更新功率和负荷 island_power(i) = island_power(i) + island_power(j); island_load(i) = island_load(i) + island_load(j); % 移除被融合孤岛 island_node_cross(j) = []; island_cross_DG(j) = []; island_num = island_num - 1; end end end ``` 3. **优先负荷连接策略**: ```matlab % BFS最短路径搜索 function [nearest_dg, min_path] = find_nearest_dg(...) while ~isempty(queue) current_path = queue{1}; current_node = current_path(end); if ismember(current_node, dg_nodes) % 找到最近DG if length(current_path)-1 < min_dist min_dist = length(current_path)-1; nearest_dg = current_node; min_path = current_path; end end % 扩展邻居 for neighbor = graph{current_node} if ~visited(neighbor) && (~ismember(neighbor, island_node_all) || ...) new_path = [current_path, neighbor]; queue{end+1} = new_path; end end end end ``` ### 算法优势与创新点 1. **高效洪水算法**: - 采用队列实现广度优先搜索 - 按负荷重要度排序邻居节点 - 实时功率约束校验(5%裕度) 2. **动态孤岛融合**: - 自动检测交叉孤岛 - 动态更新孤岛功率和负荷 - 避免重复节点和功率计算 3. **智能负荷管理**: - 三级负荷削减策略(完全可控→50%可控→按重要度) - 优先恢复关键负荷 - 负荷重要度动态排序 4. **鲁棒性设计**: - 处理未连通节点 - 故障支路排除 - 边界条件检查
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