function mpc = case118
%CASE118 Power flow data for IEEE 118 bus test case.
% Please see CASEFORMAT for details on the case file format.
% This data was converted from IEEE Common Data Format
% (ieee118cdf.txt) on 15-Oct-2014 by cdf2matp, rev. 2393
% See end of file for warnings generated during conversion.
%
% Converted from IEEE CDF file from:
% https://labs.ece.uw.edu/pstca/
% With baseKV data take from the PSAP format file from the same site,
% added manually on 10-Mar-2006.
% Branches 86--87, 68--116 changed from transmission lines (tap ratio = 0)
% to transformers (tap ratio = 1) for consistency with bus base voltages
% on 2019-02-15.
%
% 08/25/93 UW ARCHIVE 100.0 1961 W IEEE 118 Bus Test Case
% MATPOWER
%% MATPOWER Case Format : Version 2
mpc.version = '2';
%%----- Power Flow Data -----%%
%% system MVA base
mpc.baseMVA = 100;
%% bus data
% bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin
mpc.bus = [
1 2 51 27 0 0 1 0.955 10.67 138 1 1.06 0.94;
2 1 20 9 0 0 1 0.971 11.22 138 1 1.06 0.94;
3 1 39 10 0 0 1 0.968 11.56 138 1 1.06 0.94;
4 2 39 12 0 0 1 0.998 15.28 138 1 1.06 0.94;
5 1 0 0 0 -40 1 1.002 15.73 138 1 1.06 0.94;
6 2 52 22 0 0 1 0.99 13 138 1 1.06 0.94;
7 1 19 2 0 0 1 0.989 12.56 138 1 1.06 0.94;
8 2 28 0 0 0 1 1.015 20.77 345 1 1.06 0.94;
9 1 0 0 0 0 1 1.043 28.02 345 1 1.06 0.94;
10 2 0 0 0 0 1 1.05 35.61 345 1 1.06 0.94;
11 1 70 23 0 0 1 0.985 12.72 138 1 1.06 0.94;
12 2 47 10 0 0 1 0.99 12.2 138 1 1.06 0.94;
13 1 34 16 0 0 1 0.968 11.35 138 1 1.06 0.94;
14 1 14 1 0 0 1 0.984 11.5 138 1 1.06 0.94;
15 2 90 30 0 0 1 0.97 11.23 138 1 1.06 0.94;
16 1 25 10 0 0 1 0.984 11.91 138 1 1.06 0.94;
17 1 11 3 0 0 1 0.995 13.74 138 1 1.06 0.94;
18 2 60 34 0 0 1 0.973 11.53 138 1 1.06 0.94;
19 2 45 25 0 0 1 0.963 11.05 138 1 1.06 0.94;
20 1 18 3 0 0 1 0.958 11.93 138 1 1.06 0.94;
21 1 14 8 0 0 1 0.959 13.52 138 1 1.06 0.94;
22 1 10 5 0 0 1 0.97 16.08 138 1 1.06 0.94;
23 1 7 3 0 0 1 1 21 138 1 1.06 0.94;
24 2 13 0 0 0 1 0.992 20.89 138 1 1.06 0.94;
25 2 0 0 0 0 1 1.05 27.93 138 1 1.06 0.94;
26 2 0 0 0 0 1 1.015 29.71 345 1 1.06 0.94;
27 2 71 13 0 0 1 0.968 15.35 138 1 1.06 0.94;
28 1 17 7 0 0 1 0.962 13.62 138 1 1.06 0.94;
29 1 24 4 0 0 1 0.963 12.63 138 1 1.06 0.94;
30 1 0 0 0 0 1 0.968 18.79 345 1 1.06 0.94;
31 2 43 27 0 0 1 0.967 12.75 138 1 1.06 0.94;
32 2 59 23 0 0 1 0.964 14.8 138 1 1.06 0.94;
33 1 23 9 0 0 1 0.972 10.63 138 1 1.06 0.94;
34 2 59 26 0 14 1 0.986 11.3 138 1 1.06 0.94;
35 1 33 9 0 0 1 0.981 10.87 138 1 1.06 0.94;
36 2 31 17 0 0 1 0.98 10.87 138 1 1.06 0.94;
37 1 0 0 0 -25 1 0.992 11.77 138 1 1.06 0.94;
38 1 0 0 0 0 1 0.962 16.91 345 1 1.06 0.94;
39 1 27 11 0 0 1 0.97 8.41 138 1 1.06 0.94;
40 2 66 23 0 0 1 0.97 7.35 138 1 1.06 0.94;
41 1 37 10 0 0 1 0.967 6.92 138 1 1.06 0.94;
42 2 96 23 0 0 1 0.985 8.53 138 1 1.06 0.94;
43 1 18 7 0 0 1 0.978 11.28 138 1 1.06 0.94;
44 1 16 8 0 10 1 0.985 13.82 138 1 1.06 0.94;
45 1 53 22 0 10 1 0.987 15.67 138 1 1.06 0.94;
46 2 28 10 0 10 1 1.005 18.49 138 1 1.06 0.94;
47 1 34 0 0 0 1 1.017 20.73 138 1 1.06 0.94;
48 1 20 11 0 15 1 1.021 19.93 138 1 1.06 0.94;
49 2 87 30 0 0 1 1.025 20.94 138 1 1.06 0.94;
50 1 17 4 0 0 1 1.001 18.9 138 1 1.06 0.94;
51 1 17 8 0 0 1 0.967 16.28 138 1 1.06 0.94;
52 1 18 5 0 0 1 0.957 15.32 138 1 1.06 0.94;
53 1 23 11 0 0 1 0.946 14.35 138 1 1.06 0.94;
54 2 113 32 0 0 1 0.955 15.26 138 1 1.06 0.94;
55 2 63 22 0 0 1 0.952 14.97 138 1 1.06 0.94;
56 2 84 18 0 0 1 0.954 15.16 138 1 1.06 0.94;
57 1 12 3 0 0 1 0.971 16.36 138 1 1.06 0.94;
58 1 12 3 0 0 1 0.959 15.51 138 1 1.06 0.94;
59 2 277 113 0 0 1 0.985 19.37 138 1 1.06 0.94;
60 1 78 3 0 0 1 0.993 23.15 138 1 1.06 0.94;
61 2 0 0 0 0 1 0.995 24.04 138 1 1.06 0.94;
62 2 77 14 0 0 1 0.998 23.43 138 1 1.06 0.94;
63 1 0 0 0 0 1 0.969 22.75 345 1 1.06 0.94;
64 1 0 0 0 0 1 0.984 24.52 345 1 1.06 0.94;
65 2 0 0 0 0 1 1.005 27.65 345 1 1.06 0.94;
66 2 39 18 0 0 1 1.05 27.48 138 1 1.06 0.94;
67 1 28 7 0 0 1 1.02 24.84 138 1 1.06 0.94;
68 1 0 0 0 0 1 1.003 27.55 345 1 1.06 0.94;
69 3 0 0 0 0 1 1.035 30 138 1 1.06 0.94;
70 2 66 20 0 0 1 0.984 22.58 138 1 1.06 0.94;
71 1 0 0 0 0 1 0.987 22.15 138 1 1.06 0.94;
72 2 12 0 0 0 1 0.98 20.98 138 1 1.06 0.94;
73 2 6 0 0 0 1 0.991 21.94 138 1 1.06 0.94;
74 2 68 27 0 12 1 0.958 21.64 138 1 1.06 0.94;
75 1 47 11 0 0 1 0.967 22.91 138 1 1.06 0.94;
76 2 68 36 0 0 1 0.943 21.77 138 1 1.06 0.94;
77 2 61 28 0 0 1 1.006 26.72 138 1 1.06 0.94;
78 1 71 26 0 0 1 1.003 26.42 138 1 1.06 0.94;
79 1 39 32 0 20 1 1.009 26.72 138 1 1.06 0.94;
80 2 130 26 0 0 1 1.04 28.96 138 1 1.06 0.94;
81 1 0 0 0 0 1 0.997 28.1 345 1 1.06 0.94;
82 1 54 27 0 20 1 0.989 27.24 138 1 1.06 0.94;
83 1 20 10 0 10 1 0.985 28.42 138 1 1.06 0.94;
84 1 11 7 0 0 1 0.98 30.95 138 1 1.06 0.94;
85 2 24 15 0 0 1 0.985 32.51 138 1 1.06 0.94;
86 1 21 10 0 0 1 0.987 31.14 138 1 1.06 0.94;
87 2 0 0 0 0 1 1.015 31.4 161 1 1.06 0.94;
88 1 48 10 0 0 1 0.987 35.64 138 1 1.06 0.94;
89 2 0 0 0 0 1 1.005 39.69 138 1 1.06 0.94;
90 2 163 42 0 0 1 0.985 33.29 138 1 1.06 0.94;
91 2 10 0 0 0 1 0.98 33.31 138 1 1.06 0.94;
92 2 65 10 0 0 1 0.993 33.8 138 1 1.06 0.94;
93 1 12 7 0 0 1 0.987 30.79 138 1 1.06 0.94;
94 1 30 16 0 0 1 0.991 28.64 138 1 1.06 0.94;
95 1 42 31 0 0 1 0.981 27.67 138 1 1.06 0.94;
96 1 38 15 0 0 1 0.993 27.51 138 1 1.06 0.94;
97 1 15 9 0 0 1 1.011 27.88 138 1 1.06 0.94;
98 1 34 8 0 0 1 1.024 27.4 138 1 1.06 0.94;
99 2 42 0 0 0 1 1.01 27.04 138 1 1.06 0.94;
100 2 37 18 0 0 1 1.017 28.03 138 1 1.06 0.94;
101 1 22 15 0 0 1 0.993 29.61 138 1 1.06 0.94;
102 1 5 3 0 0 1 0.991 32.3 138 1 1.06 0.94;
103 2 23 16 0 0 1 1.001 24.44 138 1 1.06 0.94;
104 2 38 25 0 0 1 0.971 21.69 138 1 1.06 0.94;
105 2 31 26 0 20 1 0.965 20.57 138 1 1.06 0.94;
106 1 43 16 0 0 1 0.962 20.32 138 1 1.06 0.94;
107 2 50 12 0 6 1 0.952 17.53 138 1 1.06 0.94;
108 1 2 1 0 0 1 0.967 19.38 138 1 1.06 0.94;
109 1 8 3 0 0 1 0.967 18.93 138 1 1.06 0.94;
110 2 39 30 0 6 1 0.973 18.09 138 1 1.06 0.94;
111 2 0 0 0 0 1 0.98 19.74 138 1 1.06 0.94;
112 2 68 13 0 0 1 0.975 14.99 138 1 1.06 0.94;
113 2 6 0 0 0 1 0.993 13.74 138 1 1.06 0.94;
114 1 8 3 0 0 1 0.96 14.46 138 1 1.06 0.94;
115 1 22 7 0 0 1 0.96 14.46 138 1 1.06 0.94;
116 2 184 0 0 0 1 1.005 27.12 138 1 1.06 0.94;
117 1 20 8 0 0 1 0.974 10.67 138 1 1.06 0.94;
118 1 33 15 0 0 1 0.949 21.92 138 1 1.06 0.94;
];
%% 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
mpc.gen = [
1 0 0 15 -5 0.955 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
4 0 0 300 -300 0.998 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
6 0 0 50 -13 0.99 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
8 0 0 300 -300 1.015 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
10 450 0 200 -147 1.05 100 1 550 0 0 0 0 0 0 0 0 0 0 0 0;
12 85 0 120 -35 0.99 100 1 185 0 0 0 0 0 0 0 0 0 0 0 0;
15 0 0 30 -10 0.97 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
18 0 0 50 -16 0.973 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
19 0 0 24 -8 0.962 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
24 0 0 300 -300 0.992 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
25 220 0 140 -47 1.05 100 1 320 0 0 0 0 0 0 0 0 0 0 0 0;
26 314 0 1000 -1000 1.015 100 1 414 0 0 0 0 0 0 0 0 0 0 0 0;
27 0 0 300 -300 0.968 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
31 7 0 300 -300 0.967 100 1 107 0 0 0 0 0 0 0 0 0 0 0 0;
32 0 0 42 -14 0.963 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
34 0 0 24 -8 0.984 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
36 0 0 24 -8 0.98 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
40 0 0 300 -300 0.97 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
42 0 0 300 -300 0.985 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
46 19 0 100 -100 1.005 100 1 119 0 0 0 0 0 0 0 0 0 0 0 0;
49 204 0 210 -85 1.025 100 1 304 0 0 0 0 0 0 0 0 0 0 0 0;
54 48 0 300 -300 0.955 100 1 148 0 0 0 0 0 0 0 0 0 0 0 0;
55 0 0 23 -8 0.952 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
56 0 0 15 -8 0.954 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0;
59 155 0 180 -60 0.985 100 1 255 0 0 0 0 0 0 0 0 0 0 0 0;
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25号资源-源程序:论文可在知网下载《多源动态最优潮流的分布鲁棒优化方法》本人博客有解读
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该资源详细解读可关注博主免费专栏《论文与完整程序》25号博文 相关文献可参考: 1.多源动态最优潮流的分布鲁棒优化方法_竺如洁 2.A_state-independent linear power flow model with accurate_estimation of voltage magnitude 3.Wasserstein Metric Based Distributionally Robust Approximate Framework For Unit 针对大规模清洁能源接入电网引起的系统鲁棒性和经济性协调问题,提出含风–光–水–火多种能源的分布鲁棒动态最优潮流模型。采用分布鲁棒优化方法将风光不确定性描述为包含概率分布信息的模糊不确定集。将模糊不确定集构造为一个以风光预测误差经验分布为中心,以 Wasserstein距离为半径的 Wasserstein 球。在满足风光预测误差服从模糊不确定集中极端概率分布情况下最小化运行费用。由于梯级水电厂模型为混合整数模型,为了提高计算效率,将交流潮流近似为解耦线性潮流。
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1010【分布鲁棒】多源动态最优潮流的分布式鲁棒优化.rar (15个子文件)
1010【分布鲁棒】多源动态最优潮流的分布式鲁棒优化
case118.m 33KB
fljl.mat 4KB
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118系统图.jpg 93KB
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ab.lp 309KB
newton1.m 9KB
getConssGen.m 780B
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~$多源动态.docx 162B
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