case.m

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)

function [baseMVA, bus, gen, branch, area, gencost] = case(p,q,c,t,k)

%%-----  Power Flow Data  -----%%
%% system MVA base
baseMVA = 100.0000;     

%% bus data
bus = [
         1   3   0.0        0.0       0.0    0.0    1   1.0000  0.0000  236.5000    1   1.0700  1.0000;
         2   1   0.0        0.0       0.0    0.0    1   1.0000  0.0000  236.5000    1   1.0700  1.0000;
         3   1   0.0        0.0       0.0    0.0    1   1.0000  0.0000  110.0000    1   1.0700  1.0000;
         4   1   0.0        0.0       0.0    0.0    1   1.0000  0.0000  115.0000    1   1.0700  1.0000;
         5   1   0.0        0.0       0.0    0.0    1   1.0000  0.0000  115.0000    1   1.0700  1.0000;
         6   1   0.0        0.0       0.0    0.0    1   1.0000  0.0000  110.0000    1   1.0700  1.0000;
         7   1   0.0        0.0       0.0    0.0    1   1.0000  0.0000  110.0000    1   1.0700  1.0000;
         8   1   p(1)+p(2)  q(1)+q(2) 0.0    c(1)   1   1.0000  0.0000  11.00000    1   1.0700  1.0000;
         9   1   p(3)+p(4)  q(3)+q(4) 0.0    c(2)   1   1.0000  0.0000  11.00000    1   1.0700  1.0000;
         10  1   p(5)       q(5)      0.0    c(3)   1   1.0000  0.0000  10.50000    1   1.0700  1.0000;
         11  1   p(6)       q(6)      0.0    c(4)   1   1.0000  0.0000  11.00000    1   1.0700  1.0000;
         12  1   p(8)+(9)   q(8)+q(9) 0.0    c(5)   1   1.0000  0.0000  10.50000    1   1.0700  1.0000;
         13  1   p(7)       q(7)      0.0    c(6)   1   1.0000  0.0000  10.50000    1   1.0700  1.0000;
         14  1   0.0	    0.0	      0.0	 0.0	1	1.0000	0.0000	1.000000 	1	1.1000	0.9000;  
     ];
     
%% generator data
gen = [
	     1  0.000	0.0000	300.0000	0.0000	k	100.0000	1	150.0000	0.0000;
      ];
      
%% branch data
branch = [
	       1	2	0.0008	0.0028	0.09744 	360.00	360.00  360.00	0.0000	0.0000	1;
           2    14  0.0015  0.0892  0.00000     180.00  180.00  180.00  t(1)    0.0000  1;
           14   3   0.0007  -0.007  0.00000     180.00  180.00  180.00  1.1000  0.0000  1;
           14   8   0.0013  0.0550  0.00000     90.000  90.000  90.000  1.1000  0.0000  1;
           3    4   0.0018  0.0047  0.00912     130.00  130.00  130.00  0.0000  0.0000  1;
           3    5   0.0013  0.0033  0.00638     130.00  130.00  130.00  0.0000  0.0000  1;
           5    6   0.001   0.0026  0.00524     130.00  130.00  130.00  0.0000  0.0000  1;
           5    7   0.001   0.0026  0.00524     130.00  130.00  130.00  0.0000  0.0000  1;
           4    9   0.0120  0.2620  0.00000     40.000  40.000  40.000  t(2)    0.0000  1;
           4    10  0.0101  0.2580  0.00000     40.000  40.000  40.000  t(3)    0.0000  1;
           5    11  0.0109  0.2540  0.00000     40.000  40.000  40.000  t(4)    0.0000  1;
           6    12  0.0108  0.2590  0.00000     40.000  40.000  40.000  t(5)    0.0000  1;
           7    13  0.0102  0.2650  0.00000     40.000  40.000  40.000  t(6)    0.0000  1;
       ];
 
      
      %%-----  OPF Data  -----%%
%% area data
area = [
	1	8;
	2	23;
	3	26;
];

%% generator cost data
gencost = [
	2	0.00	0.00	3	0.02	2	   0    0.015	  1.5	      0;
];

return;