I want to optimize the 5 objective function

I want to optimize five objective problems and having 5 variables

and it has some historical values of the variable and it has total 96 values. thats why i have choosen T=96

Function values

function [F g h]=wo_mpc(x)


T=96;
P_load=x(:,1);
P_pa=x(:,2);
P_bes=x(:,3);
P_ev=x(:,4);
P_grid=x(:,5);

%%%%%%%%%%%LOAD HISTORICAL VALUE%%%%%%%%%%

P_hist=[ 2000  2088.4	2023.2	2023.2	2023.2	2023.2	1958.3	1958.3	1958.3	1958.3	1918.6	1918.6	1918.6	1918.6	1915.0	1915.0	1915.0	1915.0			1964.2			1964.2			1964.2			1964.2			2091.8			2091.8			2091.8			2091.8			2300.0			2300.0			2300.0			2300.0			2399.9			2399.9			2399.9			2399.9			2345.8			2345.8			2345.8			2345.8			2191.9			2191.9			2191.9			2191.9			2055.5			2055.5			2055.5			2055.5			1958.3			1958.3			1958.3			1958.3			1910.7			1910.7			1910.7			1910.7			1901.2			1901.2			1901.2			1901.2			1932.2			1932.2			1932.2			1932.2			2028.9			2028.9			2028.9			2028.9			2144.2			2144.2			2144.2			2144.2			2328.5			2328.5			2328.5			2328.5			2502.8			2502.8			2502.8			2502.8			2652.1			2652.1			2652.1			2652.1			2635.1			2635.1			2635.1			2635.1			2536.4			2536.4			2536.4			2536.4			2371.5			2371.5			2371.5			2371.5			2218.1			2218.1			2218.1];
%%LOAD OBJECTIVE FUNCTION%%
f1=sum(((P_hist(1:T))-(P_load(1:T))).^2);


%%%%%%%%%PV HISTORICAL VALUE%%%%%%%%%%%%%

Ppa_hist=[0			0			0			0			0			0			0		0			0			0			0			0			0			0			0			0			0			0			0			0			0			0			0			0			0		0			.1			1.2			14.0			70.4			181.5			327.7			523.8			695.3			878.9			1025.1			1083.0			1101.6			1183.8			1170.8			1183.6			1158.9			1198.9			1188.9			1194.3			1195.1			1173.8			1185.8			1186.8			1227.1			1210.0			1195.1			1192.4			1175.9			1234.0			1254.5			1249.0			1268.0			1224.4			1226.4			1182.5			1222.3			1163.6			1140.4			1162.9			1019.7			996.0			1022.3			994.2			860.9			863.3			731.3			549.1			355.4			197.0			90.5			36.9			14.7			14.8			12.0			8.1			4.9			4.6			4.7			4.4			3.2			3.3			3.2			3.1			0			0			0			0			0			0			0];
%%PV OBJECTIVE FUNCTION%%
f2=sum(((Ppa_hist(1:T))-(P_pa(1:T))).^2);


%%%%%%%%%BES HISTORICAL VALUE%%%%%%%%%%%%%

P_bes_hist=[-73	-57	-88	-100			-112			-124			-71			-86			-82			-83			-122			-149			-130			-76			-130			-29			-37			-31			-18			42			61			69			85			8			67			69			87			164			201			272			200			361			414			203			120			472			369			-5			-156			339			20			-82			-204			-193			-330			-340			-368			-355			-125			-352			-419			-531			-484			-486			-448			-372			-479			-383			-133			-390			-136			-141			139			-129			274			247			66			328			447			229			213			85			-2			197			310			519			809			830			821			771			786			829			812			830			829			826			778			932			942			864			672			447			335			64			116			173];
%%BES OBJECTIVE FUNCTION%%
f3=sum((P_bes_hist(2:T))-P_bes(1:T-1));


%%%%%%%%%EV HISTORICAL VALUE%%%%%%%%%%%%%%%

P_ev_hist=[	0	0	0	400	425	435	440	455	475	500	700	850	900	960	1000	1100			1250			1365			1145			1055			1000			996			970			950			935			600			520			200			-200			-350			-435			-765			-1356			-1400			-1156			-985			-915			-895			-850			-800			-650			-600			-545			-520			-300			-250			-150			315			350			368			400			412			420			430			460			500			485			470			465			462			458			452			448			442			440			437			550			570			475			460			462			465			470			475			450			435			420			360			320			225			195			160			-120			-135			-155			-175			-190			-220			-185			-160			-140			500			550			750			1000 1200];
%%EV OBJECTIVE FUNCTION%%
f4=sum(abs((P_ev_hist(2:T))-(P_ev(1:T-1))));

%%%%%%%%Grid HISTORICAL VALUE%%%%%%%%%%%%%%%

P_grid_hist=[2143.2			2146.4			2121.7			2108.7			2074.9			2073.9			2074.8			2075.6			2068.8			2073.9			2064.8			2045.5			2026.4			2003.7			1999.7			1997.4			2005.8			2024.9			2040.2			2043.6			2066.4			2135.2			2160.2			2204.3			2259.0			2361.4			2399.4			2443.4			2476.9			2540.3			2550.3			2542.6			2519.9			2501.7			2475.6			2475.5			2455.5			2412.9			2382.5			2360.2			2341.7			2325.2			2299.7			2261.0			2243.1			2240.7			2197.9			2210.5			2200.6			2192.5			2181.0			2187.2			2197.6			2177.4			2179.3			2178.3			2158.3			2198.4			2185.5			2207.9			2210.5			2232.6			2214.4			2222.4			2246.5			2251.5			2278.0			2334.3			2364.2			2393.7			2451.4			2483.8			2555.6			2559.4			2605.7			2650.0			2685.4			2734.6			2760.8			2762.3			2734.8			2708.9			2698.1			2673.0			2662.8			2657.1			2622.5			2589.5			2542.3			2501.6			2460.8			2422.2			2359.9			2334.2			2301.3			2268.4];
a=max(P_grid_hist);
b=min(P_grid_hist);

Cost=[1700.015 1700.030 1600.086 1600.085 1600.100 1700.075 1700.046 1700.030 1500.010 1500.043 1252.081 1252.051 1083.994 1052.050 1052.009 1020.452 979.590 1020.497 1020.434 1017.435 1083.988 1411.538 1452.039 149023 1083.941 1083.965 999.940 950.092 1400.032 1083.991 1300.071 1400.030 1441.643 1300.019 999.952 749.971 750.074 750.061 1020.464 1020.481 1380.237 1481.096 1390.430 1152.098 1020.496 852.064 852.007 700.084 700.079 699.935 564.528 440.008 400.075 565.514 699.966 852.001 852.060 852.088 1200.033 1200.078 1302.011 1302.082 1302.096 1382.475 1800.082 1803.621 1803.287 1803.111 1600.089 750.062 800.060 749.901 1020.480 1020.432 1600.040 1600.057 1800.679 1800.639 1800.619 1600.083 1370.539 1200.044 1200.021 1560.526 1500.073 1381.151 1380.190 1430.197 1083.936 1020.483 1150.152 1083.980 1801.742 1802.024 1786.004 1805.639];
g=(a-b)+(sum(abs((P_grid(2:T))-(P_grid(1:T-1)))))+(sum(((P_grid(1:T-2))-(2*P_grid(2:T-1))+(P_grid(3:T))).^2));
h=sum((Cost(1:T)).*(P_grid(1:T)));
f5=g+h;
F=[ f1 f2 f3 f4 f5]
    end

To create a equality constraints

    function [c ceq]=nonlin(x)
P_load=x(:,1);
P_pa=x(:,2);
P_bes=x(:,3);
P_ev=x(:,4);
P_grid=x(:,5);
ceq=P_load+P_pa+P_bes+P_ev+P_grid;
c=[]
    end

and finally the main program

    T=96;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
P_hist=[2088.4	2023.2	2023.2	2023.2	2023.2	1958.3	1958.3	1958.3	1958.3	1918.6	1918.6	1918.6	1918.6	1915.0	1915.0	1915.0	1915.0			1964.2			1964.2			1964.2			1964.2			2091.8			2091.8			2091.8			2091.8			2300.0			2300.0			2300.0			2300.0			2399.9			2399.9			2399.9			2399.9			2345.8			2345.8			2345.8			2345.8			2191.9			2191.9			2191.9			2191.9			2055.5			2055.5			2055.5			2055.5			1958.3			1958.3			1958.3			1958.3			1910.7			1910.7			1910.7			1910.7			1901.2			1901.2			1901.2			1901.2			1932.2			1932.2			1932.2			1932.2			2028.9			2028.9			2028.9			2028.9			2144.2			2144.2			2144.2			2144.2			2328.5			2328.5			2328.5			2328.5			2502.8			2502.8			2502.8			2502.8			2652.1			2652.1			2652.1			2652.1			2635.1			2635.1			2635.1			2635.1			2536.4			2536.4			2536.4			2536.4			2371.5			2371.5			2371.5			2371.5			2218.1			2218.1			2218.1];
Ppa_hist=[0			0			0			0			0			0			0		0			0			0			0			0			0			0			0			0			0			0			0			0			0			0			0			0			0		0			.1			1.2			14.0			70.4			181.5			327.7			523.8			695.3			878.9			1025.1			1083.0			1101.6			1183.8			1170.8			1183.6			1158.9			1198.9			1188.9			1194.3			1195.1			1173.8			1185.8			1186.8			1227.1			1210.0			1195.1			1192.4			1175.9			1234.0			1254.5			1249.0			1268.0			1224.4			1226.4			1182.5			1222.3			1163.6			1140.4			1162.9			1019.7			996.0			1022.3			994.2			860.9			863.3			731.3			549.1			355.4			197.0			90.5			36.9			14.7			14.8			12.0			8.1			4.9			4.6			4.7			4.4			3.2			3.3			3.2			3.1			0			0			0			0			0			0			0];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
x0=ones(5,96).*[0];
A=[];
b=[];
Aeq=[];                                                          
Beq=[];

lb=ones(5,96).*[(0.5*(P_hist(1:T))),0,-100,-140,-3000];

ub=ones(5,96).*[(P_hist(1:T)),(Ppa_hist(1:T)),55,140,3000];
fun=@wo_mpc;
nlcon=@nonlin;

[x,fval,exitflag,output]=paretosearch(fun,5,A,b,Aeq,Beq,lb,ub,nlcon)

after that it showing that matrix daimension must agree

and error is comming in lower bound and upper bound

[Matt J]

after that it showing that matrix daimension must agree

You should show that error to us as well. Either copy/paste the errors or, better yet, Run the code here for us (not on your local computer).

[SPANDY1993]