[Optimized scheduling] Based on particle swarm to realize economic scheduling optimization of microgrid under grid-connected model with matlab code

1 内容介绍

In recent years, the global environmental pollution problem and energy crisis have become increasingly prominent,People's awareness of environmental protection and energy conservation is constantly improving,Microgrid has become one of the research hotspots in the field of power system.Compared with the traditional large power grid,Microgrid has its own characteristics and advantages,The power generation process produces less pollutants、发电效率高、The utilization rate of fossil energy is low、Power transmission losses are negligible.In the engineering application of microgrid,It is of great practical significance to optimize the scheduling model of microgrid,The operating cost of microgrid is ultimately due to the use and maintenance consumption of mechanical equipment,The operating condition of each mechanical equipment is good and the service cycle is long, and the cost will be reduced accordingly,The optimal scheduling model can optimize the operating state of the user-side mechanical equipment while optimizing the use state of the distributed power equipment in the microgrid.The operating state of mechanical equipment is the inherent essence of economic cost,This paper studies the microgrid considering demand-side response,Comprehensive consideration of demand-side response、经济成本、环境成本,The optimal microgrid day-ahead scheduling model is obtained,Make the operation state of the supply-side and demand-side mechanical equipment reach the optimal state.

2 仿真代码

function cost=shiyingzhi_grid(PGT,iii,PL_new)

%适应值函数

%--------------------------------------------------------------------------

PGT=PGT;

T=1;

N=5;

% 输入原始数据,Including the load size of each period,The upper and lower limits of generator active output,Generator consumption cost factor,The predicted average output power of the wind farm in each period

pmax(1)=115;pmin(1)=0;

pmax(2)=40;pmin(2)=10; % FC

pmax(3)=40;pmin(3)=10; % MT

pmax(4)=30;pmin(4)=0; % DG

% Pollutant emission cost

% aa表示NOX,bb表示so2,cc表示co2,从左到右依次为Grid,DG,MT,FC

aa=[3.6,21.8,0.03,0.44];

bb=[4.54,0.454,0.006,0.008];

cc=[23,1.432,1.078,1.596];

% Consumption parameters of diesel generators

a=0.4333; b=0.2333;c=0.0071;

% 微型燃气轮机,P_mtIndicates the power produced by the gas turbine,Xl_mtIndicates the efficiency of the gas turbine

Xl_mt=0.5;

Price_mt=0.39;

L=9.7; % Low calorific value of natural gas,单位是kW.h/m3,kWh per cubic meter

% Xl_mt=0.0753*(P_mt/65)^3-0.3095*(P_mt/65)^2+0.1068;

% 燃料电池

Xl_fc=0.6;

Price_fc=0.39;

Price_grid=0.07; % The average electricity price of the grid

Price_grid_sell=0.07; % The price of electricity sold to the grid for excess energy from the microgrid

% Price_grid=[0.03 0.03 0.03 0.03  0.03 0.06 0.06 0.06 0.06 0.06 0.06 0.09 0.09 0.09 0.06 0.06 0.06 0.09 0.09 0.09 0.09 0.06 0.03 0.03];

%Price_grid=[0.01 0.01 0.01 0.01 0.01 0.04 0.04 0.04 0.04 0.04 0.04 0.1 0.1 0.1 0.04 0.04 0.04 0.1 0.1 0.1 0.1 0.04 0.01 0.01];

%%%%%%%%%%=[  1    2   3     4    5    6    7    8    9    10   11  12  13  14  15   16   17   18  19  20  21  22   23   24]

%--------------------------------------------------------------------------

C=zeros(1,T);

Cost=0;

% %     s=a+b*PGT(1)+c*PGT(1)*PGT(1)+Price_fc*PGT(2)/Xl_fc+Price_mt*PGT(3)/(0.0753*(PGT(3)/65)^3-0.3095*(PGT(3)/65)^2+0.4174*PGT(3)/65+0.1068)+1000000*(sum(PGT(1:N))-PL_new)^2;

   % 目标1 

   if PGT(1)>=0

       s=a+b*PGT(4)+c*PGT(4)*PGT(4)+(Price_fc/L)*PGT(2)/Xl_fc+(Price_mt/L)*PGT(3)/Xl_mt+Price_grid*PGT(1)+10000000000000000*(sum(PGT(1:N))-PL_new)^2;

    else 

       s=a+b*PGT(4)+c*PGT(4)*PGT(4)+(Price_fc/L)*PGT(2)/Xl_fc+(Price_mt/L)*PGT(3)/Xl_mt+Price_grid_sell*PGT(1)+10000000000000000*(sum(PGT(1:N))-PL_new)^2;

    end

    Cost=Cost+s;

    % 目标2

%     if PGT(1)>=0

%         s=0.0042*(aa(1)*PGT(1)+aa(2)*PGT(4)+aa(3)*PGT(3)+aa(4)*PGT(2))+0.00099*(bb(1)*PGT(1)+bb(2)*PGT(4)+bb(3)*PGT(3)+bb(4)*PGT(2))+0.000014*(cc(1)*PGT(1)+cc(2)*PGT(4)+cc(3)*PGT(3)+cc(4)*PGT(2))+10000000000*(sum(PGT(1:N))-PL_new)^2;

%     else 

%         s=0.0042*(aa(2)*PGT(4)+aa(3)*PGT(3)+aa(4)*PGT(2))+0.00099*(bb(2)*PGT(4)+bb(3)*PGT(3)+bb(4)*PGT(2))+0.000014*(cc(2)*PGT(4)+cc(3)*PGT(3)+cc(4)*PGT(2))+10000000000*(sum(PGT(1:N))-PL_new)^2;

%     end

%     Cost=Cost+s;

    % 目标 3

%     if PGT(1)>=0

%        s1=a+b*PGT(4)+c*PGT(4)*PGT(4)+(Price_fc/L)*PGT(2)/Xl_fc+(Price_mt/L)*PGT(3)/Xl_mt+Price_grid*PGT(1)+10000000000000000*(sum(PGT(1:N))-PL_new)^2;

%        s2=0.0042*(aa(1)*PGT(1)+aa(2)*PGT(4)+aa(3)*PGT(3)+aa(4)*PGT(2))+0.00099*(bb(1)*PGT(1)+bb(2)*PGT(4)+bb(3)*PGT(3)+bb(4)*PGT(2))+0.000014*(cc(1)*PGT(1)+cc(2)*PGT(4)+cc(3)*PGT(3)+cc(4)*PGT(2));

%        s=s1+s2;

%     else 

%        s1=a+b*PGT(4)+c*PGT(4)*PGT(4)+(Price_fc/L)*PGT(2)/Xl_fc+(Price_mt/L)*PGT(3)/Xl_mt+Price_grid_sell*PGT(1)+10000000000000000*(sum(PGT(1:N))-PL_new)^2;

%        s2=0.0042*(aa(2)*PGT(4)+aa(3)*PGT(3)+aa(4)*PGT(2))+0.00099*(bb(2)*PGT(4)+bb(3)*PGT(3)+bb(4)*PGT(2))+0.000014*(cc(2)*PGT(4)+cc(3)*PGT(3)+cc(4)*PGT(2));

%        s=s1+s2;

%     end

%     Cost=Cost+s;

cost=Cost;

3 运行结果

4 参考文献

[1]孙阳. Research on microgrid scheduling model and optimal operation strategy based on particle swarm optimization[D]. 吉林大学.

[2]Li Yanzhao, 王击. Optimal scheduling of grid-connected operation of microgrid based on particle swarm algorithm[J]. 信息通信, 2018(4):2.​

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