【微电网_储能】基于启发式状态机策略和线性程序策略优化方法的微电网中的储能研究【给定系统约束和定价的情况下】（Matlab代码实现）

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📋📋📋本文目录如下：🎁🎁🎁

目录

💥1 概述

📚2 运行结果

🎉3 参考文献

🌈4 Matlab代码实现

💥1 概述

能源管理系统（EMS）有助于优化微电网中分布式能源（DER）的使用，特别是在涉及可变定价和发电时。本文使用预测定价和负荷条件来优化存储/销售来自电网规模电池系统的能量。演示了两种方法：启发式状态机策略和基于线性程序的优化方法。

📚2 运行结果

for i = 1:numSim
    if i <= numOffset*numel(pvDataSet)
        heuristicCost(end+1) = out(i).logsout{1}.Values.Data(end);
    else
        optCost(end+1)= out(i).logsout{1}.Values.Data(end);
    end
end
histogram(heuristicCost); hold on;
histogram(optCost);
legend('Heuristic','Optimization');
xlabel('Cost per Day ($)'); hold off;

部分代码：

function [Pgrid,Pbatt,Ebatt] = battSolarOptimize(N,dt,Ppv,Pload,Einit,Cost,FinalWeight,batteryMinMax)

% Minimize the cost of power from the grid while meeting load with power
% from PV, battery and grid

prob = optimproblem;

% Decision variables
PgridV = optimvar('PgridV',N);
PbattV = optimvar('PbattV',N,'LowerBound',batteryMinMax.Pmin,'UpperBound',batteryMinMax.Pmax);
EbattV = optimvar('EbattV',N,'LowerBound',batteryMinMax.Emin,'UpperBound',batteryMinMax.Emax);

% Minimize cost of electricity from the grid
prob.ObjectiveSense = 'minimize';
prob.Objective = dt*Cost'*PgridV - FinalWeight*EbattV(N);

% Power input/output to battery
prob.Constraints.energyBalance = optimconstr(N);
prob.Constraints.energyBalance(1) = EbattV(1) == Einit;
prob.Constraints.energyBalance(2:N) = EbattV(2:N) == EbattV(1:N-1) - PbattV(1:N-1)*dt;

% Satisfy power load with power from PV, grid and battery
prob.Constraints.loadBalance = Ppv + PgridV + PbattV == Pload;

% Solve the linear program
options = optimoptions(prob.optimoptions,'Display','none');
[values,~,exitflag] = solve(prob,'Options',options);

% Parse optmization results
if exitflag <= 0
Pgrid = zeros(N,1);
Pbatt = zeros(N,1);
Ebatt = zeros(N,1);
else
Pgrid = values.PgridV;
Pbatt = values.PbattV;
Ebatt = values.EbattV;
end