【需求响应】基于进化算法的住宅光伏电池系统需求响应研究(Matlab代码实现)

简介: 【需求响应】基于进化算法的住宅光伏电池系统需求响应研究(Matlab代码实现)

💥1 概述

文献来源:

摘要:配备分布式能源的家庭,可以根据运营情况自行使用现场发电,也可以将能源出售给电网,或者两者兼而有之。本文建立了能源服务提供商对家庭能源资源进行优化的模型。我们考虑配备了技术的房屋,这些技术支持实际减少能源账单,从而执行需求响应行动。建立了一个数学公式,以求得家用设备的最优调度,以最小化能源账单和需求响应削减行动。本文的创新方法除了采用调度模型外,还采用了进化算法来解决两种优化方法下的问题:(a)非并行方法将所有家庭的变量同时组合;(b)基于并行的方法利用多人口机制和独立优化,利用家庭之间变量的独立性。


结果表明,基于并行的方法可以提高测试的进化算法在更大的问题实例中的性能。因此,虽然增加了问题的规模,即增加了家庭的数目,但拟议的方法将更为有利。总体而言,涡旋搜索克服了所有其他测试算法(包括众所周知的差分进化和粒子群优化),在所有情况下的适应度值都提高了30%左右,证明了它在解决所提出问题方面的有效性。


关键词:需求响应;能源服务提供商;储能系统;进化算法;优化;太阳能光伏发电


原文摘要:


Abstract: Households equipped with distributed energy resources, such as storage units and renewables, open the possibility of self-consumption of on-site generation, sell energy to the grid, or do both according to the context of operation. In this paper, a model for optimizing the energy resources of households by an energy service provider is developed. W e consider houses equipped with technologies that support the actual reduction of energy bills and therefore perform demand response actions. A mathematical formulation is developed to obtain the optimal scheduling of household devices that minimizes energy bill and demand response curtailment actions. In addition to the scheduling model, the innovative approach in this paper includes evolutionary algorithms used to solve the problem under two optimization approaches: (a) the non-parallel approach combine the variables of all households at once; (b) the parallel-based approach takes advantage of the independence of variables between households using a multi-population mechanism and independent optimizations. Results show that the parallel-based approach can improve the performance of the tested evolutionary algorithms for larger instances of the problem. Thus, while increasing the size of the problem, namely increasing the number of households, the proposed methodology will be more advantageous. Overall, vortex search overcomes all other tested algorithms (including the well-known differential evolution and

particle swarm optimization) achieving around 30% better fitness value in all the cases, demonstrating its effectiveness in solving the proposed problem.

Keywords: demand response; energy service provider; energy storage system; evolutionary

algorithms; optimization; photovoltaic generation


本文流程图:  


📚2 运行结果

部分代码:

%% Load MH parameters (e.g., get MH parameters from DEparameters.m file)
    switch Select_Algorithm
        case 1
            addpath('DEalg')
            algorithm='DE_rand'; %'The participants should include their algorithm here'
            DEparameters %Function defined by the participant
            No_solutions=deParameters.I_NP; %Notice that some algorithms are limited to one individual
        case 2
            addpath('PSOalg')
            algorithm='PSO_LVS'; %'The participants should include their algorithm here'
            psoParameters %Function defined by the participant
            No_solutions=PSOparameters.nPop; %Notice that some algorithms are limited to one individual
        case 3
            addpath('alg_HyDEDF')
            algorithm='HyDE_DF'; %'The participants should include their algorithm here'
            HyDEparameters %Function defined by the participant
            No_solutions=deParameters.I_NP; %Notice that some algorithms are limited to one individual
            deParameters.I_strategy=3;
            deParameters.I_strategyVersion=2;
        case 4
            addpath('alg_HyDEDF')
            algorithm='HyDE'; %'The participants should include their algorithm here'
            HyDEparameters %Function defined by the participant
            No_solutions=deParameters.I_NP; %Notice that some algorithms are limited to one individual
            deParameters.I_strategy=3;
            deParameters.I_strategyVersion=3;
        case 5
            addpath('alg_HyDEDF')
            algorithm='VS'; %'The participants should include their algorithm here'
            HyDEparameters %Function defined by the participant
            No_solutions=deParameters.I_NP; %Notice that some algorithms are limited to one individual
            deParameters.I_strategy=3;
            deParameters.I_strategyVersion=1;
        case 6
            addpath('alg_HyDEDF')
            algorithm='DE_best'; %'The participants should include their algorithm here'
            HyDEparameters %Function defined by the participant
            No_solutions=deParameters.I_NP; %Notice that some algorithms are limited to one individual
            deParameters.I_strategy=2;
            deParameters.I_strategyVersion=1;
        otherwise
            fprintf(1,'No algorithm selected\n');
    end
    fileResultsname=['Results\' algorithm 'NP_20_Nplayers' num2str(Nplayers) '.mat'];

🎉3 参考文献

部分理论来源于网络,如有侵权请联系删除。

🌈4 Matlab代码实现


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