# DR-of-Residential-Houses-Equipped-with-PV-BB-Systems-An-Application-Study-Using-EAs_Energies2020
This repository contains the experimental set up used for the paper: "Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms" Published in Energies journal MDPI 2020
To replicate the experiments, access to codes folder and run
"RunningExperiments.m" for EA approach (C1 and C3 case studies)
"RunningExperiments_distributed" for EA distributed approach (C2 and C4 case studies)
*Note:Result files are not included because of space, but can be optained by running the experiments
file Send me an email for further questions ing.flezama@gmail.com
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温馨提示
该程序是《Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms》完全复现程序,主要做的是一个家庭微网能量管理系统,考虑家庭微网是并网运行的,可以向电网出售电量或者购入电量,同时家庭微网中含有空调、热水器、洗衣机等需求响应负荷资源,在电价的引导下积极进行需求响应,算法的求解方面采用了一种并行的差分进化算法,使得对于大规模家庭用户场景下依然求解速度很快,对比多种粒子群算法以及其余差分进化算法,求解效果比较突出。 参考文献:《Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms》
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