Scavenging differential evolution algorithm for smart grid demand side management

被引：11

作者：

Essiet, Ima ^{[1
]}

Sun, Yanxia ^{[1
]}

Wang, Zenghui ^{[2
]}

机构：

[1] Univ Johannesburg, Dept Elect & Elect Engn Sci, ZA-2006 Johannesburg, South Africa

[2] Univ South Africa, Dept Elect & Min Engn, ZA-1710 Florida, South Africa

来源：

2ND INTERNATIONAL CONFERENCE ON SUSTAINABLE MATERIALS PROCESSING AND MANUFACTURING (SMPM 2019) | 2019年 / 35卷

基金：

新加坡国家研究基金会;

关键词：

Renewable energy sources; demand response; differential evolution; optimization;

D O I：

10.1016/j.promfg.2019.05.084

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Demand side management (DSM) has gained a lot of attention in recent years as a result of increased deployment of alternative renewable energy resources in electric power grids. This paper presents a novel scavenging differential evolution algorithm which reuses unfit population agents in previous generations of a genetic algorithm. The performance of the proposed algorithm is compared to another popular evolutionary algorithm in literature: enhanced differential evolution (EDE). The cost minimization model consists of parameters which describe consumer energy cost savings for weekdays in Johannesburg using home energy management system (HEMS). The HEMS is incorporated with solar photovoltaic (PV) panel and plug-in electric vehicle (PHEV). Preliminary results show that the proposed algorithm outperforms EDE with regard to flattening consumer energy usage profile and minimizing discomfort related to load scheduling. (C) 2019 The Authors. Published by Elsevier B.V.

引用

页码：595 / 600

页数：6

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