Scavenging differential evolution algorithm for smart grid demand side management

Ima Essiet, Yanxia Sun, Zenghui Wang

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)595-600
Number of pages6
JournalProcedia Manufacturing
Publication statusPublished - 2019
Event2nd International Conference on Sustainable Materials Processing and Manufacturing, SMPM 2019 - Sun City, South Africa
Duration: 8 Mar 201910 Mar 2019


  • Demand response
  • Differential evolution
  • Optimization
  • Renewable energy sources

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence


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