Multiple group search optimization based on decomposition for multi-objective dispatch with electric vehicle and wind power uncertainties

Xian Zhang, Ka Wing Chan, Huaizhi Wang, Bin Zhou, Guibin Wang, Jing Qiu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

While the number of plug-in electric vehicles (PEVs) increases rapidly, the application potential of PEVs should be accounted in electric power dispatch with several conflicting and competing objectives such as providing vehicle-to-grid (V2G) service or coordinating with wind power. To solve this highly constrained multi-objective optimization problem (MOOP), a multiple group search optimization based on decomposition (MGSO/D) is proposed considering the uncertainties of PEVs and wind power. Specifically, the decomposition approach effectively reduces the computational complexity, and the innovatively incorporated producer-scrounger model effectively improves the diversity and spanning of the Pareto-optimal front (PF). Meanwhile, the estimation error punishment is utilized to take into account of uncertainties. The performance of MGSO/D and the effectiveness of the uncertainty model are investigated on the IEEE 30-bus and 118-bus system with wind farms and PEV aggregators. Simulation results demonstrate the superiority of MGSO/D to solve this MOOP with practical uncertainties by comparing with well-established Pareto heuristic methods.

Original languageEnglish
Article number114507
JournalApplied Energy
Volume262
DOIs
Publication statusPublished - 15 Mar 2020

Keywords

  • Multi-objective optimization
  • Multiple group search optimization based on decomposition
  • Pareto-optimal front
  • Plug-in electric vehicles

ASJC Scopus subject areas

  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

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