A weight-aggregation multi-objective PSO algorithm for load scheduling of PHEVs

Shuwei Feng, Qi Kang, Xiaoshuang Chen, Mengchu Zhou, Sisi Li, Jing An

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

As the supply potential has declined gradually and the pressure for better environment intensifies, the demand for clean energy arises continuously. The consumptive demand for Plug-in Hybrid Electric Vehicle (PHEV) thus increases. However, load peak caused by their disordered charging can be detrimental to an entire power grid. Several methods have been proposed to establish ordered PHEV charging. While focusing on single-objective optimal load scheduling, they fail to meet the real requirements for efficient multiple objective optimization. This work proposes a novel weight-aggregation based multi-objective particle swarm optimization method to solve the load scheduling problem. Its effectiveness and efficiency to generate a Pareto front of this problem are verified and compared with those of the state-of-the-art approaches.

Original languageEnglish (US)
Title of host publication2016 IEEE Congress on Evolutionary Computation, CEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2896-2902
Number of pages7
ISBN (Electronic)9781509006229
DOIs
StatePublished - Nov 14 2016
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
Duration: Jul 24 2016Jul 29 2016

Publication series

Name2016 IEEE Congress on Evolutionary Computation, CEC 2016

Other

Other2016 IEEE Congress on Evolutionary Computation, CEC 2016
CountryCanada
CityVancouver
Period7/24/167/29/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Modeling and Simulation
  • Computer Science Applications
  • Control and Optimization

Keywords

  • Load Scheduling
  • Multi-objective Optimization
  • PHEV
  • PSO
  • Weight Aggregation

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