A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles

Zhile Yang, Kang Li, Yuanjun Guo, Shengzhong Feng, Qun Niu, Yusheng Xue, Aoife Foley

Research output: Contribution to journalArticle

11 Citations (Scopus)
73 Downloads (Pure)

Abstract

Conventional unit commitment is a mixed integer optimization problem and has long been a key issue for power system operators. The complexity of this problem has increased in recent years given the emergence of new participants such as large penetration of plug-in electric vehicles. In this paper, a new model is established for simultaneously considering the day-ahead hourly based power system scheduling and a significant number of plug-in electric vehicles charging and discharging behaviours. For solving the problem, a novel hybrid mixed coding meta-heuristic algorithm is proposed, where V-shape symmetric transfer functions based binary particle swarm optimization are employed. The impact of transfer functions utilised in binary optimization on solving unit commitment and plug-in electric vehicle integration are investigated in a 10 unit power system with 50,000 plug-in electric vehicles. In addition, two unidirectional modes including grid to vehicle and vehicle to grid, as well as a bi-directional mode combining plug-in electric vehicle charging and discharging are comparatively examined. The numerical results show that the novel symmetric transfer function based optimization algorithm demonstrates competitive performance in reducing the fossil fuel cost and increasing the scheduling flexibility of plug-in electric vehicles in three intelligent scheduling modes.
Original languageEnglish
Pages (from-to)889-905
Number of pages17
JournalEnergy
Volume170
Early online date04 Jan 2019
DOIs
Publication statusPublished - 01 Mar 2019

Keywords

  • Plug-in electric vehicles
  • Unit commitment
  • Vehicle to grid
  • Symmetric transfer function
  • Binary particle swarm optimization
  • Meta-heuristic

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