Multi-objective optimal load dispatch of microgrid with stochastic access of electric vehicles

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

29 Scopus Citations
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  • Xinhui Lu
  • Kaile Zhou
  • Shanlin Yang
  • Huizhou Liu

Related Research Unit(s)


Original languageEnglish
Pages (from-to)187-199
Journal / PublicationJournal of Cleaner Production
Online published23 May 2018
Publication statusPublished - 10 Sep 2018


Large-scale uncoordinated charging of electric vehicles (EVs) will become a reality in the near future, which will have a great impact on the stability and security of power system operation. In this regard, this paper proposes a multi-objective optimal load dispatch model of microgrid with the stochastic access of EVs. The uncertainties of EVs are modeled by using the Monte Carlo simulation. The objective function of the model includes the operating cost, pollutant treatment cost and load variance. Distributed generations (DGs) are considered in the model, including photovoltaic (PV) array, wind turbine (WT), diesel engine (DE) and micro turbine (MT). In order to solve the proposed model effectively, an improved particle swarm optimization (PSO) algorithm is proposed. Then we discuss the dispatch results under three different scheduling scenarios, i.e., uncoordinated charging scenario, coordinated charging scenario with and without DGs. The simulation results show that charging loads will be shifted form high-priced periods to low-priced periods under the coordinated charging mode of EVs, which can reduce daily costs by 3.09% and effectively improve the stability of power system operation. Meanwhile, the penetration of DGs can further reduce 6.43% of total cost by managing output of DGs. Further, the influence of cost weight factor on dispatch results is discussed. It illustrates that the cost weight factor is a trade-off between the total cost and the load variance. The experimental results demonstrate the effectiveness of the model under different charging situations.

Research Area(s)

  • Distributed generations, Electric vehicles, Microgrid, Multi-objective optimization, Optimal load dispatch

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).