A Model to Mitigate Forecast Uncertainties in Distribution Systems Using the Temporal Flexibility of EVAs

Xi Lu, Ka Wing Chan, Shiwei Xia, Xian Zhang, Guibin Wang, Furong Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)


Electric vehicles (EVs) provide new options for energy balancing of power systems. One possible way to use EVs in energy balancing is to let each distribution system mitigate its forecast uncertainties through the flexibility of EVs. In consideration of the difficulties to directly govern a large number of EVs, it is more reasonable for distribution systems to dispatch electric vehicle aggregators (EVAs). Without influencing driving activities of EVs in the next day, a model is established for distribution systems to make use of EVAs, whose contributions are delaying uncertainties through their temporal flexibility and thus creating opportunities for uncertainties from different hours to offset each other. In the established model, a scheme of uncertainty transferring is proposed to relieve interruption to EVAs and distributionally robust optimization is adopted to evaluate the operation plans' average performance with temporal and spatial uncertainty correlations considered. Comprehensive case studies are carried out based on charging demands of EVAs simulated from real traffic data to verify the effectiveness of the proposed model.

Original languageEnglish
Article number8890715
Pages (from-to)2212-2221
Number of pages10
JournalIEEE Transactions on Power Systems
Issue number3
Publication statusPublished - May 2020


  • day-ahead planning
  • Distribution system
  • distributionally robust optimization
  • electric vehicle aggregator
  • temporal flexibility
  • uncertainty mitigation

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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