An Operation Model for Distribution Companies Using the Flexibility of Electric Vehicle Aggregators

Xi Lu, Ka Wing Chan, Shiwei Xia, Mohammad Shahidehpour, Wing Ho Ng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

An operation model for distribution companies (DISCOs) is proposed to reduce their operation costs by fully utilizing the flexibility of electric vehicle aggregators (EVAs). In the proposed model, linear decision rules approximation is adopted to achieve mathematical tractability, and distributionally robust optimization is applied to evaluate costs affected by uncertainties in renewable power outputs and EVA charging demands. Case studies are conducted under various settings. With the proposed model, using EVAs to mitigate uncertainties is achieved and is further classified into delaying uncertainties and eliminating uncertainties. As a result, average penalties for DISCO's deviations from its planned energy portfolio are reduced. Besides, EVA charging demands are shifted to hours with lower energy prices to reduce energy costs of DISCO. Using EVAs to mitigate uncertainties and shifting EVA charging demands are properly coordinated under the proposed model. Moreover, power losses in EVA charging and discharging are utilized to reduce the scale of uncertainties, which decreases average penalties for energy deviations of DISCO.

Original languageEnglish
Article number51
Pages (from-to)1507-1518
Number of pages12
JournalIEEE Transactions on Smart Grid
Volume12
Issue number2
DOIs
Publication statusPublished - Mar 2021

Keywords

  • Distribution company
  • distributionally robust optimization
  • electric vehicle aggregator
  • renewable energy
  • uncertainty

ASJC Scopus subject areas

  • Computer Science(all)

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