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Day-Ahead Charging Capacity Market for Power Distribution Network With Electric Vehicle Charging Stations

  • Xi'an Jiaotong University
  • Illinois Institute of Technology

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The rapidly increasing charging load of electric vehicles (EV) calls for large charging capacities, which brings operational and economic challenges to power distribution network (PDN) with limited supply capacities. To tackle such challenges, this work proposes the concept of charging capacity market that enables the day-ahead allocation and trading of charging capacity for EV charging stations (EVCSs). Different from traditional day-ahead power markets, the day-ahead charging capacity market ensures secure grid operations by preventively supplying EVCSs with additional PDN resources that can counter the EV load uncertainty. First, the market clearing model is established as a two-stage robust problem, which offers preventive charging capacity adjustments in the first stage to ensure secure operations of power networks. Second, an improved column and constraint generation algorithm is proposed to solve the proposed robust charging capacity market clearing problem with operation-dependent uncertainty sets, which tackles the potential infeasibility and computational intractability in the traditional market clearing algorithm. Third, the day-ahead capacity pricing is devised within the robust framework. Finally, case studies on modified real-world networks validate the effectiveness of the proposed market, which outperforms the traditional market clearing solutions for enhancing the grid security and economy.

Original languageEnglish
Pages (from-to)2258-2271
Number of pages14
JournalIEEE Transactions on Power Systems
Volume40
Issue number3
DOIs
StatePublished - 2025

Keywords

  • Electric vehicle
  • capacity pricing
  • column and constraint generation algorithm
  • day-ahead charging capacity market
  • market clearing
  • two-stage robust optimization

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