Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations

Brian Hodge, Qifang Chen, Fei Wang, Jianhua Zhang, Zhigang Li, Miadreza Shafie-Khah, Joao Catalao

Research output: Contribution to journalArticlepeer-review

221 Scopus Citations


A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.

Original languageAmerican English
Article number7896607
Pages (from-to)2903-2915
Number of pages13
JournalIEEE Transactions on Smart Grid
Issue number6
StatePublished - Nov 2017

Bibliographical note

Publisher Copyright:
© 2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-70597


  • Automatic demand response
  • charging station
  • electric vehicle
  • PV system
  • real-time price


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