Dynamic Pricing for EV Charging Stations: A Deep Reinforcement Learning Approach

Zhonghao Zhao, Carman K.M. Lee

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Dynamic pricing, which aims to dynamically adjust the charging price in a timely fashion to unlock the flexibility of electric vehicle (EV) customers, has been extensively studied with the rapid development of charging technologies. Many existing works on dynamic pricing focus on maximizing the social welfare of charging service providers and EV customers. Cases of high-dimensional charging environments, which are often encountered with the rapid growth of EV market penetration, have been rarely considered to date. This article proposes a new dynamic pricing framework for EV charging stations that can offer multiple charging options to customers over a finite-time horizon. The charging price can be dynamically adjusted to maximize the quality of service (QoS) with a differentiated service requirement level (SRL) whenever the arrival rates and queuing system capacities of the charging systems are given at the end of a time period. The dynamic pricing problem is formulated as a finite-discrete horizon Markov decision process (MDP) with a mixed state space. A customized deep reinforcement learning (DRL) approach is employed to solve the examined EV dynamic pricing problem. The simulation results demonstrate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)2456-2468
Number of pages13
JournalIEEE Transactions on Transportation Electrification
Volume8
Issue number2
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • Deep reinforcement learning (DRL)
  • dynamic pricing
  • electric vehicle (EV) charging station
  • quality of service (QoS)

ASJC Scopus subject areas

  • Automotive Engineering
  • Transportation
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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