Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach
Artikel i vetenskaplig tidskrift, 2024

With the increasing number of fast-electric vehicle charging stations (fast-EVCSs) and the popularization of information technology, electricity price competition between fast-EVCSs is highly expected, in which the utilization of public and/or privacy-preserved information will play a crucial role. Self-interest electric vehicle (EV) users, on the other hand, try to select a fast-EVCS for charging in a way to maximize their utilities based on electricity price, estimated waiting time, and their state of charge. While existing studies have largely focused on finding equilibrium prices, this study proposes a personalized dynamic pricing policy (PeDP) for a fast-EVCS to maximize revenue using a reinforcement learning (RL) approach. We first propose a multiple fast-EVCSs competing simulation environment to model the selfish behavior of EV users using a game-based charging station selection model with a monetary utility function. In the environment, we propose a Q-learning-based PeDP to maximize fast-EVCS' revenue. Through numerical simulations based on the environment: (1) we identify the importance of waiting time in the EV charging market by comparing the classic Bertrand competition model with the proposed PeDP for fast-EVCSs (from the system perspective); (2) we evaluate the performance of the proposed PeDP and analyze the effects of the information on the policy (from the service provider perspective) and the robustness of the proposed approach; and (3) it can be seen that privacy-preserved information sharing can be misused by artificial intelligence-based PeDP in a certain situation in the EV charging market (from the customer perspective).

Electric vehicle

Reinforcement learning

Personalized dynamic pricing

Fast-electric vehicle charging station

Game theory

Författare

Sangjun Bae

Sejong cyber university

Balázs Adam Kulcsár

Chalmers, Elektroteknik, System- och reglerteknik

Sébastien Gros

Norges teknisk-naturvitenskapelige universitet

Chalmers, Elektroteknik, System- och reglerteknik

Transportation Research, Part C: Emerging Technologies

0968-090X (ISSN)

Vol. 161 104540

ServiCe OPtimization of charging station for Electrified vehicles

Swedish Electromobility Centre, 2017-10-18 -- 2019-10-17.

E-Laas: Energioptimal urban logistik som tjänst

Energimyndigheten (2023-00021), 2023-05-02 -- 2025-04-30.

Europeiska kommissionen (EU) (F-ENUAC-2022-0003), 2023-05-01 -- 2025-04-30.

Styrkeområden

Transport

Ämneskategorier

Transportteknik och logistik

Elektroteknik och elektronik

Reglerteknik

DOI

10.1016/j.trc.2024.104540

Mer information

Senast uppdaterat

2024-08-09