frankliumin/reinforcement-learning-for-real-time-pricing-and-scheduling-control-in-ev-charging-stations

Reinforcement Learning for Real time Pricing and Scheduling Control in EV Charging Stations

Cite this work (The TII version is prefered)

This is the offical implementation of the published papers 'Reinforcement Learning for Real-time Pricing and Scheduling Control in EV Charging Stations' (ESI Highly Cited) and 'A Reinforcement Learning Approach for EV Charging Station Dynamic Pricing and Scheduling Control'.

S. Wang, S. Bi, and Y. J. Zhang, “Reinforcement Learning for Real-time Pricing and Scheduling Control in EV Charging Stations,” in IEEE Transactions on Industrial Informatics, vol. 17, no. 2, pp. 849-859, Feb. 2021, doi: 10.1109/TII.2019.2950809.

Abstract

This paper proposes a Reinforcement-Learning (RL) approach for optimizing charging scheduling and pricing strategies that maximize the system objective of a public electric vehicle (EV) charging station. The proposed algorithm is ”on-line” in the sense that the charging and pricing decisions depend only on the observation of past events, and is ”model-free” in the sense that the algorithm does not rely on any assumed stochastic models of uncertain events. To cope with the challenge arising from the time-varying continuous state and action spaces in the RL problem, we first show that it suffices to optimize the total charging rates to fulfill the charging requests before departure times. Then, we propose a feature-based linear function approximator for the state-value function to further enhance the generalization ability. Through numerical simulations with real-world data, we show that the proposed RL algorithm achieves on average 138.5% higher profit than representative benchmark algorithms.

About authors

• Shuoyao WANG, sywang AT szu.edu.cn

• Suzhi BI, bsz AT szu.edu.cn

• Ying Jun (Angela) Zhang, yjzhang AT ie.cuhk.edu.hk

Required packages

• Tensorflow

• numpy

• scipy

Thank You for Reading!!!