Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning

TL;DR

This paper introduces a novel aggregation-based reinforcement learning approach for efficient electric vehicle charging station operations, improving policy effectiveness and computational efficiency.

Contribution

It proposes a new state aggregation scheme based on EV charging urgency and a policy gradient method for optimized EVCS management.

Findings

Higher rewards achieved with the proposed method

More effective policies compared to existing approaches

Improved convergence and efficiency in RL algorithms

Abstract

Effectively operating electrical vehicle charging station (EVCS) is crucial for enabling the rapid transition of electrified transportation. To solve this problem using reinforcement learning (RL), the dimension of state/action spaces scales with the number of EVs and is thus very large and time-varying. This dimensionality issue affects the efficiency and convergence properties of generic RL algorithms. We develop aggregation schemes that are based on the emergency of EV charging, namely the laxity value. A least-laxity first (LLF) rule is adopted to consider only the total charging power of the EVCS which ensures the feasibility of individual EV schedules. In addition, we propose an equivalent state aggregation that can guarantee to attain the same optimal policy. Based on the proposed representation, policy gradient method is used to find the best parameters for the linear Gaussian policy . Numerical results have validated the performance improvement of the proposed representation approaches in attaining higher rewards and more effective policies as compared to existing approximation based approach.