EV charging station deployment on coupled transportation and power distribution networks via reinforcement learning

This study addresses the optimal electric vehicle (EV) charging station deployment problem (CSDP) on coupled transportation and power distribution networks, which is one of the critical issues with the mass adoption of EVs in the recent years. In contrast to existing works that mainly employ heuristics and exact algorithms, we propose a finite-discrete Markov decision process (MDP) formulation defined in a reinforcement learning (RL) framework to mitigate the curse of dimensionality problem. The RL-based approach aims to determine the location of a set of EV charging stations with limited capacity by minimizing the total investment cost while satisfying the coupled network constraints. Specifically, a long short-term memory (LSTM)-based recurrent neural network (RNN) with an attention mechanism is used to train the model based on an offline strategy. The model parameters are learned by the policy gradient algorithm with a learned baseline function. Numerical experiments on multiple problem sizes are conducted to assess the efficiency and feasibility of the proposed solution method. We experimentally show that our approach is efficient to solve the CSDP and outperforms other baseline approaches in solution quality with competitive computational time.

Automated summary

This study addresses the optimal deployment of electric vehicle charging stations in the transportation and power distribution networks, which is a critical issue for the mass adoption of EVs. A finite-discrete Markov decision process formulation is proposed in a reinforcement learning framework to solve the curse of dimensionality problem. The proposed approach, which utilizes a LSTM-based recurrent neural network with an attention mechanism, outperforms other baseline approaches in terms of solution quality and computational time.