MARL for Decentralized Electric Vehicle Charging Coordination with V2V Energy Exchange

TL;DR

This paper proposes a multi-agent reinforcement learning approach for decentralized electric vehicle charging coordination that incorporates vehicle-to-vehicle energy exchange, considering uncertainties and user satisfaction, demonstrating superior performance over traditional methods.

Contribution

It introduces a novel MARL framework with parameter noise for EV charging coordination with V2V energy exchange, addressing uncertainties and user experience.

Findings

Outperforms traditional optimization baselines in experiments

Demonstrates scalability and robustness in decentralized execution

Effectively manages uncertainties in EV arrival, energy prices, and solar generation

Abstract

Effective energy management of electric vehicle (EV) charging stations is critical to supporting the transport sector's sustainable energy transition. This paper addresses the EV charging coordination by considering vehicle-to-vehicle (V2V) energy exchange as the flexibility to harness in EV charging stations. Moreover, this paper takes into account EV user experiences, such as charging satisfaction and fairness. We propose a Multi-Agent Reinforcement Learning (MARL) approach to coordinate EV charging with V2V energy exchange while considering uncertainties in the EV arrival time, energy price, and solar energy generation. The exploration capability of MARL is enhanced by introducing parameter noise into MARL's neural network models. Experimental results demonstrate the superior performance and scalability of our proposed method compared to traditional optimization baselines. The decentralized execution of the algorithm enables it to effectively deal with partial system faults in the charging station.