Incentive-Aligned Vehicle-to-Vehicle Energy Trading via Nash-Integrated Multi-Agent Reinforcement Learning

TL;DR

This paper proposes Nash-MADDPG, a multi-agent reinforcement learning approach integrating Nash bargaining for incentive-aligned vehicle-to-vehicle energy trading, improving efficiency, fairness, and scalability.

Contribution

It introduces Nash-MADDPG, combining Nash bargaining with multi-agent RL to enable decentralized, fair, and scalable V2V energy trading among EVs.

Findings

61.6% improvement in social welfare over Double Auction

62.9% increase in trading volume

40.1% better fairness as per Jain's index

Abstract

Vehicle-to-vehicle (V2V) energy trading enables decentralized peer-to-peer energy exchange among electric vehicles (EVs), reducing grid dependency while monetizing surplus capacity. However, coordinating self-interested EV agents with diverse charging needs and uncertain arrival-departure schedules remains challenging. Existing approaches either require centralized optimization with computational limitations or lack fairness guarantees. This paper integrates Nash Bargaining Solution into Multi-Agent Deep Deterministic Policy Gradient, namely Nash-MADDPG, for incentive-aligned V2V energy trading. Nash bargaining determines efficient bilateral pricing, while Nash-guided price proximity rewards align agent learning toward bargaining-optimal strategies. Evaluation over 30-day continuous operation demonstrates an improvement of 61.6% in social welfare and 62.9% improvement in trading volume over Double Auction, while achieving superior fairness, such as 40.1% improvement in Jain's index. Testing across 6-100 agents over a 30-day horizon with continuous vehicle turnover confirms scalability across population size and empirically stable pricing near the Nash Bargaining benchmark.