Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approach

TL;DR

This paper introduces a multi-agent reinforcement learning framework for peer-to-peer energy trading that automates bidding, manages renewable resources, and ensures physical network constraints for sustainable local energy systems.

Contribution

It develops MARL-based methods for P2P energy trading that incorporate network constraints and fair pricing, advancing practical deployment of decentralized energy markets.

Findings

MARL frameworks effectively automate energy trading processes.

The approach ensures physical feasibility through voltage control.

Results demonstrate improved resilience and sustainability in local energy networks.

Abstract

Utilizing distributed renewable and energy storage resources in local distribution networks via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy systems' resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have the expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose multi-agent reinforcement learning (MARL) frameworks to help automate consumers' bidding and management of their solar PV and energy storage resources, under a specific P2P clearing mechanism that utilizes the so-called supply-demand ratio. In addition, we show how the MARL frameworks can integrate physical network constraints to realize voltage control, hence ensuring physical feasibility of the P2P energy trading and paving way for real-world implementations.