Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network

TL;DR

This paper presents a multi-agent reinforcement learning framework for decentralized voltage control and fair peer-to-peer energy trading in distribution networks, addressing market fairness and physical constraints.

Contribution

It introduces a MARL-based approach that automates trading and manages network constraints, enabling practical and decentralized P2P energy trading with voltage regulation.

Findings

MARL effectively automates consumer bidding and resource management.

The framework ensures physical feasibility through integrated network constraints.

Decentralized voltage control is achieved alongside P2P trading.

Abstract

Utilizing distributed renewable and energy storage resources via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy system's resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have 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 a multi-agent reinforcement learning (MARL) framework 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 framework can integrate physical network constraints to realize decentralized voltage control, hence ensuring physical feasibility of the P2P energy trading and paving ways for real-world implementations.