A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids

TL;DR

This paper introduces a multi-agent deep reinforcement learning model for a distributed energy marketplace in smart grids, enabling real-time pricing, improved economic benefits, and efficient grid support through prosumer storage utilization.

Contribution

It proposes a novel RL-based market model that enhances real-time pricing and leverages prosumer storage for grid support, demonstrating significant economic and operational improvements.

Findings

Increased 24-hour profit for prosumers and grid operator

Reduced grid reserve power utilization

Effective real-time demand-dependent pricing

Abstract

This paper presents a Reinforcement Learning (RL) based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers. Furthermore, this market model enables the grid operator to leverage prosumers storage capacity as a dispatchable asset for grid support applications. Simulation results based on the Deep QNetwork (DQN) framework demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the grid operator, as well as major reductions in grid reserve power utilization.