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.
@article{arxiv.2009.10905,
title = {A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids},
author = {Arman Ghasemi and Amin Shojaeighadikolaei and Kailani Jones and Morteza Hashemi and Alexandru G. Bardas and Reza Ahmadi},
journal= {arXiv preprint arXiv:2009.10905},
year = {2020}
}