Trading Strategy in a Local Energy Market, a Deep Reinforcement Learning Approach

Jogunola, Olamide ORCID: https://orcid.org/0000-0002-2701-9524, Tsado, Yakubu, Adebisi, Bamidele and Nawaz, Raheel (2021) Trading Strategy in a Local Energy Market, a Deep Reinforcement Learning Approach. In: 2021 IEEE Electrical Power and Energy Conference (EPEC), 22 October 2021 - 31 October 2021, Toronto, ON, Canada.

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Abstract

In response to energy transition fueled by the increasing energy generation mix and dynamic environment, this paper presents an energy trading strategy utilising real microgrid data. Specifically, we adapted the deep Q-network (DQN) with prioritised experience replay (PER) to develop a DQN-PER-based energy market algorithm to optimise the utility derived by prosumers participating in a local energy market (LEM). The problem of exercising energy trading actions is formulated as a sequential decision-making problem to optimise the prosumer’s utility in a variety of energy trading scenarios. This includes the contingency or flexibility provided by the energy storage system (ESS), the incorporation of solar photovoltaic (PV) sources and the decision to trade energy with the grid or in a LEM. The results show the benefit achieved in trading energy in LEM with higher benefits when more sources of renewable energy are incorporated. For instance, the average benefit of trading in the LEM over the grid with ESS is 35%, which increased to 54% when PV and ESS are incorporated.