Local Energy Trading Behavior Modeling With Deep Reinforcement Learning

Abstract

In this paper, we model prosumers’ energy trading behavior, with the operation of an energy storage system, in a proposed event-driven local energy market. Through modeling local energy trading strategies of a prosumer in the proposed holistic market model, the prosumer’s decision-making process will be built as a Markov decision process with many continuous variables. Then, this decision-making process of local market participation will be solved by deep reinforcement learning technology with experience replay mechanism. Specifically, a deep Q-learning for local energy trading algorithm is modified from deep Q-network to facilitate such a decision-making within an intelligent energy system and promote prosumers’ willingness to participate in the localized energy ecosystem.