Automatic P2P Energy Trading Model Based on Reinforcement Learning Using Long Short-Term Delayed Reward

• Published in: Energies (Basel) Vol. 13; no. 20; p. 5359
• Main Authors: Kim, Jin-Gyeom, Lee, Bowon
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 14.10.2020
• Subjects: Algorithms; Alternative energy sources; automatic P2P energy trading; Climate change; Consumers; Consumption; Decision making; deep Q-network; deep reinforcement learning; Electric rates; Electricity generation; Energy consumption; Energy industry; Energy resources; Energy storage; Game theory; Industrialized nations; long short-term delayed reward; Markov decision process; Markov processes; Peer to peer computing; Profits; Reinforcement; Renewable resources; Social networks; United States > US; South Korea
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13205359

Automatic peer-to-peer energy trading can be defined as a Markov decision process and designed using deep reinforcement learning. We consider prosumer as an entity that consumes and produces electric energy with an energy storage system, and define the prosumer’s objective as maximizing the profit through participation in peer-to-peer energy trading, similar to that of the agents in stock trading. In this paper, we propose an automatic peer-to-peer energy trading model by adopting a deep Q-network-based automatic trading algorithm originally designed for stock trading. Unlike in stock trading, the assets held by a prosumer may change owing to factors such as the consumption and generation of energy by the prosumer in addition to the changes from trading activities. Therefore, we propose a new trading evaluation criterion that considers these factors by defining profit as the sum of the gains from four components: electricity bill, trading, electric energy stored in the energy storage system, and virtual loss. For the proposed automatic peer-to-peer energy trading algorithm, we adopt a long-term delayed reward method that evaluates the delayed reward that occurs once per month by generating the termination point of an episode at each month and propose a long short-term delayed reward method that compensates for the issue with the long-term delayed reward method having only a single evaluation per month. This long short-term delayed reward method enables effective learning of the monthly long-term trading patterns and the short-term trading patterns at the same time, leading to a better trading strategy. The experimental results showed that the long short-term delayed reward method-based energy trading model achieves higher profits every month both in the progressive and fixed rate systems throughout the year and that prosumer participating in the trading not only earns profits every month but also reduces loss from over-generation of electric energy in the case of South Korea. Further experiments with various progressive rate systems of Japan, Taiwan, and the United States as well as in different prosumer environments indicate the general applicability of the proposed method.