Chen, T. and Bu, S. (2019) Realistic Peer-to-Peer Energy Trading Model for Microgrids Using Deep Reinforcement Learning. In: 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), Bucharest, Romania, 29 Sep - 02 Oct 2019, ISBN 9781538682180 (doi: 10.1109/ISGTEurope.2019.8905731)
|
Text
190489.pdf - Accepted Version 469kB |
Abstract
In this paper, we integrate deep reinforcement learning with our realistic peer-to-peer (P2P) energy trading model to address a decision-making problem for microgrids (MGs) in the local energy market. First, an hour-ahead P2P energy trading model with a set of critical physical constraints is formed. Then, the decision-making process of energy trading is built as a Markov decision process, which is used to find the optimal strategies for MGs using a deep reinforcement learning (DRL) algorithm. Specifically, a modified deep Q-network (DQN) algorithm helps the MGs to utilise their resources and make better strategies. Finally, we choose several real-world electricity data sets to perform the simulations. The DQN-based energy trading strategies improve the utilities of the MGs and significantly reduce the power plant schedule with a virtual penalty function. Moreover, the model can determine the best battery for the selected MG. The results show that this P2P energy trading model can be applied to real-world situations.
Item Type: | Conference Proceedings |
---|---|
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Chen, Tianyi and Bu, Dr Shengrong |
Authors: | Chen, T., and Bu, S. |
College/School: | College of Science and Engineering > School of Engineering > Systems Power and Energy |
ISBN: | 9781538682180 |
Copyright Holders: | Copyright © 2019 IEEE |
Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
Related URLs: |
University Staff: Request a correction | Enlighten Editors: Update this record