Abstract
This paper introduced a reinforcement learning based method for developing operational strategy for an energy storage system (ESS) to achieve energy arbitrage in a microgrid or power system. In comparison to conventional energy resources such as gas turbines units or wind plant, it is more challenging to design an optimal strategy for ESS because of their limited energy and the impact of future electricity prices. The energy arbitrage problem also presents unique challenges than the economic dispatch problem because the ESS owner has very limited information of the system compared to those available to grid operators. In this work, reinforcement learning method was applied to determine the best time for charge/discharge in order to maximize the profit. Moreover, different scenarios were designed, and the performance of proposed reinforcement learning algorithm was analyzed by comparing the results with those of optimization-based methods.
Recommended Citation
H. Chen et al., "Developing Optimal Energy Arbitrage Strategy for Energy Storage System using Reinforcement Learning," IET Conference Proceedings, vol. 2021, no. 6, pp. 2266 - 2270, Institute of Electrical and Electronics Engineers, Jan 2021.
The definitive version is available at https://doi.org/10.1049/icp.2021.1752
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
ARBITRAGE STRATEGY; ELECTRICITY MARKETS; ESS; REINFORCEMENT LEARNING
International Standard Book Number (ISBN)
978-183953591-8
International Standard Serial Number (ISSN)
2732-4494
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2021
Comments
Office of Energy Efficiency and Renewable Energy, Grant D18AP00054