Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145713
Title: Probability-based energy reinforced management of electric vehicle aggregation in the electrical grid frequency regulation
Authors: Dong, Chaoyu
Sun, Jianwen
Wu, Feng
Jia, Hongjie
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Dong, C., Sun, J., Wu, F., & Jia, H. (2020). Probability-based energy reinforced management of electric vehicle aggregation in the electrical grid frequency regulation. IEEE Access, 8, 110598-110610. doi:10.1109/ACCESS.2020.3002693
Journal: IEEE Access
Abstract: The model uncertainties and the heterogeneous energy states burden the effective aggregation of electric vehicles (EVs), especially coupling with the real-time frequency dynamic of the electrical grid. Integrating the advantages of deep learning and reinforcement learning, deep reinforcement learning shows its potential to relieve this challenge, where an intelligent agent fully considers the individual state of charge (SOC) difference of EV and the grid state to optimize the aggregation performance. However, existing policies of deep reinforcement learning usually provide deterministic and certain actions, and it is difficult to deal with the increasing uncertainties and randomness in modern electrical systems. In this paper, a probability-based management strategy is proposed with continuous action space based on the deep reinforcement learning, which provides fine-grained energy management and addresses the time-varying dynamics from EVs and electrical grid simultaneously. Moreover, an optimization based on the proximal policy is further introduced to clip the policy upgradation speed to enhance the training stability. The effectiveness of proposed energy management structure and policy optimization strategy are verified on various scenarios and uncertainties, which demonstrates advantageous performance in the SOC management and frequency maintenance. Besides the performance merits, the training procedure is also presented revealing the evolution reason for the proposed approach.
URI: https://hdl.handle.net/10356/145713
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3002693
Rights: © 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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