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Deep-Learning-Based Predictive Control of Battery Management for Frequency Regulation
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    Deep-Learning-Based Predictive Control of Battery Management for Frequency Regulation
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    • Yun Li
      Yun Li
      Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada
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    • Yixiu Wang
      Yixiu Wang
      Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada
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    • Yifu Chen
      Yifu Chen
      Department of Chemical and Biological Engineering, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
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    • Yifan Lu
      Yifan Lu
      Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada
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    • Kaixun Hua
      Kaixun Hua
      Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada
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    • Jiayang Ren
      Jiayang Ren
      Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada
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    • Ghazaleh Mozafari
      Ghazaleh Mozafari
      Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada
    • Qiugang Lu
      Qiugang Lu
      Department of Chemical Engineering, Texas Tech University, Lubbock, Texas 79409, United States
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    • Yankai Cao*
      Yankai Cao
      Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada
      *Email: [email protected]
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    Industrial & Engineering Chemistry Research

    Cite this: Ind. Eng. Chem. Res. 2022, 61, 24, 8432–8442
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    https://doi.org/10.1021/acs.iecr.1c04903
    Published March 16, 2022
    Copyright © 2022 American Chemical Society

    Abstract

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    This paper proposes a deep-learning-based optimal battery management scheme for frequency regulation (FR) by integrating model predictive control (MPC), supervised learning (SL), reinforcement learning (RL), and high-fidelity battery models. By taking advantage of deep neural networks (DNNs), the derived DNN-approximated policy is computationally efficient in online implementation. The design procedure of the proposed scheme consists of two sequential processes: (1) the SL process, in which we first run a simulation with an MPC embedding a low-fidelity battery model to generate a training data set, and then, based on the generated data set, we optimize a DNN-approximated policy using SL algorithms; (2) the RL process, in which we utilize RL algorithms to improve the performance of the DNN-approximated policy by balancing short-term economic incentives and long-term battery degradation. The SL process speeds up the subsequent RL process by providing a good initialization. By utilizing RL algorithms, one prominent property of the proposed scheme is that it can learn from the data generated by simulating the FR policy on the high-fidelity battery simulator to adjust the DNN-approximated policy, which is originally initialized using a low-fidelity battery model. A case study using real-world data of FR signals and prices is performed. Simulation results show that, compared to conventional MPC schemes, the proposed deep-learning-based scheme can effectively achieve higher economic benefits of FR participation while maintaining lower online computational cost.

    Copyright © 2022 American Chemical Society

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    This article is cited by 3 publications.

    1. Xu Chen, Jie Wang, Jiaqi Yue, Chunhui Zhao. Semantic Knowledge Integrated Graph Convolutional Network for Zero-Sample Tracing of Control Performance Degradation. Industrial & Engineering Chemistry Research 2023, 62 (49) , 21265-21277. https://doi.org/10.1021/acs.iecr.3c02489
    2. Hanyu Gao, Li-Tao Zhu, Zheng-Hong Luo, Marco A. Fraga, I-Ming Hsing. Machine Learning and Data Science in Chemical Engineering. Industrial & Engineering Chemistry Research 2022, 61 (24) , 8357-8358. https://doi.org/10.1021/acs.iecr.2c01788
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    Industrial & Engineering Chemistry Research

    Cite this: Ind. Eng. Chem. Res. 2022, 61, 24, 8432–8442
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.iecr.1c04903
    Published March 16, 2022
    Copyright © 2022 American Chemical Society

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