Deep-Learning-Based Predictive Control of Battery Management for Frequency RegulationClick to copy article linkArticle link copied!
- Yun LiYun LiDepartment of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, CanadaMore by Yun Li
- Yixiu WangYixiu WangDepartment of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, CanadaMore by Yixiu Wang
- Yifu ChenYifu ChenDepartment of Chemical and Biological Engineering, University of Wisconsin─Madison, Madison, Wisconsin 53706, United StatesMore by Yifu Chen
- Yifan LuYifan LuDepartment of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, CanadaMore by Yifan Lu
- Kaixun HuaKaixun HuaDepartment of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, CanadaMore by Kaixun Hua
- Jiayang RenJiayang RenDepartment of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, CanadaMore by Jiayang Ren
- Ghazaleh MozafariGhazaleh MozafariDepartment of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, CanadaMore by Ghazaleh Mozafari
- Qiugang LuQiugang LuDepartment of Chemical Engineering, Texas Tech University, Lubbock, Texas 79409, United StatesMore by Qiugang Lu
- Yankai Cao*Yankai Cao*Email: [email protected]Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, CanadaMore by Yankai Cao
Abstract

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.
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