Deep-Learning-Based Predictive Control of Battery Management for Frequency Regulation

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 lowfidelity 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 longterm 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.

Semantic Knowledge Integrated Graph Convolutional Network for Zero-Sample Tracing of Control Performance Degradation

“…Benefiting from the development of deep learning, the deep neural network (DNN)-based methods have been verified to have a strong feature extraction ability in the image domain, , traffic monitoring, , industrial applications, ,− etc. In CPD applications, the feedback mechanisms with a certain time delay and the close cross-linking of loop signals within the control system lead to complicated temporal and correlated interactions hidden in the input time-series data.…”

Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process

“…By employing DNNs, the online implementation of the approximated MPC law simplifies function evaluation and significantly reduces the computational burden. Fast MPC designs using DNNs have been successful in areas like battery management [ 24 ], vehicle dynamics [ 25 ], and chemical technology [ 26 ]. However, to the best of our knowledge, there have been no previous studies that specifically explore the application of fast MPC for designing control systems in paddy deep-bed drying processes.…”

Machine Learning and Data Science in Chemical Engineering

“…This special issue of Industrial & Engineering Chemistry Research presents an excellent collection of articles from internationally renowned researchers from all around the world to showcase the application of machine learning and data science in the aforementioned chemical engineering problems. − We truly appreciate the efforts from all contributing authors to make it happen. We hope these articles provide new insights and perspectives as to how machine learning can be used in a wide variety of chemical engineering problems, and stimulate more creative solutions to existing and future challenges.…”