Battery energy storage system scheduling based on variable-step prediction and online dynamic nonlinear programming

The increased penetration of renewable energy sources has exacerbated the issue of peak shaving in power systems. To address this challenge, Battery Energy Storage Systems (BESS) emerge as a crucial solution. However, due to the limitations of BESS capacity, unrestricted charging/discharging is not...

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Published in:	Journal of energy storage Vol. 127; p. 116874
Main Authors:	Cai, Jun, Cai, Yuxin, Chen, Liang, Yan, Ying, Cheok, Adrian David, Chen, Zhong, Zhang, Xin
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15.08.2025
Subjects:	Battery Energy Storage System (BESS) Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) Online Updating Dynamic Nonlinear Programming Algorithm Based on Variable-Step Prediction (OUDNLP-VSP) Peak shaving Peak shaving Battery Energy Storage System (BESS) Long Short-Term Memory (LSTM) Online Updating Dynamic Nonlinear Programming Algorithm Based on Variable-Step Prediction (OUDNLP-VSP) Convolutional Neural Network (CNN)
ISSN:	2352-152X
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Abstract	The increased penetration of renewable energy sources has exacerbated the issue of peak shaving in power systems. To address this challenge, Battery Energy Storage Systems (BESS) emerge as a crucial solution. However, due to the limitations of BESS capacity, unrestricted charging/discharging is not feasible. Present short-term programming strategies overlook this factor, leading to BESS being either fully charged or insufficiently charged, thereby impacting subsequent programming. In response, an Online Updating Dynamic Nonlinear Programming Algorithm Based on Variable-Step Prediction (OUDNLP-VSP) is proposed, aiming to minimize power fluctuations in the grid. OUDNLP-VSP is divided into two phases. In the day-ahead phase, a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network based forecasting model is utilized to obtain an approximate load profile for the following day, which is then used to formulate a charging/discharging strategy. In the intraday phase, the forecasting model is employed to predict the load value for the next time step, which can enable real-time optimization of the charging/discharging strategy established in the day-ahead phase. •OUDNLP-VSP algorithm resolves BESS scheduling via dynamic nonlinear programming with online updates.•Day-ahead uses CNN-LSTM 96-step predictions for BESS strategy. Intraday refines via single-step predictions.•PI controller iteratively adjusts real-time predictions to enhance accuracy at each timestep.•Nonlinear programming updates sequences/constraints in real-time using latest forecast data.
AbstractList	The increased penetration of renewable energy sources has exacerbated the issue of peak shaving in power systems. To address this challenge, Battery Energy Storage Systems (BESS) emerge as a crucial solution. However, due to the limitations of BESS capacity, unrestricted charging/discharging is not feasible. Present short-term programming strategies overlook this factor, leading to BESS being either fully charged or insufficiently charged, thereby impacting subsequent programming. In response, an Online Updating Dynamic Nonlinear Programming Algorithm Based on Variable-Step Prediction (OUDNLP-VSP) is proposed, aiming to minimize power fluctuations in the grid. OUDNLP-VSP is divided into two phases. In the day-ahead phase, a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network based forecasting model is utilized to obtain an approximate load profile for the following day, which is then used to formulate a charging/discharging strategy. In the intraday phase, the forecasting model is employed to predict the load value for the next time step, which can enable real-time optimization of the charging/discharging strategy established in the day-ahead phase. •OUDNLP-VSP algorithm resolves BESS scheduling via dynamic nonlinear programming with online updates.•Day-ahead uses CNN-LSTM 96-step predictions for BESS strategy. Intraday refines via single-step predictions.•PI controller iteratively adjusts real-time predictions to enhance accuracy at each timestep.•Nonlinear programming updates sequences/constraints in real-time using latest forecast data.
ArticleNumber	116874
Author	Zhang, Xin Chen, Liang Cai, Jun Cheok, Adrian David Chen, Zhong Cai, Yuxin Yan, Ying
Author_xml	– sequence: 1 givenname: Jun surname: Cai fullname: Cai, Jun email: j.cai@nuist.edu.cn organization: C-MEIC, CICAEET, and School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 2 givenname: Yuxin surname: Cai fullname: Cai, Yuxin email: yuxin.cai@nuist.edu.cn organization: C-MEIC, CICAEET, and School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 3 givenname: Liang surname: Chen fullname: Chen, Liang email: ch.lg@nuist.edu.cn organization: C-MEIC, CICAEET, and School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 4 givenname: Ying surname: Yan fullname: Yan, Ying email: ying.yan@nuist.edu.cn organization: C-MEIC, CICAEET, and School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 5 givenname: Adrian David surname: Cheok fullname: Cheok, Adrian David email: adrian@imagineeringinstitute.org organization: C-MEIC, CICAEET, and School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 6 givenname: Zhong surname: Chen fullname: Chen, Zhong email: cz1982@ahjzu.edu.cn organization: School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230009, China – sequence: 7 givenname: Xin surname: Zhang fullname: Zhang, Xin email: zhangxin_ieee@zju.edu.cn organization: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
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Cites_doi	10.1109/TSTE.2021.3058252 10.1109/TIA.2023.3305318 10.1109/TSTE.2022.3194718 10.1109/JSYST.2020.3045020 10.1109/TPWRS.2023.3282368 10.1109/TPWRS.2022.3231044 10.1109/TII.2022.3230726 10.1016/j.enconman.2020.113711 10.1109/TSTE.2021.3103444 10.1109/TSTE.2020.2982205 10.1109/TPWRS.2022.3159493 10.1080/01430750.2024.2397672 10.1109/TSG.2020.3048648 10.1016/j.enconman.2023.117391 10.1109/TSG.2020.3041620 10.1016/j.enconman.2023.117594 10.1109/TSTE.2020.2995101 10.1109/TII.2021.3130237 10.1109/TPWRS.2022.3227178 10.1109/TPWRS.2022.3190847 10.1109/TIA.2023.3289939 10.1109/TPWRS.2022.3187069 10.1109/TIA.2022.3170385 10.1109/TII.2022.3165890 10.1109/TIE.2020.2969128 10.1016/j.enconman.2023.116888 10.1109/TPWRS.2023.3317309 10.1109/TSG.2022.3179567 10.1109/TPWRD.2022.3178822 10.1109/TII.2020.3000184
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Keywords	Peak shaving Battery Energy Storage System (BESS) Long Short-Term Memory (LSTM) Online Updating Dynamic Nonlinear Programming Algorithm Based on Variable-Step Prediction (OUDNLP-VSP) Convolutional Neural Network (CNN)
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Snippet	The increased penetration of renewable energy sources has exacerbated the issue of peak shaving in power systems. To address this challenge, Battery Energy...
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SubjectTerms	Battery Energy Storage System (BESS) Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) Online Updating Dynamic Nonlinear Programming Algorithm Based on Variable-Step Prediction (OUDNLP-VSP) Peak shaving
Title	Battery energy storage system scheduling based on variable-step prediction and online dynamic nonlinear programming
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