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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Veröffentlicht in:	Journal of energy storage Jg. 127; S. 116874
Hauptverfasser:	Cai, Jun, Cai, Yuxin, Chen, Liang, Yan, Ying, Cheok, Adrian David, Chen, Zhong, Zhang, Xin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.08.2025
Schlagworte:	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
Online-Zugang:	Volltext
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