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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Vydáno v:	Journal of energy storage Ročník 127; s. 116874
Hlavní autoři:	Cai, Jun, Cai, Yuxin, Chen, Liang, Yan, Ying, Cheok, Adrian David, Chen, Zhong, Zhang, Xin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 15.08.2025
Témata:	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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