A deep learning-based battery sizing optimization tool for hybridizing generation plants

Yingqian Lin, Binghui Li, Vivek Kumar Singh, Thomas M. Mosier, Sangwook Kim, Tanvir R. Tanim, L. Michael Griffel, S.M. Shafiul Alam, Hill Balliet, Matthew R. Mahalik and Jonghwan Kwon

Renewable Energy, 2024, vol. 223, issue C

Abstract: Hybrid generation and energy storage systems can enhance asset flexibility, enabling various services and optimizing financial performance. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary or too complex for most asset owners to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run).

Keywords: Renewable energy; Energy storage; Hydropower; Deep learning; Battery sizing optimization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:223:y:2024:i:c:s0960148123018268

DOI: 10.1016/j.renene.2023.119911

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