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

Journal Article · · Renewable Energy
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  1. Idaho National Laboratory (INL), Idaho Falls, ID (United States)
  2. Argonne National Laboratory (ANL), Argonne, IL (United States)
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Hybrid generation and energy storage systems offer the ability to increase flexibility of the combined asset. This flexibility can be used to increase provision of services already provided by the generation asset, such as timing sale of electricity to the energy market during high price periods, and also enable provision of additional services, such as ancillary services or contribute to resource adequacy. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that, among other factors, maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary (i.e., based on “rules of thumb”) or too complex to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a novel deep learning-based battery sizing optimization tool that is designed to help generation asset owners easily assess preliminary sizing considerations for potential battery investments to hybridize their generation facility. The tool uses deep learning to predict revenue over a broad search space of potential battery sizes, estimates capital and operating costs (including accounting for battery degradation), and computes financial performance of each potential battery system investment, recommending a system with maximum financial performance. The tool is tested and validated for hydropower assets. Finally, this tool will help a greater cross-section of industry consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.
Research Organization:
Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office
Grant/Contract Number:
AC07-05ID14517
OSTI ID:
2373075
Report Number(s):
INL/JOU--21-64617-Rev000
Journal Information:
Renewable Energy, Journal Name: Renewable Energy Vol. 223; ISSN 0960-1481
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

13 HYDRO ENERGY
25 ENERGY STORAGE
Battery sizing optimization
Deep learning
Energy Storage
Financial performance optimization
Hydropower
Renewable energy