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

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). This work presents a deep learning-based battery sizing optimization tool for hybridizing generation facilities. The tool uses deep learning technique to predict revenue over a broad search space of potential battery sizes, estimate capital and operations costs (including accounting for battery degradation), and calculate financial performance of each potential battery system investment; an output is a recommendation of battery that maximizes financial performance. The tool is tested and validated for hydropower generation and is publicly available on Idaho National Laboratory's GitHub page (https://github.com/idaholab/Hydro_Hybrids), documented in Zenodo (https://zenodo.org/record/7562692#.Y9Q7anbMKUm), and accessible through an intuitive web app (hydrohybrids.inl.gov). This tool will help a greater cross-section of generation owners consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.