A method for selecting the type of energy storage for power systems with high penetration of renewable energy with multi-application scenarios

Sen Wang, Fengting Li, Ye Zhang, Ting Wang and Yuan Li

Renewable Energy, 2024, vol. 235, issue C

Abstract: Energy storage (ES) configurations effectively relieve regulatory pressure on power systems with a high penetration of renewable energy. However, it is difficult for a single ES type to satisfy the complex regulatory demands of a power system. There has been little research on the selection methods for multiple types of ES that meet the demands of multiple application scenarios of power systems. This study introduces a method for the selection of ES types for power systems with a high penetration of renewable energy to determine the optimal ES types for multi-application scenarios. First, a generation method for ES schemes is proposed based on K-means clustering and combination theory. Then, considering the demands of peak shaving, frequency regulation and reserve of power systems with high penetration of renewable energy, an optimisation model of the expected indices of the ES is constructed to meet the demands of multi-application scenarios. Finally, the comprehensive evaluation method for ES schemes based on the error mean and the selection method for optimal ES types based on projection technology are proposed. Numerical studies show that for the power system in the example whose demand coefficients for peak shaving, frequency regulation and reserve are 30.59 %, 26.29 % and 43.11 %, respectively, a combination of pumped storage and colloidal battery, lithium iron phosphate battery, or lithium titanate battery is the most suitable ES type for the power system. This study offers a reference for the selection of ES types when an ES is configured.

Keywords: Energy storage; Multi-application scenarios; Selection of energy storage types; Expected indices of energy storage (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:235:y:2024:i:c:s0960148124014113

DOI: 10.1016/j.renene.2024.121343

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