A novel long-term power forecasting based smart grid hybrid energy storage system optimal sizing method considering uncertainties

Luo Zhao, Tingze Zhang, Xiuyan Peng, Xinan Zhang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

With the penetration of renewable generation, the reliability of modern power systems is increasingly challenged. This is especially true for power systems with comparatively low inertia, such as smart grids. To mitigate the impact of renewable intermittency on smart grid operation, a hybrid energy storage system (HESS) is widely employed. Nonetheless, the proper sizing of HESS in smart grids remains a technical challenge. Most of the existing energy storage sizing methods rely on historical data or deterministic renewable generation/load forecasting. Their results can be unconvincing with the presence of uncertainties. To figure out the optimal size of HESS in smart grids using probability-based long-term forecasting, this paper proposes a novel sizing approach with an improved forecasting accuracy. It considers the impact of uncertainties and the life cycle cost of HESS.
Original languageEnglish
Pages (from-to)326-344
Number of pages19
JournalInformation Sciences
Volume610
DOIs
Publication statusPublished - Sep 2022

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