Artificial intelligence computational techniques of flywheel energy storage systems integrated with green energy: A comprehensive review

Abstract

In recent years, the operation of the electric power grid has become more efficient and resilient due to the integration of renewable energy sources (RESs). Solar and wind energy are being incorporated aggressively into the main grid, while other RESs like biomass and geothermal energy are also on the rise. However, the intermittent nature of these RESs necessitates the use of energy storage devices (ESDs) as a backup for electricity generation such as batteries, supercapacitors, and flywheel energy storage systems (FESS). This paper provides a thorough review of the standardization, market applications, and grid integration of FESS. It examines the components of FESS, including the electric motor/generator set, power converters, bearings, and control techniques. The paper also highlights the application of modern artificial intelligence (AI) methodologies in optimizing FESS operations, referencing over 240 recent publications in reputable journals. Metaheuristic optimizers, machine learning techniques, and well-matures software's are the main AI aspects discussed in this paper. Additionally, it explores the use of FESS in commercial sectors such as marine, space, and transportation, and its integration with RESs for participating in green energy. Finally, the paper emphasizes the role of AI in enhancing the synergy between FESS and RESs to contribute to a more sustainable and secure energy future.