Development and Comparison of Rule- and Machine Learning-Based EMS for HESS Providing Grid Services
Hakan Polat,
Eneko Unamuno,
David Cabezuelo
et al.
Abstract:In this paper, a smart machine-learning-based energy management system (MLBEMS) is developed for a hybrid energy storage system (HESS). This HBESS consists of batteries with high-energy (HE) and high-power (HP) characteristics, to provide grid-supporting services. The aim of the MLBEMS is to improve the overall battery lifetime and achieve state-of-charge (SoC) balancing for two different use cases (UC). UC1 involves enhanced frequency regulation for the Pan-European grid, while UC2 pertains to an electric veh… Show more
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