Development of a multi-timescale method for classifying hybrid energy storage systems in grid applications

TL;DR

This paper introduces a multi-timescale classification method for hybrid energy storage systems, enhancing energy management in grid applications by evaluating various criteria and experimental load profiles.

Contribution

It presents a novel mathematical approach combining multiple evaluation criteria to classify hybrid energy storage systems based on their roles in different applications.

Findings

Hybrid energy storage systems can be effectively classified using multi-criteria thresholds.

Experimental load profiles demonstrate the method's applicability across diverse real-world scenarios.

Clustering reveals distinct roles of VRFB and SC in energy management.

Abstract

An extended use of renewable energies and a trend towards increasing energy consumption lead to challenges such as temporal and spatial decoupling of energy generation and consumption. This work evaluates the possible applications and advantages of hybrid energy storage systems compared to conventional, single energy storage applications. In a mathematical approach, evaluation criteria such as frequency, probability of power transients, as well as absolute power peaks are combined to identify suitable thresholds for energy management systems on a multi-timescale basis. With experimental load profiles from a municipal application, an airport, and an industrial application, four categories, clustering similar roles of the VRFB and the SC, are developed.