Genetic Algorithm Optimisation of Hybrid Energy Storage System providing Dynamic Frequency Response

Abstract

Frequency Response services such as Dynamic Frequency Response (DFR) are an integral part of the safe operation of the electricity grid in the United Kingdom. Hybrid Energy Storage Systems (HESSs) are increasingly being deployed to provide frequency response services to mitigate the drawbacks that any singular energy storage system may be susceptible to. In the case of Battery Energy Storage Systems (BESSs), this drawback is a vulnerability to intense cycling either through rapid changes in SOC or from operating outside of ideal operational ranges such as at high C-Rates. Flywheels by comparison are largely unaffected by cycle quantity or intensity but commonly have poor energy density. Co-location of these devices can therefore lead to an extended operational lifetime and increased economic returns. In this paper, a novel investigation into using a genetic algorithm to optimize the configuration of a HESS providing Dynamic Frequency Response (DFR) on the Great Britain Grid is presented. Two control schemes are assessed to determine the relative merits of varying control schemes under hybrid scenarios. The work presented in this study provides key commentary on the narrow window of profitability experienced by HESSs and provides a framework for further studies to be undertaken on new and emerging frequency response services.