A Study of Battery Energy Storage Systems for Power System Frequency Regulation

  • Bo Lian

Student thesis: Doctoral ThesisPhD


In the UK power system, there is currently no battery energy storage system (BESS) for providing grid-scale frequency response. According to the UK future energy plan, however, with a high penetration of renewable energy sources, battery technologies have become increasingly attractive to providing frequency-response-related services. The frequency response characteristics of the UK system with reduced inertia and the incorporation of BESSs should be investigated. BESSs can either be deployed as an independent commercial frequency response provider or be coordinated with renewable generation for complementing and fulfilling mandatory frequency response required by the grid. Economic optimization of the parameters of BESSs should be made according to the regulations of the UK market. Due to the high capital costs of batteries, BESSs should be carefully deployed to guarantee the profits of the BESS investments in the grid. The main focus of this research project is to optimize the control algorithm and capacities of BESSs for frequency regulation in the UK system. A review of the UK balancing services and the method of selecting energy storage technologies is the initial part of this work. To investigate the effects of the incorporation of BESSs in the UK system under current inertia and future low inertia generation, a UK frequency response model was developed and studied. The energy offset strategy and energy/ power capacities of BESSs are very important to the reliability and economics of BESSs projects. BESSs for firm frequency response service and coordinated wind-farm-battery system in the UK system to provide frequency regulation were studied respectively. This research extends previous studies about BESS energy offset by optimizing the battery energy/power ratio, energy offset interval and the preferred SOC. The optimal parameters of the studied BESSs were firstly obtained by using gradient search. For comparison, Genetic Algorithm searching technique was used and has been found to have better performance in terms of reduced computing time and improved accuracy. Results suggest that it is profitable to deploy BESSs in the UK market. The optimal parameters of BESSs for firm frequency response service differ with those for coordinated wind-farm-battery system.
Date of Award11 Jan 2017
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorRoderick Dunn (Supervisor)

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