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Frequency Restoration Control and Its Impact on Model Predictive Control of Island Power Systems

Lucas Reus*, Marco Alferink, Kai Michels, Lutz Hofmann

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Abstract

Advanced Energy Management Systems (EMSs) are proposed to achieve the economic and stable operation of low-inertia island power systems with high shares of Renewable Energy Sources (RES). However, fast power fluctuations severely affect the frequency dynamics, which are not addressed by the EMS. Thus, this paper presents the extension of a Model Predictive Control (MPC)-based EMS by a Frequency Restoration Control (FRC) that shifts the droop characteristic of a grid-forming Battery Energy Storage System (BESS) to restore the frequency. The power system of Suðuroy, Faroe Islands, consisting of RES, synchronous machines and a grid-forming BESS, serves as a case study. Quasi-stationary simulations are used to investigate both the impact of the generation units’ droop controls and the FRC on the system stability and economics. The improved frequency behavior results in less deviations of the distributed energy resources from the optimal setpoints determined by the EMS. As a result, the economic performance is improved while ensuring a stable operation, highlighting the advantages of the MPC-based EMS with local FRC.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Power System Technology, PowerCon 2024 - Proceedings
PublisherIEEE
ISBN (Electronic)979-8-3503-5128-6
ISBN (Print)979-8-3503-5129-3
DOIs
Publication statusPublished - 4 Nov 2024

UN Sustainable Development Goals (SDGs)

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • battery energy storage system
  • energy management system
  • grid-forming
  • hybrid power system
  • microgrid

ASJC Scopus subject areas

  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Modelling and Simulation

Research Area (based on ÖFOS 2012)

  • Electric power engineering

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