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Integration of renewable energy systems and challenges for dynamics, control, and automation of electrical power systems

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  • Amirhossein Sajadi
  • Luka Strezoski
  • Vladimir Strezoski
  • Marija Prica
  • Kenneth A. Loparo

Abstract

This paper tackles the key challenges for dynamics, control, and automation of power systems that are imposed by the integration of renewable power plants. First, the current practice of automation and control in large‐scale power systems are reviewed. Then, dynamics and control of electrical transmission systems are discussed and the issues associated with the integration of large‐scale wind and solar power plants are exploited. The discussion carries on with a focus on control of electrical distribution systems and the key issues associated with the integration of distributed generation power plants. An emerging concern in power and energy industry is the dynamic interaction between transmission and distribution systems as a result of technological and topological changes in power systems that can put their control at risk. These topics are also covered in this paper. In terms of automation, the key challenges and opportunities for accommodation of higher penetration and share of renewable energy, as part of the vision for grid modernization, are explored in this paper. Throughout the discussion, some results from the recent studies are shown. This article is categorized under: Energy Infrastructure > Systems and Infrastructure

Suggested Citation

  • Amirhossein Sajadi & Luka Strezoski & Vladimir Strezoski & Marija Prica & Kenneth A. Loparo, 2019. "Integration of renewable energy systems and challenges for dynamics, control, and automation of electrical power systems," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(1), January.
    • Handle: RePEc:bla:wireae:v:8:y:2019:i:1:n:e321
    DOI: 10.1002/wene.321
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Manuel S. Alvarez-Alvarado & Johnny Rengifo & Rommel M. Gallegos-Núñez & José G. Rivera-Mora & Holguer H. Noriega & Washington Velasquez & Daniel L. Donaldson & Carlos D. Rodríguez-Gallegos, 2022. "Particle Swarm Optimization for Optimal Frequency Response with High Penetration of Photovoltaic and Wind Generation," Energies, MDPI, vol. 15(22), pages 1-12, November.
    2. Peter D. Lund & John Byrne, 2020. "Little time left to reverse emissions—Growing hope despite disappointing CO2 trend," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(1), January.
    3. Alarico Macor & Alberto Benato, 2020. "Regulated Emissions of Biogas Engines—On Site Experimental Measurements and Damage Assessment on Human Health," Energies, MDPI, vol. 13(5), pages 1-38, February.
    4. Luka Strezoski, 2023. "Distributed energy resource management systems—DERMS: State of the art and how to move forward," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(1), January.
    5. Zoran Pajić & Zoran Janković & Aleksandar Selakov, 2024. "Autoencoder-Driven Training Data Selection Based on Hidden Features for Improved Accuracy of ANN Short-Term Load Forecasting in ADMS," Energies, MDPI, vol. 17(20), pages 1-14, October.
    6. Kim, Hyunggeun & Park, Sangkyu & Lee, Jongsu, 2021. "Is renewable energy acceptable with power grid expansion? A quantitative study of South Korea's renewable energy acceptance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    7. Lv, Jiaqing & Zheng, Xiaodong & Pawlak, Mirosław & Mo, Weike & Miśkowicz, Marek, 2021. "Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms," Renewable Energy, Elsevier, vol. 177(C), pages 181-192.
    8. Peng Tian & Zetao Li & Zhenghang Hao, 2019. "A Doubly-Fed Induction Generator Adaptive Control Strategy and Coordination Technology Compatible with Feeder Automation," Energies, MDPI, vol. 12(23), pages 1-21, November.
    9. Amirhossein Sajadi & Rick Wallace Kenyon & Bri-Mathias Hodge, 2022. "Synchronization in electric power networks with inherent heterogeneity up to 100% inverter-based renewable generation," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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