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A Survey of Real-Time Optimal Power Flow

Author

Listed:
  • Erfan Mohagheghi

    (Department of Process Optimization, Institute of Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany)

  • Mansour Alramlawi

    (Department of Process Optimization, Institute of Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany)

  • Aouss Gabash

    (Department of Process Optimization, Institute of Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany)

  • Pu Li

    (Department of Process Optimization, Institute of Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany)

Abstract

There has been a strong increase of penetration of renewable energies into power systems. However, the renewables pose new challenges for the operation of the networks. Particularly, wind power is intermittently fluctuating, and, therefore, the network operator has to fast update the operations correspondingly. This task should be performed by an online optimization. Therefore, real-time optimal power flow (RT-OPF) has become an attractive topic in recent years. This paper presents an overview of recent studies on RT-OPF under wind energy penetration, offering a critical review of the major advancements in RT-OPF. It describes the challenges in the realization of the RT-OPF and presents available approaches to address these challenges. The paper focuses on a number of topics which are reviewed in chronological order of appearance: offline energy management systems (EMSs) (deterministic and stochastic approaches) and real-time EMSs (constraint satisfaction-based and OPF-based methods). The particular challenges associated with the incorporation of battery storage systems in the networks are explored, and it is concluded that the current research on RT-OPF is not sufficient, and new solution approaches are needed.

Suggested Citation

  • Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Pu Li, 2018. "A Survey of Real-Time Optimal Power Flow," Energies, MDPI, vol. 11(11), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3142-:d:182548
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    References listed on IDEAS

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    Cited by:

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    3. Kumar, Abhishek & Meena, Nand K. & Singh, Arvind R. & Deng, Yan & He, Xiangning & Bansal, R.C. & Kumar, Praveen, 2019. "Strategic integration of battery energy storage systems with the provision of distributed ancillary services in active distribution systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Frede Blaabjerg & Pu Li, 2020. "Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems," Energies, MDPI, vol. 13(7), pages 1-17, April.
    5. Li, Shuijia & Gong, Wenyin & Hu, Chengyu & Yan, Xuesong & Wang, Ling & Gu, Qiong, 2021. "Adaptive constraint differential evolution for optimal power flow," Energy, Elsevier, vol. 235(C).
    6. Iver Bakken Sperstad & Magnus Korpås, 2019. "Energy Storage Scheduling in Distribution Systems Considering Wind and Photovoltaic Generation Uncertainties," Energies, MDPI, vol. 12(7), pages 1-24, March.
    7. Ding, Jie & Xu, Yujie & Chen, Haisheng & Sun, Wenwen & Hu, Shan & Sun, Shuang, 2019. "Value and economic estimation model for grid-scale energy storage in monopoly power markets," Applied Energy, Elsevier, vol. 240(C), pages 986-1002.
    8. Suresh Chavhan & Subhi R. M. Zeebaree & Ahmed Alkhayyat & Sachin Kumar, 2022. "Design of Space Efficient Electric Vehicle Charging Infrastructure Integration Impact on Power Grid Network," Mathematics, MDPI, vol. 10(19), pages 1-20, September.
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