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A Simulation-Based Optimization Method for Hybrid Frequency Regulation System Configuration

Author

Listed:
  • Jie Song

    (Department of Industrial Engineering, College of Engineering, Peking University, Beijing 100871, China)

  • Xin Pan

    (Department of Industrial Engineering, College of Engineering, Peking University, Beijing 100871, China)

  • Chao Lu

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Hanchen Xu

    (Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA)

Abstract

Frequency regulation is essential for the stability of a power grid with high load fluctuation and integration of new energies. Constrained by the large ramping, a generator alone is not capable of conducting load frequency controls effectively and economically. In this paper, an energy storage system (ESS) is introduced to coordinate with generators in automatic generation control (AGC), where ESS and the generator respectively deal with high-frequency load fluctuation and low-portion. We develop a system configuration framework for such a hybrid system, including the operation strategy and capacity optimization. Due to the complexity of the hybrid system, the operation process is captured by a simulation model which considers practical constraints as well as remaining energy management of ESS. Taking advantage of the gradient-based approximation algorithm, we are then able to optimize the capacity of a hybrid system. According to the numerical experiments with real historical AGC data, the hybrid system is shown to perform well in cost reduction and to achieve the regulation tasks.

Suggested Citation

  • Jie Song & Xin Pan & Chao Lu & Hanchen Xu, 2017. "A Simulation-Based Optimization Method for Hybrid Frequency Regulation System Configuration," Energies, MDPI, vol. 10(9), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1302-:d:110302
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    References listed on IDEAS

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    1. Cihan Turhan & Silvio Simani & Ivan Zajic & Gulden Gokcen Akkurt, 2017. "Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model," Energies, MDPI, vol. 10(1), pages 1-20, January.
    2. Chao Lu & Hanchen Xu & Xin Pan & Jie Song, 2014. "Optimal Sizing and Control of Battery Energy Storage System for Peak Load Shaving," Energies, MDPI, vol. 7(12), pages 1-15, December.
    3. Jingyi Zhang & Chao Lu & Jie Song, 2016. "Dynamic performance-based automatic generation control unit allocation with frequency sensitivity identification," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6532-6547, November.
    4. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    5. Su Su & Hao Li & David Wenzhong Gao, 2017. "Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits," Energies, MDPI, vol. 10(7), pages 1-15, July.
    6. Jie Xu & Edward Huang & Chun-Hung Chen & Loo Hay Lee, 2015. "Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 32(03), pages 1-34.
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    Cited by:

    1. Mahmut Temel ÖZDEMİR & Dursun ÖZTÜRK, 2017. "Comparative Performance Analysis of Optimal PID Parameters Tuning Based on the Optics Inspired Optimization Methods for Automatic Generation Control," Energies, MDPI, vol. 10(12), pages 1-19, December.
    2. Yongsik Lee & Hyunchul Lee & Jaehyeon Gim & Inyong Seo & Guenjoon Lee, 2020. "Technical Measures to Mitigate Load Fluctuation for Large-Scale Customers to Improve Power System Energy Efficiency," Energies, MDPI, vol. 13(18), pages 1-27, September.

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