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Secondary Frequency Stochastic Optimal Control in Independent Microgrids with Virtual Synchronous Generator-Controlled Energy Storage Systems

Author

Listed:
  • Ting Yang

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Nankai District, Tianjin 300072, China)

  • Yajian Zhang

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Nankai District, Tianjin 300072, China)

  • Zhaoxia Wang

    (Agency for Science, Technology and Research, Singapore 138632, Singapore)

  • Haibo Pen

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Nankai District, Tianjin 300072, China)

Abstract

With the increasing proportion of renewable energy in microgrids (MGs), its stochastic fluctuation of output power has posed challenges to system safety and operation, especially frequency stability. Virtual synchronous generator (VSG) technology, as one effective method, was used to smoothen frequency fluctuation and improve the system’s dynamic performance, which can simulate the inertia and damping of the traditional synchronous generator. This study outlines the integration of VSG-controlled energy storage systems (ESSs) and traditional synchronous generators so they jointly participate in secondary frequency regulation in an independent MG. Firstly, a new uncertain state-space model for secondary frequency control is established, considering the measurement noises and modelling error. Then, an improved linear quadratic Gaussian (LQG) controller is designed based on stochastic optimal control theory, in which the dynamic performance index weighting matrices are optimized by combining loop transfer recovery (LTR) technology and the distribution estimation algorithm. On the issue of secondary frequency devices’ output power allocation, the dynamic participation factors based on the ESS’s current state of charge (SOC) are proposed to prevent the batteries’ overcharging and overdischarging problems. The energy storage devices’ service life can be prolonged and OPEX (operational expenditure) decreased. Multiple experimental scenarios with real parameters of MGs are employed to evaluate the performance of the proposed algorithm. The results show that, compared with the lead-compensated-proportional-integral-derivative (LC-PID) control and robust μ-control algorithms, the proposed stochastic optimal control method has a faster dynamic response and is more robust, and the fluctuations from renewable energy and power loads can be smoothened more effectively.

Suggested Citation

  • Ting Yang & Yajian Zhang & Zhaoxia Wang & Haibo Pen, 2018. "Secondary Frequency Stochastic Optimal Control in Independent Microgrids with Virtual Synchronous Generator-Controlled Energy Storage Systems," Energies, MDPI, vol. 11(9), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2388-:d:168989
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    References listed on IDEAS

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    Cited by:

    1. Latif, Abdul & Hussain, S.M. Suhail & Das, Dulal Chandra & Ustun, Taha Selim, 2020. "State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based power systems," Applied Energy, Elsevier, vol. 266(C).
    2. Eng Tseng Lau & Kok Keong Chai & Yue Chen & Jonathan Loo, 2018. "Efficient Economic and Resilience-Based Optimization for Disaster Recovery Management of Critical Infrastructures," Energies, MDPI, vol. 11(12), pages 1-20, December.
    3. Chengshun Yang & Fan Yang & Dezhi Xu & Xiaoning Huang & Dongdong Zhang, 2019. "Adaptive Command-Filtered Backstepping Control for Virtual Synchronous Generators," Energies, MDPI, vol. 12(14), pages 1-17, July.
    4. Gholam Ali Alizadeh & Tohid Rahimi & Mohsen Hasan Babayi Nozadian & Sanjeevikumar Padmanaban & Zbigniew Leonowicz, 2019. "Improving Microgrid Frequency Regulation Based on the Virtual Inertia Concept while Considering Communication System Delay," Energies, MDPI, vol. 12(10), pages 1-15, May.
    5. Min-Rong Chen & Guo-Qiang Zeng & Yu-Xing Dai & Kang-Di Lu & Da-Qiang Bi, 2018. "Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid," Energies, MDPI, vol. 12(1), pages 1-21, December.

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