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An Improved Multi-Timescale Coordinated Control Strategy for Stand-Alone Microgrid with Hybrid Energy Storage System

Author

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  • Jingfeng Chen

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, Guangzhou 511458, China)

  • Ping Yang

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Key Laboratory of Clean Energy Technology, Guangzhou 511458, China
    National-Local Joint Engineering Laboratory for Wind Power Control and Integration Technology, South China University of Technology, Guangzhou 511458, China)

  • Jiajun Peng

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Yuqi Huang

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Yaosheng Chen

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Zhiji Zeng

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

Abstract

A scientific and effective coordinated control strategy is crucial to the safe and economic operation of a microgrid (MG). With the continuous improvement of the renewable energy source (RES) penetration rate in MG, the randomness and intermittency of its output lead to the increasing regulation pressure of the conventional controllable units, the increase of the operating risk of MG and the difficulty in improving the operational economy. To solve the mentioned problems and take advantage of hybrid energy storage system (HESS), this study proposes a multi-time scale coordinated control scheme of “day-ahead optimization (DAO) + intraday rolling (IDR) + quasi-real-time correction (QRTC) + real-time coordinated control (RTCC).” Considering the shortcomings of existing low prediction accuracy of distributed RES and loads, the soft constraints such as unit commitment scheduling errors and load switching scheduling errors are introduced in the intraday rolling model, allowing the correction of day-ahead unit commitment and load switching schedule. In the quasi-real-time coordinated control, an integrated criterion is introduced to decide the adjustment priority of the distributed generations. In the real-time coordinated control, the HESS adopts an improved first order low pass filtering algorithm to adaptively compensate the second-level unbalanced power. Compared with the traditional coordinated control strategy, the proposed improved model has the advantages of good robustness and fast solving speed and provides some guidance for the intelligent solution for stable and economic operation of stand-alone MG with HESS.

Suggested Citation

  • Jingfeng Chen & Ping Yang & Jiajun Peng & Yuqi Huang & Yaosheng Chen & Zhiji Zeng, 2018. "An Improved Multi-Timescale Coordinated Control Strategy for Stand-Alone Microgrid with Hybrid Energy Storage System," Energies, MDPI, vol. 11(8), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2150-:d:164301
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    References listed on IDEAS

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    1. Songli Fan & Qian Ai & Longjian Piao, 2018. "Hierarchical Energy Management of Microgrids including Storage and Demand Response," Energies, MDPI, vol. 11(5), pages 1-23, May.
    2. Won-Poong Lee & Jin-Young Choi & Dong-Jun Won, 2017. "Coordination Strategy for Optimal Scheduling of Multiple Microgrids Based on Hierarchical System," Energies, MDPI, vol. 10(9), pages 1-18, September.
    3. Cagnano, A. & Caldarulo Bugliari, A. & De Tuglie, E., 2018. "A cooperative control for the reserve management of isolated microgrids," Applied Energy, Elsevier, vol. 218(C), pages 256-265.
    4. Chongxin Huang & Dong Yue & Song Deng & Jun Xie, 2017. "Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization," Energies, MDPI, vol. 10(3), pages 1-23, March.
    5. Xianyong Zhang & Wei-chang Yeh & Yunzhi Jiang & Yaohong Huang & Yingwang Xiao & Li Li, 2018. "A Case Study of Control and Improved Simplified Swarm Optimization for Economic Dispatch of a Stand-Alone Modular Microgrid," Energies, MDPI, vol. 11(4), pages 1-21, March.
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    2. Pan, Chenyun & Fan, Hongtao & Zhang, Ruixiang & Sun, Jie & Wang, Yu & Sun, Yaojie, 2023. "An improved multi-timescale coordinated control strategy for an integrated energy system with a hybrid energy storage system," Applied Energy, Elsevier, vol. 343(C).
    3. Md. Sanwar Hossain & Abdullah G. Alharbi & Khondoker Ziaul Islam & Md. Rabiul Islam, 2021. "Techno-Economic Analysis of the Hybrid Solar PV/H/Fuel Cell Based Supply Scheme for Green Mobile Communication," Sustainability, MDPI, vol. 13(22), pages 1-29, November.
    4. Furat Dawood & GM Shafiullah & Martin Anda, 2020. "Stand-Alone Microgrid with 100% Renewable Energy: A Case Study with Hybrid Solar PV-Battery-Hydrogen," Sustainability, MDPI, vol. 12(5), pages 1-17, March.

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