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Coordination control of distributed generators and load resources for frequency restoration in isolated urban microgrids

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  • Hui, Hongxun
  • Chen, Yulin
  • Yang, Shaohua
  • Zhang, Hongcai
  • Jiang, Tao

Abstract

Urban microgrids have become the main body of energy consumption in modern power systems. To reduce carbon emissions, distributed generators (DGs), such as roof photovoltaics (PVs), are increasing rapidly in urban microgrids. By utilizing the power output from local DGs, more microgrids have the ability to operate in isolated mode. However, isolated microgrids have less regulation capacities compared with large interconnected power systems, which significantly raises the difficulty for maintaining the stable operation of isolated microgrids. To address this issue, this paper proposes a coordination control method to tap flexibility from both DGs in supply-side and load resources in demand-side. First, the microgrid model with high-penetration DGs is developed considering virtual power plants (VPPs) by aggregating flexible loads. Then, a coordination control framework is proposed for DGs and VPPs in multi-scenarios, including stable operation, uncertain load disturbances, and accidental DG outages. Based on this framework, a distributed consensus algorithm (DCA) and a local control algorithm (LCA) are designed for DGs and VPPs, respectively. The DCA can achieve a quick regulation of DGs with high plug-and-play expansibility. The LCA can control VPPs considering both the quality of regulation services and comfortable requirements of heterogeneous users. Finally, numerical studies verify that the proposed models and methods can awaken fragmented DGs and VPPs to improve the stability and enhance the resilience of isolated microgrids.

Suggested Citation

  • Hui, Hongxun & Chen, Yulin & Yang, Shaohua & Zhang, Hongcai & Jiang, Tao, 2022. "Coordination control of distributed generators and load resources for frequency restoration in isolated urban microgrids," Applied Energy, Elsevier, vol. 327(C).
  • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922013733
    DOI: 10.1016/j.apenergy.2022.120116
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    References listed on IDEAS

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    1. Zhang, Xiangyu & Pipattanasomporn, Manisa & Rahman, Saifur, 2017. "A self-learning algorithm for coordinated control of rooftop units in small- and medium-sized commercial buildings," Applied Energy, Elsevier, vol. 205(C), pages 1034-1049.
    2. Zheng, Menglian & Wang, Xinhao & Meinrenken, Christoph J. & Ding, Yi, 2018. "Economic and environmental benefits of coordinating dispatch among distributed electricity storage," Applied Energy, Elsevier, vol. 210(C), pages 842-855.
    3. Gan, Wei & Yan, Mingyu & Yao, Wei & Wen, Jinyu, 2021. "Peer to peer transactive energy for multiple energy hub with the penetration of high-level renewable energy," Applied Energy, Elsevier, vol. 295(C).
    4. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    5. Kim, Youngjin & Norford, Leslie K., 2017. "Optimal use of thermal energy storage resources in commercial buildings through price-based demand response considering distribution network operation," Applied Energy, Elsevier, vol. 193(C), pages 308-324.
    6. Jiang, Tao & Li, Zening & Jin, Xiaolong & Chen, Houhe & Li, Xue & Mu, Yunfei, 2018. "Flexible operation of active distribution network using integrated smart buildings with heating, ventilation and air-conditioning systems," Applied Energy, Elsevier, vol. 226(C), pages 181-196.
    7. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
    8. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2014. "Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response," Applied Energy, Elsevier, vol. 126(C), pages 297-306.
    9. Zhong, Haiwang & Zhang, Guanglun & Tan, Zhenfei & Ruan, Guangchun & Wang, Xuan, 2022. "Hierarchical collaborative expansion planning for transmission and distribution networks considering transmission cost allocation," Applied Energy, Elsevier, vol. 307(C).
    10. Li, Yinxiao & Wang, Yi & Chen, Qixin, 2020. "Study on the impacts of meteorological factors on distributed photovoltaic accommodation considering dynamic line parameters," Applied Energy, Elsevier, vol. 259(C).
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    Cited by:

    1. Li, Zhihao & Yang, Lun & Xu, Yinliang, 2023. "A dynamics-constrained method for distributed frequency regulation in low-inertia power systems," Applied Energy, Elsevier, vol. 344(C).
    2. Erdal Irmak & Ersan Kabalci & Yasin Kabalci, 2023. "Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity," Energies, MDPI, vol. 16(12), pages 1-58, June.
    3. Yang, Shaohua & Lao, Keng-Weng & Hui, Hongxun & Chen, Yulin, 2023. "A robustness-enhanced frequency regulation scheme for power system against multiple cyber and physical emergency events," Applied Energy, Elsevier, vol. 350(C).

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