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Cooperative energy dispatch for multiple autonomous microgrids with distributed renewable sources and storages

Author

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  • Fang, Xinli
  • Ma, Shihao
  • Yang, Qiang
  • Zhang, Jintao

Abstract

The increasing number of intermittent renewable DGs (distributed generators) penetrating into current MGs (microgrids) brings direct challenges in operation, e.g. voltage raise effect, power quality, protection and stability, energy management, e.g. DG-demand matching energy utilization efficiency, as well as power supply security of CLs (critical loads). This paper presents a collective energy dispatch solution to optimally coordinate DGs, distributed SUs (storage units) and critical demands across multiple AMGs (autonomous MGs) based on a “tree stem-leaves” approach. The energy distribution network consisting of multiple AMGs are modeled mathematically as a weighted matrix simultaneously considering power loss and reliability statistics. The revised MST (minimum spanning tree) algorithm is adopted to identify the optimal DG-CL and SU-CL mappings (“tree stems”) for energy supply, and the LMI (linear matrix inequality) algorithm determines the NLs (non-critical loads) to be supplied and added to the “stems” as “tree leaves”. Such energy network structure formed by “stem” and “leaves” can vary over time in case that significant changes are identified during MG operation (e.g. DG and demand dynamics), and the functionalities can be implemented through intelligent system management tools, e.g. multi-agent systems. As a result, it can consistently lead to optimal energy management with significantly improved CL supply security, global DG utilization efficiency, and generation-demand matching performance. The suggested solution is verified by carrying out a set of simulation experiments for a range of network scenarios (e.g. various renewable penetration ratios) based on IEEE 33-bus network model, and the numerical result clearly confirms the effectiveness and technical benefits.

Suggested Citation

  • Fang, Xinli & Ma, Shihao & Yang, Qiang & Zhang, Jintao, 2016. "Cooperative energy dispatch for multiple autonomous microgrids with distributed renewable sources and storages," Energy, Elsevier, vol. 99(C), pages 48-57.
    • Handle: RePEc:eee:energy:v:99:y:2016:i:c:p:48-57
    DOI: 10.1016/j.energy.2016.01.036
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Xiaoshun & Yu, Tao & Xu, Zhao & Fan, Zhun, 2018. "A cyber-physical-social system with parallel learning for distributed energy management of a microgrid," Energy, Elsevier, vol. 165(PA), pages 205-221.
    2. José Luis Ruiz Duarte & Neng Fan, 2022. "Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies," Energies, MDPI, vol. 15(7), pages 1-24, March.
    3. Zhao, Zhigao & Yang, Jiandong & Chung, C.Y. & Yang, Weijia & He, Xianghui & Chen, Man, 2021. "Performance enhancement of pumped storage units for system frequency support based on a novel small signal model," Energy, Elsevier, vol. 234(C).
    4. Baghaee, H.R. & Mirsalim, M. & Gharehpetian, G.B. & Talebi, H.A., 2016. "Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system," Energy, Elsevier, vol. 115(P1), pages 1022-1041.
    5. Jalali, Mehdi & Zare, Kazem & Seyedi, Heresh, 2017. "Strategic decision-making of distribution network operator with multi-microgrids considering demand response program," Energy, Elsevier, vol. 141(C), pages 1059-1071.
    6. Nawaz, Arshad & Zhou, Min & Wu, Jing & Long, Chengnian, 2022. "A comprehensive review on energy management, demand response, and coordination schemes utilization in multi-microgrids network," Applied Energy, Elsevier, vol. 323(C).
    7. Li, Qiang & Gao, Mengkai & Lin, Houfei & Chen, Ziyu & Chen, Minyou, 2019. "MAS-based distributed control method for multi-microgrids with high-penetration renewable energy," Energy, Elsevier, vol. 171(C), pages 284-295.
    8. Farihan Mohamad & Jiashen Teh, 2018. "Impacts of Energy Storage System on Power System Reliability: A Systematic Review," Energies, MDPI, vol. 11(7), pages 1-23, July.
    9. Xiuyun Wang & Shaoxin Chen & Yibing Zhou & Jian Wang & Yang Cui, 2018. "Optimal Dispatch of Microgrid with Combined Heat and Power System Considering Environmental Cost," Energies, MDPI, vol. 11(10), pages 1-23, September.

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