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Optimal multi-objective sizing of renewable energy sources and battery energy storage systems for formation of a multi-microgrid system considering diverse load patterns

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  • Ibrahim, Nurul Nadia
  • Jamian, Jasrul Jamani
  • Md Rasid, Madihah

Abstract

Optimal microgrid design is pivotal in planning active distribution networks (ADNs) with intermittent renewable energy sources (RESs) and battery energy storage systems (BESSs). This paper introduces an innovative approach to clustering existing ADN systems, incorporating RESs and BESSs into a set of microgrids (MGs) termed a multi-microgrid (MMG). The approach accommodates diverse load patterns, considering the dynamic nature of residential, commercial, and industrial loads. The proposed methodology employs an innovative k-means algorithm that utilizes end node characteristics based on the graph partitioning method and the Silhouette technique, transforming the existing system into an MMG framework. Furthermore, an iterative pareto-fuzzy (IPF) method is suggested to determine optimal RES and BESS sizes, considering reliability, total cost, and the unutilized surplus power function. The framework integrates total line losses into the generation adequacy assessment of energy management strategy (EMS) for each MG within the MMG system. The design and performance analysis of the proposed multi-objective optimization algorithm is tested on the IEEE 33-bus distribution system. The implementation of the independent operation index (IOI) and round-trip efficiency (RTE) metrics demonstrates the effectiveness of the proposed approach in constructing an MMG, with achieved values exceeding 90 percent for IOI and 77 percent for RTE.

Suggested Citation

  • Ibrahim, Nurul Nadia & Jamian, Jasrul Jamani & Md Rasid, Madihah, 2024. "Optimal multi-objective sizing of renewable energy sources and battery energy storage systems for formation of a multi-microgrid system considering diverse load patterns," Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:energy:v:304:y:2024:i:c:s0360544224016943
    DOI: 10.1016/j.energy.2024.131921
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    References listed on IDEAS

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    2. Lin, Hai & Wu, Yan & Cui, Ziyuan & Yang, Sen & Yu, Xinhai & Wang, Yufei, 2025. "An integrated optimization method for distributed wind farm design considering three-dimensional complex terrain," Renewable Energy, Elsevier, vol. 255(C).
    3. Astrid Esparza & Maude Blondin & João Pedro F. Trovão, 2025. "A Review of Optimization Strategies for Energy Management in Microgrids," Energies, MDPI, vol. 18(13), pages 1-32, June.
    4. Ye, Tong & Huang, Yuping & Yang, Weijia & Cai, Guotian & Yang, Yuyao & Pan, Feng, 2025. "Safe multi-agent deep reinforcement learning for decentralized low-carbon operation in active distribution networks and multi-microgrids," Applied Energy, Elsevier, vol. 387(C).
    5. Li, Jinmeng & Liu, Da & Wang, Jiaying & Bao, Guanglu & Shi, Mengfei, 2025. "Collaborative configuration optimization of renewable energy generation capacity for islanded microgrid clusters: A decision-making framework based on multi-criteria flexible interaction and shared mobile energy storage," Energy, Elsevier, vol. 333(C).
    6. Stanek, Wojciech & Szostok, Agnieszka, 2025. "Thermo-ecological assessment of microgrid supported with renewable energy," Energy, Elsevier, vol. 314(C).

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