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Reduction in Microgrid Topology Selection Time via Hybrid Branch and Bound and k-Nearest Neighbors Techniques

Author

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  • Inoussa Legrene

    (Systems Engineering Department, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

  • Tony Wong

    (Systems Engineering Department, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

  • Nicolas Mary

    (Electrical Engineering Department, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

  • Louis-A. Dessaint

    (Electrical Engineering Department, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

Abstract

The global adoption of hybrid renewable energy systems (HRESs) is accelerating as a strategic response to escalating energy demands and the imperative to mitigate greenhouse gas emissions. Despite the development of various technological tools, such as pre-feasibility analysis, sizing, and simulation tools, challenges persist due to their limited flexibility in modifying system architectures and their typically long computation times, which hinder their practical efficiency. This study introduces a novel hybrid method that integrates the Branch and Bound (BB) heuristic search algorithm with the k-Nearest Neighbors (kNN) algorithm to drastically reduce the simulation time of microgrid models in Simulink. Validation considering four distinct case studies reveals that our method can decrease the simulation time by up to 94.68% while maintaining an acceptable accuracy. Specifically, simulation times in certain cases were reduced from approximately 21,780 and 118,580 s to 1442.7969 and 6306.0625 s, respectively. This significant reduction facilitates the rapid evaluation and selection of optimal HRES configurations, enhancing the efficiency of both editable and non-editable systems. Through streamlining the simulation process, this approach not only accelerates the design and analysis phases but also supports the broader adoption and deployment of HRESs, which is critical for achieving a sustainable future. This advancement offers a robust and efficient methodology for optimizing simulation times, thereby addressing a key bottleneck in the development and implementation of hybrid renewable energy solutions.

Suggested Citation

  • Inoussa Legrene & Tony Wong & Nicolas Mary & Louis-A. Dessaint, 2025. "Reduction in Microgrid Topology Selection Time via Hybrid Branch and Bound and k-Nearest Neighbors Techniques," Mathematics, MDPI, vol. 13(3), pages 1-16, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:360-:d:1574478
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    References listed on IDEAS

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    1. Javed, Muhammad Shahzad & Ma, Tao & Jurasz, Jakub & Mikulik, Jerzy, 2021. "A hybrid method for scenario-based techno-economic-environmental analysis of off-grid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    2. Xiaoming Liu & Liang Wang & Yongji Cao & Ruicong Ma & Yao Wang & Changgang Li & Rui Liu & Shihao Zou, 2023. "Renewable Scenario Generation Based on the Hybrid Genetic Algorithm with Variable Chromosome Length," Energies, MDPI, vol. 16(7), pages 1-16, March.
    3. Saxena, Vivek & Kumar, Narendra & Manna, Saibal & Rajput, Saurabh Kumar & Agarwal, Kusum Lata & Diwania, Sourav & Gupta, Varun, 2025. "Modelling, solution and application of optimization techniques in HRES: From conventional to artificial intelligence," Applied Energy, Elsevier, vol. 380(C).
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