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Optimal Reconfiguration of Electrical Distribution System Using Heuristic Methods with Geopositioning Constraints

Author

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  • Edy Quintana

    (Department of Posgraduate in Electricity, Universidad Politécnica Salesiana, Quito 170525, Ecuador
    Current address: Postgraduate Department, Girón Campus, Av. 12 de Octubre N 23-52, Quito 170525, Ecuador.)

  • Esteban Inga

    (Department of Posgraduate ICT for Education, Smart Grid Research Group, Universidad Politécnica Salesiana, Quito 170525, Ecuador)

Abstract

Natural disasters have great destructive power and can potentially wipe out great lengths of power lines. A resilient grid can recover from adverse conditions and restore service quickly. Therefore, the present work proposes a novel methodology to reconfigure power grids through graph theory after an extreme event. The least-cost solution through a minimum spanning tree (MST) with a radial topology that connects all grid users is identified. To this end, the authors have developed an iterative minimum-path heuristic algorithm. The optimal location of transformers and maintenance holes in the grid is obtained with the modified Prim algorithm, and the Greedy algorithm complements the process. The span distance and capacity restrictions define the transformer’s number, where larger spans and capacities reduce the number of components in the grid. The performance of the procedure has been tested in the urban zone Quito Tenis of Ecuador, and the algorithm proved to be scalable. Grid reconfiguration is pushed through a powerful tool to model distribution systems such as CYMDIST, where the voltage drops were minor than 3.5%.

Suggested Citation

  • Edy Quintana & Esteban Inga, 2022. "Optimal Reconfiguration of Electrical Distribution System Using Heuristic Methods with Geopositioning Constraints," Energies, MDPI, vol. 15(15), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5317-:d:868896
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    References listed on IDEAS

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    1. Verner Püvi & Robert J. Millar & Eero Saarijärvi & Ken Hayami & Tahitoa Arbelot & Matti Lehtonen, 2020. "Slime Mold Inspired Distribution Network Initial Solution," Energies, MDPI, vol. 13(23), pages 1-17, November.
    2. Alex Valenzuela & Iván Montalvo & Esteban Inga, 2019. "A Decision-Making Tool for Electric Distribution Network Planning Based on Heuristics and Georeferenced Data," Energies, MDPI, vol. 12(21), pages 1-18, October.
    3. Jesus Beyza & Eduardo Garcia-Paricio & Jose M. Yusta, 2019. "Applying Complex Network Theory to the Vulnerability Assessment of Interdependent Energy Infrastructures," Energies, MDPI, vol. 12(3), pages 1-16, January.
    4. Sachin Kumar & Kumari Sarita & Akanksha Singh S Vardhan & Rajvikram Madurai Elavarasan & R. K. Saket & Narottam Das, 2020. "Reliability Assessment of Wind-Solar PV Integrated Distribution System Using Electrical Loss Minimization Technique," Energies, MDPI, vol. 13(21), pages 1-30, October.
    5. Alex Valenzuela & Esteban Inga & Silvio Simani, 2019. "Planning of a Resilient Underground Distribution Network Using Georeferenced Data," Energies, MDPI, vol. 12(4), pages 1-20, February.
    6. Qin, Chun & Wang, Linqing & Han, Zhongyang & Zhao, Jun & Liu, Quanli, 2021. "Weighted directed graph based matrix modeling of integrated energy systems," Energy, Elsevier, vol. 214(C).
    7. Teketay Mulu Beza & Yen-Chih Huang & Cheng-Chien Kuo, 2020. "A Hybrid Optimization Approach for Power Loss Reduction and DG Penetration Level Increment in Electrical Distribution Network," Energies, MDPI, vol. 13(22), pages 1-17, November.
    8. Abeysinghe, Sathsara & Wu, Jianzhong & Sooriyabandara, Mahesh & Abeysekera, Muditha & Xu, Tao & Wang, Chengshan, 2018. "Topological properties of medium voltage electricity distribution networks," Applied Energy, Elsevier, vol. 210(C), pages 1101-1112.
    9. Wilson Pavón & Esteban Inga & Silvio Simani, 2019. "Optimal Routing an Ungrounded Electrical Distribution System Based on Heuristic Method with Micro Grids Integration," Sustainability, MDPI, vol. 11(6), pages 1-18, March.
    10. Ibrahim Mohamed Diaaeldin & Shady H. E. Abdel Aleem & Ahmed El-Rafei & Almoataz Y. Abdelaziz & Ahmed F. Zobaa, 2019. "A Novel Graphically-Based Network Reconfiguration for Power Loss Minimization in Large Distribution Systems," Mathematics, MDPI, vol. 7(12), pages 1-17, December.
    11. Artur Łukaszewski & Łukasz Nogal & Sylwester Robak, 2020. "Weight Calculation Alternative Methods in Prime’s Algorithm Dedicated for Power System Restoration Strategies," Energies, MDPI, vol. 13(22), pages 1-20, November.
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    Cited by:

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    2. Min Zhu & Saber Arabi Nowdeh & Aspassia Daskalopulu, 2023. "An Improved Human-Inspired Algorithm for Distribution Network Stochastic Reconfiguration Using a Multi-Objective Intelligent Framework and Unscented Transformation," Mathematics, MDPI, vol. 11(17), pages 1-23, August.

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