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Planning of a Resilient Underground Distribution Network Using Georeferenced Data

Author

Listed:
  • Alex Valenzuela

    (Electrical Engineering, Universidad Poliécnica Salesiana, Quito EC170146, Ecuador
    These authors contributed equally to this work.)

  • Esteban Inga

    (Electrical Engineering, Universidad Poliécnica Salesiana, Quito EC170146, Ecuador
    These authors contributed equally to this work.)

  • Silvio Simani

    (Department of Engineering, University of Ferrara, 44121 Ferrara, Italy)

Abstract

This study describes a practical methodology for a resilient planning and routing of power distribution networks considering real scenarios based on georeferenced data. Customers’ demand and their location are the basis for distribution transformer allocation considering the minimal construction costs and reduction of utility’s budget. MST (Minimum Spanning Tree) techniques are implemented to determine the optimal location of distribution transformers and Medium voltage network routing. Additionally, the allocation of tie points is determined to minimise the total load shedding when unusual and extreme events are faced by the distribution grid, improving reliability and resilience reducing downtime during those events. The proposed methodology provides a coverage of 100%, supplying electricity to the totality of customers within statutory limits during normal and unusual conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:644-:d:206652
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    References listed on IDEAS

    as
    1. Mousavizadeh, Saeed & Haghifam, Mahmoud-Reza & Shariatkhah, Mohammad-Hossein, 2018. "A linear two-stage method for resiliency analysis in distribution systems considering renewable energy and demand response resources," Applied Energy, Elsevier, vol. 211(C), pages 443-460.
    2. Salman, Abdullahi M. & Li, Yue & Stewart, Mark G., 2015. "Evaluating system reliability and targeted hardening strategies of power distribution systems subjected to hurricanes," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 319-333.
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    Citations

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    Cited by:

    1. 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.
    2. Zhai, Chengwei & Chen, Thomas Ying-jeh & White, Anna Grace & Guikema, Seth David, 2021. "Power outage prediction for natural hazards using synthetic power distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    3. 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.
    4. Alex Valenzuela & Silvio Simani & Esteban Inga, 2021. "Automatic Overcurrent Protection Coordination after Distribution Network Reconfiguration Based on Peer-To-Peer Communication," Energies, MDPI, vol. 14(11), pages 1-22, June.
    5. Syed Ali Abbas Kazmi & Usama Ameer Khan & Hafiz Waleed Ahmad & Sajid Ali & Dong Ryeol Shin, 2020. "A Techno-Economic Centric Integrated Decision-Making Planning Approach for Optimal Assets Placement in Meshed Distribution Network Across the Load Growth," Energies, MDPI, vol. 13(6), pages 1-71, March.
    6. Alex Guamán & Alex Valenzuela, 2021. "Distribution Network Reconfiguration Applied to Multiple Faulty Branches Based on Spanning Tree and Genetic Algorithms," Energies, MDPI, vol. 14(20), pages 1-16, October.
    7. Ghosh, Puspendu & De, Mala, 2023. "A stochastic investment decision making method for distribution system resilience enhancement considering automation, hardening and distributed energy resources," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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