Planning Underground Power Distribution Networks to Minimize Negative Visual Impact in Resilient Smart Cities

Francisco Pabón, Esteban Inga, Miguel Campaña

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This paper presents the application of heuristic methods in conjunction with graph theory in the optimal routing and sizing of underground distribution networks in georeferenced (GIS) scenarios, which are modeled and simulated in the advanced engineering tool CYMDIST. The tool allows the deployment of underground networks to facilitate the design, planning, and implementation of networks, taking into consideration distribution company regulations, thus allowing overview and future planning in the growth of distribution systems. Further, this method is modeled in real georeferenced scenarios, where the coverage of the electric service to all users connected to the network is guaranteed according to population density and energy demand while minimizing the number of distribution transformers used. The applied method considers the location of transformer chambers, the capacity and coverage of the distribution transformers, and the voltage drops over the line section, which should not exceed 5% of the nominal value as described in the ANSI C84.1 standard. Consequently, to verify the efficiency of the applied method, the limitations and restrictions of the mathematical model are considered, as well as the characteristics of the georeferenced system and a comparison with different research studies that address the subject presented here. In addition, supply coverage is guaranteed to be 100%.
Translated title of the contributionPlanificación de redes subterráneas de distribución de energía para minimizar el impacto visual negativo en ciudades inteligentes resilientes
Original languageEnglish
Pages (from-to)463-479
Number of pages17
Issue number3
StatePublished - 16 Sep 2022

Bibliographical note

Funding Information:
This work was financially funded by the Smart Grid Research Group (GIREI) of Universidad Politécnica Salesiana and Power Grids and Smart Cities (RECI), Salesian Institutions of Higher Education (IUS).

Publisher Copyright:
© 2022 by the authors.


  • georeferenced
  • graph theory
  • heuristic techniques
  • optimization

CACES Knowledge Areas

  • 317A Electricity and Energy


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