Planning and Sizing of Electrical Networks of Underground Distribution by Metaheuristic Method

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Fabricio Villacres
Esteban Inga


Optimization, planning, distribution networks, geolocation, sizing, metaheuristics


The introduction of new loads to the traditional electrical distribution systems can lead to the overloading in the power equipment. This on the sizing makes the useful life of the power equipment decrease considerably, in addition, the reliability and stability of the system begins to be compromised. Therefore, through the present investigation it is possible to solve the problem of the planning of electrical distribution networks by integrating the possibility of migrating from the concept of traditional electric networks to smart electric networks, the same ones that only electrical distribution systems of robust networks are achieved heterogeneous bidirectional communication. The present work focused on the development of a model capable of locating the distribution transformers in the best sites to satisfy the majority of users of the electrical network and obtain the best topology by applying the theory of graphs In addition, the presented model contemplates the development of a heuristic capable of executing georeferenced planning processes through the management and use of geolocated information from OpenStreetMap through the .osm file that this free platform offers us. The heuristic proposed in the present document is modeled using the Matlab software and to validate the information, the Cymdist software is required. 


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[1] Salamat Sharif, Salama, and Vannelli, “Optimal model for future expansion of radial distribution networks using mixed integer programming,” Proceedings of Canadian Conference on Electrical and Computer Engineering CCECE-94, pp. 152–155 vol.1, 1994. [Online]. Available:

[2] E. Miguez, J. Cidras, E. Diaz-Dorado, and J. L. Garcia-Dornelas, “An Improved Branch Exchange Algorithm for Large Scale Distribution Network Planning,” IEEE Power Engineering Review, vol. 22, no. 9, pp. 58–58, 2002. [Online]. Available:

[3] G. A. Jiménez-Estévez, L. S. Vargas, and R. Palma-Behnke, “An evolutionary approach for the greenfield planning problem in distribution networks,” IEEE International Conference on Neural Networks - Conference Proceedings, pp. 1744–1749, 2007. [Online]. Available:

[4] J. Fletcher, T. Fernando, H. Iu, M. Reynolds, and S. Fani, “A case study on optimizing an electrical distribution network using a genetic algorithm,” 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE), pp. 20–25, 2015. [Online]. Available:

[5] P. Zeng, Qinyong Zhou, Z. Wu, X.-p. Zhang, and Hao Fu, “A fast and automatic candidate lines selection approach for transmission expansion planning,” in IET International Conference on Resilience of Transmission and Distribution Networks (RTDN) 2015, 2015, pp. 1–6. [Online]. Available:

[6] H. E. Farag and S. M. Kandil, “Optimum Planning of Renewable Energy Resources in Conjunction with Battery Energy Storage Systems,” in 2015 4th International Conference on Electric Power and Energy Conversion Systems (EPECS), 2015, pp. 1–6. [Online]. Available:

[7] J. Inga, E. Inga, C. Gómez, and R. Hincapié, “Evaluación de la Infraestructura de Medición y la Respuesta de la Demanda,” Revista Técnica Energía, no. 12, pp. 262–269, 2016.

[8] M. Hemphill and N. South, “Electricity Distribution System Planning for an Increasing Penetration of Plug-In Electric Vehicles in New South Wales,” University of New South Wales, pp. 1–6, 2011.

[9] G. A. Jiménez-Estévez, L. Vargas, and R. Palma-Behnke, “Genetic Algorithms and Voronoi Polygons applied to decision making in the Distribution Systems expansion problem,” 2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America, T and D-LA, pp. 1–7, 2008. [Online]. Available:

[10] W. Yuan, S. Member, J. Wang, S. Member, F. Qiu, C. Kang, S. Member, and B. Zeng, “Robust Optimization Based Resilient Distribution Network Planning Against Natural Disasters,” Ieeee Transactions on Smart Grid, vol. 7, no. 6, pp. 2817–2826, 2016. [Online]. Available:

[11] G. A. Jiménez-Estévez, L. S. Vargas, and V. Marianov, “Determination of feeder areas for the design of large distribution networks,” IEEE Transactions on Power Delivery, vol. 25, no. 3, pp. 1912–1922, 2010. [Online]. Available:

[12] D. P. Montoya and J. M. Ramirez, “A minimal spanning tree algorithm for distribution networks configuration,” IEEE Power and Energy Society General Meeting, pp. 1–7, 2012. [Online]. Available:

[13] J. Li, X. Y. Ma, C. C. Liu, and K. P. Schneider, “Distribution system restoration with microgrids using spanning tree search,” IEEE Transactions on Power Systems, vol. 29, no. 6, pp. 3021–3029, 2014. [Online]. Available:

[14] A. Nagarajan and R. Ayyanar, “Application of Minimum Spanning Tree Algorithm for Network Reduction of Distribution Systems,” in IEEE, 2014.

[15] B. C. Neagu and G. Georgescu, “Wind Farm Cable Route Optimization Using a Simple Approach,” Electrical and Power Engineering (EPE), 2014 International Conference and Exposition on, Iasi, no. Epe, pp. 1004–1009, 2014

[16] P. Balakrishna, K. Rajagopal, and K. S. Swarup, “AMI / GIS based Distribution System Load Flow for Extended Situational Awareness .” in IEEE, 2014, pp. 1–6.

[17] M. Esmaeeli, A. Kazemi, H.-a. Shayanfar, and M.-r. Haghifam, “Sizing and placement of distribution substations considering optimal loading of transformers,” International Transactions on Electrical Energy Systems Int., 2014. [Online]. Available:

[18] M. Campaña, E. Inga, and R. Hincapié, “Optimal placement of universal data aggregation points for smart electric metering based on hybrid wireless,” in CEUR Workshop Proceedings, vol. 1950, 2017, pp. 6–9.

[19] E. Inga, S. Céspedes, R. Hincapié, and C. Andy, “Scalable Route Map for Advanced Metering Infrastructure Based on Optimal Routing of Wireless Heterogeneous Networks,” IEEE Wireless Communications, vol. 24, no. April, pp. 1–227, 2017.

[20] D. Carrión, E. Inga, J. W. Gonzalez, and R. Hincapié, “Optimal Geographical Placement of Phasor Measurement Units based on Clustering Techniques,” 51st International Universities’ Power Engineering Conference, p. 6, 2016. [Online]. Available:

[21] C. Mateo, G. Prettico, T. Gómez, R. Cossent, F. Gangale, P. Frías, and G. Fulli, “European representative electricity distribution networks,” International Journal of Electrical Power and Energy Systems, vol. 99, no. January, pp. 273–280, 2018. [Online]. Available:

[22] E. M. Rodríguez-Herrera, M. A. Angamarca-Guamán, E. M. Inga-Ortega, E. M. Rodríguez-Herrera, M. A. Angamarcauamán, and E. M. Inga-Ortega, “Optimización de cobertura para lugares georreferenciados,” ITECKNE Innovación e Investigación en Ingeniería, vol. 14, no. 2, p. 140, 2017. [Online]. Available:

[23] A. Peralta-Sevilla, E. Inga, R. Cumbal, and R. Hincapié, “Optimum deployment of FiWi Networks using wireless sensors based on Universal Data Aggregation Points,” 2015 IEEE Colombian Conference on Communications and Computing, COLCOM 2015 - Conference Proceedings, 2015. [Online]. Available:

[24] D. Pérez, E. Inga, and R. Hincapié, “Optimal Sizing of a Network for Smart Metering,” IEEE Latin America Transactions, vol. 14, no. 5, pp. 2114–2119, 2016.

[25] V. Gouin, M. C. Alvarez-Hérault, and B. Raison, “Innovative planning method for the construction of electrical distribution network master plans,” Sustainable Energy, Grids and Networks, vol. 10, pp. 84–91, 2017. [Online]. Available: