Optimal Location of Transformers in Electrical Distribution Networks Using Geographic Information Systems
DOI:
https://doi.org/10.29019/enfoque.v11n1.593Keywords:
Optimal deployment; sizing; planning; transformation centers.Abstract
This research shows a heuristic model for the design of scalable and reliable electrical distribution networks. The algorithms presented allow to optimize the location of transformation centers using on their database geographic information systems from which it is possible to define user locations, candidate sites, possible routes for the deployment of the electricity grid and, in general, data for the reconstruction of the scenario. The model employs clustering and triangulation methods, as well as algorithms for creating a minimally expanding tree and the consequent site assignment for transformer placement. After setting the optimal locations for the transformer site, the algorithms compute voltage drops in secondary circuits, required transformation capability, execution times, and coverage achieved. The results obtained are adjusted to the requirements of an actual distribution power grid and show a good performance on the proposed scenario.
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Carrión Galarza, D. F., & González Sánchez, J. W. (2019). Ubicación óptima de PMU considerando restricciones de contingencias N-1 en sistemas eléctricos de potencia. Enfoque UTE, 10(1), 1–12. https://doi.org/10.29019/enfoqueute.v10n1.437
Ciechanowicz, D., Pelzer, D., Bartenschlager, B., & Knoll, A. (2017). A modular power system planning and power flow simulation framework for generating and evaluating power network models. IEEE Transactions on Power Systems, 32(3), 2214–2224. https://doi.org/10.1109/TPWRS.2016.2602479
Gholizadeh-Roshanagh, R., Najafi-Ravadanegh, S., & Hosseinian, S. H. (2018). A framework for optimal coordinated primary-secondary planning of distribution systems considering MV distributed generation. IEEE Transactions on Smart Grid, 9(2), 1408–1415. https://doi.org/10.1109/TSG.2016.2590425
Li, H., Mao, W., Zhang, A., & Li, C. (2016). An improved distribution network reconfiguration method based on minimum spanning tree algorithm and heuristic rules. International Journal of Electrical Power and Energy Systems, 82, 466–473. https://doi.org/10.1016/j.ijepes.2016.04.017
Liu, J., Cheng, H., Xu, Q., Lan, Z., Zeng, P., & Yao, L. (2017). Stochastic expansion planning of interconnected distribution networks with renewable sources considering uncertainties and power transfer capability. 2017(October), 1600–1604. https://doi.org/10.1049/joe.2017.0602
Maldonado, M. G. R. (2017). Wireless Sensor Network for Smart Home Services Using Optimal Communications. 2017 International Conference on Information Systems and Computer Science (INCISCOS), 27–32. https://doi.org/10.1109/INCISCOS.2017.21
Once, M. (2017). Scalable Route Map for Advanced Metering Infrastructure Based on Optimal Routing of Wireless Heterogeneous Networks. (April), 2–9.
Quishpe, S., Padilla, M., & Ruiz, M. (2019). Despliegue Óptimo de Redes Inalámbricas para Medición Inteligente Optimal Deployment of Wireless Networks for Smart Metering. (16), 105–113.
Ruiz, M., & Inga, E. (2019). Despliegue óptimo de redes ópticas para comunicaciones en redes eléctricas inteligentes. I+D Tecnológico, 15(2), 79–85. https://doi.org/10.33412/idt.v15.2.2249
Ruiz, M., Masache, P., & Dominguez, J. (2018). High availability network for critical communications on smart grids. CEUR Workshop Proceedings, 2178, 13–17.
Ruiz, M., Masache, P., & Inga, E. (2018). Optimal Communications for Smart Measurement of Electric Energy Reusing Cellular Networks. 2018 International Conference on Information Systems and Computer Science (INCISCOS), 198–204. https://doi.org/10.1109/INCISCOS.2018.00036
Ruiz Maldonado, M. G., & Inga, E. (2019). Asignación óptima de recursos de comunicaciones para sistemas de gestión de energía. Enfoque UTE, 10(1), 141–152. https://doi.org/10.29019/enfoqueute.v10n1.447
Xie, S., Hu, Z., Zhou, D., Li, Y., Kong, S., Lin, W., & Zheng, Y. (2018). Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network. Applied Energy, 219(March), 207–225. https://doi.org/10.1016/j.apenergy.2018.03.023
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