Optimal Location of Reclosers in Distribution Networks with Distributed Generation Using Genetic Algorithms and Service Quality Indexes
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Abstract
This paper presents a mathematical model for optimal location of reclosers placement using the genetic algorithm optimization technique in distribution systems, considering the insertion of distributed resources, such as distributed systems. The methodology includes the power flow solution using the open-source software OpenDSS, through the interface developed by Electric Power Research Institute - EPRI. The values of voltages, currents, powers and SAIFI/SAIDI indicators are transferred to Matlab software. The optimization algorithm determines the proper positions to install reclosers.
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La Revista Técnica "energía" está bajo licencia internacional Creative Commons Reconocimiento-NoComercial 4.0.
References
G. Casillas, "Ubicación de equipos de regulación de voltaje en redes de Distribución considerando RD's, São Paulo." 2017.
J. Smith et al, " “Momento y Ubicación,” ," IEEE Power & Energy, Vol. 15, N.º 2, pp. 34-42, 2017.
R. Dugan, J. Smith and M. Rylander, "It's All in the Plans. Maximizing the Benefits and Minimizing the Impacts of DERs in an integrated Grid ," IEEE Power and Energy Magazine, Vol. 13, N. º 2, pp. 10, 2015.
G. Burani and M. Morales, "O Cenário dos Recursos Energéticos Distribuídos no Estado de São Paulo,” São Paulo," 2004.
R. Brown, "Electric Power Distribution Reliability," Segunda Edición Ed., New York: Taylor & Francis Group, LLC, 2009.
G. Peponis, "New dynamic, branch exchange method for optimal distribution system planning ," IEE Proceedings - Generation, Transmission and Distribution, Vol. 144, N. º 3, pp. 7, 1997.
K. Nara, "Algorithm for expansion planning in distribution systems taking faults into consideration ," IEEE Transactions on Power Systems, Vol. 9, N. º 1, 1994.
J. Cebrian and N. Kagan, "Evolutionary Algorithms for the reconfiguration of radial distribution networks considering long interruption and voltage sag disruption costs," 13th International Conference on Harmonics and Quality of Power IEEE, pp. 7, 2008.
A. Mendes, "(N-1) Contingency planning in radial distribution networks using genetic algorithms ," Transmission and Distribution Conference and Exposition: Latin America (T&D-LA), IEEE/PES, pp. 7, 2010.
R. Dugan, "Value of Sequential-Time Simulations in Distribution Planning ," IEEE Transactions on Industry Applications, Vol. 50, N. º 6, pp. 5, 2014.
G. Casillas, N. Kagan and J. Cebrian, "Ubicación de Reconectadores de Voltaje em Redes de Distribución Considerando Recursos Energéticos distribuidos ," IEEE, pp. 8, 2016.
EPRI, "Electrical Power Research Institute - Smart Grid Resource Center - OpenDSS - help ," 2004.
A. Dubey et al, "Allanando el camino para las aplicaciones de sistemas de gestión de distribución avanzados ," IEEE Power & Energy, Vol. 18, pp. 76, 2020.
M. Sadeghi, "Probabilistic Analysis of Wind Turbine planning using," Thermal Power Plants (CTPP) IEEE, pp. 6, 2014.
D. Souza and N. Kagan, "Impact of Distributed Generation on the Operational Planning of Medium Voltage Distribution Networks Using Genetic Algorithms ," IEEE PES Innovative Smart Grid Technologies, Europe, pp. 5, 2014.
G. Casillas, N. Kagan and J. Cebrian, "Voltage Regulators Allocation in Distribution Networks System Considering Energy Resources Distributed ," IEEE International Conference - Automatic (ICA-ACCA), N.º 16525644, pp. 8, 2016.
D. Goldberg, "Genetic Algoritthms in Seaech, Optimization, and Machine Learning," Addison-Wesley Publishing Company, Inc, 1989.
W. Power et al, "IEEE," pp. 8, 2012.
