Optimización Multi-objetivo Aplicada a la Planificación de Instalación de Estaciones de Carga para Vehículos Eléctricos
Palabras clave:
FCLM, método ε-restringido, OPF, Vehículo eléctrico
Resumen
La óptima ubicación de electrolineras es un reto para constructores y diseñadores de redes eléctricas. Por este motivo, en la presente investigación, se propone el método ε-restringido capaz de resolver problemas multiobjetivos. Este método minimiza las pérdidas de potencia activa y maximiza el flujo de tráfico vehicular capturado simultáneamente. Además, se determinan las ubicaciones óptimas georreferenciadas de las estaciones de carga elegidas. Los resultados de las simulaciones, muestran que el cambio de ubicación de las electrolineras, genera mayor impacto en la cantidad de tráfico vehicular capturado que sobre las pérdidas de potencia en la red de subtransmisión.
Citas
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[4] M. Spitzer, J. Schlund, E. Apostolaki-Iosifidou, M. Pruckner, Optimized integration of electric vehicles in low voltage distribution grids, Energies 12 (21) (2019) 1–19. doi:10.3390/en12214059.
[5] A. Ul-Haq, M. Azhar, Y. Mahmoud, A. Perwaiz, E. A. Al-Ammar, Probabilistic modeling of electric vehicle charging pattern associated with residential load for voltage unbalance assessment, Energies 10 (9) (2017) 1–18. doi:10.3390/en10091351.
[6] S. Deb, E. al, Impact of electric vehicle charging station load on distribution network, Energies 11 (1) (2018) 1–25. doi:10.3390/en11010178.
[7] T. Gnann, S. Funke, N. Jakobsson, P. Plotz, F. Sprei, A. Bennehag, Fast charging infrastructure for electric vehicles: Today’s situation and future needs, Transportation Research Part D: Transport and Environment 62 (2018) 314–329. doi:10.1016/j.trd.2018.03.004.
[8] M. Campaña, E. Inga, Despliegue óptimo georreferenciado de estaciones de carga vehicular pública considerando capacidad de flujo y distancias máximas habilitantes, I+D Tecnológico 15 (2) (2019) 68–78. doi:10.33412/idt.v15.2.2248.
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[10] M. M. Vazifeh, E. al., Optimizing the deployment of electric vehicle charging stations using pervasive mobility data, Transportation Research Part A: Policy and Practice 121 (January 2017) (2019) 75–91. arXiv:1511.00615, doi:10.1016/j.tra.2019.01.002.
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[12] X. Wang, E. al., Coordinated Planning Strategy for Electric Vehicle Charging Stations and Coupled Traffic-Electric Networks, IEEE Transactions on Power Systems 34 (1) (2019) 268–279. doi:10.1109/TPWRS.2018. 2867176.10
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[15] Y. Xiang, E. al., Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates, Applied Energy 178 (2016) 647–659. doi:10.1016/j.apenergy.2016.06.021.
[16] B. Wang, E. al., Electrical Safety Considerations in Large-Scale Electric Vehicle Charging Stations, IEEE Transactions on Industry Applications 55 (6) (2019) 6603–6612. doi:10.1109/TIA.2019.2936474.
[17] X. Ren, E. al., Location of electric vehicle charging stations: A perspective using the grey decision-making model, Energy 173 (2019) 548–553. doi:10.1016/j.energy.2019.02.015.
[18] S. Faddel, A. T. Elsayed, O. A. Mohammed, Bilayer Multi-Objective Optimal Allocation and Sizing of Electric Vehicle Parking Garage, IEEE Transactions on Industry Applications 54 (3) (2018) 1992–2001. doi:10.1109/TIA.2018.2803151.
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[20] I. Safak Bayram, S. Bayhan, Location Analysis of Electric Vehicle Charging Stations for Maximum Capacity and Coverage, Proceedings - 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020 (2020) 409–414doi:10.1109/CPE-POWERENG48600.2020.9161639.
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[23] A. F. Porras-Ortiz, J. Layedra, H. Arcos, Active power loss minimization in the Santa Cruz and Baltra hybrid energy system using particle swarm optimization, 2015 IEEE PES Innovative Smart Grid Technologies Latin America, ISGT LATAM 2015 (2016) 429–434doi:10.1109/ISGT-LA.2015.7381193.
[24] K. Y. Lee, M. A. El-Sharkawi, Modern Heuristic Optimization Techniques, Vol. 4, John Wiley & Sons, Inc, Hoboken, 2008.
[25] P. Pardalos, A. Zilinskas, J. Zilinskas, Non-Convex Multi-Objective Optimization, 123rd Edition, Springer International, Cham, 2017.
[26] H. Saber, M. Moeini-Aghtaie, M. Ehsan, Developing a multi-objective framework for expansion planning studies of distributed energy storage systems (DESSs), Energy 157 (2018) 1079–1089. doi:10.1016/j.energy.2018.06.081.
[27] N. Tabrizi, E. Babaei, M. Mehdinejad, An interactive fuzzy satisfying method based on particle swarm optimization for multi-objective function in reactive power market, Iranian Journal of Electrical and Electronic Engineering 12 (1) (2016) 65–72. doi:10.22068/IJEEE.12.1.65.
[28] M. Masoud, H. A. Khalifa, S. Q. Liu, M. Elhenawy, P. Wu, A fuzzy goal programming approach for solving fuzzy multi-objective stochastic linear programming problem, Proceedings of the 2019 International Conference on Industrial Engineering and Systems Management, IESM 2019 (2019) 1–6doi:10.1109/IESM45758.2019.8948204.
[29] Marlon René Martínez Rodas, Carlos Gustavo Morocho Ortiz, Diagnóstico del sistema de subtransmisión de la Empresa Eléctrica Regional Centro Sur frente al impacto masivo de las cocinas de inducción, Ph.D. thesis (2014).
Publicado
2023-04-21
Sección
Artículos de Investigación
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