Monte Carlo simulation of uncontrolled Electric Vehicle charging impact on distributed generation
Main Article Content
Abstract
The uncontrolled charging of electric vehicles poses a great challenge for distribution network operators and power system planners. Instead of focusing on controlling this uncontrolled load, a model that uses contingency analysis variables to calculate the power capacity needed in the power system is proposed. The unserved power variable is used to evaluate the amount of uncovered load power at each bus of the system, followed by the calculation of the additional power capacity required using a photovoltaic and storage system and another constant generation alternative in the 14-bus IEEE power system with information on some electric vehicles and daily load in the power system of Peru. The results obtained in the power system with distributed generation, the absence of unserved power, corroborate the success of the methodology used. This model provides tools to both distribution network operators and power system planners, reducing the impact on the power system of electric vehicles and providing a methodology applicable to other electric distribution systems with uncontrolled loads.
Keywords
Fuentes de energía renovable, Potencia no servida, sistema de Potencia, sistemas de almacenamiento de energía, Simulación de Monte Carlo, vehículos eléctricos Electric Vehicles, Monte Carlo Simulation, Optimal Power Flow, Photovoltaic and Storage System, Not Served Power, Power System
References
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[7] S. Kumar, R. Saket, D. K. Dheer, J. B. Holm-Nielsen, and P. Sanjeevikumar, “Reliability enhancement of electrical power system including impacts of renewable energy sources: a comprehensive review,” IET Generation, Transmission & Distribution, vol. 14, no. 10, pp. 1799–1815, 2020. [Online]. Available: https://doi.org/10.1049/iet-gtd.2019.1402
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[9] S. Pirouzi, J. Aghaei, T. Niknam, H. Farahmand, and M. Korpås, “Exploring prospective benefits of electric vehicles for optimal energy conditioning in distribution networks,” Energy, vol. 157, pp. 679–689, 2018. [Online]. Available: https://doi.org/10.1016/j.energy.2018.05.195
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[12] AAP, Los Protagonistas de la nueva era automotriz: Vehículos eléctricos e híbridos en el Perú. Asociación Automotríz del Per”u, 2019. [Online]. Available: https://bit.ly/465w2kl
[13] C. Villanueva, J. Luyo, and C. Delgado, A. Carbajal, “Electric vehicle aggregator participation in energy markets considering uncertainty travel patterns,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 12, pp. 4994–4998, 2019. [Online]. Available: https://doi.org/10.35940/ijitee.L3747.1081219
[14] R. Jing, M. Wang, Z. Zhang, X. Wang, N. Li, N. Shah, and Y. Zhao, “Distributed or centralized? designing district-level urban energy systems by a hierarchical approach considering demand uncertainties,” Applied Energy, vol. 252, p. 113424, 2019. [Online]. Available: https://doi.org/10.1016/j.apenergy.2019.113424
[15] A. Dammert, R. García Carpio, and F. Molinelli, Regulación y Supervisión del Sector Eléctrico. Fondo Editorial Pontificia Universidad Católica del Perú, 2013. [Online]. Available: https://bit.ly/3p7rIQW
[16] PSCAD, IEEE 14 Bus System. Manitoba HVDC Research Centre, 2014. [Online]. Available: https://bit.ly/3CxmAZt
[17] T. Khatib and W. Elmenreich, “A model for hourly solar radiation data generation from daily solar radiation data using a generalized regression artificial neural network,” International Journal of Photoenergy, vol. 2015, p. 968024, Oct 2015. [Online]. Available: https://doi.org/10.1155/2015/968024
[18] A. Babajide and M. C. Brito, “Solar pv systems to eliminate or reduce the use of diesel generators at no additional cost: A case study of lagos, nigeria,” Renewable Energy, vol. 172, pp. 209–218, 2021. [Online]. Available: https://doi.org/10.1016/j.renene.2021.02.088
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Published
Nov 22, 2023
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Automotive Engineering
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