Estimation of the Effect of Electric Vehicles on the Aging of Distribution Transformers Using Fuzzy Logic

Yıl 2024, Cilt: 12 Sayı: 3, 199 - 205, 30.09.2024

Onur Akar

https://doi.org/10.17694/bajece.1503781

Öz

Depending on industrialization and technological advancements worldwide, the demand for electrical energy, recognized as clean and dependable energy, is on the rise. Presently, electric energy consumption has notably increased alongside the rise in Electric Vehicles (EVs). The surge in EVs necessitates a thorough examination of the situation, anticipating the widespread adoption of Electric Vehicle Fast Charging Stations (EVFCS) in the near future and the subsequent escalation of their adverse impact on the grid. To mitigate these negative effects on the grid, proactive measures are essential. EVs function as capacitive loads due to their battery composition, and the harmonics produced during their grid connection detrimentally affect the quality of grid electricity, leading to constraints. Furthermore, the escalating EVFCS loads resulting from the rapid growth in EV numbers distribute the burden on distribution networks, posing a threat to network adequacy and reliability. Therefore, integrating EVFCS with distribution and generation units to minimize overloading, additional losses, and voltage fluctuations in the grid will enhance the efficiency of both systems. In addition, each EVFCS is only connected to the distribution transformer assigned to it or to the distribution transformers considered suitable in the city. Depending on the current drawn by one or more EVFCS linked to the feeder of each transformer, it can lead to overloading in transformers and chemical changes in windings and oils, resulting in the aging of transformers. In this context, a FL-based estimation is conducted to assess the impact of EVs' charging loads on transformer aging. The FL method utilizes transformer current load, EVFCS load, transformer temperature, and harmonic power quality data. The data utilized are derived from statistical information about a local distribution network and measured values from a feeder, and the aging effects on EVFCS distribution transformers are examined.

Anahtar Kelimeler

Distribution network, fuzzy logic, distribution transformers, transformer aging, electric vehicle fast charging station.

Kaynakça

• [1] P. Ahmadi, “Environmental impacts and behavioral drivers of deep decarbonization for transportation through electric vehicles”, J. Clean. Prod. (2019). [2] J. R. Weeberb, M. Moataz, D. H. Christopher, A. Altaf, F. Mark, “How Clean Are Electric Vehicles? Evidence-Based Review of the Effects of Electric Mobility on Air Pollutants, Greenhouse Gas Emissions and Human Health”, Atmospheric Environment, 185(2018), 64-77.
• [3] L. Calearo, M. Marinelli, & C. Ziras, “A review of data sources for electric vehicle integration studies”, Renewable and Sustainable Energy Reviews, 151, 111518, 2021.
• [4] U.K. Terzi, et. al. , “A review of commercial electric vehicle charging methods”, Promet - Traffic - Traffico, vol.32, no.2, pp.291-307, 2020
• [5] I. Diahovchenko, A. Chuprun, & Z. Čonka, “Assessment and mitigation of the influence of rising charging demand of electric vehicles on the aging of distribution transformers”, Electric Power Systems Research, 221, 109455, 2023.
• [6] A. Srivastava, M. Manas, R. K. Dubey, “Integration of power systems with electric vehicles: A comprehensive review of impact on power quality and relevant enhancements”, Electric Power Systems Research, 234, 110572, 2024.
• [7] S. Powell, E. Kara, R. Sevlian, G. V. Cezar, S. Kiliccote, & R. Rajagopal, “Controlled workplace charging of electric vehicles: The impact of rate schedules on transformer aging”, Applied Energy, 276, 115352, 2020.
• [8] US Energy, 2016, US Department of Energy, “Developing Infrastructure to Charge Plug-In Electric vehicles”, date received: 11.05.2024.
• [9] F. Kaya, O. Akar. “Short Circuit Effects on HV Feeders of Optimally Located Electric Vehicle Fast Charging Stations”, IEEE Access, vol.12, 47842-47853, 2024.
• [10] G. P. Tal, D.P.Chakraborty, A.P.Jenn, J.H.P.Lee, D.P. Bunch,“Factors affecting demand for plug-in harging infrastructure: an analysis of plug-in electric vehicle commuters”, UC off. Pres. ITS Rep, 2020.
• [11] C.B. Jones, M.Lave, W.Vining, B.M. Garcia, “Uncontrolled electric vehicle charging impacts on distribution electric power systems with primarily residential”, commercial or industrial loads’, Energies, 2021.
• [12] V. Volokhin, I. Diahovchenko, V. Kurochkina, M. Kanálik, “The influence of nonsinusoidal supply voltage on the amount of power consumption and electricity meter readings”, Energetika 63 (1), 2017.
• [13] O. Sundstrom, C. Binding, “Flexible charging optimization for electric vehicles considering distribution grid constraints”, IEEE Transactions on Smart Grid, 2012, 3(1), 26-37.
• [14] P. Richardson, D. Flynn, A. Keane, “Optimal charging of electric vehicles in low voltage distribution systems.” IEEE Transactions on Power Systems, 2012, 27(1), 268-279.
• [15] R. C. Leou, C. L. Su, C. N. Lu, “Stochastic analyses of electric vehicle charging impacts on distribution network”, IEEE Transactions on Power Systems, 2014, 29(3), 1055-1063.
• [16] T. Au, M.Ortega-Vazquez, “Assessment of plug-in electric vehicles charging on distribution networks”, IEEE Power & Energy Society General Meeting, Vancouver, Canada, 21 – 25 July 2013.
• [17] G. Li, X. Zhang, “Modeling of plug-in hybrid electric vehicle charging demand in probabilistic power flow calculations”, IEEE Transactions on Smart Grid, 2012, 3(1), 492-499.
• [18] R. A. Verzijlbergh, M. O. W. Grond, Z. Lukszo, J. G. Slootweg, M. D. Ilic, “Network impacts and cost savings of controlled EV charging”, IEEE Transactions on Smart Grid, 2012, 3(3), 1203-1212.
• [19] Z. Liu, F. Wen, G. Ledwich, “Optimal planning of electric vehicle charging stations in distribution systems”, IEEE Transactions on Power Delivery, 2013, 28(1), 102-110.
• [20] S. Weckx, R. D’Hulst, B. Claessens , J. Driesensam,“Multiagent charging of electric vehicles respecting distribution transformer loading and voltage limits”, IEEE Transactions on Smart Grid, 2014, 5(6), 2857-2867.
• [21] L Hua, J. Wang, C. Zhou, “Adaptive electric vehicle charging coordination on distribution network”, IEEE Transactions on Smart Grid, 2014, 5(6), 2666- 2675.
• [22] S. Shafiee, M. Fotuhi-Firuzabad, M. Rastegar, “Investigating the impact of plug-in hybrid electric vehicles on power distribution systems”, IEEE Transactions on Smart Grid, 2013, 4(3), 1351-1360.
• [23] J. De Hoog, T. Alpcan, M. Brazil, D. A. Thomas, I. Mareels, “Optimal charging of electric vehicles taking distribution network constraints into account”, IEEE Transactions on Power Systems,2015, 30(1), 365-375.
• [24] F. Shahnia, A. Ghosh, G. Ledwich, F. Zare, “Predicting voltage unbalance impacts of plug-in electric vehicles penetration in residential low voltage distribution networks”, Electric Power Components and Systems, 2013, 41(16), 1594-1616.
• [25] F. Kaya, ‘‘Analysis of the effects of electric vehicle fast charging stations on the network’’, M.S. thesis, Dept. Electr. Educ., Marmara Univ., Istanbul, Türkiye, 2023.
• [26] O. Akar, ‘‘Estimation through ANN of Voltage Drop Resulting from Overloads on Power Transformers’’, European Journal of Technique, vol.12, no.2, pp.198-203, 2022
• [27] I. Diahovchenko, B. Dolník, M. Kan´alik, J. Kurimský, “Contemporary electric energy meters testing under simulated nonsinusoidal field conditions’’, Electr. Eng. 104 (2022) 1077–1092.
• [28] B. R. Klingenberg, et al. “Fuzzy logic application for time-varying harmonics”, in Time-Varying Waveform Distortions in Power Systems, 2009.
• [29] W. R. Tamma, R. A. Prasojo, S. Suwarno,“Assessment of high voltage power transformer aging condition based on health index value considering its apparent and actual age’’, In 2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 292-296). IEEE, 2020, October.
• [30] A. D. Hilshey, P. D. Hines, P. Rezaei, J. R. Dowds, “Estimating the impact of electric vehicle smart charging on distribution transformer aging”, IEEE Transactions on Smart Grid, 4(2), 905-913, 2012.
• [31] F. Guerbas, M. Aoudjit and S. Abba, “Comparative Study of Transformer aged oil Reclamation by Fuller's earth and activated Bentonite”, 2017 IEEE 19th International Conference on Dielectric Liquids (ICDL). IEEE, 2017.
• [32] A. Vatsa, & A. S. Hati, “Insulation aging condition assessment of transformer in the visual domain based on SE-CNN,” Engineering Applications of Artificial Intelligence, 128, 107409, 2024.
• [33] A. Srivastava, M., Manas, & R. K. Dubey, “Integration of power systems with electric vehicles: A comprehensive review of impact on power quality and relevant enhancements,” Electric Power Systems Research, 234, 110572, 2024.
• [34] G. Kaliappan, & M. Rengaraj, “Aging assessment of transformer solid insulation: A review. Materials Today: Proceedings,” 47, 272-277, 2021.
• [35] Z. Ok Davarcı, O. Akar, “Estimation of the electricity to be generated at different wind speeds and turbines through fuzzy logic and ANN, A case study of Balıkesir,” Int J Energy Studies 2024; 9(1): 115-133, e-ISSN: 2717-7513.
• [36] Z. Ok Davarcı, O. Akar, U.K. Terzi, “Comparison of Road Luminance Data Estimated by Fuzzy Logic and ANN”, A Case study of Kocaeli Sakıp Sabancı Caddesi, European Journal of Technique , vol.13, no.2, 150-158, 2023.