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

Year 2024, Volume: 12 Issue: 3, 199 - 205, 30.09.2024

Onur Akar

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

Abstract

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.

Keywords

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

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