Electrical Vehicles Charging Coordination by Fuzzy Logical System

Yıl 2018, Cilt: 10 Sayı: 2, 53 - 59, 29.06.2018

Meral Kılıçarslan Volkan Ateş , Hüseyin Aydilek , Ertuğrul Çam

https://doi.org/10.29137/umagd.426804

Öz

Today,
with the rapid technological development, interest in electric vehicles is also
increasing. This raises the question of what the effects of the vehicles on the
electric power network will be. In this article, the adverse effects of
charging scenarios of electric vehicles' batteries on the electric power
network are examined and fuzzy logic based solutions are proposed to prevent or
reduce the effects of charging electric vehicles during peak hours. In this
article a fuzzy logic system that is providing the cheapest charging prices,
also reducing the impact on the load curve of Turkey’s electrical network is
provided.

Anahtar Kelimeler

Electrical Vehicles, Fuzzy Logic, Least Squares Method, Load

Kaynakça

• Advantages and Disadvantages of Electric Cars - Conserve Energy Future. (2014, 2014-05-07). Retrieved from http://www.conserve-energy-future.com/advantages-and-disadvantages-of-electric-cars.php
• Andersen, P. H., Mathews, J. A., & Rask, M. (2009). Integrating private transport into renewable energy policy: The strategy of creating intelligent recharging grids for electric vehicles. Energy Policy, 37(7), 2481-2486. doi:https://doi.org/10.1016/j.enpol.2009.03.032
• Apostolaki-Iosifidou, E., Codani, P., & Kempton, W. (2017). Measurement of power loss during electric vehicle charging and discharging. Energy, 127, 730-742. doi:https://doi.org/10.1016/j.energy.2017.03.015
• Arias, M. B., & Bae, S. (2016). Electric vehicle charging demand forecasting model based on big data technologies. Applied Energy, 183, 327-339. doi:https://doi.org/10.1016/j.apenergy.2016.08.080
• Arias, M. B., Kim, M., & Bae, S. (2017). Prediction of electric vehicle charging-power demand in realistic urban traffic networks. Applied Energy, 195, 738-753. doi:https://doi.org/10.1016/j.apenergy.2017.02.021
• Di Nola, A., Lettieri, A., Perfilieva, I., & Novák, V. (2007). Algebraic analysis of fuzzy systems. Fuzzy Sets and Systems, 158(1), 1-22. doi:https://doi.org/10.1016/j.fss.2006.09.003
• Farahani, H. F. (2017). Improving voltage unbalance of low-voltage distribution networks using plug-in electric vehicles. Journal of Cleaner Production, 148, 336-346. doi:https://doi.org/10.1016/j.jclepro.2017.01.178
• Harris, C. B., & Webber, M. E. (2014). An empirically-validated methodology to simulate electricity demand for electric vehicle charging. Applied Energy, 126, 172-181. doi:https://doi.org/10.1016/j.apenergy.2014.03.078
• Hu, Z., Zhan, K., Zhang, H., & Song, Y. (2016). Pricing mechanisms design for guiding electric vehicle charging to fill load valley. Applied Energy, 178, 155-163. doi:https://doi.org/10.1016/j.apenergy.2016.06.025
• Khemakhem, S., Rekik, M., & Krichen, L. (2017). A flexible control strategy of plug-in electric vehicles operating in seven modes for smoothing load power curves in smart grid. Energy, 118, 197-208. doi:https://doi.org/10.1016/j.energy.2016.12.039
• Luo, Y., Zhu, T., Wan, S., Zhang, S., & Li, K. (2016). Optimal charging scheduling for large-scale EV (electric vehicle) deployment based on the interaction of the smart-grid and intelligent-transport systems. Energy, 97, 359-368. doi:https://doi.org/10.1016/j.energy.2015.12.140
• Moon, S.-K., & Kim, J.-O. (2017). Balanced charging strategies for electric vehicles on power systems. Applied Energy, 189, 44-54. doi:https://doi.org/10.1016/j.apenergy.2016.12.025
• Morrissey, P., Weldon, P., & O’Mahony, M. (2016). Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour. Energy Policy, 89, 257-270. doi:https://doi.org/10.1016/j.enpol.2015.12.001
• Neaimeh, M., Wardle, R., Jenkins, A. M., Yi, J., Hill, G., Lyons, P. F., . . . Taylor, P. C. (2015). A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts. Applied Energy, 157, 688-698. doi:https://doi.org/10.1016/j.apenergy.2015.01.144
• Poullikkas, A. (2015). Sustainable options for electric vehicle technologies. Renewable and Sustainable Energy Reviews, 41, 1277-1287. doi:https://doi.org/10.1016/j.rser.2014.09.016
• Richardson, D. B. (2013). Encouraging vehicle-to-grid (V2G) participation through premium tariff rates. Journal of Power Sources, 243, 219-224. doi:https://doi.org/10.1016/j.jpowsour.2013.06.024
• Sadeghi, M., & Kalantar, M. (2015). The analysis of the effects of clean technologies from economic point of view. Journal of Cleaner Production, 102, 394-407. doi:https://doi.org/10.1016/j.jclepro.2015.04.042
• Salah, F., Ilg, J. P., Flath, C. M., Basse, H., & Dinther, C. v. (2015). Impact of electric vehicles on distribution substations: A Swiss case study. Applied Energy, 137, 88-96. doi:https://doi.org/10.1016/j.apenergy.2014.09.091
• Soares, J., Ghazvini, M. A. F., Borges, N., & Vale, Z. (2017). Dynamic electricity pricing for electric vehicles using stochastic programming. Energy, 122, 111-127. doi:https://doi.org/10.1016/j.energy.2016.12.108
• Sovacool, B. K., & Hirsh, R. F. (2009). Beyond batteries: An examination of the benefits and barriers to plug-in hybrid electric vehicles (PHEVs) and a vehicle-to-grid (V2G) transition. Energy Policy, 37(3), 1095-1103. doi:https://doi.org/10.1016/j.enpol.2008.10.005
• Temiz, A., & Guven, A. N. (2016, 4-8 April 2016). Assessment of impacts of Electric Vehicles on LV distribution networks in Turkey. Paper presented at the 2016 IEEE International Energy Conference (ENERGYCON).
• teslaccessories. (2017). Number of electric cars worldwide climbs to 1.3 million; Tesla Model S takes top spot among new EV registrations. Retrieved from https://evannex.com/blogs/news/77801925-number-of-electric-cars-worldwide-climbs-to-1-3-million-tesla-model-s-takes-top-spot-among-new-ev-registrations
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:https://doi.org/10.1016/S0019-9958(65)90241-X
• Zadeh, L. A. (1975). Fuzzy logic and approximate reasoning. Synthese, 30(3), 407-428. doi:10.1007/bf00485052