Sivas İlindeki Elektrikli Araç Şarj İstasyonlarının Elektrik Şebekelerine Etkisinin Değerlendirilmesi

Yıl 2023, Cilt: 26 Sayı: 3, 1215 - 1231, 01.10.2023

Mutlu Tarık Çakır , Musa Faruk Çakır

https://doi.org/10.2339/politeknik.1232637

Cited By: 1

Öz

Fosil yakıtların çevreye verdiği zararlar nedeniyle, elektrikli araçlara ilgi sürekli artmaktadır. Elektrikli/klasik araç değişimi ve trafoya ek yük trendinin belirlenmesi, enerji dağıtım şirketlerinin yatırım planları için hayati önem taşımaktadır. Bu çalışmada elektrik dağıtım şirketlerinin yatırım planlamasına elektrikli araç ikamelerini dahil etmek için kapsamlı bir yöntem sunulmaktadır. Elektrikli araçların yaygınlaşmasıyla klasik araçların yerini alacak yeni bir model önerilmektedir. Farklı senaryolar için önerilen modeller kullanılarak güç yoğunluğu ve trafo kapasite oranı incelenmiştir. Son on yıllık elektrik tüketimi, hat uzunluğu, trafo kurulu gücü, nüfus ve araç sayısı verileri ÇEDAŞ ve Türkiye İstatistik Kurumu'ndan alınmıştır. 30 yıllık planlama üzerinden senaryo karşılaştırmalarına göre, en iyi ihtimalle 2029-2030 yılları arasında şebekedeki J değeri mevcut değeri aşacak, R-değeri ise 2031-2032 yılları arasında mevcut değerin altına düşecektir. 2029-2030 yıllarında elektrik dağıtım hatlarına, 2031-2032 yıllarında ise trafo kapasitelerine yatırım yapılması gerekmektedir. Elde edilen sonuçlar, geliştirilen yöntemin elektrikli araçların yaygınlaşması nedeniyle hat ve trafo kapasitelerinin belirlenmesinde kullanılabileceğini göstermiştir.

Anahtar Kelimeler

Elektrikli araçlar, enerji dağılımı, güç kapasitesi, güç ağı, zaman serileri analizi, enerji tüketimi

Evaluating the Effect of Electric Vehicle Charging Stations on Power Grids in Sivas Province

Öz

Due to the damage of fossil fuels to the environment, fossil fuels will finish soon, the interest in electric vehicles has increased. Determining the trend of electric/classic vehicle replacement and additional load on the transformer is vital importance for the investment plans of energy distribution companies. It presents a comprehensive method for including electric vehicle replacements in the investment planning of electric distribution companies. A new model is proposed, used to replace classical vehicles by becoming widespread of electric vehicles. The power density and transformer capacity ratio were examined using the proposed model for scenarios. Electricity consumption, line length, transformer installed power capacity, population, and the number of vehicles data for the last ten years were obtained from ÇEDAŞ and Turkish Statistical Institute. According to scenario comparisons over the 30-year planning, the J value on the grid will exceed the current value between 2029 and 2030, R-value will fall below the current value between 2031 and 2032 in the best case. It is necessary to invest in electricity distribution lines in 2029-2030 and transformer capacities in 2031-2032. The results showed the developed method could be used to determine the line and transformer capacities due to the prevalence of electric vehicles.

Anahtar Kelimeler

Electric vehicles (EVs), energy distribution, power capacity, power network, time series analysis, energy consumption

Kaynakça

• [1] Albanese, L., et al., "The impact of electric vehicles on the power market", Energy Science & Engineering, 3(4): 300-309, (2015).
• [2] Khan, S., et al., "A comprehensive review on solar powered electric vehicle charging system", Smart Science, 6(1): 54-79, (2018).
• [3] Larminie, J. and J. Lowry., "Electric vehicle technology explained”, John Wiley & Sons, (2012).
• [4] Bockarjova, M. and L. Steg., "Can Protection Motivation Theory predict pro-environmental behavior? Explaining the adoption of electric vehicles in the Netherlands", Global environmental change, 28: 276-288, (2014).
• [5] Rapa, M., L. Gobbi, and R. Ruggieri, "Environmental and economic sustainability of electric vehicles: Life cycle assessment and life cycle costing evaluation of electricity sources", Energies, 13(23): 6292, (2020).
• [6] Martins, M.C. and F.C. Trindade., "Time series studies for optimal allocation of electric charging stations in urban area in 2015", IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), IEEE, (2015).
• [7] Buzna, L., et al., "Electric vehicle load forecasting: A comparison between time series and machine learning approaches in 2019", 1st International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED), IEEE, (2019).
• [8] Nurmuhammed, M. and T. Karadağ, "Elektrikli Araç Şarj İstasyonlarının Konumlandırılması ve Enerji Şebekesi Üzerine Etkisi Konulu Derleme Çalışması", Gazi University Journal of Science Part A: Engineering and Innovation, 8(2): 218-233, (2021).
• [9] Richardson, P., D. Flynn, and A. Keane, "Optimal charging of electric vehicles in low-voltage distribution systems", IEEE Transactions on Power Systems, 27(1): 268-279, (2011).
• [10] Shareef, H., M.M. Islam, and A. Mohamed, "A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles", Renewable and Sustainable Energy Reviews, 64: 403-420, (2016).
• [11] Mukherjee, J.C. and A. Gupta, "A review of charge scheduling of electric vehicles in smart grid", IEEE Systems Journal, 9(4): 1541-1553, (2014).
• [12] Clement-Nyns, K., E. Haesen, and J. Driesen., "The impact of charging plug-in hybrid electric vehicles on a residential distribution grid", IEEE Transactions on power systems, 25(1): 371-380, (2009).
• [13] Mohammad, A., R. Zamora, and T.T. Lie, "Integration of electric vehicles in the distribution network: A review of P.V. based electric vehicle modelling", Energies, 13(17): 4541, (2020).
• [14] Zhu, X., B. Mather, and P. Mishra., "Grid impact analysis of heavy-duty electric vehicle charging stations in 2020", IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), IEEE, (2020).
• [15] Ahmadi, S., et al., "Optimal use of vehicle-to-grid technology to modify the load profile of the distribution system", Journal of Energy Storage, 31: 101627, (2020).
• [16] Tan, K.M., et al., "Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling", Energies, 10(11): 1880, (2017).
• [17] Alegre, S., J.V. Míguez, and J. Carpio., "Modelling of electric and parallel-hybrid electric vehicle using Matlab/Simulink environment and planning of charging stations through a geographic information system and genetic algorithms", Renewable and Sustainable Energy Reviews, 74: 1020-1027, (2017).
• [18] Xie, F., et al., "Long-term strategic planning of inter-city fast charging infrastructure for battery electric vehicles", Transportation Research Part E: Logistics and Transportation Review, 109: 261-276, (2018).
• [19] Abushnaf, J. and A. Rassau., "Impact of energy management system on the sizing of a grid-connected P.V./Battery system", The Electricity Journal, 31(2): 58-66, (2018).
• [20] Awasthi, A., et al., "Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm", Energy, 133: 70-78, (2017).
• [21] Dong, J., C. Liu, and Z. Lin, "Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data", Transportation Research Part C: Emerging Technologies, 38: 44-55, (2014).
• [22] DeCarlo, L.T., "On the meaning and use of kurtosis", Psychological Methods, 2(3): 292-307, (1997).
• [23] Groeneveld, R.A. and G. Meeden., "Measuring skewness and kurtosis", Journal of the Royal Statistical Society: Series D (The Statistician), 33(4): 391-399, (1984).
• [24] Hopkins, K.D. and D.L. Weeks., "Tests for normality and measures of skewness and kurtosis: Their place in research reporting", Educational and psychological measurement, 50(4): 717-729, (1990).
• [25] Moors, J.J.A., "The meaning of kurtosis: Darlington reexamined", The American Statistician, 40(4): 283- 284, (1986).
• [26] Chatfield, C., "Time-series forecasting”, Chapman and Hall/CRC, (2000).
• [27] Montgomery, D.C., C.L. Jennings, and M. Kulahci., "Introduction to time series analysis and forecasting”, John Wiley & Sons, (2015).
• [28] Brown, R.G., "Exponential smoothing for predicting demand. in Operations Research", Inst Operations Research Management Sciences 901 Elkridge Landing Rd, (1957).
• [29] Hunter, J.S., "The exponentially weighted moving average", Journal of quality technology, 18(4): 203- 210, (1986).
• [30] Winters, P.R., "Forecasting sales by exponentially weighted moving averages", Management science, 6(3): 324-342, (1960).
• [31] Booranawong, T. and A. Booranawong., "Simple and Double Exponential Smoothing Methods with Designed Input Data for Forecasting A Seasonal Time Series: In an Application for Lime Prices in Thailand", Suranaree Journal of Science & Technology, 24(3), (2017).
• [32] Hunter, J.E., "Cognitive ability, cognitive aptitudes, job knowledge, and job performance", Journal of vocational behavior, 29(3): 340-362, (1986).
• [33] Mori, M. and M. Sugihara, "The double-exponential transformation in numerical analysis", Journal of Computational and Applied Mathematics, 127(1-2): 287-296, (2001).
• [34] Takahasi, H. and M. Mori., "Double exponential formulas for numerical integration", Publications of the Research Institute for Mathematical Sciences, 9(3): 721-741, (1974).
• [35] Hansun, S., "A new approach of brown's double exponential smoothing method in time series analysis", Balkan Journal of Electrical and Computer Engineering, 4(2): 75-78, (2016).
• [36] https://esarj.com (date of access: 30.01.2022).
• [37] https://www.tesla.com (date of access: 30.01.2022).