Dynamic Pricing-Based Energy Management Model for Electric Vehicles Considering Real Traffic Information

Öz

Increasing charging demand with the widespread use of electric vehicles leads to negative effects such as load imbalance, sudden load changes, harmonics and voltage fluctuations in the electricity distribution network. Furthermore, irregular charging demand negatively impacts electric vehicle user comfort and traffic management. This study presents a dynamic pricing-based energy management model developed for use in urban electric vehicle charging infrastructures to address these challenges. The proposed model considers price not only as an economic output but also as a control variable that manages grid load balance. There are four input parameters (traffic, station occupancy rate, location and state of charge) in the pricing model and these parameters are dynamically updated at each iteration. The model was developed in MATLAB environment and was employed real-time traffic data obtained through the Google Maps API. The model tested for ten iterations. The results show that the pricing model prioritizes low charge levels vehicles. But the model maintaining balanced grid load simultaneously. Furthermore, price output increases high occupancy rates charging stations in order to encourage users to choose stations with lower occupancy rates. Results of this study demonstrates that pricing mechanism can be used as a decision variable both economic reasons and system efficiency. In future works, the model might be extended with artificial intelligence and optimization-based methods. Pricing model serves as a potential solution to challenges in energy and transportation networks with the help of test systems.

Anahtar Kelimeler

• Electric vehicles
• Dynamic pricing
• Energy management
• Grid
• Transportation systems

Elektrikli Araçlar için Gerçek Trafik Bilgilerini Dikkate Alan Dinamik Fiyatlandırma Tabanlı Enerji Yönetim Modeli

Öz

Elektrikli araçların kullanımının yaygınlaşmasıyla birlikte artan şarj talebi, elektrik dağıtım şebekesinde yük dengesizliği, ani yük değişimleri, harmonikler ve gerilim dalgalanmaları gibi negatif etkilere yol açmaktadır. Ayrıca şarj talebinin düzensiz olması elektrikli araç kullanıcı konforunu ve trafik yönetimini de etkileyen negatif bir durumdur. Bu çalışmada, bu negatif durumları ortadan kaldırmak amacıyla şehir içi elektrikli araç şarj altyapısında kullanılmak üzere geliştirilen dinamik fiyatlandırma tabanlı bir enerji yönetim modeli sunulmaktadır. Bu bağlamda önerilen model, fiyatı yalnızca ekonomik bir çıktı olarak değil aynı zamanda şebeke yük dengesini yönlendiren bir kontrol değişkeni olarak ele almaktadır. Modelde dört temel giriş parametresi (trafik, istasyon doluluk oranı, konum ve araç şarj seviyesi) vardır ve her iterasyonda dinamik bir şekilde güncellenmektedir. MATLAB ortamında geliştirilen model, Google Maps API aracılığıyla elde edilen gerçek trafik verileriyle desteklenmiş ve on iterasyon boyunca test edilmiştir. Sonuçlar, geliştirilen modelin düşük şarj seviyesine sahip araçları önceliklendirdiğini ancak bunu yaparken de şebeke yükünü dengeli biçimde gözettiğini göstermektedir. Ayrıca, yüksek doluluk oranına sahip istasyonlarda fiyat artışı gözlenmiş ve bu durumda kullanıcıların daha düşük doluluk oranına sahip istasyonlara teşvik edildiği görülmüştür. Çalışma, fiyatlandırmanın yalnızca ekonomik değil aynı zamanda sistemsel verimlilik açısından da bir karar değişkeni olarak kullanılabileceğini ortaya koymaktadır. Gelecek çalışmalarda model, yapay zekâ ve optimizasyon tabanlı yöntemlerle genişletilebilme ve test sistemleri yardımıyla enerji ve ulaşım sistemlerindeki problemlere çözüm olarak kullanılabilme potansiyeline sahiptir.

Anahtar Kelimeler

• Elektrikli araçlar
• Dinamik fiyatlandırma
• Enerji yönetimi
• Şebeke
• Ulaşım sistemleri

Kaynakça

1. A. A. Arefin, S. T. Meraj, M. H. Lipu, M. S. Rahman, T. Rahman, K. Hasan and K. M. Muttaqi, “Societal, environmental and economic impacts of electric vehicles towards achieving sustainable development goals,” Results Eng., p. 107060, 2025.
2. S. R. Mekapati and N. B. D. Choudhury, “An overview of sustainable transportation: Solar modules integrated electric vehicles,” J. Power Sources, vol. 659, p. 238424, 2025.
3. M. A. Abdelkareem, A. G. Olabi, M. N. AlMallahi, M. Mahmoud and M. Elgendi, “Contributions of electric vehicles towards the sustainable development goals,” Energy Convers. Manag. X, p. 101170, 2025.
4. M. S. Çetin, M. T. Gençoğlu and A. Dobrzycki, “Investigation of charging technologies for electric vehicles,” Turk. J. Sci. Technol., vol. 19, no. 1, pp. 97–106, 2024.
5. J. Sarda, N. Patel, H. Patel, R. Vaghela, B. Brahma, A. K. Bhoi and P. Barsocchi, “A review of the electric vehicle charging technology, impact on grid integration, policy consequences, challenges and future trends,” Energy Rep., vol. 12, pp. 5671–5692, 2024.
6. L. Sun, J. Teh, W. Liu, C. M. Lai and L. R. Chen, “Impact of electric vehicles on power system reliability and related improvements: A review,” Electr. Power Syst. Res., vol. 247, p. 111838, 2025.
7. P. Arulkumar, R. Karthick, R. Saravanan and K. Balamurugan, “Design of adaptive control framework for collaborative electric vehicle charging to improve hosting capacity in constrained distribution networks,” J. Energy Storage, vol. 140, p. 118980, 2025.
8. EPDK, “Şarj hizmeti piyasası aylık istatistikleri – Temmuz,” [Online]. Available: https://www.epdk.gov.tr/Detay/Icerik/4-16153/sarj-hizmeti-piyasasi-aylik-istatistikleri-temmuz. [Accessed: Jan. 10, 2025].

9. A. Dobrzycki, L. Kasprzyk, M. S. Çetin and M. T. Gençoğlu, “Analysis of the influence of the charging process of an electrical vehicle on voltage distortions in the electrical installation,” Appl. Sci., vol. 14, no. 17, p. 7691, 2024.
10. S. Torres, I. Durán, A. Marulanda, A. Pavas and J. Quirós-Tortós, “Electric vehicles and power quality in low voltage networks: Real data analysis and modeling,” Appl. Energy, vol. 305, p. 117718, 2022.
11. A. Dobrzycki, M. S. Çetin and M. T. Gençoğlu, “Harmonics generated during the electric vehicle charging process,” in Proc. 2nd Int. Conf. Adv. Innov. Eng. (ICAIE), Elazığ, Turkey, 2023, pp. 173–178.
12. N. Aung, W. Zhang, K. Sultan, S. Dhelim and Y. Ai, “Dynamic traffic congestion pricing and electric vehicle charging management system for the Internet of Vehicles in smart cities,” Digit. Commun. Netw., vol. 7, no. 4, pp. 492–504, 2021.
13. W. Gan, J. Wen, M. Yan, Y. Zhou and W. Yao, “Enhancing resilience with electric vehicles charging redispatching and vehicle-to-grid in traffic-electric networks,” IEEE Trans. Ind. Appl., vol. 60, no. 1, pp. 953–965, 2023.
14. M. S. Çetin, M. T. Gençoğlu and H. Şahin, “A review of electric vehicles: Their impact on the electricity grid and artificial intelligence-based approaches for charging load management,” Int. J. Energy Smart Grid, vol. 9, no. 1, pp. 51–59, 2024.
15. Z. Ding, Y. Lu, K. Lai, M. Yang and W. J. Lee, “Optimal coordinated operation scheduling for electric vehicle aggregator and charging stations in an integrated electricity-transportation system,” Int. J. Electr. Power Energy Syst., vol. 121, p. 106040, 2020.
16. M. A. Gharibi, H. Nafisi, H. Askarian-Abyaneh and A. Hajizadeh, “Deep learning framework for day-ahead optimal charging scheduling of electric vehicles in parking lot,” Appl. Energy, vol. 349, p. 121614, 2023.
17. J. Jin and Y. Xu, “Optimal policy characterization enhanced actor-critic approach for electric vehicle charging scheduling in a power distribution network,” IEEE Trans. Smart Grid, vol. 12, no. 2, pp. 1416–1428, 2020.
18. M. Tang, W. Zhuang, B. Li, H. Liu, Z. Song and G. Yin, “Energy-optimal routing for electric vehicles using deep reinforcement learning with transformer,” Appl. Energy, vol. 350, p. 121711, 2023.
19. R. Basso, B. Kulcsár, I. Sanchez-Diaz and X. Qu, “Dynamic stochastic electric vehicle routing with safe reinforcement learning,” Transp. Res. Part E Logist. Transp. Rev., vol. 157, p. 102496, 2022.
20. A. Ahmadian, K. Sedghisigarchi and R. Gadh, “Empowering dynamic active and reactive power control: A deep reinforcement learning controller for three-phase grid-connected electric vehicles,” IEEE Access, vol. 12, pp. 66068–66084, 2024.
21. Y. Zhang, Q. Yang, D. An, D. Li and Z. Wu, “Multistep multiagent reinforcement learning for optimal energy schedule strategy of charging stations in smart grid,” IEEE Trans. Cybern., vol. 53, no. 7, pp. 4292–4305, 2022.
22. S. Shahriar, A. R. Al-Ali, A. H. Osman, S. Dhou and M. Nijim, “Prediction of EV charging behavior using machine learning,” IEEE Access, vol. 9, pp. 111576–111586, 2021.
23. T. Lan, K. Jermsittiparsert, S. T. Alrashood, M. Rezaei, L. Al-Ghussain and M. A. Mohamed, “An advanced machine learning based energy management of renewable microgrids considering hybrid electric vehicles’ charging demand,” Energies, vol. 14, no. 3, p. 569, 2021.
24. J. Zhong, J. Liu and X. Zhang, “Charging navigation strategy for electric vehicles considering empty-loading ratio and dynamic electricity price,” SSRN Electron. J., Art. no. 4203560, 2023.
25. Z. Zhao and C. K. Lee, “Dynamic pricing for EV charging stations: A deep reinforcement learning approach,” IEEE Trans. Transp. Electrif., vol. 8, no. 2, pp. 2456–2468, 2021.
26. H. Lin, Y. Zhou, Y. Li and H. Zheng, “Aggregator pricing and electric vehicles charging strategy based on a two-layer deep learning model,” Electr. Power Syst. Res., vol. 227, p. 109971, 2024.
27. M. B. Rasheed, A. Llamazares, M. Ocana and P. Revenga, “A game-theoretic approach to mitigate charging anxiety for electric vehicle users through multi-parameter dynamic pricing and real-time traffic flow,” Energy, vol. 304, p. 132103, 2024.
28. B. Palaniyappan and T. Vinopraba, “Dynamic pricing for load shifting: Reducing electric vehicle charging impacts on the grid through machine learning-based demand response,” Sustain. Cities Soc., vol. 103, p. 105256, 2024.