Keywords
charging station
hybrid energy sources
energy balance
load distribution
optimization
efficiency coefficient
MATLAB/Simulink
Python
How to Cite
Abstract
The increase in electric vehicles has made ensuring energy balance at charging stations a critical challenge. In this study, a hybrid model based on solar panels, wind turbines, the central grid, and battery storage was developed, and its efficiency was evaluated. Simulation results showed that the proposed approach increases efficiency by 18–25%, reduces losses by 8–10%, and decreases the grid load by up to 30%.
In the study, the energy balance of the charging station was modeled. Photovoltaic, wind, and battery power were evaluated using appropriate models. Optimization methods were applied for balancing, and calculations were carried out in MATLAB/Simulink and Python environments.
According to the simulation results, 45–55% of the charging station load was supplied by renewable sources, 20–30% by the battery, and the remaining part by the central grid. The hybrid strategy reduced losses, increased efficiency up to 78%, and alleviated grid stress during peak loads.
The study results showed that hybrid energy-based charging stations ensure technical stability, reduce grid load, and improve efficiency. The proposed model can be applied in real projects
References
[1] Mwasilu, F., Justo, J. J., Kim, E. K., Do, T. D., & Jung, J. W. (2014). Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration. Renewable and Sustainable Energy Reviews, 34, 501–516. Elsevier. https://doi.org/10.1016/j.rser.2014.03.031
[2] Lund, H., Ostergaard, P. A., Connolly, D., & Mathiesen, B. V. (2017). Smart energy and smart energy systems. Energy, 137, 556–565. Elsevier. https://doi.org/10.1016/j.energy.2017.05.123
[3] Yilmaz, M., & Krein, P. T. (2013). Review of charging power levels and infrastructure for plug-in electric and hybrid vehicles. IEEE International Electric Vehicle Conference (IEVC), 1–8. IEEE. https://doi.org/10.1109/IEVC.2013.6799610
[4] Nykamp, S., Pruckner, M., & Heuvelink, E. (2019). Integration of electric vehicle charging stations into energy systems with renewable energy sources. Applied Energy, 254, 113600. Elsevier. https://doi.org/10.1016/j.apenergy.2019.113600
[5] Bahrami, S., Wong, V. W. S., & Jatskevich, J. (2019). Demand response for smart charging of electric vehicles in smart grids. IEEE Transactions on Smart Grid, 10(3), 2829–2839. https://doi.org/10.1109/TSG.2018.2887299
[6] Li, R., Su, H., & Wang, H. (2020). Optimal scheduling of renewable energy integrated EV charging stations considering demand response. Journal of Cleaner Production, 256, 120389. Elsevier. https://doi.org/10.1016/j.jclepro.2020.120389
[7] González, J. M., Rivas, E., & Aguado, J. A. (2018). Energy management of microgrids with electric vehicles and renewable sources: A literature review. Renewable Energy, 123, 282–294. Elsevier. https://doi.org/10.1016/j.renene.2018.02.003
[8] Zhou, Y., Wu, Y., & Xu, W. (2018). Stochastic modeling and optimization of EV charging load in renewable energy integrated microgrids. IEEE Transactions on Sustainable Energy, 9(3), 1071–1081. https://doi.org/10.1109/TSTE.2017.2762364
[9] G‘ayimnazarov I. X. UDC 532.543: 627.157: Calculation of the parameters of the base rows in a non-stationary flow //Innovatsion texnologiyalar. – 2025. – Т. 59. – №. 3. – С. 62-66.
[10] G‘ayimnazarov, I., Eshev, S., Bazarov, O., Latipov, S., Rakhimov, A., & Guliyeva, S. (2025, July). Investigation of the initiation of sediment movement in mixed flows. InAIP Conference Proceedings (Vol. 3256, No. 1, p. 020041). AIP Publishing LLC.
[11] Chen, X., Liu, J., & Song, Y. (2018). Energy management of hybrid renewable energy systems for EV charging stations: A comprehensive review. Energy Conversion and Management, 171, 1247–1268. Elsevier. https://doi.org/10.1016/j.enconman.2018.06.051
[12] Khalid, M., & Savkin, A. V. (2016). Optimal control of fast charging stations for electric vehicles under renewable energy generation and grid power constraints. Energy, 113, 1247–1258. Elsevier. https://doi.org/10.1016/j.energy.2016.07.065
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2026 Esanov T. B. (Muallif)