Optimizing the Electric Vehicle Charging Stations Operation in Radial Networks
DOI:
https://doi.org/10.24160/1993-6982-2026-4-29-38Keywords:
electric vehicle charging stations, power distribution grids, optimization, photovoltaic systems, reactive power compensationAbstract
The article examines the introduction of electric vehicles as a key factor in reducing the negative environmental impact of transport and improving energy sustainability by reducing greenhouse gas emissions and decreasing the dependence on fossil fuels. Electric vehicle infrastructure has a significant impact on power systems, increasing their complexity, but on the other hand it opens new opportunities for stabilizing energy supply through vehicle-to-grid (V2G) communication technologies. To effectively manage these processes, a new optimization method based on a genetic algorithm (GA) has been developed. It focuses on the optimal placement of charging infrastructure and on the integration of V2G charging devices, solar photovoltaic panels, and reactive power compensation devices (RPCs). The proposed algorithm contributes to distribution grid efficiency enhancement by improving power quality and reducing operating costs. This effect is achieved through applying a multi-criteria objective function that includes the performance of photovoltaic systems, V2G technologies, grid operating costs, and the operation of reactive power compensation devices. The method also includes determining the optimal capacity and location of V2G stations, along with defining their operating schedules. The model has been implemented in the Matlab/Simulink environment with using a GA that supports parallel processing and real-valued encoding. The performance of the method has been validated using an IEEE 69-node test network; the simulation results have demonstrated high efficiency of the proposed approach. Integration of a photovoltaic system with a capacity of approximately 66.86% of the grid's daily consumption and an electric vehicle charging infrastructure with a total capacity of 1776 kW made it possible to reduce active energy losses by 50.5%, while the use of an RPC system with a capacity of approximately 65.02% reduced reactive power losses by 47.29% and increased the minimum voltage level to 0.95 units. The obtained results confirm the stability and versatility of the developed algorithm under various operating conditions and electrical grid configurations.
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Для цитирования: Солиман И.А.И., Тульский В.Н., Соловьева А.С. Оптимизация работы зарядных станций электромобилей в радиальных сетях // Вестник МЭИ. 2026. № 4. С. 29—38. DOI: 10.24160/1993-6982-2026-4-29-38
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Конфликт интересов: авторы заявляют об отсутствии конфликта интересов
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6. Hasan K.N. e. a. Distribution Network Voltage Analysis with Data-driven Electric Vehicle Load Profiles. Sustain. Energy, Grids and Networks. 2023;36:101216.
7. Shi X. e. a. Day-ahead Distributionally Robust Optimization-based Scheduling for Distribution Systems with Electric Vehicles. IEEE Trans. Smart Grid. 2023;14(4):2837—2850.
8. Wu G. e. a. Multi-objective Optimization of Integrated Energy Systems Considering Renewable Energy Uncertainty and Electric Vehicles. IEEE Trans. Smart Grid. 2023;14(6):4322—4332.
9. Gabbar H.A., Siddique A.B. Technical and Economic Evaluation of Nuclear-powered Hybrid Renewable Energy System for Fast Charging Station. Energy Conversion and Management: X. 2023;17:100342.
10. Siddique A.B., Gabbar H.A. Adaptive Mixed-integer Linear Programming-based Energy Management System of Fast Charging Station with Nuclear–renewable Hybrid Energy System. Energies. 2023;16(2):685.
11. Bilal M., Rizwan M. Integration of Electric Vehicle Charging Stations and Capacitors in Distribution Systems with Vehicle-to-grid Facility. Energy Sources, Pt. A: Recovery, Utilization, and Environmental Effects. 2021;47(1):7700—7729.
12. Erdinç O. e. a. Comprehensive Optimization Model for Sizing and Siting of DG Units, EV Charging Stations, and Energy Storage Systems. IEEE Trans. Smart Grid. 2018;9(4):3871—3882.
13. Zare P., Dejamkhooy A., Davoudkhani I.F. Efficient Expansion Planning of Modern Multi-energy Distribution Networks with Electric Vehicle Charging Stations: a Stochastic MILP Model. Sustain. Energy, Grids and Networks. 2024;38:101225.
14. Kumar B.V., Farhan M.A.A. Optimal Simultaneous Allocation of Electric Vehicle Charging Stations and Capacitors in Radial Distribution Network Considering Reliability. J. Modern Power Syst. and Clean Energy. 2024;12(5):1584—1595.
15. Li M. e. a. Two-stage Allocation of Electric Vehicle Charging Stations Considering Coordinated Charging Scenario. Proc. IEEE V Intern. Electrical and Energy Conf. Nangjing, 2022:4982—4987.
16. Li K., Shao C., Hu Z., Shahidehpour M. An MILP Method for Optimal Planning of Electric Vehicle Charging Stations in Coordinated Urban Power and Transportation Networks. IEEE Trans. Power Syst. 2023;38(6):5406—5419.
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19. Lee Z.J. e. a. Adaptive Charging Networks: a Framework for Smart Electric Vehicle Charging. IEEE Trans. Smart Grid. 2021;12(5):4339—4350.
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21. Abdel-Ghany H.A., Azmy A.M., Elkalashy N.I., Rashad E.M. Optimizing DG Penetration in Distribution Networks Concerning Protection Schemes and Technical Impact. Electric Power Syst. Research. 2015;128:113—122
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For citation: Soliman I.A.I., Tulsky V.N., Solovyeva A.S. Optimizing the Electric Vehicle Charging Stations Operation in Radial Networks. Bulletin of MPEI. 2026;4:29—38. (in Russian). DOI: 10.24160/1993-6982-2026-4-29-38
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Conflict of interests: the authors declare no conflict of interest
