Estimation of uncertainty in electrical loads due to electric vehicle charging

Relevance. This study addresses the need to develop a method for modeling the load profile of electric vehicle charging stations (EVCS) while accounting for parameter uncertainties, including the stochastic intensity of electric vehicle (EV) connections over a given period.

The Purpose. Analyze challenges in modeling EVCS load profiles under uncertainty. Develop a simulation method for EVCS load profiles that incorporates multiple stochastic components. Simulate load profiles reflecting the intensity of charging start times using empirical data. Evaluate EVCS load uncertainty, model convergence, and sensitivity to input parameter variations.

Methods. The study analyzes existing methods for modeling EVCS load profiles. A Monte Carlo method, implemented in MatLab®, was used to construct a mathematical model of the EVCS load profile.

Results. Experimental data on the number of connected EVs are processed to derive an average daily EVCS load profile. A combined probability distribution law, aligned with empirical data and reflecting EV connection intensity, is applied. A parametric model is developed to generate the temporal load profile of EVCS, incorporating key uncertainty factors: charging start time, EV power consumption, charging duration, and the number of EVs.

Conclusions. A method and model for simulating EVCS load profiles are proposed, enabling the generation of temporal power profiles under input data uncertainty. The model can be applied to plan EVCS placement, evaluate power imbalance and select parameters for energy storage systems to integrate EVCS and ensure their stable operation in distribution grids.

1. Analytical agency "AUTOSTAT" URL: https://www.autostat.ru/ (accessed October 20, 2024)

2. RBC. URL: https://www.rbc.ru/ (accessed October 20, 2024).

3. Valeeva, Yu. S. Encouraging the development of electric transport as a tool for territorial development/ JS. Valeeva, MV. Kalinina, TG. Zorina, IG. Akhmetova// Bulletin of Kazan State Power Engineering University,– 2022. – vol. 14, № 1(53). – С. 155-172. – EDN UCKMFG.

4. Safin AR. Study of design features of mobile charging units for electric transport for development of sketch design documentation / AR. Safin, IV. Ivshin, AN. Tsvetkov, TI. Petrov, VR. Basenko VA. Manakhov// Power engineering: research, equipment, technology. – 2021. – vol. 23, № 5. – С. 100-114. – DOI 10.30724/1998-9903-2021-23-5-100-114. – EDN YIGCHH.

5. Deb, S. Charging station placement for electric vehicles: A case study of Guwahati city, India/ S. Deb, K. Tammi, K. Kalita, P. Mahanta//IEEE access. – 2019. – vol. 7. – pp. 100270-100282.

6. Karmaker, A. K., Roy S., Ahmed M. R. Analysis of the impact of electric vehicle charging station on power quality issues/ A. K. Karmaker, S. Roy, M. R. Ahmed //2019 international conference on electrical, computer and communication engineering (ECCE). – IEEE, 2019. – pp. 1-6.

7. Ahmed, A. A. NEPLAN-Based Analysis of Impacts of Electric Vehicle Charging Strategies on Power Distribution System/ A. A. Ahmed, M. A. Abdullah, M. Mansor, M. B. Marsadek, Y. J. Ying, M. S. Abd Rahman, N. A. Salim //IOP Conference Series: Materials Science and Engineering. – IOP Publishing, 2021. – vol. 1127. – №. 1. – 012033.

8. Shkitina, N. Analysis of the Impact of Stochastic Electric Vehicle Load on the Distribution Network / N. Shkitina, D. Akimov // Electricity. Transmission and Distribution. – 2021. – No. S1. – Pp. 40-45

9. Soluyanov, Yu.I. Determination of estimated electrical loads of charging infrastructure for electric vehicles integrated into electrical installations of residential and public buildings / Yu.I. Soluyanov, A.I. Fedotov, A.R. Akhmetshin, E.A. Fedotov, V.A. Khalturin // Power engineering: research, equipment, technology. 2024;26(6):94-107.– DOI 10.30724/1998-9903-2024-26-6-94-107. – EDN XFGOGS.

10. Soluyanov, Yu.I. Selection of charging stations aligned with existing power supply systems for apartment buildings and educational institutions in a metropolis./ A. I.Fedotov, V. I. Soluyanov et al. // Electrichestvo. – 2025. – № 1. – p. 32-44. – DOI 10.24160/0013-5380-2025-1-32-44. – EDN HOCOCV.

11. Alasseur C. et al. Regression Monte Carlo for microgrid management //ESAIM: proceedings and surveys. – 2019. – Т. 65. – С. 46-67.

12. Jospin L. V. et al. Hands-on Bayesian neural networks—A tutorial for deep learning users //IEEE Computational Intelligence Magazine. – 2022. – Т. 17. – №. 2. – С. 29-48.

13. Goodfellow I. et al. Generative adversarial networks //Communications of the ACM. – 2020. – Т. 63. – №. 11. – С. 139-144

14. Feng J. Load forecasting of electric vehicle charging station based on grey theory and neural network/ J. Feng, J. Yang, Y. Li, H. Wang, H. Ji, W. Yang, K. Wang//Energy Reports. – 2021. – vol. 7. – pp. 487-492

15. Zhu J. et al. Short-term load forecasting for electric vehicle charging stations based on deep learning approaches //Applied sciences. – 2019. – Т. 9. – №. 9. – С. 1723.

16. Buzna L. et al. An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations //Applied Energy. – 2021. – Т. 283. – С. 116337.

17. Zhang J. et al. Daily electric vehicle charging load profiles considering demographics of vehicle users //Applied Energy. – 2020. – Т. 274. – С. 115063.

18. Tang, D.Probabilistic modeling of nodal charging demand based on spatial-temporal dynamics of moving electric vehicles/ D. Tang, P.Wang //IEEE Transactions on Smart Grid. – 2015. – vol. 7. – №. 2. – pp. 627-636.

19. Palomino A., Parvania M. Bayesian hierarchical model for characterizing electric vehicle charging flexibility //2020 IEEE Power & Energy Society General Meeting (PESGM). – IEEE, 2020. – С. 1-5.

20. Wang B. et al. EV charging algorithm implementation with user price preference //2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). – IEEE, 2015. – С. 1-5.

21. Voronin, V. A. Multi-Agent Modeling of the Development of Electric Charging Infrastructure in the City of Kemerovo / V. A. Voronin, F. S. Nepsha // Electricity. Transmission and Distribution. – 2023. – No. 3(78). – Pp. 10-17. – EDN XVUSOW.

22. Mehboob N. et al. Smart operation of electric vehicles with four-quadrant chargers considering uncertainties //IEEE Transactions on Smart Grid. – 2018. – Т. 10. – №. 3. – С. 2999-3009