Designing a Centralized Charging and Discharging Management Strategy for Electric Vehicles to Enhance Transformer Lifespan in Distribution Networks

Document Type : Research paper

Authors

1 Kimyo International University in Tashkent, Shota Rustaveli Street 156, 100121, Тashkent, Uzbekistan.

2 Termiz University of Economics and Service, Farovon Street 4-b, Termez, Surxondaryo, Uzbekistan.

3 Department of Traumatology and Orthopedics, Fergana Medical Institute of Public Health, 2A Yangi Turon Street, Fergana, Uzbekistan.

4 PhD, Assistant Professor, Alfraganus University, Uzbekistan.

5 Andijan State University, 170100 Andijan, Uzbekistan.

6 “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, 39, Street Kari Niyaziy, 100000, Tashkent, Uzbekistan.

7 Department of Chemistry, Urgench State University named after Abu Rayhan Biruni, 220100 Urgench, Uzbekistan.

8 Department of General Professional Subjects, Mamun University, Khiva, Uzbekistan.

Abstract

The widespread adoption of electric vehicles (EVs) is increasing the loading of distribution transformers and accelerating insulation aging. This paper proposes a centralized charging and discharging strategy that jointly co-optimizes EV operating cost and the monetized cost of transformer loss-of-life, explicitly linking technical asset degradation to economic decision-making. Unlike prior centralized EV-scheduling approaches that either constrain temperature or evaluate aging only in post-processing, the proposed framework embeds an IEEE C57.91-based aging model directly into the optimization objective and converts aging into an equivalent financial cost.The model further introduces a stakeholder compensation mechanism in which part of the deferred transformer replacement savings is redistributed to EV owners, allowing an independent aggregator (or the distribution utility) to coordinate EV charging while preserving consumer economic incentives. The framework considers grid-to-vehicle (G2V), vehicle-to-home (V2H), and vehicle-to-grid (V2G) modes and is formulated as a mixed-integer nonlinear optimization problem. Simulation results for a residential network with six EVs demonstrate that centralized coordination can reduce transformer loss-of-life by up to 80% compared with decentralized charging, while increasing daily EV operating costs by only 4-6%. The remuneration mechanism enables all stakeholders—transformer owner, aggregator, and consumers—to benefit economically. These findings show that integrating monetized transformer aging into EV scheduling, combined with explicit profit-sharing, provides a technically effective and financially viable pathway for extending transformer lifespan under growing EV penetration.

Keywords

Main Subjects

References

  1. R. Kakkar, S. Agrawal, and S. Tanwar, “A systematic survey on demand response management schemes for electric vehicles,” Renew. Sustain. Energy Rev., vol. 203, p. 114748, 2024.
  2. J. Pasha, B. Li, Z. Elmi, A. M. Fathollahi-Fard, Y.-y. Lau, A. Roshani, T. Kawasaki, and M. A. Dulebenets, “Electric vehicle scheduling: State of the art, critical challenges, and future research opportunities,” J. Ind. Inf. Integr., vol. 38, p. 100561, 2024.
  3. F. S. Hwang, T. Confrey, C. Reidy, D. Picovici, D. Callaghan, D. Culliton, and C. Nolan, “Review of battery thermal management systems in electric vehicles,” Renew. Sustain. Energy Rev., vol. 192, p. 114171, 2024.
  4. M. Saklani, D. K. Saini, M. Yadav, and Y. C. Gupta, “Navigating the challenges of EV integration and demand-side management for India’s sustainable EV growth,” IEEE Access, vol. 12, pp. 143767–143796, 2024.
  5. S. M. Mortezaie, “Analysis and design of a soft-switching interleaved boost converter using auxiliary resonant circuit for electric vehicle applications,” Procedia Environ. Sci. Eng. Manag., vol. 12, no. 1, pp. 47–54, 2025.
  6. S. Adhikary, P. K. Biswas, T. S. Babu, and M. Balasubbareddy, “Bidirectional operation of electric vehicle charger incorporating grids and home energy storage: V2G/G2V/V2H/V2X,” in Renewable Energy for Plug-In Electric Vehicles, pp. 191–207, Elsevier, 2024.
  7. P. Shyam Sundar, P. Shrisrinivas, S. Patra, and G. Kanimozhi, “A short review on bidirectional converters for EV applications,” Smart Grids as Cyber Physical Systems, vol. 2, pp. 289–311, 2024.
  8. V. Monteiro and J. L. Afonso, “Control, optimization, and management of vehicle electrification in modern power grids,” in Vehicle Electr. Modern Power Grids, pp. 279–302, Elsevier, 2024.
  9. F. Bashir, Y. Yang, M. H. M. Alhaj, and W. Ding, “Modeling and analysis of bidirectional power flow grid-to-vehicle and vehicle-to-grid,” in Proc. Int. Conf. Electrical, Electronic and Networked Energy Syst., Springer, 2024.
  10. S. Sadeq, M. Sattar, D. Mohsen, H. Furaijl, N. Nimah, A. Shubaa, J. Mansi, and K. Khudaybergan, “Sustainable economic growth: Evaluating the role of green investments and renewable energy,” Procedia Environ. Sci. Eng. Manag., vol. 12, no. 2, pp. 519–530, 2025.
  11. A. Q. Al-Shetwi, I. E. Atawi, M. A. El-Hameed, and A. Abuelrub, “Digital twin technology for renewable energy, smart grids, energy storage and vehicle-to-grid integration,” IET Smart Grid, vol. 8, no. 1, p. e70026, 2025.
  12. V. Karlusov and D. Yarkov, “Cooperation between Russia and China in the oil and gas energy sector,” Econ. Ann.-XXI, vol. 193, no. 9-10, pp. 15–24, 2021.
  13. M. Montazeri-Gh, Z. Pourbafarani, and M. Mahmoodi-k, “Comparative study of different PHEV optimal control strategies,” Proc. IMechE, Part D: J. Automob. Eng., vol. 232, no. 12, pp. 1597–1610, 2018.
  14. A. Demirci, S. M. Tercan, E. E. Ahmed, U. Cali, and I. Nakir, “A novel electric vehicle charging management with dynamic active power curtailment framework,” IEEE Access, 2024.
  15. M. J. Chihota, C. Devine, and B. Bekker, “Probabilistic analysis of the technical impacts of high penetrations of electric vehicles,” in Adv. Technol. Electr. Veh., pp. 229–257, Elsevier, 2024.
  16. S. D. Vasconcelos, F. Gouveia, A. V. de Moura Lacerda Filho, R. F. Buzo, L. H. A. de Medeiros, L. R. Limongi, D. da Costa Marques, A. L. Fernandes, J. Chai, N. K. L. Dantas, et al., “Assessment of electric vehicles charging grid impact via predictive indicator,” IEEE Access, 2024.
  17. S. R. Moro, P. A. Cauchick-Miguel, T. T. de Sousa-Zomer, and G. H. de Sousa Mendes, “Design of a sustainable electric vehicle sharing business model in the Brazilian context,” Int. J. Ind. Eng. Manage., vol. 14, no. 2, pp. 147–161, 2023.
  18. M. Koc, O. B. Tor, and S. Demirbas, “Analysis of the effects of electric vehicles on distribution networks,” Gazi Univ. J. Sci. Part C, vol. 9, no. 1, pp. 95–107, 2021.
  19. D. D. Atkar, P. Chaturvedi, H. M. Suryawanshi, P. P. Nachankar, D. Yadeo, and S. K. Saketi, “Three-phase integrated optimized power electronic transformer for EV charging infrastructure,” IEEE Trans. Ind. Appl., vol. 58, no. 4, pp. 5198–5213, 2022.
  20. M. Mahmoodi-k, M. Montazeri, and V. Madanipour, “Simultaneous multi-objective optimization of a PHEV power management system,” Energy, vol. 233, p. 121111, 2021.
  21. A. Visakh and M. Selvan, “Seasonal effects of electric vehicle charging on the aging of distribution transformers,” in Proc. IEEE PES APPEEC, 2021.
  22. I. Diahovchenko, “Analyzing the influence of electric vehicle charging scheduling on distribution transformer lifespan,” Heliyon, vol. 10, no. 21, 2024.
  23. A. Visakh and M. P. Selvan, “Analysis and mitigation of the impact of electric vehicle charging on service disruption,” Sustain. Energy Grids Netw., vol. 35, p. 101096, 2023.
  24. M. Senol and I. S. Bayram, “Impact assessment and mitigation of electric vehicle smart charging harmonics,” IEEE Access, vol. 13, pp. 207412–207432, 2025.
  25. A. Visakh and M. P. Selvan, “Smart charging of electric vehicles to minimize cost and transformer aging,” Turk. J. Electr. Eng. Comput. Sci., vol. 30, no. 3, pp. 927–942, 2022.
  26. Z. Li, S. Su, X. Jin, H. Chen, Y. Li, and R. Zhang, “A hierarchical scheduling method of active distribution networks,” Int. J. Electr. Power Energy Syst., vol. 131, p. 106768, 2021.
  27. F. G. Venegas, M. Petit, and Y. Perez, “Active integration of electric vehicles into distribution grids,” Renew. Sustain. Energy Rev., vol. 145, p. 111060, 2021.
  28. O. Valarezo, T. Gomez, J. P. Chaves-Avila, L. Lind, M. Correa, D. Ulrich Ziegler, and R. Escobar, “Analysis of new flexibility market models in Europe,” Energies, vol. 14, no. 12, p. 3521, 2021.
  29. J. E. Bistline and G. J. Blanford, “The role of the power sector in net-zero energy systems,” Energy Clim. Change, vol. 2, p. 100045, 2021.
  30. T. Smith, J. Garcia, and G. Washington, “Novel PEV charging approaches for extending transformer life,” Energies, vol. 15, no. 12, p. 4454, 2022.
  31. H. Dehghani and B. Vahidi, “Evaluating the effects of demand response programs on transformer life expectancy,” Sci. Iranica, vol. 30, no. 5, pp. 1764–1779, 2023.
  32. S. W. T. Al-Mashhadani and S. Kurnaz, “A novel research process based on power IoT architecture for smart grid demand schemes,” Appl. Sci., vol. 14, no. 13, p. 5799, 2024.
  33. K. Guri and K. Najdenkoski, “Impact of wind velocity on power transformer loss of life estimation using improved thermal models in MATLAB/Simulink,” Int. J. Energy Convers., vol. 13, no. 3, pp. 103–112, 2025.
  34. H. Gorginpour, H. Ghimatgar, and M. S. Toulabi, “Lifetime estimation and optimal maintenance scheduling of urban oil-immersed distribution transformers considering weatherdependent intelligent load model and unbalanced loading,” IEEE Trans. Power Del., vol. 37, no. 5, pp. 4154–4165, 2022.
  35. V. Saxena, N. Kumar, and U. Nangia, “Computation and optimization of BESS in the modeling of renewable-energy-based framework,” Arch. Comput. Methods Eng., vol. 31, no. 4, pp. 2385–2416, 2024.
  36. B. Shi, W. Xiao, L. Zhang, T. Wang, Y. Jiang, J. Shang, Z. Li, X. Chen, and M. Li, “Multi-objective optimization method for power transformer design based on surrogate modeling and hybrid heuristic algorithm,” Electron., vol. 14, no. 6, p. 1198, 2025.
  37. D. Yang, Q. Chong, B. Hu, and M. Ma, “Optimal operation of energy internet based on user electricity anxiety,” Tsinghua Sci. Technol., vol. 23, no. 3, pp. 243–253, 2018.
  38. C. Zhai, Z. Cao, Y. Wang, M. Abdou-Tankari, J. Yu, and Z. Lei, “A reverse incentive-based demand response strategy for shared energy storage,” Energy, p. 136882, 2025.
  39. F. Rodriguez-Gomez, J. del Campo-Avila, and L. MoraLopez, “A novel clustering-based method for household electricity consumption profiles,” Eng. Appl. Artif. Intell., vol. 129, p. 107653, 2024.
  40. P. Dalaliyan Miandoab, P. Nazarian, and M. Moradlou, “Mathematical model for exploring the effect of demand response on transmission network expansion planning,” Elect. Eng., vol. 106, no. 5, pp. 5403–5416, 2024.
  41. G. Vigano, G. Lattanzio, and M. Rossi, “Review of projects and challenges in flexibility markets for distribution networks,” Energies, vol. 17, no. 11, p. 2781, 2024.
Journal of Operation and Automation in Power Engineering
Volume 13, Special Issue
Intelligent and Sustainable Power Systems (ISPS): AI-Driven Innovations for Renewable Integration and Smart Grid Resilience
2025
Pages 75-88

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History

  • Receive Date: 27 November 2025
  • Revise Date: 27 December 2025
  • Accept Date: 28 December 2025
  • First Publish Date: 28 December 2025

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