Document Type : Research paper
Authors
1
Kimyo international university in Tashkent, Shota Rustaveli street 156, 100121, Тashkent, Uzbekistan
2
Teacher, 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
DSc, professor, Andijan State University, 170100 Andijan, Uzbekistan
6
Candidate of Technical Sciences, Associate Professor, “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, 39, Street Kari Niyaziy, 100000, Tashkent, Uzbekistan
7
Candidate of Chemical Sciences, Associate Professor, Department of Chemistry, Urgench State University named after Abu Rayhan Biruni, 220100 Urgench, Uzbekistan
8
Department of General Professional Subjects, Mamun university, Khiva, Uzbekistan
10.22098/joape.2025.18919.2470
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.
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