Thermal-Aging-Constrained Distribution Transformer Sizing for Demand-Side Resource Integration

Distribution transformers (DTs) must accommodate rapidly changing net-load profiles driven by electric vehicles (EVs), rooftop photovoltaic (PV) systems, heat pumps (HPs), load growth, and ambient temperature. However, these factors are rarely considered jointly in long-term DT planning, particularly when thermal aging and utility-standard candidate ratings are included. This paper proposes a multiyear optimization framework that jointly determines DT rating and replacement timing. The mixed-integer linear programming model minimizes the present value of the investment, energy-loss, replacement, expected outage, and salvage-value components. Transformer loss of life is calculated using IEEE Std. C57.91, while Monte Carlo simulation represents heterogeneous residential EV charging. Scenario-based PV, HP, load-growth, and ambient-temperature profiles are then integrated into the planning model. In the reported comparison, the optimized plan reduced total present-value cost by approximately 16.5% relative to the conventional sizing rule while avoiding a later replacement. These results show how thermal aging and evolving demand-side resources can be incorporated into auditable, utility-relevant DT upgrade decisions.