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Mathematical optimization study demonstrates that reallocating power transformers reduces energy losses by 5% in electrical substations, indicating net economic savings for grid planning.
Economic expansion is commonly accompanied by accelerated growth in electricity consumption. In this context, distribution networks play a fundamental role in delivering electricity to end users; however, they remain subject to technical losses. Among system components, power transformers are particularly relevant because, despite their high efficiency, they still generate significant losses due to continuous operation and their widespread presence throughout the electrical system. The literature addresses the relocation of distribution transformers as a strategy to minimize technical losses. However, the application of this approach to equipment installed in electrical substations remains less explored. Therefore, this study proposes an optimization model for reallocating a pool of existing power transformers among electrical substations, matching transformer rated capacities to substation demands to reduce technical losses and enhance energy efficiency. The research methodology involved the mathematical formulation of the problem, adopting the minimization of accumulated total cost as the objective function, which incorporates both technical losses and relocation costs. The model was applied to a case study using technical and operational data from power transformers commonly installed in HV/MV substations. The results demonstrate that the optimal relocation reduced transformer energy losses by approximately 5% over the 10-year planning horizon, generating net economic savings that exceeded the equipment relocation costs. The optimization model suggests adjustments in transformer allocation, aligning loading levels with more appropriate operating ranges and thereby increasing transformer efficiency. Consequently, the proposed approach provides an engineering design-oriented decision-support method for optimizing transformer allocation, contributing to more energy-efficient and economically effective substation planning.
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Vieira et al. (2026) studied this question.
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