Stochastic Assessment of Industrial Loads in the Automotive Industry for Transformer Sizing: Contribution and Responsibility

This study applies the Monte Carlo simulation method to support decision-making in the design of industrial electrical infrastructure under uncertainty. The case focuses on determining the optimal sizing of the main power transformer in an automotive manufacturing plant. Given the complexity of coordinating multiple power-consuming processes, each with different levels of demand, simultaneity, and uncertainty, the Monte Carlo simulation provides a probabilistic framework to estimate expected power requirements and their confidence intervals. By iteratively simulating consumption scenarios derived from statistical distributions, the method enables a more robust sizing of such technically and economically critical electrical equipment. Simulation results in values with enough confidence and robustness suitable for educated decisions in the sizing process. Also, two archetypes are used to categorize the involvement degree and nature of each load in the system.