Optimal Location of Spare Transformers and Mobile Units for Distribution Substations via Genetic Algorithm and Monte Carlo Simulation

This paper proposes a new optimization method based on genetic algorithm (GA) and chronological Monte Carlo simulation (CMCS) techniques, which are simultaneously applied to optimize the location of regular spare transformers (RSTs) and mobile unit substations (MUS), among predefined distribution substations with storage capabilities. Given a certain number of RST and MUS equipment per year, available to serve a set of distribution substations, the goal is to minimize the costs associated with energy interruption, no billing and equipment displacement due to transformer failures in operation. Based on the chronological features of the main probabilistic assessment algorithm, the proposed method can model all waiting times inherent to system actions, e.g.: RSTs installation, MUS connection, stock replenishment etc. A real system with different characteristics is used to illustrate the application of the proposed method, allowing the analysis of the results obtained from different scenarios.