Optimal Requirements of Spare Transformers and Mobile Units for Distribution Substations via Genetic Algorithm and Monte Carlo Techniques

This paper proposes a new optimization method based on enhanced genetic algorithm (GA) and Monte Carlo simulation (MCS) techniques, which are simultaneously applied to size regular spare transformer (RST) and mobile unit substations (MUS) stocks for distribution substations. The aim is to serve a group of electrical energy distribution substations to mitigate possible losses caused by load curtailments due to major failures that affect the substation transformers. The proposed method includes the use of resources such as MUS and load transfer, in addition to representing the expansion of the transformers group in operation and the increase in power demand, over a specified planning horizon, considering all waiting times inherent to system actions, e.g.,: RST installation, MUS connection, stock replenishment, etc. Two real systems with different characteristics are used to illustrate the proposed method, allowing the analysis of results obtained from different scenarios and parameters.