Optimal Sizing of a Hybrid Renewable Energy System for Auxiliary Services in Substations Through Genetic Algorithm and Variable Neighborhood Search

Auxiliary services in substations are fundamental systems for the operation and coordination of power electrical systems. Because of their importance, backup systems are utilized to support critical loads during main power supply failures. Hybrid renewable backup systems, comprising renewable generation and batteries, offer a more sustainable alternative compared to the current use of fossil fuel generators in substations. However, sizing these systems can be a complex task due to the uncertainty associated with interruptions and renewable source production. This paper proposes an optimization model to size hybrid renewable energy systems for auxiliary services in substations. Uncertainties related to wind and photovoltaic generation, as well as power outages start time and durations, are addressed through Monte Carlo simulations. Furthermore, a hybrid algorithm, based on Genetic Algorithm (GA) and Variable Neighborhood Search (VNS), is introduced. The proposed approach considers the costs associated with diesel generator usage and instances of non-supply. A comparison of the metaheuristics GA, VNS, and the new hybrid algorithm is presented, emphasizing the advantages of the hybrid approach. The algorithms are validated by comparing them with results from the literature, demonstrating their ability to achieve solutions with high accuracy and low computational time. The results demonstrate that the proposed sizing approach successfully minimizes costs while ensuring reliability, highlighting its potential to optimize hybrid renewable backup systems for critical substation loads.