Application of metaheuristics genetic algorithm and variable neighborhood search for the optimal sizing of a hybrid backup system for auxiliary services in substations

• Grant number: 22/04826-0
• Support Opportunities: Scholarships in Brazil - Master
• Effective date (Start): June 01, 2023
• Effective date (End): June 30, 2025
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: John Fredy Franco Baquero
• Grantee: Matheus Holzbach
• Host Institution: Faculdade de Engenharia (FEIS). Universidade Estadual Paulista (UNESP). Campus de Ilha Solteira. Ilha Solteira , SP, Brazil
• Associated scholarship(s): 24/02027-8 - Hierarchical approach to managing auxiliary service loads in substations during power outages, BE.EP.MS

Abstract

Power substations are one of the main components of the electrical power system, allowing the connection of networks of different voltage levels and exercising coordination and control functions of vital importance for the stability of the system. In substations, auxiliary services must ensure the supply of loads that make up the monitoring, communication, and switching systems. In cases of contingency in supply, substations require the maintenance of auxiliary services to guarantee essential functions in the recovery process, which is why backup systems are used, consisting of power sources that often use a generator set supplied by fossil fuels. Another alternative is the adoption of a microgrid powered by a hybrid system, composed of renewable energy sources (wind and photovoltaic) and batteries, adding greater availability. However, this alternative requires a careful analysis for its implementation due to its cost and the intermittence of the generating sources. In this context, the development of two optimization methods for the optimal sizing of a hybrid backup system to supply auxiliary services in a substation, based on the metaheuristics Genetic Algorithm and Variable Neighborhood Search is proposed. Those methods must consider uncertainties regarding the intermittence of generating sources and the duration of power outages. A comparative analysis of the performance of the application of these meta-heuristics in the problem is carried out. The criteria used for the analysis of the results will be the best cost-benefit considering both the investment and the capacity to reduce substation downtime in cases of contingency.