Hierarchical approach to managing auxiliary service loads in substations during power outages

• Grant number: 24/02027-8
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Effective date (Start): July 01, 2024
• Effective date (End): September 30, 2024
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: John Fredy Franco Baquero
• Grantee: Matheus Holzbach
• Supervisor: Javier Contreras Sanz
• Host Institution: Faculdade de Engenharia (FEIS). Universidade Estadual Paulista (UNESP). Campus de Ilha Solteira. Ilha Solteira , SP, Brazil
• Research place: Universidad de Castilla-La Mancha, Ciudad Real (UCLM), Spain
• Associated to the scholarship: 22/04826-0 - Application of metaheuristics genetic algorithm and variable neighborhood search for the optimal sizing of a hybrid backup system for auxiliary services in substations, BP.MS

Abstract

Substation auxiliary services play a crucial role in the reliability and stability of the entire electrical system. They also contribute to emergency response, ensuring the safety of operators and allowing the rapid restoration of electrical service. During outages, auxiliary service loads must be continuously supplied by backup systems, which can consist of a microgrid with renewable generation. To guarantee this service, it is proposed to develop an algorithm based on a hierarchical approach for managing non-essential auxiliary service loads in substations. The algorithm will determine the operation of the loads taking into account the charge level of the batteries, the availability of renewable generation resources in the microgrid, and the presence of electric vehicles from the electric company, which can be used in the Vehicle-to-charge (V2L) scheme. If the battery system has little energy available, or renewable generation systems do not produce enough energy, the demand for programmable air conditioning or lighting loads can be adjusted to extend the supply of other essential loads. The algorithm will be implemented in Matlab and validated through stochastic simulations using the Monte Carlo method, where its implementation will be tested using different renewable generation conditions, as well as start times and duration of power faults. It is expected to improve the availability rate and implementation cost within the microgrid sizing project for auxiliary services associated with the exchange in the country (process FAPESP 2022/04826-0).