Portugues (BR) Este repositorio contem os codigos-fonte e dados da pesquisa de Mestrado sobre o dimensionamento otimo de sistemas de backup hibridos para servicos auxiliares em subestacoes. O projeto implementa e compara meta-heuristicas, incluindo Algoritmo Genetico (GA) e Busca em Vizinhanca Variavel (VNS), para encontrar configuracoes que minimizem o custo e garantam a confiabilidade do suprimento de energia. As incertezas relacionadas as falhas de energia e a geracao renovavel sao tratadas com simulacoes de Monte Carlo.
Palavras-chave: Otimizacao, Meta-heuristicas, Sistemas Híbridos de Energia Renovavel, Subestacoes, Algoritmo Genetico, Busca em Vizinhanca Variavel.
Pesquisa desenvolvida no Programa de Pos-Graduacao em Engenharia Eletrica da Universidade Estadual Paulista - UNESP, com fomento da Fundacao de Amparo a Pesquisa de Sao Paulo - FAPESP (Processo 2022/04826-0).
English (US) This repository contains the source codes and data from the Master's research on the optimal sizing of hybrid backup systems for auxiliary services in substations. The project implements and compares metaheuristics, including Genetic Algorithm (GA) and Variable Neighborhood Search (VNS), to find configurations that minimize cost and ensure power supply reliability. Uncertainties related to power outages and renewable generation are addressed using Monte Carlo simulations.
Keywords: Optimization, Metaheuristics, Hybrid Renewable Energy Systems, Substations, Genetic Algorithm, Variable Neighborhood Search.
Research developed at the Graduate Program in Electrical Engineering at Sao Paulo State University - UNESP, with funding from Sao Paulo Research Foundation -FAPESP (Grant 2022/04826-0).
Author: Matheus Holzbach Supervisor: Prof. Dr. Jhon Fredy Franco Baquero