Étude de la répartition optimale des puissances dans les réseaux de transport de d'énergie électrique pour un écoulement de puissance optimal

Abstract

The electrical network is a complex and interconnected system comprising transmission lines, substations, and transformers. Its purpose is to transport electricity from power plants to end users, such as households, businesses, and industries. However, energy losses within the network pose challenges and lead to inefficiencies in resource utilization. This research focuses on optimizing power flow to minimize active losses, using mathematical models to represent network characteristics and power distribution. The goal is to find optimal network configurations that ensure stability and reliability while minimizing active losses. Specifically, the study explores the Modified Teaching Learning Algorithm (MTLA) as a meta-heuristic optimization method and the K-means algorithm for clustering in power flow optimization (OPF) of electrical networks. By leveraging these approaches, the research aims to reduce active losses caused by resistance in network components. This reduction not only enhances overall network efficiency but also has significant financial implications for electricity companies. The study analyzes modern artificial intelligence and machine learning techniques to mitigate active losses, contributing to the development of more efficient and sustainable electrical systems.