Aplicação de técnicas de estimação de estado e aprendizado de máquina para o gerenciamento energético das microrredes

Author(s)

Byron Alejandro Acuña Acurio

• Document type: Doctoral Thesis
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação
• Defense date: 2025-03-14

Examining board members

Luiz Carlos Pereira da Silva; Carlos Alberto de Castro Junior; Roberto Perillo Barbosa da Silva; Wendy Yadira Eras Herrera; Romis Ribeiro de Faissol Attux

• Advisor: Juan Camilo López Amézquita; Luiz Carlos Pereira da Silva

Abstract

Microgrids are integrated sustainable energy systems capable of operating both connected to and independently from the main power grid. These systems typically incorporate various distributed energy resources (DERs), including photovoltaic panels, storage systems, and thermal generators. However, the intermittent nature of renewable power generation in microgrids creates complex stochastic challenges for real-time control and system reliability. To address this, advanced machine learning algorithms and state estimation techniques are essential for monitoring and responding to dynamic, random events beyond human control, enabling accurate monitoring of microgrid components across various operational scenarios. State estimation serves two crucial functions in microgrids: converting smart meter readings and available data into reliable estimates of unmonitored variables, and identifying significant measurement errors. Meanwhile, machine learning can aid state estimation algorithms with predictions of unobservable variables. This research project focuses on developing and evaluating hybrid tools that combine machine learning and state estimation algorithms to enhance microgrid data-driven energy management functionalities (AU)

• FAPESP's process: 20/03069-5 - Application of state estimation and machine learning techniques for the energy management of microgrids
• Grantee: Byron Alejandro Acuña Acurio
• Support Opportunities: Scholarships in Brazil - Doctorate