Otimização multiobjetivo estocástica para geração distribuída usando sistema híbrido de energia com sistema de armazenamento em bateria

• Ano de defesa: 2024
• Autor(a) principal: Barbosa, Aglaucibelly Maciel
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Engenharia de Produção; Programa de Pós-Graduação em Engenharia de Produção e Sistema; UFPB
• Palavras-chave em Português: Energia Híbrida; Design de Experimentos; Fontes alternativas de energia; Hybrid energy; Design of experiments; Multi-Objective Optimization; Alternative energy sources; Battery storage; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/33367

The global energy landscape is undergoing rapid transformation, driven by the need to reduce carbon emissions and the growing demand for renewable energy sources. In this context, hybrid energy systems that combine wind and photovoltaic sources with battery storage have emerged as a sustainable and efficient alternative. These factors have fueled research and the development of optimization methods to configure hybrid energy systems in a way that maximizes their efficiency and sustainability. The objective of this research is to propose a stochastic optimization method that identifies the ideal configuration for residential wind-photovoltaic hybrid systems with battery storage. This approach aims not only to optimize technical and economic performance but also to incorporate environmental aspects, making it a valuable tool for regulators and energy policy planners. The study begins with a Systematic Literature Review (SLR), which served as the foundation for the development of an advanced mathematical model. Utilizing the Response Surface Methodology (RSM) and a Robust Parameters approach, the model is designed to explore critical variables such as the proportion of wind and solar energy, energy demand, and storage capacity, all while incorporating the inherent uncertainty in these systems. The key findings indicated that the optimal configuration of hybrid systems depends significantly on the proportion of wind energy and energy demand. Additionally, the study revealed that temperature, as an environmental variable, has a lesser influence on system efficiency compared to other factors such as demand and the proportion of renewable sources. In conclusion, the research underscores the importance of a multidimensional approach to the optimization of hybrid energy systems. The proposed methodology not only offers an ideal configuration but also integrates environmental considerations, becoming an essential tool for the development of more sustainable energy policies. In a global scenario marked by climate change, optimizing these systems represents a crucial step in the transition to cleaner and more efficient energy matrices.