Algoritmo híbrido de otimização por enxame de partículas aplicado ao gerenciamento de cargas residenciais

• Ano de defesa: 2018
• Autor(a) principal: Cavalca, Diego Luiz
• Orientador(a): Fernandes, Ricardo Augusto Souza
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: Otimização; Redes inteligentes; Resposta à demanda; Meta-heurísticas; Enxame de partículas
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/9972

Demand side power management and a useful solution and guarantee in the context of Smart Grids, since it allows to reduce energy consumption in periods of greater network consumption, aiming to ensure system reliability and minimize resource wastage. Thus, production efficiency and energy consumption are a key feature in this context, and optimization methods are highly relevant, being a crucial part of the planning, operation and control of energy systems. In this work, a new particle swarm optimization algorithm approach is proposed for the feasible time resolution of the load operation planning in a smart home, aiming to mitigate problems related to the high dimensionality and the presence of constraints, inherent problems in this field. Assuming a robust mathematical model, the results of the computational simulations carried out show that the proposed approach, even when compared to alternative methods, contributes significantly to reduction of energy costs in relation to tariff variations, as well as minimizing the use of residential loads at peak times for a group of consumers, obtaining optimized solutions in adequate time, expressing to the applicability of the proposed algorithm.