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Authors
Abstract(s)
Esta dissertação apresenta uma abordagem de otimização para sistemas de micro-cogeração
com motores de combustão interna integrados em redes residenciais, abordando falhas
na procura de energia causadas por fontes de energia renováveis intermitentes. O método
proposto utiliza técnicas de machine learning, estratégias de controlo e dados da rede
para melhorar a flexibilidade e a eficiência do sistema na satisfação das necessidades de
eletricidade e água quente sanitária. Foram analisados dados históricos da rede residencial
para desenvolver um modelo de previsão da procura de eletricidade e água quente baseado
em machine learning. O armazenamento de energia térmica foi integrado no sistema
de micro-cogeração para aumentar a flexibilidade. Foi criado um modelo de otimização,
considerando a eficiência, as emissões e o custo, adaptando-se às alterações da procura
em tempo real. Foi concebida uma estratégia de controlo para o funcionamento flexível
do sistema de micro-cogeração, tendo em conta o excesso de armazenamento de energia
térmica e a atribuição de recursos. A eficácia da solução proposta foi validada através de
simulações, com resultados que demonstram a capacidade do sistema de micro-cogeração
para responder eficazmente a períodos de elevada procura de eletricidade e água quente,
ao mesmo tempo que atenua as falhas de procura de energia proveniente de fontes de
energia renováveis. A investigação apresenta uma nova abordagem com potencial para
melhorar significativamente a resiliência da rede, a eficiência energética e a integração
das energias renováveis nas redes residenciais, contribuindo para sistemas energéticos
mais sustentáveis e fiáveis.
This work presents an optimization approach for micro-cogeneration systems with internal combustion engines integrated into residential grids, addressing power demand failures caused by intermittent renewable energy sources. The proposed method leverages machine learning techniques, control strategies, and grid data to improve system flexibility and efficiency in meeting electricity and domestic hot water demands. Historical residential grid data were analysed to develop a machine learning-based demand prediction model for electricity and hot water. Thermal energy storage was integrated into the micro-cogeneration system to enhance flexibility. An optimization model was created, considering efficiency, emissions, and cost while adapting to real-time demand changes. A control strategy was designed for the flexible operation of the micro-cogeneration system, addressing excess thermal energy storage and resource allocation. The proposed solution’s effectiveness was validated through simulations, with results demonstrating the micro-cogeneration system’s ability to efficiently address high electricity and hot water demand periods while mitigating power demand failures from renewable energy sources. The research presents a novel approach with the potential to significantly improve grid resilience, energy efficiency, and renewable energy integration in residential grids, contributing to more sustainable and reliable energy systems.
This work presents an optimization approach for micro-cogeneration systems with internal combustion engines integrated into residential grids, addressing power demand failures caused by intermittent renewable energy sources. The proposed method leverages machine learning techniques, control strategies, and grid data to improve system flexibility and efficiency in meeting electricity and domestic hot water demands. Historical residential grid data were analysed to develop a machine learning-based demand prediction model for electricity and hot water. Thermal energy storage was integrated into the micro-cogeneration system to enhance flexibility. An optimization model was created, considering efficiency, emissions, and cost while adapting to real-time demand changes. A control strategy was designed for the flexible operation of the micro-cogeneration system, addressing excess thermal energy storage and resource allocation. The proposed solution’s effectiveness was validated through simulations, with results demonstrating the micro-cogeneration system’s ability to efficiently address high electricity and hot water demand periods while mitigating power demand failures from renewable energy sources. The research presents a novel approach with the potential to significantly improve grid resilience, energy efficiency, and renewable energy integration in residential grids, contributing to more sustainable and reliable energy systems.
Description
Keywords
Energia Elétrica Energia Térmica Estratégias de Controlo Flexibilidade da Rede Gestão de Energia Integração de Energias Renováveis Machinelearning Motores de Combustão Interna Redes Inteligen-Tes Redes Residenciais Sistemas de Micro-Cogeração