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Orientador(es)
Resumo(s)
A preocupação com a redução das emissões de gases com efeito de estufa tem aumentado signi-
ficativamente, dada a sua contribuição direta para as mudanças climáticas, o que levou as instituições
europeias, como a Comissão Europeia, a estabelecer metas e limites para as emissões de CO₂. O con-
sumo de energia elétrica no sector residencial tem uma forte expressão no consumo de energia a nível
global (representa cerca de 25% da energia elétrica global), sendo que dentro desta, 15% a 20% da ener-
gia elétrica é utilizada para o aquecimento de sistemas de águas quentes sanitárias.
Uma das ferramentas a considerar na evolução do sistema elétrico é a Flexibilidade Energética
(FE). Esta representa a capacidade de um sistema alterar o seu consumo ou produção de energia em
resposta a sinais externos, respeitando as necessidades da rede e do consumidor. Uma das formas de
utilizar a FE é através de um algoritmo de otimização, como por exemplo o Binary Particle Swarm Op-
timization (BOEP). O estudo realizado considerou a redução das emissões de CO₂ associadas ao con-
sumo de energia elétrica num sistema de aquecimento de águas quentes sanitárias utilizando a flexibi-
lidade energética através do BOEP.
The concern with reducing greenhouse gas emissions has increased significantly, given their di- rect contribution to climate change, which has led European institutions, such as the European Commis- sion, to set targets and limits for CO₂ emissions. Electricity consumption in the residential sector has a significant share of global energy consumption (representing about 25% of global electricity), with 15% to 20% of this electricity being used for heating domestic hot water systems. One of the tools to consider in the evolution of the electricity system is Energy Flexibility (FE EF). This represents the ability of a system to change its energy consumption or production in response to external signals, while meeting the needs of both the grid and the consumer. One way to utilize EF is through an optimization algorithm, such as Binary Particle Swarm Optimization (BOEP). The case study considered the reduction of CO₂ emissions associated with electricity consumption in a domestic hot water heating system using energy flexibility through a BOEP.
The concern with reducing greenhouse gas emissions has increased significantly, given their di- rect contribution to climate change, which has led European institutions, such as the European Commis- sion, to set targets and limits for CO₂ emissions. Electricity consumption in the residential sector has a significant share of global energy consumption (representing about 25% of global electricity), with 15% to 20% of this electricity being used for heating domestic hot water systems. One of the tools to consider in the evolution of the electricity system is Energy Flexibility (FE EF). This represents the ability of a system to change its energy consumption or production in response to external signals, while meeting the needs of both the grid and the consumer. One way to utilize EF is through an optimization algorithm, such as Binary Particle Swarm Optimization (BOEP). The case study considered the reduction of CO₂ emissions associated with electricity consumption in a domestic hot water heating system using energy flexibility through a BOEP.
Descrição
Palavras-chave
Energia Elétrica Flexibilidade Energética Binary Particle Swarm Optimization Sistema de Aquecimento de Águas Quentes Sanitárias
