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Motivado por políticas cada vez mais rígidas, com o objetivo de reduzir a emissão de poluentes que contribuem para o efeito estufa e aumentar a sustentabilidade do setor elétrico, buscam-se alternativas para tornar a geração e o consumo de energia mais eficiente. Com mais de um terço do consumo mundial de energia, os edifícios apresentam grande potencial para contribuir de maneira sustentável no setor. Adicionalmente, a combinar com o crescente incentivo na indústria de geração distribuída (GD) e de veículos elétricos (VE), acredita-se que os Smart Buildings (SBs) podem ser uma chave no campo da sustentabilidade energética residencial no futuro. Desta forma, este trabalho apresenta uma revisão dos principais trabalhos relacionados ao âmbito de otimização de recursos energéticos de SBs e propõe um modelo de otimização de recursos energéticos de um edifício coletivo residencial. Para isto, é apresentado uma introdução ao método de programação linear, bem como a formulação matemática do modelo desenvolvido. Para a validação do modelo, apresenta-se um estudo de caso envolvendo quatro cenários e a sua respetiva análise económica. Os resultados comprovam a efetividade do modelo, sendo possível atingir 24% de redução no pico da potência consumida da rede e 20% de redução de custos relacionados ao consumo de energia elétrica.
Efficient alternatives in the energy production and consumption are constantly investigated, motivated by increasingly strict policies aiming the reduction of pollutants emissions that contribute to the greenhouse effect and increase the sustainability of the electricity sector. With more than a third of the world's energy consumption, buildings have great potential to contribute to these sustainability goals. Additionally, with the growing incentives in the Distributed Generation (GD) and Electric Vehicle (VE) industry, it is believed that Smart Buildings (SBs) can be a key in the field of residential energy sustainability in the future. In this way, this work presents a review of the main works related to the scope of energy resources optimization of SBs and proposes a model to optimize the energy resources of a residential collective building. Therefore, an introduction to the method of linear programming optimization is presented, as well as the mathematical formulation of the developed model. For its validation, a case study involving four scenarios and an economic analysis is presented. The results confirm the effectiveness of the model, being able to reach a 24% reduction in the peak load consumed by the grid and a 20% reduction in electricity consumption costs.
Efficient alternatives in the energy production and consumption are constantly investigated, motivated by increasingly strict policies aiming the reduction of pollutants emissions that contribute to the greenhouse effect and increase the sustainability of the electricity sector. With more than a third of the world's energy consumption, buildings have great potential to contribute to these sustainability goals. Additionally, with the growing incentives in the Distributed Generation (GD) and Electric Vehicle (VE) industry, it is believed that Smart Buildings (SBs) can be a key in the field of residential energy sustainability in the future. In this way, this work presents a review of the main works related to the scope of energy resources optimization of SBs and proposes a model to optimize the energy resources of a residential collective building. Therefore, an introduction to the method of linear programming optimization is presented, as well as the mathematical formulation of the developed model. For its validation, a case study involving four scenarios and an economic analysis is presented. The results confirm the effectiveness of the model, being able to reach a 24% reduction in the peak load consumed by the grid and a 20% reduction in electricity consumption costs.
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Geração Distribuída Smart Buildings Programação Linear Otimização Gestão de Recursos de Energia Distributed Generation Smart Buildings Linear Programming Optimization Energy Resource Management