Navegando por Assunto "Retrofit"

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Acesso aberto (Open Access)
Metodologia de Avaliação do desempenho energético da integração de carros elétricos à edificações
(Universidade Federal do Pará, 2024-09-06) SOUZA, Ana Carolina Dias Barreto de; CARVALHO, Carminda Célia Moura de Moura; http://lattes.cnpq.br/1778332169942633; TOSTES, Maria Emília de Lima; http://lattes.cnpq.br/4197618044519148
Energy diagnosis methodologies have been incorporating energy consumption and energy generation systems into their analysis, making it possible to classify energy-self-sufficient buildings as Near Zero Energy Buildings (NZEB) or Positive Energy Buildings (PEB). In electric mobility, the increased use of electric vehicles (EVs) brings challenges and opportunities in electricity consumption, management and efficiency. The impact of this robust and growing load when integrated into new and existing buildings is not yet considered in performance assessments. Consequently, the methodologies for obtaining certifications and labels do not consider the load of this system as an individual end-use. For buildings with energy efficiency (EE) and self-sufficiency labels, introducing EVs can result in the rating being downgraded due to increased energy consumption. Therefore, analyzing the impact of integrating EVs into buildings aims to support the formulation or revision of energy diagnosis methodologies that include EV charging systems integrated into buildings. This thesis evaluates the influence of EV charging in buildings with the NZEB/PEB label from the Brazilian Building Labeling Program (PBE Edifica). Using on-site surveys, computer modelling and thermo energetic analysis with software such as OpenStudio and EnergyPlus, an energy rating was carried out on a building in Belém, State of Pará, Brazil. Subsequently, energy flow simulations using probabilistic models with the Monte Carlo method were run in OpenDSS software to examine the impact of integrating EVs without (scenario 01) and with (scenario 02) the implementation of demand-side management techniques. Analysis using the labelling methodology showed that the building has an EE level C rating and NZEB self-sufficiency. Scenario 01 generated a 69.28% increase in energy consumption, reducing the EE level to D and resulting in the loss of the NZEB class. Scenario 02 increased consumption by 40.50%, a lower percentage than scenario 01 and guaranteed the return of the NZEB class lost in scenario 1, but did not return the EE level to class C. The results highlight the need for immediate and comprehensive energy management strategies. However, these strategies are not sufficient if other consumption restrictions or EE measures are not applied to other systems in the building. To this end, EE measures were proposed and evaluated in the air conditioning and lighting systems. Subsequently, an equation was drawn up to indicate the maximum level of energy X consumption that could be increased without compromising the building's energy performance and NZEB rating. Finally, OpenDSS software was used to simulate the increased availability of EV charging after the retrofit. With the proposed retrofit, the building improved its EE ratings by three levels, and the NZEB rating percentage increased by 33.28%. These measures also increased the EV charging load by 20% while maintaining the maximum EE level and NZEB rating.
Acesso aberto (Open Access)
SmartLVEnergy: um framework para gestão energética inteligente e descentralizada de sistemas legados de baixa tensão
(Universidade Federal do Pará, 2024-07-11) FERNANDES, Rubens de Andrade; GOMES, Raimundo Cláudio Souza; http://lattes.cnpq.br/4244097441063312; COSTA JÚNIOR, Carlos Tavares da; http://lattes.cnpq.br/6328549183075122
Essential for technological and economic progress, electrical energy requires well-founded solutions and strategies for efficient and sustainable management. Existing consumer units, lacking modern technological resources, need gradual alternatives to optimize energy use, making the most of pre-established resources. In this context, retrofit offers an effective update for these infrastructures. Systematic models and strategies can standardize and ensure the replication of these solutions in different contexts through abstractions known as frameworks. However, there is a lack of frameworks to enable the implementation of systematic retrofit strategies for energy management, especially in the low-voltage energy sector. To fill this gap, this thesis presents the SmartLVEnergy framework, proposed to guide the design of innovative retrofit strategies to modernize legacy low-voltage installations with IoT, AIoT, and distributed computing solutions, optimizing energy management with distributed technological resources and advanced predictive capabilities. The experiments conducted in this thesis are presented in the format of aggregated scientific articles, which contributed to the conception of the SmartLVEnergy framework. As a result, it was possible to implement energy management tools in existing building and industrial scenarios in a systematic manner, based on the premises of the proposed framework. The main focus was the analysis and prediction of the energy demand of the installations and their respective circuits, allowing to anticipate and mitigate demand overrun events of the consumer units, following the guidelines of the Brazilian National Electric Energy Agency. The strategies conceived included the development, use, and integration of sensing, communication, and computing resources, distributed locally, in the cloud, and at the edge, according to the principles of the SmartLVEnergy framework, maximizing the use of existing resources according to the specific needs of each installation. The proposed framework is flexible and allows the integration, expandability, and interoperability of technological solutions across legacy systems, enabling operations according to the peculiarities and resources of each pre-existing context. This versatility confirms the relevance of this work as a robust and sustainable proposal to promote energy efficiency today, especially in legacy low-voltage systems.