Electric vehicles and photovoltaic power stations can play an important role in replacing fossil fuels. This article presents a case study on the placement of charging stations powered by photovoltaic energy along an important highway in Brazil. A demand model was adopted to elaborate three scenarios for 2030 with different participation levels of electric vehicles in the Brazilian market. An optimized allocation model was used to derive the location and number of charging stations required to meet the charging demand. The results provided a list of adequate locations for installing the charging stations and offered insights into the consumed electricity and greenhouse gas emissions that could be mitigated by these actions. A financial analysis was conducted, and it was determined that the charging costs, based on the Internal Rate of Return calculation, were 10%. These costs were compared to the fueling costs of other traditional vehicles. The results showed that the costs can be 72% lower than the cost of refueling current conventional automobiles. The results of this study can serve as a reference in the public policy debate, as well as for investors in fast charging stations.

This study describes how to build a prototype Stirling engine named Amazon, focusing on how to apply it to generate electric power in remote areas, with an expected power of 8 kW. Such engine was assessed theoretically by using two different computer software, i.e. PROSA and 'a model to simulate Stirling engines by the theory of Schmidt', which was formulated in Mathcad. The results from Mathcad software were on average 6.8% higher than the results from PROSA software. Such fact occurs since Mathcad does not consider the losses in a real system. Hence, the PROSA is generally more accurate in its results. Modeling performed in Mathcad presents higher values for partial loads and similar values for the project operating point.

In order to increase the flexibility and performance of gas turbines, generally their manufacturers and research centers involved in their development are constantly seeking the expansion of their operational envelope as well as their efficiency, making the engine more dynamic, less polluting and able to respond promptly to variations in load demands. An important aspect that should be considered when analyzing these prime movers is the assessment of its behavior under transients due to load changes, which can be accomplished through the development of a detailed, accurate and effective computational model. Considering this scenario, the present work aims to develop a model for the simulation and analysis of the dynamic behavior of stationary gas turbines. The engine considered in this analysis has a nominal capacity of 30.7 MW (ISO conditions) and is composed by a two-spool gas generator and a free power turbine. The model was developed using T-MATS, an integrated Simulink/Matlab toolbox, develop by NASA. The gas turbine was evaluated under both permanent and transient regimes and each one of its component was analyzed individually. The assessment made it possible to determine the engine performance parameters such as efficiency, heat rate and specific fuel consumption and its operational limits (surge limits, stall, turbine inlet temperatures, etc.) under different load conditions and regimes. The results obtained were compared with available field data, and the relative deviations for the considered parameters were all lower than 1%.

O presente estudo concentra-se no cenário energético brasileiro e destaca o aumento progressivo do uso de fontes renováveis na matriz de energia elétrica do país. O principal objetivo deste trabalho é contribuir para a busca de soluções e impulsionar debates e reflexões sobre as ações futuras necessárias para o planejamento energético. Para isso, a pesquisa emprega ferramentas computacionais baseadas em machine learning e mineração de dados, utilizando fontes de dados governamentais e de mercado de energia. A metodologia abrange a utilização de ferramentas computacionais para projetar a previsão do mercado de energia elétrica no país, inclui a execução de modelos de previsão, destacando o comportamento do mercado energético ao longo do tempo, a partir dos métodos Redes Neurais Multilayer Perceptron (MLP), Regressão do Processo Gaussiano (GPR) e Regressão Linear para projetar a geração elétrica por fonte no Brasil. Os resultados indicam um crescimento considerável das fontes renováveis no mercado energético nacional até o ano de 2030, aproximando-se do objetivo do Plano Decenal de Expansão de Energia de atingir 90% de renovabilidade, abrangendo fontes como hidrelétrica, biomassa, eólica e solar. O método de Regressão Linear alcançou 86% de renovabilidade, enquanto o método de GPR atingiu 90%, e o método das MLP chegou a 88%. A projeção da previsão do mercado de energia elétrica, possibilitou a identificação dos padrões de comportamento mercadológico, permitindo antecipar as tendências e mudanças no mercado. Essas previsões têm o propósito de fornecer informações para apoiar o desenvolvimento de ações no processo de planejamento energético, contribuindo para a transição para fontes mais sustentáveis e renováveis de energia no Brasil.