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The emerging energy transition is particularly described as a move towards a cleaner, lower-carbon system. In the context of the global shift towards sustainable energy sources, this paper reviews the potential and roadmap for hydrogen energy as a crucial component of the clean energy landscape. The primary objective is to present a comprehensive literature overview, illuminating key themes, trends, and research gaps in the scientific discourse concerning hydrogen production and energy policy. This review focuses particularly on specified geographic contexts, with an emphasis on understanding the unique energy policies related to renewable energy in Brazil, Austria, and Germany. Given their distinct social systems and developmental stages, this paper aims to delineate the nuanced approaches these countries adopt in their pursuit of renewable energy and the integration of hydrogen within their energy frameworks. Brazil exhibits vast renewable energy potential, particularly in wind and solar energy sectors, positioning itself for substantial growth in the coming years. Germany showcases a regulatory framework that promotes innovation and technological expansion, reflecting its highly developed social system and commitment to transitioning away from fossil fuels. Austria demonstrates dedication to decarbonization, particularly through the exploration of biomethane for residential heating and cooling. [ABSTRACT FROM AUTHOR]

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs' development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]