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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 proposes a novel dynamic pricing scheme for demand response with individualized tariffs by consumption profile, aiming to benefit both customers and utility. The proposed method is based on the genetic algorithm, and a novel operator called mutagenic agent is proposed to improve algorithm performance. The demand response model is set by using price elasticity theory, and simulations are conducted based on elasticity, demand, and photovoltaic generation data from Brazil. Results are evaluated considering the integration effects of renewable energy sources and compared with other two pricing strategies currently adopted by Brazilian utilities: flat tariff and time-of-use tariff. Simulation results show the proposed dynamic tariff brings benefits to both utilities and consumers. It reduces the peak load and average cost of electricity and increases utility profit and load factor without the undesirable rebound effect. [ABSTRACT FROM AUTHOR]

Wind energy is a powerful resource contributing to the decarbonization of the electric grid. However, wind power penetration introduces uncertainty about the availability of wind energy. This article addresses the complementarity of remote offshore wind sites in Brazil, demonstrating that strategic distribution of wind farms can significantly reduce the seasonality and the risk of periods without generation and reduce dependence on fossil sources. Field observations, atmospheric reanalysis, and simplified optimization methods are combined to demonstrate generation improvement considering regions under environmental licensing and areas not yet considered for offshore development. Aggregated power results demonstrate that with the relocation of wind turbines, a 68% reduction of the grid seasonal variability is possible, with a penalty of only 9% of the generated energy. This is accomplished through optimization and the inclusion of the northern region, which presents negative correlations with all other stations. More specifically, the north and northeast of Brazil have large seasonal amplitudes. However, out-of-phase wind regimes with a strong negative correlation (R < −0.6) and high-capacity factors ( C F ) during the peak seasons occur in Jan-Feb-Mar in the north ( C F > 0.5) and in Aug-Sep-Oct in the northeast ( C F > 0.7). These complementary regimes allow for the introduction of the concept of Reserve Wind Power (RWP) plants, wind farms that can be viewed as "reserve sources" for energy security. These can replace the contracts of thermal reserve plants, with resulting economic and environmental advantages. Our analysis suggests that RWP plants can be 20 to 32% cheaper than thermal reserves in the current market. [ABSTRACT FROM AUTHOR]