Brazil has started an energy transition process through more efficient electric motors. The new legislation determining the IE3 Class as the minimum efficiency level in commercial electric motors became valid in August 2019. Currently, four induction motor classes are defined by the IEC 60034-30-1, with the IE4 class being the most efficient, and in the same way, proposals to achieve IE5 efficiency class are available in the market. Considering the upcoming new technologies, it is necessary to know the impact of power systems disturbances in IM's in view of future substitutions. This paper presents a detailed analysis of the impact of different percentages of under and over voltage unbalances on the temperature and performance of electric motors classes IE2, IE3 and IE4. Results show that the IE4 hybrid motor presents non-linear characteristics, being observed this motor shows less dependence on voltage variation, mainly undervoltage unbalance. An analysis of how this phenomenon impacts on the current harmonic distortions in the induction motors operation is also developed through Spearman's correlation matrices. Finally, models that represent the temperature increase of each motor with different voltage unbalance conditions are presented.

The search for more competitive equipment in the global market has led to the implementation of new materials and technologies in the search for greater energy efficiency. This is certainly a guideline followed by the electric motor industry that has introduced new technologies in rotating machines, such as the permanent magnet motor, evolving into increasingly efficient motor classes. However, subjecting these efficient motors under nonideal electric grid power conditions, such as voltage unbalance, can cause these efficient machines to generate additional distortions in the voltage and current waveforms, which in turn not only affect the performance of electric motors themselves but also that of the electrical system in general. This work presents a comparison between energy efficiency gain and the corresponding power quality degradation through a detailed harmonic analysis of the effects of voltage harmonics and voltage unbalance in three 0.75-kW electric motors classes: IE2, IE3, and IE4, The results show that for increasing percentages of a specific harmonic distortion, other harmonics are also increased in the higher efficiency electric motors sample analyzed, and similar responses were also observed for large percentages of voltage unbalance, mainly in the line-start permanent magnet motor class IE4. The results achieved are interesting but rigorously reflect only the tested motor sample and cannot be generalized to all motors, in other power ranges, of the respective motor classes tested. For generalization, exhaustive tests must be accomplished to formulate general conclusions according to the electric motors' classes commercially available.

This study addresses the problem of determining which non‐linear loads can be considered potential harmonic sources to the voltage harmonic distortions observed in the utility grid voltages. Statistical regression models are used to establish correlations between the simultaneously measured harmonic voltages, in the grid, and harmonic currents, at each non‐linear individual load. First, the R ‐squared statistic is used to decide the goodness of fit of the developed models, giving indication on the relevance of the model. In order to overcome the limitation of the linear regression model based on voltage and current magnitudes measurements only, that imposes a constant behaviour for the background harmonic sources, a non‐parametric regression model is suggested and a comparison between the performances of the two models is carried out. Field data from two real electrical systems, one having a 138 kV substation in which a large industrial load and a 34.5 kV rural long feeder are connected; and the other having four urban 13.8 kV feeders supplying industrial and commercial loads, were used to develop and validate the statistical models used in the application.

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.

Among the main problems faced in the context of electric mobility today, the management and monitoring of electric vehicle charging stations, the integration between the diverse types of technologies that make up its architecture, and its low scalability stand out. Therefore, we will present the implementation and complete integration of an electric vehicle charging system in an electric mobility pilot project being executed in the Amazon region in Brazil. Therefore, a literature review of related works will be presented, and its entire implementation will be addressed, from the charging infrastructure, through its back-end system and its Internet of things platform, to its front-end web system for monitoring charging stations. In addition, a complete prototype is created with a real testbed to verify the scalability of the implemented physical system. Based on the testbed evaluations performed, we observe that the implemented system performs well in receiving and sending data from up to 160 electric vehicle charging stations, achieving an average consumption of 26% for CPU and 95% for memory. In addition, it is important to mention that the deployed system supports horizontal scalability, enabling the connection of more charging stations and making it ideal for other integrated systems similar to ours. Based upon the main results obtained with the implemented system, the possibility of carrying out the management and monitoring of charging stations stands out; the integration of different technologies, from the back end and IoT middleware to its front end; a system that supports scalability, enabling the connection of more charging stations; and a reference architecture for charging station management and monitoring systems for the Amazon region.

Buildings' energy consumption has a great energetic and environmental impact worldwide. The architectural design has great potential to solve this problem because the building envelope exerts influence on the overall system performance, but this is a task that involves many objectives and constraints. In the last two decades, optimization studies applied to energy efficiency of buildings have helped specialists to choose the best design options. However, there is still a lack of optimization approaches applied to the design stage, which is the most influential stage for building energy efficiency over its entire life cycle. Therefore, this article presents a multi-objective optimization model to assist designers in the schematic building design, by means of the Pareto archived evolutionary strategies (PAES) algorithm with the EnergyPlus simulator coupled to evaluate the solutions. The search process is executed by a binary array where the array components evolve over the generations, together with the other building components. The methodology aims to find optimal solutions (OSs) with the lowest constructive cost associated with greater energy efficiency. In the case study, it was possible to simulate the process of using the optimization model and analyze the results in relation to: a standard building; energy consumption classification levels; passive design guidelines; usability and accuracy, proving that the tool serves as support in building design. The OSs reached an average of 50% energy savings over typical consumption, 50% reduction in CO₂ operating emissions, and investment return less than 3 years in the four different weathers.