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.

This study presents an approach to calculate average technical losses (TLoss) and non‐technical losses (NTLoss) in distribution grids using an equivalent operational impedance (EOI) calculated from a load flow solution for the transformers’ average loads, obtained from customers’ electric bills. If transformers are supervised, i.e. have available active and reactive power injections measurements or estimates from a state estimator program, the transformers’ average technical and non‐technical losses can be calculated by running a load flow for the grid supplied by the transformer and using the definition of EOI presented in this study. For unsupervised transformers, i.e. no measurements or estimates are available, pseudo measurements of average active and reactive power injections can be obtained by a procedure that uses active and reactive power measurements at the feeder substation. The use of this methodology by electric utilities allows the calculation of technical and non‐technical losses more accurately, as well as the inclusion of non‐technical losses in the distribution networks operation planning. Results using the IEEE 13‐bus test system and an urban real distribution grid are presented to demonstrate the effectiveness of the proposed methodology.

This paper presents a procedure to estimate the impacts on voltage harmonic distortion at a point of interest due to multiple nonlinear loads in the electrical network. Despite artificial neural networks (ANN) being a widely used technique for the solution of a large amount and variety of issues in electric power systems, including harmonics modeling, its utilization to establish relationships among the harmonic voltage at a point of interest in the electric grid and the corresponding harmonic currents generated by nonlinear loads was not found in the literature, thus this innovative procedure is considered in this article. A simultaneous measurement campaign must be carried out in all nonlinear loads and at the point of interest for data acquisition to train and test the ANN model. A sensitivity analysis is proposed to establish the percent contribution of load currents on the observed voltage distortion, which constitutes an original definition presented in this paper. Initially, alternative transient program (ATP) simulations are used to calculate harmonic voltages at points of interest in an industrial test system due to nonlinear loads whose harmonic currents are known. The resulting impacts on voltage harmonic distortions obtained by the ATP simulations are taken as reference values to compare with those obtained by using the proposed procedure based on ANN. By comparing ATP results with those obtained by the ANN model, it is observed that the proposed methodology is able to classify correctly the impact degree of nonlinear load currents on voltage harmonic distortions at points of interest, as proposed in this paper.

Europe has initiated the transition process toward IE4 efficiency motor classes, setting an example for other regions to follow in adopting higher-efficiency motor standards. However, in some regions, the operating voltage may differ from the nominal voltage according to the IEC 60038-2009. Therefore, the performance of new technologies under conditions of voltage variations (VVs) must also be assessed. This study presents a comprehensive analysis of a 0.75 kW line-start permanent-magnet motor (LSPMM) under different VV magnitudes, including undervoltage and overvoltage, while considering different load conditions. The study incorporates technical, economic, statistical, and thermal analyses to obtain important indicators related to power consumption, efficiency, power factor, and temperature. This study provides valuable insights to specialists regarding the technical and economic impacts of voltage-magnitude variation on LSPMMs.

Modern electrical systems have a significant presence of electronic loads, which in turn produce negative impacts on distribution systems and loads, this has motivated their study to be increasingly prioritized, aiming to reduce their impacts from corrective actions. Harmonics are classified as positive, negative, and zero sequence, and their impacts on loads can vary according to the harmonic present. In the case of electric motors, negative sequence harmonics result in the greatest impacts. This work presents a classifier of existing harmonics in the input waveform of electric motors classes IE2, IE3 and IE4 using artificial neural networks (ANN), for that purpose, negative (2nd), positive (7th) and zero sequence harmonics (3rd) were applied separately and combined in the electric motors, the data was exported for a classification algorithm to identify existing harmonics. The results show how the algorithm presents good approximations of the present harmonics, mainly with those of positive and negative sequence.

The Motor Monitoring and Analysis System (SMAM) was developed as a tool that can help with the predictive maintenance of electric motors through Digital Twin (DT) and the Industrial Internet of Things (IIoT). To do this, SMAM has an embedded system made up of the ESP32 microcontroller and temperature, voltage and current sensors, in which the measurements taken are stored in the Firebase web database. It is worth noting that the sensors are calibrated using the least squares and linear regression mathematical methods, as well as using the ET-5062 energy analyzer as a reference to ensure that the measurement is reliable. In addition, strong coupling simulations are carried out in the Finite Element Method Magnetics (FEMM) software, which makes it possible to visualize the magnetic flux and the internal temperature of the motor by entering the measured current data. In this way, SMAM is a promising resource for the manufacturing environment.