The fast charging of electric vehicles (EVs) has stood out prominently as an alternative for long-distance travel. These charging events typically occur at public fast charging stations (FCSs) within brief timeframes, which requires a substantial demand for power and energy in a short period. To adequately prepare the system for the widespread adoption of EVs, it is imperative to comprehend and establish standards for user behavior. This study employs agglomerative clustering, kernel density estimation, beta distribution, and data mining techniques to model and identify patterns in these charging events. They utilize telemetry data from charging events on highways, which are public and cost-free. Critical parameters such as stage of charge (SoC), energy, power, time, and location are examined to understand user dynamics during charging events. The findings of this research provide a clear insight into user behavior by separating charging events into five groups, which significantly clarifies user behavior and allows for mathematical modeling. Also, the results show that the FCSs have varying patterns according to the location. They serve as a basis for future research, including topics for further investigations, such as integrating charging events with renewable energy sources, establishing load management policies, and generating accurate load forecasting models.

Non-Technical Losses (NTL) represent a serious concern for electric companies. These losses are responsible for revenue losses, as well as reduced system reliability. Part of the revenue loss is charged to legal consumers, thus, causing social imbalance. NTL methods have been developed in order to reduce the impact in physical distribution systems and legal consumers. These methods can be classified as hardware-based and non-hardware-based. Hardware-based methods need an entirely new system infrastructure to be implemented, resulting in high investment and increased cost for energy companies, thus hampering implementation in poorer nations. With this in mind, this paper performs a review of non-hardware-based NTL detection methods. These methods use distribution systems and consumers’ data to detect abnormal energy consumption. They can be classified as network-based, which use network technical parameters to search for energy losses, data-based methods, which use data science and machine learning, and hybrid methods, which combine both. This paper focuses on reviewing non-hardware-based NTL detection methods, presenting a NTL detection methods overview and a literature search and analysis.

Energy losses in the power distribution systems represents real cost harm to the energy sector as to the final consumers. Energy losses are categorized by Technical Losses (TL) and Non-Technical Losses (NTL). The TLs are related to the energy transformation and transport processes, meaning they are inevitable. As the NTLs refers to electricity theft or frauds, meaning that they can be fixed and must be reduced to a minimum. There are several existing methodologies for detecting and estimating the NTL. This paper proposal is to clarify how the NTLs are characterized and calculated by the regulatory institution in Brazil. Presenting a contextualization and perspectives for NTLs reduce, in addition to accomplish a bibliographic review on methods and applications to solve this problem in the power distribution system.

The greater concern about climate change and the decarbonization of electric energy production plants has led to an exponential renewable energy increase in many countries, employing specially wind and solar resources. The increase investment in these new sources are necessary to support a sustainable future, with a cleaner and diversified energy matrix. However, the output of these sources is considered intermittent, depending on climatic variables, which often presents an unpredictable forecast. To minimize these effects, it is necessary to introduce energy storage systems, with the objective to simplify the transformation of the electrical system from a rigid to a more flexible platform, combining viable solutions integrating the new generation facilities. In this context, this paper aims to shine a light in the expansion of the energy storage systems combined with the renewable energy evolution around the world. Through a comprehensive review of energy storage technologies used, as well in the participation of these systems in different countries electric systems. Providing a market perspective of the storage technology in the present global scenario, along with new enterprises projections employing energy accumulators.

Electrical energy is a basic necessity for most activities in the daily life, especially for military operations. This dependency on energy is part of a national security context, especially for a military operation. Thus, the main objective of the paper is to provide a review of the energy storage and the new concepts in military facilities. Most of this energy is provided by long dated structures, both in the transmission and distribution aspect, and often not projected to a military conflict, especially in remote regions with difficult access. The electrical grid can be targeted both physically and by cyber-attacks, disrupting the energy supply to strategic regions. In response to this adversity, the military forces of several countries have been developing projects using microgrids. This new system has independent, controllable and unique energy generation, comprising renewable generation, electric vehicles, energy storage and control devices. To deploy renewable energy, it is necessary to first have an energy storage system that can support these sources. Thus, this paper proposes a review on the energy storage application in the military sector, and how this technological advance has impacted the military routine and operations, along with some real application and their economic and technical results.

Nontechnical losses (NTL) are irregularities in the consumption of electricity and mainly caused by theft and fraud. NTLs can be characterized as outliers in historical data series. The use of computational tools to identify outliers is the subject of research around the world, and in this context, artificial neural networks (ANN) are applicable. ANNs are machine learning models that learn through experience, and their performance is associated with the quality of the training data together with the optimization of the model’s architecture and hyperparameters. This article proposes a complete solution (end-to-end) using the ANN multilayer perceptron (MLP) model with supervised classification learning. For this, data mining concepts are applied to exogenous data, specifically the ambient temperature, and endogenous data from energy companies. The association of these data results in the improvement of the model’s input data that impact the identification of consumer units with NTLs. The test results show the importance of combining exogenous and endogenous data, which obtained a 0.0213 improvement in ROC-AUC and a 6.26% recall (1).