Reliability is an important issue in electricity distribution systems, with strict regulatory policies and investments needed to improve it. This paper presents a mixed integer linear programming (MILP) model for clustering electrical customers, maximizing system reliability and minimizing outage costs. However, the evaluation of reliability and its corresponding nonlinear function represent a significant challenge, making the use of mathematical programming models difficult. The proposed heuristic procedure overcomes this challenge by using a linear formulation of reliability indicators and incorporating them into the MILP model for clustering electrical customers. The model is mainly defined on a density-based heuristic that constrains the set of possible medians, thus dealing with the combinatorial complexity associated with the problem of empowered p-medians. The proposed model proved to be effective in improving the reliability of real electrical distribution systems and reducing compensation costs. Three substation cluster scenarios were explored, in which the total utility compensations were reduced by approximately USD 86,000 (1.80%), USD 67,400 (1.41%), and USD 64,000 (1.3%). The solutions suggest a direct relationship between the reduction in the compensation costs and the system reliability. In addition, the alternative modeling approach to the problem served to match the performance between the distribution system reliability indicators.

Objective: The objective of this study was to forecast the number of critical days, for the period from July 2020 to December 2022, based on time series analysis, to model changes in the number of critical power days in a prospective way. Related Studies: This section provides an overview of the use of forecasting models and methods for measuring electricity demand, applied as decision-making support and as a tool for identifying anomalous phenomena. Method: The methodology for this research involves analysis and forecasting using the Autoregressive Integrated Moving Average (ARIMA) model of order (p,d,q)(p,d,q), applied to the time series of critical days. Model development and performance evaluation followed these steps and strategies: Specification (Pre-processing), Identification and Estimation, Verification, and Forecasting. Data collection was performed by analyzing time series observations from a Brazilian electric utility company responsible for energy supply in Rio Grande do Sul. Results and Discussion: The results indicated that the seasonal ARIMA (2,1,1)(2,1,1) model performed best, with the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, as well as the lowest mean square error among the tested models. With the selected ARIMA model, forecasts were made for the following 30 months, estimating the number of monthly critical days. Research Implications: The paper discusses the practical and theoretical implications of forecasting critical days for power quality. Practically, it presents an ARIMA model to estimate these days, aiding power companies in projecting interruptions and improving preventive management and resource allocation. Theoretically, it contributes to the time series forecasting literature in the power sector, confirming the effectiveness of ARIMA models with seasonal components for grid management and adverse event forecasting in complex systems. Originality/Value: The study contributes to the literature by applying the ARIMA model with seasonal components to predict critical days of interruption in the distribution of electricity, an approach that has not yet been explored for specific adverse events. The originality lies in the application of the model in a context of quality and reliability in the supply and detailed seasonal analysis. The methodology proposes a solution for forecasting and managing these events, bringing new perspectives for proactive management in the sector. The study stands out for its potential to improve professional practice, allowing informed decisions for resource allocation and maintenance, increasing reliability and reducing costs and financial compensation.

Objective: The study aims to speed up the reconfiguration process of electrical distribution networks. Using graph theory, it explores methods for the network's topological decomposition and searches for lower-cost paths to optimize the distribution network configuration. Related Studies: The research is based on graph theory, which allows modeling the connections of an electrical network as an interconnected graph. Important references include clustering techniques and algorithms for finding minimum paths in graphs, applying methods such as Malgrange, K-means, DBSCAN, SOM, and classical graph search algorithms like Dijkstra and Floyd-Warshall. Method: The methodology includes applying different algorithms for topological reduction and path search, analyzing their performances. The IEEE 37-bus system was used as a test model. The studied algorithms included Malgrange, K-means, Ward (hierarchical), DBSCAN, SOM, and minimum path algorithms. Results and Discussion: The results indicated that the Malgrange method brought a significant reduction in the search space, with a decrease of approximately 83.5% in possible switch combinations. However, in larger and more complex networks, increasing the number of clusters could contribute to even greater efficiency. The research concluded that minimum-path search algorithms produced similar results. Research Implications: The research offers a methodology that can be applied in power distribution networks to reduce computational time and improve the efficiency of reconfiguration processes. Practical implications include direct use by power distribution companies looking to optimize their networks. Theoretically, it reinforces the use of graph theory in network optimization problems. Originality/Value: The originality lies in combining graph techniques for decomposition and minimum-path search in electrical networks, with significant optimization results. The research contributes insights for future implementations of reconfiguration in complex networks, highlighting the value of graph analysis as a robust approach to electrical distribution problems.

Objective: To evaluate the effectiveness of simple and multiple linear regression models in predicting the number of monthly critical days in an electric utility company in the state of Rio Grande do Sul, considering climatic variables as predictive factors. Method: The research used data on critical days of electrical interruption and climatic variables (precipitation, atmospheric pressure, temperature, winds, and humidity) collected between 2015 and 2020. Following a descriptive and correlation analysis, multiple linear regression models were applied, and the least significant variables were iteratively removed. Statistical tests assessed the normality, homoscedasticity, and independence of the residuals to ensure the model's adequacy. Results and Discussion: The adjusted models explained a low variability in critical days (adjusted R² up to 32%), with high error rates in predictions. The model identified a significant relationship between critical days and climatic variables (especially precipitation and winds) but with insufficient results for reliable forecasting. This suggests the need for more robust models and new variables to improve accuracy. Research Implications: Practical implications indicate the limitation of linear regression models for accurate predictions of critical days due to high data variability. Practically, it highlights the importance of seeking additional variables and alternative methodologies to support proactive planning and management of electric networks, while theoretical implications add a new perspective on the use of climatic variables as predictors of power interruptions, suggesting that while linear regression is common, it may have limitations. This encourages future investigations with nonlinear or hybrid models to improve predictions in this field. Originality/Value: The originality of the research lies in the attempt to directly correlate specific climatic indicators (precipitation, atmospheric pressure, and wind speed) with critical days, a topic still underexplored in energy reliability forecasts. This approach is innovative by investigating the impact of climatic variables on monthly interruptions, even recognizing that these linear regression models have limitations in capturing all variability of critical days. The relevance and value of the research are in highlighting how climatic factors can directly influence the reliability of the power grid, especially for energy distributors facing frequent interruptions due to adverse climatic events.

Este artigo aborda o processo de reestruturação administrativa no setor de Pós-Graduação do Centro de Tecnologia da Universidade Federal de Santa Maria (UFSM), realizado em conformidade com o Decreto 9.739/19. A transição, iniciada em junho de 2021, envolveu a subdivisão estratégica dos oito Programas de Pós-Graduação (PPGs) do Centro, consolidando-os sob uma única Secretaria Integrada. Este artigo destaca os desafios enfrentados durante a implementação dessa reestruturação, incluindo a necessidade de adaptação organizacional e cultural, bem como as estratégias adotadas para superá-los, a colaboração entre diferentes setores e níveis hierárquicos. Ademais, são apresentadas as perspectivas futuras para o aprimoramento contínuo da gestão acadêmica nesse contexto.