This paper addresses the challenge of restoring electrical service in distribution systems (DS) under contingency scenarios using a genetic algorithm (GA) implemented in MATLAB. The proposed methodology seeks to maximize restored load, considering operational constraints such as line loadability, voltage limits, and radial topology preservation. It is evaluated with simulations on the IEEE 34-bus test system under four contingency scenarios that consider the disconnection of specific branches. The algorithm’s ability to restore service is demonstrated by identifying optimal auxiliary line reconnections. The method maximizes restored load, achieving between 97% and 99% load reconnection, with an average of 98.8% across the four cases analyzed. Bus voltages remain above 0.95 pu and below the upper limit. Furthermore, test feeder results demonstrate that line loadability is mostly below 60% of the post-reconfiguration loadability.

This research focuses on evaluating the importance of the use of renewable sources through distributed generation and its implication in the operation of electrical systems given that its incorporation has a direct impact on the expansion of the capacity of the networks, the minimization losses, and the impact on end users, all supported by the growth of demand. Under this context, the study focuses on incorporating distributed generation (DG), taking scenarios of base, medium, and peak demand and the modeling of the network, and subsequently evaluating the service quality indices and operating costs in addition to the electrical variables of the system. For this purpose, the present work proposes an optimization model to be solved using the Matlab (2021b) computational program together with GAMS (37.1.0 Major release (11 November 2021)) and mixed-integer nonlinear programming, determining the optimal insertion and determination of the maximum capacity of distributed generators while complying with the technical restrictions of the system and applying optimal AC power flows. Localizing and determining maximum capacity for distributed generation (DG) in electrical systems are critical aspects of modern grid planning and operation. With the increasing penetration of renewable energy sources and the growing complexity of energy demand patterns, efficient integration of DG has become paramount for ensuring grid reliability and sustainability. In this context, the analysis of DG localization and capacity determination considering demand scenarios emerges as a critical area of research in electrical engineering. By employing advanced optimization techniques such as mixed-integer nonlinear programming (MINP), this research addresses the multidimensional challenges associated with DG deployment, including technical constraints, economic considerations, and environmental impacts. Understanding the contribution of this optimization approach to electrical engineering is fundamental for optimizing grid performance, enhancing renewable energy integration, and supporting the transition towards more resilient and sustainable energy systems. Consequently, investigating this optimization model represents a crucial step towards advancing the state-of-the-art in grid planning and facilitating the transition to a cleaner and more efficient energy future.

The following article aims to implement a hybrid modulation methodology based on the Selective Harmonic Elimination Pulse Width Modulation (SHE-PWM) technique to work with the fundamental frequency of the system and find the optimal firing angles using the PSO optimization algorithm, capable of reducing the voltage THDv present in the output signals of three-phase multilevel inverters. To develop this approach, three case studies are proposed, developed in MATLAB/Simulink software, which feature three-phase inverters with five, seven, and nine levels, respectively, of the CHB topology. The impact of adequate modulation is assessed, resulting in a voltage output signal with reduced distortion. The national regulation ARCERNNR 002/20 will be used as a reference point to evaluate the results before and after implementing the methodology. It was verified that the developed methodology can effectively eliminate the selected harmonics, especially those of lower order (3rd, 5th, 7th, 9th, 11th, 13th, and 15th), achieving an improvement of up to 17.93% in the voltage THDv concerning the standard S-PWM modulation present in the CHB-MLI.

Se presentan los resultados de una propuesta de un sistema fotovoltaico de alta eficiencia con aplicaciones multipropósito, implementado a lo largo de una carretera. Este sistema está diseñado para abordar cuestiones relacionadas con la iluminación, las telecomunicaciones y la carga de vehículos eléctricos. La necesidad de un sistema aislado surge de los apagones repentinos en el país que afectan la seguridad vial y la confiabilidad, así como la falta de señal telefónica en algunas zonas. En el ámbito de la iluminación, se realiza un análisis utilizando el software DiaLux para garantizar el cumplimiento de los estándares adecuados de iluminación para diferentes tipos de carreteras. Además, el sistema multipropósito incluye la carga de vehículos eléctricos, que se está volviendo cada vez más relevante debido al cambio hacia vehículos más ecológicos. La provisión de carga de vehículos eléctricos a lo largo de las carreteras resuelve el problema de la autonomía limitada de los vehículos eléctricos y promueve su uso confiable. La evaluación del recurso energético mediante el método Glover y McCulloch es fundamental para diseñar un sistema eficiente capaz de satisfacer diversas necesidades del usuario. Además, la reducción de las emisiones de dióxido de carbono es un aspecto importante, ya que los vehículos eléctricos pueden contribuir significativamente a la reducción de las emisiones de gases de efecto invernadero en comparación con los vehículos de combustibles fósiles

This study analyzes the intersection of energy, urban planning, decarbonization, and sustainability as a central axis for addressing urban development challenges in Latin America. A systematic search of the Scopus database selected 509 articles published between 2019 and 2024. The documents were thematically classified into urban planning (274), energy (79), and decarbonization (147), identifying only 10 studies that simultaneously integrate at least two of these dimensions in Latin American contexts. While this sample of 10 articles does not allow for generalizations about the region, the article selects representative cases to contextualize the type of research conducted, rather than offering extrapolable results. An exploratory multivariate analysis was applied to identify patterns, thematic gaps, and convergence trends, including Principal Component Analysis (PCA) to reduce the dimensionality of the set of key concepts and Hierarchical Clustering (HCC) to group terms according to their semantic proximity. These results are complemented by co-occurrence and thematic concentration maps generated from keywords extracted from the selected articles. The findings reveal a low level of integration among the topics analyzed, justifying the need to establish new lines of interdisciplinary research. The study proposes a replicable analytical tool that guides future regional research and contributes to the achievement of the Sustainable Development Goals, especially SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action).

Indoor lighting systems are a significant contributor to building energy consumption while also directly affecting occupant comfort and circadian regulation. Recent advances in smart lighting have introduced adaptive and human-centric approaches; however, the integration of optimization-oriented control strategies with interoperable automation frameworks remains only partially articulated in the literature. This study presents a systematic bibliometric review of smart indoor lighting research, with particular attention to the roles of hyper-heuristics (HH), Internet of Things (IoT) platforms, and IFTTT/Event–Condition–Action (ECA) automation. A PRISMA-based methodology was applied across Scopus, Web of Science, and IEEE Xplore for the period 2010–2025. A total of 5529 records were identified, with 5229 screened after duplicate removal, and 27 core studies included following eligibility assessment. To reduce the risk of over-interpreting null intersections, the review also incorporated a search-sensitivity analysis based on expanded query formulations and title–abstract screening. Bibliometric analysis was conducted using MATLAB and VOSviewer to identify publication trends, technological clusters, and patterns of fragmentation across the literature. The results indicate rapid growth in IoT-based and energy-aware lighting systems, alongside mature research in circadian and comfort-driven lighting. However, explicit indexed evidence connecting hyper-heuristics with IoT platforms and IFTTT/ECA frameworks remains sparse and fragmented in the available literature. Co-occurrence analysis further reveals weak metadata-level connections between optimization techniques and IoT protocols, while the sensitivity analysis confirms that broadened retrieval improves recall but still yields only limited directly relevant evidence. Overall, the review identifies a gap in the explicit convergence of optimization, interoperable IoT infrastructure, and event-driven automation for human-centric indoor lighting. On this basis, it outlines a conceptual integration framework combining hyper-heuristics, IoT middleware, and event-driven control. The findings provide a structured roadmap for future research and implementation-oriented studies aimed at improving both energy efficiency and human-centric comfort in smart indoor environments.