This study addresses the critical need for efficient laboratory methods to test battery performance, identified through a bibliometric analysis of research trends in battery technologies, integration challenges, lifespan, and recovery. A key focus is the detailed evaluation of lead-acid batteries and battery emulators in electronic applications. The study highlights the significance of lead-acid battery discharge emulators as cost-effective and safe alternatives to actual batteries in laboratory testing, enabling controlled testing conditions. The system behavior was validated by employing a resistive load module and making comparisons with manufacturer data. Using this system and a resistive load module, its behavior was verified by comparing it with the data provided by the manufacturer. The next phase of this work involved selecting components to emulate the battery's behavior using a switched-mode power supply controlled by a current source and a mathematical model chosen from the Matlab-Simulink tool through a Hardware-in-the-loop (HIL) system that interprets the battery's state of charge (SoC) to match the pre-configured model response to the lead-acid battery manufacturer's data. The emulator circuit was thoroughly evaluated against the model's expected responses to various charge levels, culminating in the implementation of an integrated prototype that simulates the discharge of lead-acid batteries in low-power applications and introduces a user-friendly interface, facilitating its application in general engineering studies. The work offers a valuable tool for battery research and development, promoting advancements in the study of lead-acid battery discharge in low-power applications.

The integration of distributed photovoltaic (PV) generation in radial distribution networks has been consolidated as a key strategy to reduce technical losses and improve voltage profiles. However, the optimal placement and sizing of these sources remain a challenge due to the nonlinear and multimodal nature of the problem. In this work, an approach based on simulated annealing with adaptive restarts, implemented in PyPower, is proposed to determine the optimal location of PV units in the IEEE 33-bus test system. The methodology considers the minimization of active power losses as the objective function, subject to operational constraints and voltage limits.The results show that the proposed strategy achieves a reduction in losses of up to 52% compared to the base scenario, in addition to improving minimum voltage profiles to values close to 0.98 pu. The comparison with non-optimized scenarios highlights the effectiveness of the method in balancing energy efficiency and quality of service. This study contributes to the literature by demonstrating the applicability of lightweight metaheuristics in distribution network planning problems and lays the groundwork for future research integrating storage and dynamic load and generation scenarios.

This article presents a comprehensive analysis of the impact of the 'Parque Solar Salinas' photovoltaic (PV) plant on the medium voltage grid of Emelnorte S.A., a power distribution company located in the northern region of Ecuador. The main objective was to evaluate the power quality delivered by the plant, in compliance with the standards set by the Electricity Regulation and Control Agency of Ecuador (ARCONEL). The study was based on a detailed review of electrical generation, specifically focusing on solar PV energy. The components and topologies of PV plants, such as inverters, solar panels, and transformers, were described in depth. Additionally, the National Electrical Code regulations related to power quality were analyzed. The technical details of the plant is described, and the steps to determine the power quality are presented in the grid. Data were extracted from the plant, including variables such as active power, reactive power, voltage, and current. Short-term (Pst) and long-term (Plt) flicker were also analyzed and interpreted about solar radiation. The results obtained in this study were presented in a technical procedure manual that allowed for the evaluation of power quality for a photovoltaic plant when connected to a medium voltage grid.

Introduction: Raptors play a critical role in ecosystem stability, yet many species face significant population declines due to habitat loss, climate change, and human-induced mortality. Technological advancements such as satellite telemetry, machine learning, bioacoustics, and radar tracking have transformed raptor research, enabling precise monitoring and data-driven conservation strategies.Methods: A systematic review using the PRISMA methodology was conducted on the most relevant methodologies and technologies used in raptor research. Data from multiple studies employing satellite telemetry, habitat modeling, genetic analysis, bioacoustics, and conservation management tools were synthesized to evaluate their effectiveness.Results: Findings indicated that satellite telemetry remains the most widely used tool for tracking raptor movements, while machine learning and bioacoustics are emerging as powerful methods for habitat assessment. Population viability models frequently overlook key demographic factors, such as the age of first breeding, which can significantly impact conservation outcomes.Conclusions: Integrating advanced technologies with standardized methodologies is essential for improving raptor conservation. Future research should focus on refining predictive models, enhancing data-sharing platforms, and ensuring technological advancements translate into effective conservation policies.

Artificial intelligence (AI) is increasingly used in electric power and energy systems for forecasting, fault diagnosis, stability assessment, control, renewable energy integration, and real-time monitoring. However, evidence remains fragmented across algorithms, applications, performance metrics, and deployment conditions. This study reviews recent AI applications in power and energy systems and examines their technological, economic, and operational contributions and limitations. A PRISMA-based systematic and bibliometric review was conducted using Scopus and Web of Science records published from 2021 to 2025. Bibliometrix/Biblioshiny 5.5.0 and VOSviewer 1.6.20 were used to analyze publication trends, citation patterns, geographical distribution, influential studies, and keyword networks, while 40 studies were retained for systematic evidence synthesis. In the reconciled 4854-record corpus, annual scientific production increased from 610 publications in 2021 to 1305 in 2025, with research concentrating on machine learning, deep learning, smart grids, stability, renewable energy, forecasting, fault diagnosis, optimization, and cybersecurity. The reviewed studies reported improvements in predictive accuracy, computational efficiency, fault localization, voltage control, energy savings, and real-time decision support. Nevertheless, data dependence, computational cost, limited interpretability, simulation-to-field transfer, and validation requirements remain major barriers. Future research should prioritize robust, explainable, physics-aware, and field-validated AI for critical power-system applications.