John Barco-Jiménez

In the Latin American Pacific region, rivers are the primary transportation routes for isolated and non-interconnected areas; however, river transport relies heavily on fossil fuels, resulting in high operating costs, CO₂ emissions, and energy dependence. To address this challenge, this study proposes a methodology for the optimal sizing of renewable-based charging stations specifically adapted to the environmental and operational conditions of the Colombian Pacific coast. This research fills a critical gap in the literature by moving beyond urban-centric charging models and simplified theoretical assumptions, instead integrating real river navigation data with technical modeling of electric boat energy consumption. The methodology evaluates the technical, economic, and operational performance of photovoltaic and hybrid photovoltaic–hydrokinetic microgrids designed to ensure reliability under the region’s extreme resource seasonality and bimodal pluvial regime. Results indicate that while purely photovoltaic systems offer lower initial investment costs, hybrid configurations significantly enhance energy resilience by leveraging complementary renewable sources during periods of low solar irradiation. Crucially, the transition to electric propulsion reduces annual CO₂ emissions by more than 98%, mitigating approximately 3421 kg per vessel compared to conventional 20 HP gasoline engines. A comparative analysis shows that the 1.1 kW electric boat is a cost-effective solution, with a 1.76-year return on investment. In contrast, the 4 kW model offers operational performance comparable to conventional gasoline boats, with a 4.95-year payback. This study provides a foundational framework for sustainable mobility in high-vulnerability territories by adapting technological solutions to site-specific environmental realities.

Planificación, gestión, operación y evaluación de microrredes eléctricas aisladas es el resultado de un trabajo de investigación orientado a enfrentar los retos técnicos, económicos, ambientales y sociales de las microrredes. Estos sistemas eléctricos autónomos representan una alternativa estratégica para ampliar el acceso a la electricidad en zonas remotas y no interconectadas, contribuyendo a la sostenibilidad y a la transición energética. La obra ofrece un marco integral para abordar cuatro dimensiones esenciales. En primer lugar, la planificación, entendida como el proceso de dimensionamiento tecnoeconómico y socioambiental que permite definir el tamaño y la configuración más adecuados de una microrred. En segundo lugar, la gestión, que estudia las formas más eficientes de administrar los recursos energéticos distribuidos. En tercer lugar, la operación, que plantea metodologías orientadas a garantizar un funcionamiento confiable y estable. Finalmente, la evaluación, que aporta indicadores y herramientas para medir el desempeño y orientar mejoras continuas. La investigación presentada recurre a enfoques novedosos que integran técnicas de optimización multiobjetivo, multinivel y multicriterio, computación basada en reglas, programación lineal y estrategias de control distribuido. Cada capítulo desarrolla un aspecto clave: desde la planificación en zonas no interconectadas, pasando por la gestión energética, el papel de los sistemas de almacenamiento, hasta la construcción de modelos de evaluación. Esta obra constituye una referencia fundamental para investigadores, profesionales, estudiantes y tomadores de decisión interesados en el diseño, implementación y validación de microrredes, ofreciendo metodologías prácticas y adaptadas al contexto latinoamericano.

Guinea pigs have significant economic and cultural importance in Latin America. However, their breeding is affected by various diseases that reduce productivity and animal welfare. Early detection of these diseases is crucial, but traditional methods such as palpation and visual observation can be imprecise and subjective. Additionally, guinea pigs are very sensitive to human contact, and improper handling can negatively affect their health. In this context, the question arises: Is it possible to develop a non-invasive monitoring system that combines temperature and weight measurements in guinea pigs to determine their health status more accurately than traditional methods? To answer this question, a comprehensive review of the existing literature is proposed. This review aims to identify and analyze the most relevant non-invasive technologies for measuring the temperature and weight of these animals. Furthermore, methodologies for combining these measurements to assess the health status of guinea pigs will be explored. The review will also consider current trends in artificial intelligence, specifically in machine learning, to analyze the data collected by sensors. The review methodology follows a systematic approach, establishing inclusion and exclusion criteria to ensure the quality and relevance of the information collected. Keywords are defined, and relevant research databases are selected. The article selection process is carried out in three stages: elimination of duplicates, elimination of articles with access restrictions, and filtering according to the research topic. Finally, the quality of the selected articles is evaluated to deepen the proposed discussion and draw conclusions on the feasibility and potential benefits of implementing a non-invasive monitoring system to improve guinea pig health and welfare in production settings.

El despacho de potencia en microrredes implica la consideración de problemas de optimización que permiten establecer los puntos de operación de los diferentes generadores distribuidos y de las unidades de almacenamiento. Formular y solucionar estos problemas son retos que deben abordarse en los sistemas de potencia emergentes para garantizar que las microrredes operen de manera eficiente, a un bajo costo e incrementen la seguridad y la confiabilidad. Adicionalmente, el comportamiento estocástico de los generadores renovables y de las dinámicas de consumo de los usuarios, demandan la incorporación de técnicas de control que permitan afrontar la incertidumbre desde la gestión energética de las microrredes. En este sentido, este trabajo de investigación propone un enfoque basado en control predictivo por modelo (MPC) para el despacho óptimo de potencia activa y reactiva en microrredes aisladas con alta penetración de generación renovable. Para ello, se formulan dos problemas de optimización convexos, uno para el despacho económico de potencia activa y otro para la minimización de pérdidas mediante el despacho de potencia reactiva. Las ecuaciones de flujo de potencia se simplifican utilizando una aproximación desarrollada recientemente basada en el cálculo de Wirtinger y se incorporan dentro de las restricciones, por lo que, los puntos óptimos de operación se establecen considerando las restricciones físicas y técnicas de la microrred. El algoritmo propuesto optimiza los costos por generación, programa la carga-descarga de las unidades de almacenamiento, garantiza el balance generación-demanda, realiza control de tensión y reduce las pérdidas en potencia activa. La validación se realiza en un esquema de co-simulación entre sobre una versión modificada de la red 13 IEEE de 13 nodos.

This work presents a distributed in-line strategy to manage an isolated microgrid by optimizing active and reactive power dispatch. The proposed objective function leads to minimize the operation costs and addresses some technical requirements such as diminishing power losses and voltage deviation. Additionally, the strategy deals with temporal multi-scale goals, i.e., robustness to demand disturbances and variation of renewable resources (a short-term objective), and preservation of the health of battery-based storage systems (a long-term objective). The technique uses alternating directions method of multipliers (ADMM), accelerated consensus, and a novel battery degradation model (Quadratic AH-Throughput model). We test the proposed solution in a case study that includes renewable resources and lead-acid and lithium batteries. To obtain the results of the case study, we employ a co-simulation scheme that uses Matlab and DIgSILENT. Finally, the performance of the method is compared with a centralized optimization technique.

Microgrids adoption around all world is increasing with different aims as to integrate renewable generation, providing energy to rural zones, reducing CO2 emissions, or simply as an alternative for energy independence. However, the planning and operation of microgrids can be a challenging task because it involves many economical, technical, environmental aspects. This work presents a methodology for planning and validating a grid-connected microgrid using two well-known software that facilitates the design. The planning process is performed in HOMER Pro to size and select appropriate generation sources, while the possible implementation on a real distribution network is validated in DigSILENT. Numerical results show the optimal sizing process of a grid-connected microgrid for a university campus based on real data and the technical validation based on Colombian regulatory aspects.

Context: This work presents different models based on artificial neural networks, among them NNARX, for estimating global solar radiation from UV index measurements. The objective is to determine the efficiency of the models studied to estimate global solar radiation in terms of the coefficient of determination (R2), the root-mean-square error (RMSE), and the mean absolute error (MAE). Methodology: It is divided into four stages: i) conformation of the training dataset (in this case, it uses a training set of 213.019 data collected over five years in the city of Pasto, Colombia, with the Davis Vantage Pro 2.0 station); ii) pre-processing of data to remove erroneous and unusual data; iii) definition of models based on recurrent and conventional artificial neural networks according to an analysis of topologies, e.g. NNFIR and NNARX; iv) training of the models and evaluation of the estimation efficiency through metrics such as R2, RMSE, and MAE. To validate the model, a new dataset collected during the last year was used, which was not included in the data training. Results: The global solar radiation estimation models based on NNARX show the best estimation efficiency compared to conventional neural networks. The NNARX221 model has an RMSE of 54,32 and a MAE of 18,06 w/m2. Conclusions: NNARX models are highly efficient at estimating global solar radiation, with a coefficient of determination of 0,9697 in the best of cases. The most efficient models are characterized by using two past times and the current UV index instant, and they feed from two past times of their own estimated radiation output. Furthermore, the numerical results show that the contribution of temperature and relative humidity is not relevant to improving the efficiency of the estimation of global solar radiation. These models can be particularly important since they only use measurements made with UV index sensors, which are less expensive than solar radiation ones.