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Conflict of interest: No Introduction and Objectives: The impact of cystic fibrosis (CF) transmembrane conductance regulator (CFTR) modulators on the natural history of liver disease is unknown. The objective of this study is to assess changes in the rates of liver transplantation compared to lung transplantation since the approval of the new CFTR modulators in October 2019. Patients / Materials and Methods: Patients with CF (PwCF) who were listed for liver or lung transplantation were identified in the OPTN/UNOS database. We compared outcomes between the pre- and post-CFTR modulators eras, 2012-2019 and 2020-2022, respectively. Results and Discussion: Between 2012-2023, 95,254 liver and 28,715 lung transplants were performed, including 138 (0.09%) and 2,129 (7.4%) transplants in PwCF, respectively. The rate of death on the waitlist was not significantly different between eras in either group. For liver transplantation, the median percentage of CF-related listings per year was similar between the two eras 0.13% (0.11-0.17%) in 2012-2019 vs. 0.12% (0.11-0.13%, p=0.18) in 2020-2023. Similarly, the median percentage of CF-related liver transplants per year was 0.14% (0.12-0.20%) vs. 0.14% (0.11-0.16%, p=0.450) (see figure). For lung transplantation waitlist additions per year decreased from 7.58% (6.72-8.17%) to 1.11% (0.95-1.52%) per year from the pre- to the post-modulator era (p

Acetone-Butanol-Ethanol (ABE) fermentation has regained interest due to the environmental problems generated by fossil fuels and the need to modify production chains including considerations of sustainability and circular economy. This work focuses on the modelling of ABE fermentation, with a Clostridium acetobutylicum mutated strain, to support the synthesis and improvement of butanol and acetone production process. To reach this objective, bioreactor fermentations with different glucose concentrations were developed. Results show that a two phases model (with a set of equations for the acidogenesis and another for the solventogenesis) generates satisfactory process predictions. In acidogenesis, the main effect on growth and acid production is due to the inhibition by acids, which can be quantified as a function of the total concentration of acids. While in the solventogenesis, inhibition must be quantified as a function of the total concentration of solvents. This model agrees with metabolic analysis that considers solvents generation being produced, simultaneously, from acids and sugars. For the studied system, acetone and butanol production is based on re-assimilated acids and pyruvate from glycolysis, with a distribution that changes with glucose availability.

Background. The prevalence of obesity is rising in the general population. Donor obesity (body mass index ≥30 kg/m2) may potentially reduce the donor pool and impact outcomes in living donor liver transplantation (LDLT). Methods. We utilized the national transplant database to investigate the impact of donor obesity on donor and recipient outcomes. This was a retrospective cohort study of all LDLTs performed in the United States between January 2010 and June 2023. Outcomes of interest were analyzed by univariable and multivariable logistic regression. Patient and graft survival was evaluated using Kaplan-Meier and Cox proportional analysis. Results. Six hundred seventy-four donors with obesity and 3498 donors without obesity were analyzed. Donors with obesity had higher rates of readmission within 1 y of donation (15.9% versus 11.6%; P = 0.003). The risk of readmission was significantly different between 6 wk and 6 mo of donation (8.8% versus 5.9%; P = 0.036). Donor body mass index (odds ratio [OR], 1.460; 95% confidence interval [CI], 1.129-1.999; P = 0.004) and preoperative alkaline phosphatase levels (OR, 1.005; 95% CI, 1.000-1.011; P = 0.038) were independent predictors of donor readmission. High LDLT center volume was associated with reduced odds of donor readmission (OR, 0.509; 95% CI, 0.373-0.694; P

This article introduces a novel approach for developing an electrical model of the lithium batteries used in an electric ultralight aircraft. Currently, no method exists in the technical literature for accurately modeling the electrical characteristics of batteries in an electric aircraft, making this study a valuable contribution to the field. The proposed method was validated with an all-electric ultralight aircraft designed and constructed at the Pascual Bravo University Institution. To build the detailed model, a kinematic analysis was first conducted through takeoff tests, where data on the speed, acceleration, time, and distance required for takeoff were collected, along with measurements of the current and power consumed by the batteries. The maximum speed and acceleration of the aircraft were also recorded. These kinematic results were obtained using two batteries made from Samsung INR-18650-35E lithium-ion cells, and different wing configurations of the aircraft were analyzed to assess their impacts on the battery energy consumption. Additionally, the discharge cycles of the batteries were evaluated. In the second phase, laboratory tests were performed on the individual battery cells, and the Peukert coefficient was estimated based on the experimental data. Finally, using the Peukert coefficient and the kinematic results from the takeoff tests, the electrical model of the battery was fine tuned. This model allows for the creation of charging and discharging equations for ultralight lithium batteries. With the final electrical model and energy consumption data during takeoff, it becomes possible to determine the energy usage and flight range of an electric aircraft. The model indicated that the aircraft did not require a long distance to takeoff, as it reached the necessary takeoff speed in a very short time. The equations used to simulate the discharge cycles of the batteries and lithium cells accurately described their energy capacities.

This paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provides valuable insights for optimizing charging infrastructure and grid operations. Based on collected local data, mathematical models are developed and coded to accurately reflect the characteristics of EV charging. Through a rigorous analysis of criteria, indices, and mathematical relationships, the most suitable model for the city is selected. By combining probabilistic modeling with neural networks, this study offers a comprehensive approach to predicting future energy demand as EV penetration increases. The EV charging model effectively captures the charging behavior of various EV types, while the neural network accurately forecasts energy demand. The findings can inform decision-making regarding charging infrastructure planning, investment strategies, and policy development to support the sustainable integration of electric vehicles into the power grid.

Context: Time is a crucial issue in the simulation of power electronics (PE) devices, even more when these elements are integrated into microgrids. Method: This paper deals with the deduction of the average switch model for PE devices with the purpose of reducing simulation times. For doing this, the average model is only applied over the power switches of PE devices, not being applied over the complete topology as traditionally done. The proposed average model switch permits eliminating the ripple of voltage and currents but keeping the transient of the signals. The average model switch is derived for Boost and Buck converter switches and then generalized to power inverter switches. The proposed approach is validated using OpenModelica software. Results: A system featuring a battery, a DC/DC converter, and an inverter connected to the power grid was simulated. A comparison was performed between a simulation that considers the power switches and a simulation that uses the proposed average model switch, the time simulation was reduced up to 99.788 %, which validates the proposed approach. Conclusions: The proposed average switch model significantly reduces simulation times. This method offers a promising way to streamline power electronics device simulations, particularly in the context of microgrids and other applications where time efficiency is critical.

Context: In power electronics applications, it is important to make comparisons between converters to choose the device that best suits a particular application. This paper compares the Boost converter and the hybrid Boost converter. The operating models of both converters under study are developed and explained in detail to allow for a proper comparison and analysis. Method: Using the passive sign law, the differential equations that govern the behavior of each converter are determined upon the basis of their switching states. Then, circuit simulations are performed by using the OpenModelica software to analyze the output signals of both converters with the same input parameters. Results: Comparisons of voltage and current gains, current and voltage time response, and ripple were obtained. Additionally, the efficiency was analyzed by adding resistive losses in each passive element of both converters. Conclusions: For high duties, the hybrid Boost converter has a greater capacity to increase the output voltage than the Boost converter. It was also found that the hybrid Boost converter has a low overshoot and a low ripple in the time response of its output signals. However, this converter is less efficient.

This paper presents a methodology for obtaining the voltage and current references that permits the modelling of Hybrid Cuk (HC) Converters. The passive sign convention when system elements absorb energy is used to derive the differential equations governing the behaviour of the converter. This allows the explanation of the operation principle of the converter that is performed through the switching states. The behavior of the converter is studied by means of an energy interchange analysis between inductors and capacitors. After the deduction of the references and operating principle, the differential equations (DEs) that allow the modelling of the converter are deduced. Finally, a comparison between the mathematical model and the circuital representation of the converter is performed in OpenModelica for validation purposes. The simulations’ results allow to conclude that the obtained references and differential equations are coherent with the circuital implementation.

The data presented in this paper are related to the paper entitled “A Numerical Method for Solving Caputo's and Riemann-Liouville's Fractional Differential Equations which includes multi-order fractional derivatives and variable coefficients”, available in the “Communications in Nonlinear Science and Numerical Simulation” journal. Here, data are included for three of the four examples of Fractional Differential Equation (FDE) reported in [1], the other data is already available in [1]. Data for each example contain: the interval of the solution, the solution by using the proposed method, the analytic solution and the absolute error. Data were obtained through Octave 5.1.0 simulations. For a better comprehension of the data, a pseudo-code of three stages and nine steps is included.

Improving the magnitude processing during early childhood is essential for further developing numerical cognition and mathematical skills. In this regard, there is an intense debate about whether numbers are processed using a number-specific system or a general magnitude processing system. Additionally, the evidence available focuses on the magnitude’s interference but not on the translation and integration process. This study aims to analyze the ability to map between non-symbolic and symbolic numerical information and integrate numbers and space. For this purpose, we designed an observational between-subjects study using two-cross format comparison tasks involving the integration and interconversion between magnitudes and formats.We will assess for each task the relation between ratio and performance, and the discrimination thresholds in 8- and 12-years old Colombian children to explore the developmental trajectory of these numerical cognition processes.

Acetone-Butanol-Ethanol (ABE) fermentation has regained interest due to the environmental problems generated by fossil fuels and the need to modify production chains including considerations of sustainability and circular economy. This work focuses on the modelling of ABE fermentation, with a Clostridium acetobutylicum mutated strain, to support the synthesis and improvement of butanol and acetone production process. To reach this objective, bioreactor fermentations with different glucose concentrations were developed. Results show that a two phases model (with a set of equations for the acidogenesis and another for the solventogenesis) generates satisfactory process predictions. In acidogenesis, the main effect on growth and acid production is due to the inhibition by acids, which can be quantified as a function of the total concentration of acids. While in the solventogenesis, inhibition must be quantified as a function of the total concentration of solvents. This model agrees with metabolic analysis that considers solvents generation being produced, simultaneously, from acids and sugars. For the studied system, acetone and butanol production is based on re-assimilated acids and pyruvate from glycolysis, with a distribution that changes with glucose availability. [ABSTRACT FROM AUTHOR]

Este documento presenta un análisis profundo de la topología Z-source clásica para la protección de sistemas de energía DC, un área previamente no explorada con tal detalle. Utilizando simulaciones en OpenModelica, el estudio ofrece valiosos conocimientos sobre el comportamiento de la topología Z-source, los mecanismos de protección y los procesos de disipación de energía. Incluye diagramas detallados, estados de conmutación y explicaciones de los principios operativos, apoyados por formas de onda que ilustran el flujo de energía a través de los componentes Z-source. Los hallazgos demuestran que el interruptor Z-source maneja eficazmente las condiciones de fallo al desconectar la fuente de la carga casi instantáneamente, en decenas de microsegundos. Las simulaciones confirman los modelos teóricos, mostrando que la topología Z-source disipa eficientemente la energía de fallo a través de inductores, capacitores y resistencias, protegiendo así el equipo sensible. Este análisis exhaustivo mejora la comprensión de la topología Z-source en sistemas de energía DC y establece una base sólida para futuras investigaciones y aplicaciones prácticas.

Protecting AC microgrids (MGs) is a challenging task due to their dual operating modes—grid-connected and islanded—which cause sudden variations in fault currents. Traditional protection methods may no longer ensure network security. This paper presents a novel approach to protection coordination in AC MGs using non-standard features of directional over-current relays (DOCRs). Three key optimization variables are considered: Time Multiplier Setting (TMS), the plug setting multiplier’s (PSM) maximum limit, and the standard characteristic curve (SCC). The proposed model is formulated as a mixed-integer nonlinear programming problem and solved using four metaheuristic techniques: the genetic algorithm (GA), Imperialist Competitive Algorithm (ICA), Harmonic Search (HS), and Firefly Algorithm (FA). Tests on a benchmark IEC MG with distributed generation and various operating modes demonstrate that this approach reduces coordination times compared to existing methods. This paper’s main contributions are threefold: (1) introducing a methodology for assessing the optimal performance of different standard curves in MG protection; (2) utilizing non-standard characteristics for optimal coordination of DOCRs; and (3) enabling the selection of curves from both North American and European standards. This approach improves trip time performance across multiple operating modes and topologies, enhancing the reliability and efficiency of MG protection systems.

Objective This study aimed to determine the status of scientific production on biosensor usage for human health monitoring. Methods We used bibliometrics based on the data and metadata retrieved from the Web of Science between 2007 and 2022. Articles unrelated to health and medicine were excluded. The databases were processed using the VOSviewer software and auxiliary spreadsheets. Data extraction yielded 275 articles published in 161 journals, mainly concentrated on 13 journals and 881 keywords plus. Results The keywords plus of high occurrences were estimated at 27, with seven to 30 occurrences. From the 1595 identified authors, 125 were consistently connected in the coauthorship network in the total set and were grouped into nine clusters. Using Lotka's law, we identified 24 prolific authors, and Hirsch index analysis revealed that 45 articles were cited more than 45 times. Crosses were identified between 17 articles in the Hirsch index and 17 prolific authors, highlighting the presence of a large set of prolific authors from various interconnected clusters, a triad, and a solitary prolific author. Conclusion An exponential trend was observed in biosensor research for health monitoring, identifying areas of innovation, collaboration, and technological challenges that can guide future research on this topic.

Passive Optical Networks (PONs) are telecommunication technologies that use fiber-optic cables to deliver high-speed internet and other communication services to end users. PONs split optical signals from a single fiber into multiple fibers, serving multiple homes or businesses without requiring active electronic components. PONs planning involves designing and optimizing the infrastructure for delivering fiber-optic communications to end users. The main contribution of this paper is the introduction of tailored operators within a genetic algorithm (GA) optimization approach for PONs planning. A three vector and an aggregator vector are devised to account, respectively, for physical and logical connections of the network, facilitating the execution of GA operators. This codification and these operators are versatile and can be applied to any population-based algorithm, not limited to GAs alone. Furthermore, the proposed operators are specifically designed to exploit the unique characteristics of PONs, thereby minimizing the occurrence of unfeasible solutions and accelerating convergence towards an optimal network design. By incorporating these specialized operators, this research aims to enhance the efficiency of PONs planning, ultimately leading to reduced costs and improved network performance.

The data presented in this paper are related to the paper entitled “Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection”, available in the Energies journal. Here, data are included to show the results of an Under-Frequency Load Shedding (UFLS) scheme that considers the injection of virtual inertia by a VSC-HVDC link. The data obtained in six cases which were considered and analyzed are shown. In this paper, each case represents a different frequency response configuration in the event of generation loss, taking into account the presence or absence of a VSC-HVDC link, traditional and optimized UFLS schemes, as well as the injection of virtual inertia by the VSC-HVDC link. Data for each example contain the state of the relay, threshold, position in every delay, load shed, and relay configuration parameters. Data were obtained through Digsilent Power Factory and Python simulations. The purpose of this dataset is so that other researchers can reproduce the results reported in our paper.

This paper introduces an improved methodology designed to address a practical deficit of existing methodologies by incorporating circuit-level analysis in the assessment of building microgrid reliability. The scientific problem at hand involves devising a systematic approach that integrates circuit modeling, Probability Density Function (PDF) selection, formulation of reliability functions, and Fault Tree Analysis (FTA) tailored specifically for the distinctive features of building microgrids. This method entails analyzing inter-component relationships to gain comprehensive insights into system behavior. By harnessing the circuit models and theoretical framework proposed herein, precise estimations of microgrid failure rates can be attained. To complement this approach, we propose a thorough investigation utilizing reliability curves and importance measures, providing valuable insights into individual device failure probabilities over time. Such time-based analysis plays a crucial role in proactively identifying potential failures and facilitating efficient maintenance planning for microgrid devices. We demonstrate the application of this methodology to the University of Antioquia (UdeA) Microgrid, a low-voltage system comprising critical components such as solar panels, microinverters, inverters/chargers, batteries, and charge controllers.

Son escasos los estudios de ciencia política que abordan la participación en la arena electoral de las demandas ambientales en América Latina. Para llenar este vacío, analizamos en este trabajo exploratorio las expresiones electorales del ambientalismo en Chile desde 1993 hasta 2021. Durante este período, el ambientalismo se ha expresado en la arena electoral mediante dos opciones, a veces complementarias y otras en competencia. Por un lado, se han constituido partidos políticos específicos, y por el otro activistas de la movilización ambiental han participado directamente. Independiente de la opción, los resultados electorales han sido bajos hasta el año 2021, cuando los candidatos ambientalistas tuvieron votaciones altas en las elecciones de gobernadores regionales y de convencionales constituyentes. Sin embargo, en las elecciones legislativas de ese mismo año el resultado volvió a ser bajo, pero al mismo tiempo las demandas ambientales fueron recogidas por actores como el candidato y luego presidente Gabriel Boric. Queda por ver si se consolidará una expresión electoral propia del ambientalismo en Chile.

Under-frequency load shedding (UFLS) schemes are the latest safety measures applied for safeguarding the integrity of the grid against abrupt frequency imbalances. The overall inertia of electrical power systems is expected to decrease with an increased penetration of renewable energy as well as elements connected through power electronic interfaces. However, voltage source converter-based high voltage direct current (VSC-HVDC) links can provide virtual inertia through a control loop that allows for a reaction to occur at certain frequency fluctuations. This paper evaluates a UFLS scheme that considers the injection of virtual inertia through a VSC-HVDC link. A genetic algorithm (GA) is used to determine the location of the UFLS relays, the activation threshold of each stage, the delay time and the percentage of load shedding at each stage. It was found that the virtual inertia causes the nadir to delay and sometimes reach a greater depth. Furthermore, the implemented GA approximates the frequency response to the limits set with the constraints, reducing the load shedding but achieving a steeper nadir and a lower steady-state frequency level than traditional UFLS. The simulations were performed using the IEEE 39-bus test system.

The world’s population is in a demographical transition, with an increase in the number of older adults and prevalence of diseases related to aging. This study evaluated in vitro the potential of using Durvillaea incurvata extract (extracted using ultrasound-assisted extraction) to inhibit key enzymes associated with the development of age-related diseases. Our results show that an extract extracted via ultrasound-assisted extracted, as well as an extract conventional extracted from Durvillaea incurvata, presented antidiabetes potential by exhibiting inhibitory activity against α-glucosidase (91.8 ± 1.0% and 93.8 ± 0.3%, respectively, at 500 µg/mL) and α-amylase (42.2 ± 1.4% and 61.9 ± 0.9%, respectively, at 1500 µg/mL) enzymes related to starch digestion and postprandial glycemic response. Also, the extracts showed inhibitory activity against the enzymes acetylcholinesterase (51.5% and 50.8%, respectively, at 500 µg/mL) and butyrylcholinesterase (32.8% and 34.4%, respectively, at 0.5 mg/mL), the biomarkers associated with Alzheimer’s disease, and angiotensin-converting enzyme (98.7 ± 7.4% and 93.0 ± 3.4%, respectively, at 2.0 mg/mL), which is key in the regulation of vascular tone and blood pressure and helps to prevent the development of hypertension. In conclusion, the extract of Durvillaea incurvata obtained from ultrasound-assisted extraction has the potential to prevent the development of age-related pathologies such as diabetes, Alzheimer’s disease, and hypertension.

This paper addresses the protection coordination problem of microgrids combining unsupervised learning techniques, metaheuristic optimization and non-standard characteristics of directional over-current relays (DOCRs). Microgrids may operate under different topologies or operative scenarios. In this case, clustering techniques such as K-means, balanced iterative reducing and clustering using hierarchies (BIRCH), Gaussian mixture, and hierarchical clustering were implemented to classify the operational scenarios of the microgrid. Such scenarios were previously defined according to the type of generation in operation and the topology of the network. Then, four metaheuristic techniques, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO), and Artificial Bee Colony (ABC) were used to solve the coordination problem of every cluster of operative scenarios. Furthermore, non-standard characteristics of DOCRs were also used. The number of clusters was limited to the maximum number of setting setting groups within commercial DOCRs. In the optimization model, each relay is evaluated based on three optimization variables, namely: time multiplier setting (TMS), the upper limit of the plug setting multiplier (PSM), and the standard characteristic curve (SCC). The effectiveness of the proposed approach is demonstrated through various tests conducted on a benchmark test microgrid.

The increasing presence of electric vehicles (EVs) requires a thorough understanding of their impact on distribution assets. EV chargers are characterized as nonlinear and multi-state loads due to their unique electrical consumption patterns. This paper presents a comprehensive study focused on modelling diverse EV charging units deployed in the industrial, commercial, and residential sectors. To conduct this analysis, a simulation environment utilizing the ETAP tool is employed, taking into account time variations. This approach facilitates the comprehension and anticipation of the effects that EV charging may impose on distribution networks, thereby supporting well-informed decision-making for the adaptation and enhancement of the electrical system. Detailed data on current and voltage from operational EV charging stations were collected to create consumption, current, voltage, and harmonic profiles for these charging units. Subsequently, general models or libraries applicable to Level 1, Level 2 and Level 3 EV chargers available in Colombia were developed. These models underwent rigorous validation and were subjected to a comprehensive analysis using the IEEE 13-bus test system. The research yielded valuable insights and conclusions regarding the integration of EV chargers into the current Colombian distribution systems, as well as the potential impact of adopting these devices on power quality issues within the distribution grid. This study contributes to the improved management of distribution assets, thereby facilitating the integration of sustainable electric mobility in the national electrical system.

This paper proposes a Python tool for the analysis of balanced, unbalanced, sinusoidal and nonsinusoidal three-phase systems based on the IEEE Std. 1459. The proposed tool was developed to be used by electrical engineers and professionals of related careers providing a free access for electrical power calculations that allows validation and power analysis in power systems. The proposed tool is available for use on a GitHub link and only requires voltage and current signals as inputs, making it possible to dispense expensive commercial network analyzers. The tool is made of two interfaces, one for making power calculations of sinusoidal three-phase systems and the other one for making calculations of nonsinusoidal three-phase systems; both interfaces can be accessed through a main menu. The proposed tool facilitates decision-making in electrical system issues, which helps improving the efficiency and power quality of electrical systems.

Main objective of this paper is the optimal distribution of the fundamental non-efficient load current terms between the inverters —Energy Gateways (EGs)— connected in grid-tied microgrids (MGs). The main feature of the presented approach is the use of the EGs as controlled current sources that can compensate fundamental non-efficient load current terms in addition to the generation of fundamental positive-sequence active current, avoiding the use of shunt active power filters. The approach relies on the so-called System of Constant References (SoCR) that is based on the symmetrical components decomposition and dq0 transformation. SoCR procedure decouples efficient and non-efficient components of the instantaneous load currents, transforming all of them into six constant references. The optimization algorithm uses a new approach for the calculation of the peak currents in each phase, avoiding non-convex problems when determining the currents of the EGs considering their operating limits. A medium-power size MG that includes photovoltaic and wind generators, as well as, a battery energy storage system is considered to evaluate the capabilities of the proposal. There were evaluated four scenarios: baseline, balanced distribution, proportional distribution, and optimal distribution. All scenarios, except optimal distribution scenario, surpass the current limits for the EGs connected. The results highlight the benefits of using the EGs as active agents in MG efficient operation and demonstrate how the optimization approach achieves the goal of maintaining the generation capabilities of EGs at the same time that compensates the non-efficient current terms demanded by the load.

Sliding-mode control (SMC) is a robust technique used in power electronics (PE) for controlling the behavior of power converters. This paper presents simulations and experimental results of an optimal SMC strategy applied to Semi-Bridgeless Boost Converters (SBBC), which includes Power Factor Correction (PFC). As the main contribution, the optimal coefficients of the SMC strategy are obtained using two metaheuristic approaches, namely the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The main objective is to obtain the sliding coefficients that ensure the best converter response in terms of the input current and output voltage, both during start-up and under disturbances (including changes in load, source, and references). The fitness function to be minimized includes two coefficients, namely the Integrative Absolute Error (IAE) and the Integral Time Absolute Error (ITAE), for both the input current and output voltage. These coefficients measure the converter’s effort to follow the control references. The IAE penalizes errors during start-up, whereas the ITAE penalizes errors in the steady state. The tests carried out demonstrated the effectiveness of the GA and PSO techniques in the optimization process; nonetheless, the GA outperformed the PSO approach, providing sliding coefficients that allowed for a reduction in the input current overshoot during start-up of up to 24.15% and a reduction in the setting time of the output voltage of up to 99%. The experimental results were very similar when tuning with the GA and PSO techniques; nevertheless, tuning with the GA technique produced a better response in the face of disturbances compared to the PSO technique.

Electric vehicles (EVs) have gained considerable attention in the last decade due to a paradigm shift in the transport sector driven by a higher awareness of environmental issues. While the importance of EVs cannot be overstated in the context of the global climate crisis, it does raise the question of whether certain countries or states are ready for their implementation. It is, therefore, necessary to analyze the impact of EVs in the power grids of these countries and states, considering factors such as line congestion and the eventual degradation of voltage profiles, to determine their hosting capacity and assess eventual expansion options. This paper proposes a representative prototype of Curaçao’s electrical system, which is used for assessing the impacts of EVs, allowing us to determine its hosting capacity. Curaçao is an island in the southern Caribbean Sea that uses fuel generators, wind energy, and solar energy to generate electricity. The idea behind this paper is to analyze the effects caused by an increase in EVs on Curaçao’s power grid and propose preventive measures to deal with such problems. Eight EV charging stations were considered, one DC super fast-charging station, three normal DC fast-charging stations, and four AC fast-charging stations. In 2022, there were an estimated 82,360 vehicles on the island. Using this information, this paper analyzes how many vehicles can be simultaneously connected to the grid before it no longer operates under acceptable values. The results showed that 3.5% of the total vehicles can be hosted by the grid. Nonetheless, this can be increased up to 4.5% with the reinforcement of a transmission line.

Nowadays, microgrids play a significant role in the development of modern distribution networks (DNs). In this context, the ever increasing presence of renewable distributed generation (DG) in microgrids has imposed new challenges in protection coordination, rendering traditional coordination schemes inefficient. Thus, it is essential to explore new approaches for protection coordination that take into account specific features of microgrids, such as the presence of DG and the fact that they may operate under different operational modes or topologies. This paper proposes a novel approach for the optimal coordination of over-current relays (OCRs) in microgrids that considers three optimization parameters for each relay: time multiplying setting, plug setting current and characteristic curve. To show the applicability of the proposed approach, several tests were carried out using an IEC benchmark microgrid and a modified version of the IEEE 30 bus test system. Three metaheuristic techniques were implemented for solving the optimal coordination problem, namely: a genetic algorithm (GA), particle swarm optimization (PSO) and a teaching–learning based optimization (TLBO) algorithm. A comparison of results with other coordination models reported in the specialized literature evidenced the applicability and effectiveness of the proposed approach.

This paper presents an assessment of capacitor banks and Distribution Static Compensator (D-STATCOM) with respect to their impact on energy quality. Tests were done with capacitor banks built with electrolytic capacitors commonly used in industrial applications and a D-STATCOM. Experimental tests were performed for power factor correction in order to reduce the reactive power demanded from the electrical grid for inductive-resistive loads. For comparative purposes, the control of the D-STATCOM was set to operate with similar compensation conditions to that of the capacitor banks. Results show that under the same test conditions capacitor banks produce higher Total Harmonic Distortion (THD) than D-STATCOM.

En este artículo se presenta un algoritmo orientado a mejorar las condiciones de seguridad de los sistemas de potencia. El algoritmo propuesto realiza una búsqueda semi-exhaustiva de posibles configuraciones de subestaciones con el objetivo de minimizar las sobrecargas presentadas ante contingencias simples (criterio N-1). Las modificaciones topológicas se hacen a través del cambio de los estados de los interruptores en las subestaciones. Se presentan resultados para el sistema IEEE de 118 barras que evidencian la efectividad del algoritmo propuesto.

Este artículo tiene como objetivo mostrar el detalle del Standard IEEE 1459-2010 para sistemas monofásicos. Este Standard tiene como objetivo cuantificar la potencia de los sistemas eléctricos; sin embargo, no muestra el detalle de la deducción de las ecuaciones partiendo del esquema circuital. Por lo que la contribución de este documento es la deducción paso a paso de las ecuaciones partiendo de los voltajes y corrientes del sistema. La deducción se realiza tanto para sistemas monofásicos lineales como para sistemas monofásicos no lineales. Se presenta el detalle del cálculo para la potencia activa, reactiva y aparente y también el cálculo del factor de potencia como de la distorsión armónica.

Protection coordination of AC microgrids (MGs) is a challenging task since they can operate either in grid-connected or islanded mode which drastically modifies the fault currents. In this context, traditional approaches to protection coordination, that only consider the time multiplier setting (TMS) as a decision variable may no longer be able to guarantee network security. This paper presents a novel approach for protection coordination in AC MGs that incorporates non-standard characteristic features of directional over-current relays (OCRs). Three optimization variables are considered for each relay: TMS, maximum limit of the plug setting multiplier (PSM) and standard characteristic curve (SCC). The proposed model corresponds to a mixed integer non-linear programming problem. Four metaheuristic techniques were implemented for solving the optimal coordination problem, namely: particle swarm optimization (PSO), genetic algorithm (GA), teaching-learning based optimization (TLBO) algorithm and shuffled frog leaping algorithm (SFLA). Numerous tests were run on an IEC MG as well as with the distribution portion of the IEEE 30-bus test system. Both systems incorporate distributed generation (DG) and feature several modes of operation. A comparison was made with other MG protection coordination approaches proposed in the specialized literature. In all cases, the proposed approach found reduced coordination times, evidencing the applicability and efficacy of the proposed approach.

This paper deals with the optimal sizing of islanded microgrids (MGs), which use diesel generators to supply energy in off-grid areas. The MG under study integrates photovoltaic (PV) and diesel generation, a battery energy storage System (BESS), and an inverter for the connection between AC and DC voltage buses. Levelised cost of energy (LCOE) and annual system cost (ASC) are considered economic indicators, while the loss of power supply probability (LPSP) is used as a reliability indicator. Fiscal incentives such as the tax benefits and accelerated depreciation applied in Colombia are considered for the optimally sizing of each MG element. Solar measurements were taken at a weather station located in the main campus of Universidad de Antioquia in Medellin, Colombia at a latitude of 6.10 and longitude of −75.38. The objective function is the minimization of the total energy delivered from the power sources that successfully meets the load. The model was implemented in Python programming language considering several scenarios. Two cases were evaluated: the first one considered PV panels, a BESS and a diesel generator, while the second one only considered PV panels and a BESS. The option that does not include the diesel generator turned out to be the most expensive, since additional PV and BESS resources are required to meet the load profile. Furthermore, it was found that the LCOE was lower when tax benefits were taken into account.