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Residential electricity generation can benefit from the successful deployment of photovoltaic (PV) and wind renewable energy sources. However, their intermittent nature poses a significant limitation. To address this, a hybrid system can be employed to enhance system reliability and efficiency. Reliability analysis plays a crucial role in evaluating the energy production capacity to meet the demand. This study aims to assess the reliability of a hybrid PV/wind microgrid through simulation. Data from a residence were collected every 10 s, and average values were computed on an hourly basis and exported for computer processing. Solar irradiation, wind speed, and temperature data were also utilized. The modeling process involved defining the PV panel, wind turbines, battery energy storage system (BESS), management strategy, and energy autonomy. The results indicate that when PV and wind systems operate independently, they are unable to consistently reduce the residential energy deficit. However, the PV/Wind/BESS configuration significantly improves system operation and proves sufficient to meet the required load in most hours. Through this configuration, only 42.5% of the total PV/wind energy utilized by the residents needs to be dispatched through the BESS. Moreover, the BESS capacity is reduced by 50% when the systems are used separately. The public grid only supplies power when the BESS is insufficient to cover the load. Therefore, the optimal sizing of the BESS plays a critical role in system viability, reducing initial installation costs, regulating microgrid parameters, and contributing to the reduction of the energy deficit. Hence, investigating the fundamental design parameters of the microgrid and BESS is essential for identifying the optimal capacity of the system and ensuring model reliability.

The dependence on fossil resources remains high, accounting for 80% of the total primary energy production worldwide. In addition, the COVID-19 pandemic has devastated several industries and electric power is no exception. Therefore, renewable energy sources have become an important aspect of the energy sector, contributing to solutions to environmental problems and the development of a sustainable future. Therefore, this study proposes a model to evaluate the energy autonomy of a photovoltaic microgrid (EAPV,MG) using a battery energy storage system (BESS). To carry out the analysis, the energy consumption history of a residence and the solar irradiation data of the location were used. Thus, this study includes the evaluation of the energy compensation of a photovoltaic microgrid that directly injects energy into the residence, and the surplus is stored in a BESS for later use. The results show the relevance of EAPV,MGmodeling for optimal system sizing. The model determines the autonomy of the microgrid without many power variations in the PV system for January, April, and October, corresponding to the summer, autumn, and spring seasons, respectively. However, in July (winter), the photovoltaic MG must increase the installation power considerably and the storage capacity of the BESS. Therefore, an optimal decision evaluating the EAPV,MGwill allow broadening an overview to opt for a PV microgrid that injects the highest amount of energy based on the actual residential load profile and the optimal capacity of the BESS. Consequently, the analysis of the EA of a microgrid based on solar irradiation and actual load data is essential for developing an optimal and stable operation of the residential energy system.

Las enfermedades cardiovasculares tienen en la actualidad un gran impacto social y económico y constituyen una de las principales causas de mortalidad y morbilidad. Los modelos computacionales personalizados del corazón están demostrando ser útiles tanto para ayudar a comprender los mecanismos subyacentes a las patologías cardiacas como para optimizar su tratamiento prediciendo la respuesta del paciente. En este contexto, se ha puesto en marcha la Red Española de Investigación en Modelización Computacional Cardiaca (V-Heart SN). El objetivo general de V-Heart SN es el desarrollo de un modelo computacional multifísico y multiescala integrado del corazón. Este objetivo general se aborda a través de los siguientes objetivos específicos: a)integrar los diferentes modelos numéricos teniendo en cuenta la especificidad de los pacientes; b)ayudar a avanzar en el conocimiento de los mecanismos asociados a las diferentes patologías cardiacas y vasculares; y c)apoyar la aplicación de terapias personalizadas. Este artículo presenta el estado actual de la modelización computacional cardiaca y los diferentes trabajos científicos desarrollados por los miembros de la red para favorecer una mayor comprensión de las características y utilidad de los modelos.

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