This article proposes a novel design methodology for hybrid isolated microgrids, integrating electric vehicles (EV) as additional loads and also as additional storage systems in the microgrid design stage. The proposed method highlights the application of electric vehicles in rural environments. Two types of electric vehicles were considered: (1) EV that only operate in charging mode; and (2) EV that can interchange power with the microgrid (V2G). For both EV types, a dispatch strategy was developed to optimize the use of the system resources during the charging process. As an application example of the proposed design methodology, a hybrid microgrid was designed for the rural Colombian village of Unguía. The results show the advantages of the inclusion of EV as ancillary services providers for the system and also as public transportation agents.

Today, air quality is one of the global concerns that governments are facing. One of the main air pollutants is the particulate matter (PM) which affects human health. This article presents the modeling of a purification system by means of negative air ions (NAIs) for air pollutant removal, using computational intelligence methods. The system uses a high-voltage booster output to ionize air molecules from stainless steel electrodes; its particle-capturing efficiency reaches up to 97%. With two devices (5 cm × 2 cm × 2.5 cm), 2 trillion negative ions are produced per second, and the particulate matter (PM 2.5) can be reduced from 999 to 0 mg/m³ in a period of approximately 5 to 7 minutes (in a 40 cm × 40 cm × 40 cm acrylic chamber). This negative ion generator is a viable and sustainable alternative to reduce polluting emissions, with beneficial effects on human health.

Air pollution caused by fine particles known as PM2.5 is a significant health concern worldwide, contributing to illnesses like asthma, heart disease, and lung cancer. To address this issue, this study focused on improving air purification systems using negative ions, which can attach to these harmful particles and help remove them from the air. This paper developed a novel mathematical model based on linear differential equations to study how PM2.5 particles interact with negative ions, making it easier to design more effective purification systems. The proposed model was validated in a small, controlled space, using common urban pollutants such as cigarette smoke, incense, coal, and gasoline. These tests were conducted at different temperatures and under two levels of ion generation. The results showed that the system could remove over $99\%$ of PM2.5 particles in five minutes when temperatures were low or moderate. However, at higher temperatures, the system’s performance dropped significantly. This research goes beyond earlier studies by examining how temperature affects the process, which had not been fully explored before. Furthermore, this approach aligns with global sustainability goals by promoting public health, reducing healthcare costs, and providing scalable solutions for sustainable urban living.

Cities globally must make urgent decisions to ensure a sustainable future as rising pollution, particularly PM2.5, poses severe health risks like respiratory and heart diseases. PM2.5’s harmful composition also impacts vegetation and the environment. Immediate government intervention is necessary to mitigate these effects. This study tackles the urgent problem of reducing PM2.5 levels in Medellín’s urban and indoor environments, where pollution presents serious health risks. To explore effective solutions, this research provides new data on the interaction between particulate matter from various pollutants and negative ions under different temperature conditions, offering valuable insights into air quality improvement strategies. Using a high-voltage system, ions bind to pollutants, accelerating their removal. Experiments measured temperature, humidity, formaldehyde, volatile organic compounds, negative ions, and PM2.5 in a 40 cm³ chamber across various conditions. Pollutants tested included cigarette smoke, incense, charcoal, and gasoline at two voltage levels and three temperature ranges. The data, available in CSV format, were based on 36,000 samples and repeated tests for reliability. This resource is designed to support studies investigating particulate matter control in urban and indoor environments, as well as to improve our understanding of negative ion-based air purification processes. The data are publicly available and structured in formats compatible with leading data analysis platforms.

The increasing penetration of renewable generation and electrified loads introduces non-Gaussian and strongly nonlinear uncertainty in power system operation. Probabilistic power flow (PPF) methods based on Monte Carlo simulation provide accurate uncertainty propagation but remain computationally demanding, while many analytical and approximation-based approaches rely on restrictive distributional assumptions. This letter proposes a Distribution-Matching Likelihood-Free Importance Sampling (DM-LFIS) framework for PPF. The method avoids explicit likelihood construction and posterior sampling, and instead propagates uncertainty by reweighting candidate operating states according to a discrepancy between simulated and observed power injection distributions. Data-driven and physics-informed proposals guided by DC and Newton--Raphson power flow solutions are used to improve sampling efficiency. Numerical results on standard IEEE test systems show that DM-LFIS accurately captures voltage and angle uncertainty with reduced computational cost compared to conventional Monte Carlo-based PPF methods.