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This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system's stability. The paper considers a space of control actions that involve both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black box counterparts. One important observation is that strategically~\emph{removing} transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts.

This study introduces a mixed-integer linear programming (MILP) model, effectively co-optimizing patrolling, damage assessment, fault isolation, repair, and load re-energization processes. The model is designed to solve a vital operational conundrum: deciding between further network exploration to obtain more comprehensive data or addressing the repair of already identified faults. As information on the fault location and repair timelines becomes available, the model allows for dynamic adaptation of crew dispatch decisions. In addition, this study proposes a conservative power flow constraint set that considers two network loading scenarios within the final network configuration. This approach results in the determination of an upper and a lower bound for node voltage levels and an upper bound for power line flows. To underscore the practicality and scalability of the proposed model, we have demonstrated its application using IEEE 123-node and 8500-node test systems, where it delivered promising results.

Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

Abstract This paper investigates the forced convection of alumina-water nanofluids within helical tubes, maintaining a constant wall temperature and assuming thermal equilibrium between the nanoparticles and the base fluid. The nanofluid model incorporates the effects of alumina (Al2O3) nanoparticle volume fraction, diameter, and temperature on thermophysical properties. The governing equations are solved using the Forward-Time Central-Space Finite Volume method in conjunction with the simple algorithm. Numerical results are validated against experimental data, demonstrating high accuracy. The study explores the effects of pitch size, curvature ratio, nanoparticle volume fraction, nanoparticle diameter, and Reynolds number on velocity contours, temperature profiles, secondary flow, thermophysical properties, friction coefficient, and heat transfer rate. Additionally, the figure of merit evaluates the impact of these parameters on the thermal performance of the system. The results indicate that an increase in Reynolds number and nanoparticle diameter negatively affects thermal performance, while higher nanoparticle volume fraction, curvature ratio, and pitch size enhance it. Furthermore, incorporating nanoparticles in straight tubes proves to be more advantageous compared to helical tubes. This study tested volumetric ratios of 1%, 2%, and 4%, which resulted in increases in heat transfer coefficients of 21%, 32%, and 43%, respectively, compared to pure water under similar conditions, such as Reynolds number and coil pitch.

Graphene oxide, a derivative of graphene, has recently emerged as a promising nanomaterial in the biomedical field due to its unique properties. Its potential as a nanocarrier in the treatment of Alzheimer's disease represents a significant advancement. This abstract outlines a study focused on utilizing graphene oxide to reduce the toxicity of Alzheimer's proteins, marking a revolutionary approach in treatment strategies. The pathological features of Alzheimer’s disease, primarily focusing on the accumulation and toxicity of amyloid-beta proteins, have been described in this review. These proteins are known to form plaques in the brain, leading to neuronal damage and the progression of Alzheimer's disease. The current therapeutic strategies and their limitations are briefly reviewed, highlighting the need for innovative approaches. Graphene oxide, with its high surface area, biocompatibility, and ability to cross the blood-brain barrier, is introduced as a novel nanocarrier. The methodology involves functionalizing graphene oxide sheets with specific ligands that target amyloid-beta proteins. This functionalization facilitates the binding and removal of these toxic proteins from the brain, potentially alleviating the symptoms of Alzheimer's disease. Preliminary findings indicate a significant reduction in amyloid-beta toxicity in neuronal cell cultures treated with graphene oxide nanocarriers. The study also explores the biocompatibility and safety profile of graphene oxide in biological systems, ensuring its suitability for clinical applications. It calls for further research and filing patents for its translational potential and benefits of this nanotechnology paying the way for a new era in neurodegenerative therapy.

The importance of early diagnosis of hepatitis B virus infection to treat and follow up this disease has led to many advances in diagnostic techniques and materials. Conventional diagnostic tests are not very useful, especially in the early stages of infection; it is therefore suggested that nanomaterials can enhance them by changing and strengthening their performance for a more accurate and rapid diagnosis. Electrochemical immunosensors with unique features such as miniaturization, low cost, specificity and simplicity have become a suitable and vital tool in the rapid diagnosis of hepatitis B since the patent. Different strategies have been presented, such as graphene oxide and gold nanorods (GO-GNRs), graphene oxide (GO), copper metal–organic framework/ electrochemically reduced graphene oxide (Cu-MOF/ErGO) composite, Label-free graphene oxide/ Fe₃O₄/Prussian Blue (GO/Fe₃O₄/PB) immunosensor, and graphene oxide–ferrocene-CS/Au (GOFc- CS/Au) nanoparticle layered electrochemical immunosensor. In this review, we discuss a group of the most widely used nanostructures, such as graphene and carbon nanotubes, which are used to develop electrochemical immunosensors for the early diagnosis of the hepatitis B virus.

Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids. However, the design of SCs usually depends on specific application requirements and may not be adaptive enough to the frequently-changing grid conditions caused by the transition from conventional to renewable power generation. This paper devises a software-defined virtual synchronous condenser (SDViSC) method to address the challenges. Our contributions are fourfold: 1) design of a virtual synchronous condenser (ViSC) to enable full converter wind turbines to provide built-in SC functionalities; 2) engineering SDViSCs to transfer hardware-based ViSC controllers into software services, where a Tustin transformation-based software-defined control algorithm guarantees accurate tracking of fast dynamics under limited communication bandwidth; 3) a software-defined networking-enhanced SDViSC communication scheme to allow enhanced communication reliability and reduced communication bandwidth occupation; and 4) Prototype of SDViSC on our real-time, cyber-in-the-loop digital twin of large-wind-farm in an RTDS environment. Extensive test results validate the excellent performance of SDViSC to support reliable and resilient operations of wind farms under various physical and cyber conditions.

Energy poverty is a complex, multifaceted problem which negatively affects many households around the world. Therefore, its mitigation is one of the most relevant policy goals for governments nowdays. The events of COVID-19, and the surge in energy prices due to the postpandemic recovery, the war in Ukraine and the energy crisis worsened an already difficult situation for many citizens, increasing energy poverty. However, these detrimental effects on energy poverty are unlikely to have been homogenous across different household types, impacting some more than others. Although energy poverty in general and the determinants of energy poverty in particular are topics which have received attention from academics in the past, the analysis of the impact of those events on specific types of households has not. The aim of this paper is to assess the impact of COVID, the post-pandemic recovery and the war in Ukraine on energy poverty through an analysis of their effects on different types of households and dwellings. Using a database of Spanish households in 2019 and 2022, and binary probit estimations, the results show that those household heads who are foreign nationals, females, older than 67 years, without education and with lower income levels and households with dependent children, whose members do not have a job and live in a detached dwelling, in a rented house, in a dwelling located in a cold region, in a rural area and in an old dwelling are more likely to be energy poor. Both events had a negative effect on energy poverty in very specific types of households: those in which the household head is a male, households whose members do not have a job, are older than 67 years, have children, are less educated, have lower income levels and live in detached dwellings. Analysing the most affected household types and most influential determinants of energy poverty is key to propose effective and efficient policy interventions which mitigate it. These findings call for the adoption of effective targeted policy interventions which focus on the factors that are more likely to reduce EP. They suggest that additional policy efforts to mitigate energy poverty should be put on specific households.