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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.

electricity. In this research, the potential of ZigZag PVNBs has been investigated. The ZigZag Solar product, developed by Wallvision, has proven to offer multiple advantages in energy yield and aesthetics for building fa & ccedil;ade applications. For noise barrier applications, the ZigZag structure could offer interesting features in safety and noise cancellation (obtained by filling the ZigZag construction with Rockwool material) on top of the advantages in aesthetics and energy yield. A ZigZag PVNB has been designed and constructed at the Brightlands Chemelot Campus in Geleen, after which the electrical performance has been automatically monitored under Dutch climate conditions. The measurements have been compared to simulated data, which allowed optimiza

The increasing commercial interest in silicon-based anode materials for Li-ion batteries has driven the development of advanced structural designs to address the challenges of poor cycling stability. This study examines the structure of commercial silicon/carbon composite materials where nano silicon clusters are embedded within a carbon matrix. The size of silicon and carbon nanoclusters is determined by comparing experimental X-ray diffraction patterns with calculated patterns based on the Debye scattering formalism, as implemented in the program DEBUSSY. The size, morphology, surface areas, and porosities of the carbon matrix and composite are measured, along with their resulting tap and true densities. Their electrochemical performance is also assessed to determine operando stack growth and cycling stability. By restricting silicon cluster sizes to sub-nanometer dimensions within a porous carbon matrix, a low specific surface area can be achieved along with a specific capacity of ∼2000 mAh g−1. Additionally, this approach results in high tap density values close to 1 g cc−1, reduces reversible stack growth, and minimizes irreversible stack growth caused by particle cracking during volume changes, thereby significantly enhancing the overall stability and performance of the anode material.

Convex relaxations of the optimal power flow (OPF) problem provide an efficient alternative to solving the intractable alternating current (AC) optimal power flow. The conic subset of OPF convex relaxations, in particular, greatly accelerate resolution while leading to high-quality approximations that are exact in several scenarios. However, the sufficient conditions guaranteeing exactness are stringent, e.g., requiring radial topologies. In this short communication, we present two equivalent ex post conditions for the exactness of any conic relaxation of the OPF. These rely on obtaining either a rank-1 voltage matrix or self-coherent cycles. Instead of relying on sufficient conditions a priori, satisfying one of the presented ex post conditions acts as an exactness certificate for the computed solution. The operator can therefore obtain an optimality guarantee when solving a conic relaxation even when a priori exactness requirements are not met. Finally, we present numerical examples from the MATPOWER library where the ex post conditions hold even though the exactness sufficient conditions do not, thereby illustrating the use of the conditions.

The conventional approach for controlling the supply temperature in collective space heating networks relies on a predefined heating curve determined by outdoor temperature and heat emitter type. This prioritises thermal comfort but lacks energetic and financial optimisation. This research proposes an adaptive supply temperature control in well-insulated dwellings, responsive to diverse environmental parameters. The approach considers variable electricity prices and accommodates different indoor temperature set points in dwellings. The study evaluates the effectiveness of two Deep Reinforcement Learning (DRL) algorithms, i.e., Proximal Policy Optimisation (PPO) and Deep Q-Network (DQN), across various scenarios. Results reveal that DQN excels in collective space heating systems with underfloor heating in each dwelling, while PPO proves superior for radiator-based systems. Both outperform the traditional heating curve, achieving up to 13.77% (DQN) and 16.15% (PPO) cost reduction while guaranteeing thermal comfort. Additionally, the research highlights the capability of DRL-based methods to dynamically set the supply temperature based on a cloud of set points, showcasing adaptability to diverse environmental factors and addressing the growing significance of indoor heat gains in well-insulated dwellings. This innovative approach holds promise for more efficient and environmentally conscious heating strategies within collective space heating networks.

This paper investigates the use of a passive in-flight battery thermal management system for an electric trainer aircraft. The proposed architecture employs a high-conductivity heat pipe coupled with a heat sink to provide cooling. Two different thermal management approaches are used for ground and flight operations. During charge, a ground-based air conditioning unit is used for thermal management. In flight, the system relies on preconditioning of the battery pack’s thermal mass to limit the temperature rise to acceptable levels, reducing the onboard weight and complexity associated with the battery thermal management system. To assess the performance of the proposed system over the entire lifetime of the battery pack, for both flight and ground operations, a coupled electrothermal-aging model based on semi-empirical data was created and validated experimentally for a notional touch-and-go training mission of a conceptual electric Cessna 172N. Resulting time-dependent performance diagrams presenting the evolution of maximum battery temperature, the ground turnaround time, and the capacity fade are used to determine operational constraints throughout the 2-year-and-a-half lifetime of the pack.

This paper aims to unveil the intellectual structure and knowledge flow within Türkiye's academic landscape, shedding light on influential research clusters and highlighting the interconnections between different research themes. The manuscript also synthesizes findings from a Web of Science database, elucidating the growth trajectories of Türkiye's contributions to the global discourse on energy, fuels, and hydrogen. Additionally, the role of interdisciplinary collaboration has been explored and the impact of Türkiye's research output on the international stage has been assessed. According to results, the oldest date goes back to 1972 for energy&fuels topic and 1989 for hydrogen topic. Whereas Ayhan Demirbas and Ibrahim Dincer are the most productive authors, Istanbul Technical University and Yildiz Technical University are the most productive institutions. Moreover, USA and Canada are the most efficient countries for colloborations. Last of all, while new trends in Energy&Fuels publications have been observed as machine learning, supercapacitor, nanoparticles, electric vehicle and graphene, new trends in hydrogen publications were observed as methanolysis, multigeneration, ammonia, thermodynamic analysis and graphene.

The recently completed Horizon-2020 project “Management and Uncertainties of Severe Accidents (MUSA)” has reviewed uncertainty sources and Uncertainty Quantification methodology for assessing Severe Accidents (SA), and has made a substantial effort at stimulating uncertainty applications in predicting the radiological Source Term of reactor and Spent Fuel Pool accident scenarios. The key motivation of the project has been to bring the advantages of the Best Estimate Plus Uncertainty approach to the field of Severe Accident modelling. With respect to deterministic analyses, expected gains are avoiding adopting conservative assumptions, identifying uncertainty bands of estimates, and gaining insights into dominating uncertain parameters. Also, the benefits for understanding and improving Accident Management were to be explored. The reactor applications brought together a large group of participants that set out to apply uncertainty analysis (UA) within their field of SA modelling expertise – in particular reactor types, but also SA code used (ASTEC, MELCOR, MAAP, RELAP/SCDAPSIM), uncertainty quantification tools used (DAKOTA, SUSA, URANIE, self-developed tools based on Python code), detailed accident scenarios, and in some cases SAM actions. The setting up of the analyses, challenges faced during that phase, and solutions explored, are described in Brumm et al. ANE 191 (2023). This paper synthesizes the reactor-application work at the end of the project. Analyses of 23 partners are presented in different categories, depending on whether their main goal is/are (i) uncertainty bands of simulation results; (ii) the understanding of dominating uncertainties in specific sub-models of the SA code; (iii) improving the understanding of specific accident scenarios, with or without the application of SAM actions; or, (iv) a demonstration of the tools used and developed, and of the capability to carry out an uncertainty analysis in the presence of the challenges faced. A cross-section of the partners’ results is presented and briefly discussed, to provide an overview of the work done, and to encourage accessing and studying the project deliverables that are open to the public. Furthermore, the partners’ experiences made during the project have been evaluated and are presented as good practice recommendations. The paper ends with conclusions on the level of readiness of UA in SA modelling, on the determination of governing uncertainties, and on the analysis of SAM actions.