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Abstract AP1000® Generation III+ reactor bases its safety concept on passive systems, differently from the previous Generation II reactors. This fact has led the approximations and methodologies previously used for modeling active safety systems to be reviewed and adapted to simulate the physics of passive systems. Diverse studies about the AP1000 containment have demonstrated the difficulty to correctly model the occurring phenomenology. In this paper, an integral AP1000 3D containment GOTHIC model is presented, including the Passive Containment Cooling System (PCCS). The model includes the compartments inside and outside the metallic containment liner that influence the thermal–hydraulic behavior. The model is tested against a Large Break Loss of Coolant Accident (LBLOCA) to assess its thermal–hydraulic performance, assuming a PCS tank malfunction, what is a conservative hypothesis. The pressure and temperature evolution predicted by the 3D containment model is analyzed and compared with a single node Lumped Parameters model, allowing to evaluate some preliminary benefits of 3D modeling for containment safety analysis. The 3D containment model allows to predict the thermal evolution in each containment compartment capturing the heterogeneity of this phenomenon, with higher resolution than the lumped parameters models traditionally used in this kind of analyses. It allows to observe the thermohydraulic conditions locally at any time during the transient.

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

Successive international commitments relating to energy and climatic change (embodied in the Kyoto Protocol) and the need to rationalise the sources of generated energy, have meant that renewable energies have started to gain a great deal of importance within the worldwide energy network. In the case of Wind Energy, and in terms of production, Spain is the second most important country at European level and the third most important country at global level. Spain holds these positions as a result of the establishment of a stable regulatory framework, better understanding of the resource and improved technology that have afforded considerable cost reduction in terms of initial investment, maintenance and exploitation. This article focuses on these circumstances in view of their relevance at international level, which is due to the highly feasible possibility of exporting Spain's experiences to other countries with guarantees of success.

Microgrids are increasingly being used as a platform to integrate distributed generation such as renewable energy sources and (RESs) conventional sources in both grid-connected and isolated power systems. Due to the inherent intermittent nature of RESs, energy storage systems (ESSs) that can absorb fluctuations have become inevitable. Nevertheless, large capacities of ESSs increase the initial cost while small capacities lead to instabilities and increase in the cost of conventional fuels. Therefore, finding the optimal size of the ESS for a given application is essential for the reliable, efficient and economical operation of a microgrid. Once the battery size is decided, maintaining its energy at appropriate levels is essential to ensure stable and safe operation of the microgrid. This paper presents a novel expert fuzzy system - grey wolf optimization (FL-GWO) based intelligent meta-heuristic method for battery sizing and energy management. The proposed energy management operation is carried out by a Grey Wolf Optimiser (GWO) that is helped to set the membership functions and rules of the fuzzy logic expert system. The unit commitment (UC) issue, which is essential for the proper operation of the isolated microgrid, has been additionally considered in this paper. To verify the performance of the proposed method, results are compared with the rules-based method and traditional GWO algorithm. It has been proven from the results that the FL-GWO has a significant convergence property and capability to minimize the Levelized Cost Of Electricity (LCOE) by 14.13% and 24.15% compared with conventional GWO algorithm and rules-based method, respectively. The weather conditions for different climates is used to verify the performance of the intelligent energy management method under different operating scenarios. The results show that the intelligent online multi-objective energy management strategy is capable of managing a smooth power flow with the same optimal configuration in the isolated microgrid, minimising the fossil fuel utilisation and reducing the CO2 emission level.

Mixed transition metal oxides with high theoretical capacity show great potential to replace carbonaceous anode materials in lithium-ion batteries.

Previous work carried out for the last eight years resulted in the proposal of a complete system of dimensionless groups in order to represent the performance of different kinematic Stirling engine configurations. When looking for experimental support for the proposed model, some differences between the performances of several prototypes were observed. In this paper an equation is introduced to be applied to all known kinematic engines and to their whole range of performance. The coefficients appearing in this equation can be computed from temperature and geometrical dimensionless ratios and from experimental measurements at the maximum indicated power operating point. The meaning of those coefficients is interpreted and their usefulness to provide an engine performance overview is shown. The quasi-static simulation and the characteristic Mach number at the maximum indicated power operating point appear to be interesting criteria in order to evaluate the performance of prototypes.

The catalytic active sites of NiFe and NiFeCr (oxy)hydroxides are revealed byoperandospectroscopic techonologies for alkaline water oxidation.

Abstractπ‐Conjugated polyelectrolytes (CPEs) have been studied as interlayers on top of a separate hole transport layer (HTL) to improve the wetting, interfacial defect passivation, and crystal growth of perovskites. However, very few CPE‐based HTLs have been reported without rational molecular design as ideal HTLs for perovskite solar cells (PeSCs). In this study, the authors synthesize a triphenylamine‐based anionic CPE (TPAFS‐TMA) as an HTL for p‐i‐n‐type PeSCs. TPAFS‐TMA has appropriate frontier molecular orbital (FMO) levels similar to those of the commonly used poly(bis(4‐phenyl)‐2,4,6‐trimethylphenylamine) (PTAA) HTL. The ionic and semiconducting TPAFS‐TMA shows high compatibility, high transmittance, appropriate FMO energy levels for hole extraction and electron blocking, as well as defect passivating properties, which are confirmed using various optical and electrical analyses. Thus, the PeSC with the TPAFS‐TMA HTL exhibits the best power conversion efficiency (PCE) of 20.86%, which is better than that of the PTAA‐based device (PCE of 19.97%). In addition, it exhibits negligible device‐to‐device variations in its photovoltaic performance, contrary to the device with PTAA. Finally, a large‐area PeSC (1 cm2) and mini‐module (3 cm2), showing PCEs of 19.46% and 18.41%, respectively, are successfully fabricated. The newly synthesized TPAFS‐TMA may suggest its great potential as an HTL for large‐area PeSCs.

Abstract Background Parabolic Trough Solar Collector (PTSC) is one of the most popular and an effective device that converts solar radiation into a heat or useful energy. However, it suffers from high temperature gradient and low thermal efficiency. The solution for this problem is to use new advanced coolants (hybrid nanofluids) in order to enhance PTSC's thermal efficiency. Methods A numerical analysis on the thermo-hydraulic performance of a PTSC receiver's tube equipped with conical turbulators is presented. The Navier-Stokes equations are solved using Finite Volume Method (FVM) coupled with Monte Carlo Ray Tracing (MCRT) method. The flow and thermal characteristics as well as entropy generation of the PTSC's receiver tube are investigated for three hybrid nanofluids (Ag-SWCNT, Ag-MWCNT, and Ag-MgO) having a mixing ratio of (50:50) dispersed in Syltherm oil 800, Reynolds number (5000 to 100,000) and fluid inlet temperatures (400 to 650 K). Significant findings The conical turbulators effectively augmented the thermal performance by 233.4% utilising Ag-SWCNT/Syltherm oil instead of pure Syltherm oil. The performance evaluation criterion is found to be in the range of 0.9–1.82. The thermal and exergetic efficiencies increased by 11.5% and 18.2%, respectively. The maximum decrement in the entropy generation rate and entropy generation ratio are 42.7% and 33.7%.