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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.

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.

Abstract Protection devices are designed to provide high sensitivity to transients produced by undesirable conditions like lightning stroke, avoiding their operation under all tolerable events like switching operations. The problem of incorrect operation due to transient phenomena can be handled by two means, one is to allow the transients and provide additional logics in the relay, other means is to damp the oscillation from source side. Protection relays’ not always must trip or send a trip signal and sometimes, only an alarm is necessary. In this context, this research presents a fast and reliable formulation for transmission lines (TLs) switching operations and lightning strokes detection and identification. The proposed methodology is based on Principal Component Analysis (PCA) and Euclidean Norm (EN); by using PCA it is possible to determine that normal operation signals describe a very well defined Ellipsoidal Pattern (EP). In this manner, by calculating the Euclidean Norm (EN) among Principal Components (PCs) for each sample and the origin of the reference Ellipsoidal Pattern, switching operations and lightning strokes are detected and identified. Test results show that the proposed algorithm presents high success on phenomena detection and identification, presenting a high potential for online applications.

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%.

Highly efficient nanocatalysts which can selectively decompose hydrous hydrazine for hydrogen production are introduced.

Abstract A new conical-cylindrical top-discharge blow tank is designed, by introducing the pulsed gas to facilitate discharging, for stable transportation for kaolin powders. A series of experimental studies on pulsed gas characteristic parameters like pulsed gas flow rate Qpulsed (5 m3/h ≤ Qpulsed ≤ 25 m3/h), pulsed interval tpulsed (1 s ≤ tpulsed ≤ 5 s) and pulsed width τpulsed (50 ms ≤ τpulsed ≤ 250 ms) are conducted with the fluidized gas flow rate of 12 m3/h. The experiments mainly test the powder mass flow rate and solid-gas ratio in the conveying process. The results indicate that the mass flow rate and solid-gas ratio range 12.6–278.04 kg/h and 0.9–19.56 kg/m3, respectively. With the increase of the pulsed gas flow rate, the mass flow rate and solid gas ratio first increase and then decrease. When the ratio of fluidized gas flow rate to pulsed gas flow rate is within 0.8–1.2, its conveying capacity reaches the maximum. Meanwhile, the increase in the pulsed interval leads to the decrease of the mass flow rate and solid-gas ratio. Moreover, the increase in the pulsed width leads to the initial increase and then the stabilization of the mass flow rate and solid gas ratio. When the pulsed width is 50 ms, the improvement of discharge would small. Conversely, increasing the pulsed width can increase the discharge, and stabilize subsequently until over 200 ms. Besides, moisture content is one of the important factors affecting kaolin powders discharge. When the moisture content is 0.83%, the pulsed gas does not improve the discharge significantly. Meanwhile, pressure distribution at different locations in the tank is also measured. The results reveal that the introduction of pulsed gas changes the pressure distribution in the tank. A pressure zone is formed on the upper part of the tank, which promotes the powder discharge.

Abstract To achieve optimal generation from a number of mixed power plants by minimizing the operational cost while meeting the electricity demand is a challenging optimization problem. When the system involves uncertain renewable energy, the problem has become harder with its operated generators may suffer a technical problem of ramp-rate violations during the periodic implementation in subsequent days. In this paper, a scenario-based dynamic economic dispatch model is proposed for periodically implementing its resources on successive days with uncertain wind speed and load demand. A set of scenarios is generated based on realistic data to characterize the random nature of load demand and wind forecast errors. In order to solve the uncertain dispatch problems, a self-adaptive differential evolution and real-coded genetic algorithm with a new heuristic are proposed. The heuristic is used to enhance the convergence rate by ensuring feasible load allocations for a given hour under the uncertain behavior of wind speed and load demand. The proposed frameworks are successfully applied to two deterministic and uncertain DED benchmarks, and their simulation results are compared with each other and state-of-the-art algorithms which reveal that the proposed method has merit in terms of solution quality and reliability.