• Energy Research
• Closed Access close
• 7. Clean energy close
• 11. Sustainability close
• CN close
• IN close
• DE close
• AU close

In order to achieve paramount economy, hybrid renewable energy sources are gaining importance, as renewable sources are costless. Over the past few years wind energy incorporation drew more consideration in the electricity market, as wind power took an affirmative role in energy saving as well as sinking emission pollutants. Recently developed Grey wolf optimizer (GWO) algorithm has conspicuous behavior for verdicting global optima, without getting ensnared in premature convergence. In the proposed research the exploitation phase of the grey wolf optimizer has been further improved using random exploratory search algorithm, which uses perturbed solutions vectors along with previously generated solution vectors. The paper presents a hybrid version of Grey Wolf Optimizer algorithm combined with random exploratory search algorithm (hGWO-RES) for the solution of combinatorial scheduling and dispatch problem of electric power systems. To validate the feasibility of the algorithm, the proposed algorithm has been tested on 23 benchmark problems. To verify the feasibility and efficacy of operation of proposed algorithm on generation scheduling and dispatch of electric power systems, small and medium scale power systems consisting of 7-, 10-, 19-, 20- and 40-generating units systems taken into consideration. Commitment and scheduling pattern has been evaluated with and without wind integration and it has been experimentally founded that proposed hybrid algorithm provides superior solution as compared to other recently reported meta-heuristics search algorithms.

Abstract Energy storage systems (ESS) are widely used in active distribution networks (ADN) to smoothen the drastic fluctuation of renewable energy sources (RES). In order to enhance the scalability and flexibility of ESS, a virtual energy storage system (VESS), which is composed of battery energy storage system (BESS), RES as well as flexible loads (FL), is developed in this paper to realize the functionalities of ESS in more cost-effective way in ADN. Aiming at achieving voltage regulation, dynamic pricing strategies based on system voltage condition are designed for VESS. A distributed real-time power management model containing dynamic pricing strategies is proposed to accomplish the voltage regulation and economic power sharing in VESS. Moreover, a set of distributed algorithms, over time-varying unbalanced directed networks, are designed for dynamic pricing strategies and optimal power management model. Furthermore, the convergence property, optimality and system voltage stability are explained by detailed mathematical analysis. Three various case studies which were ran on a real time digital simulator (OPAL-RT OP5600) were designed to validate the effectiveness of the strategy. Finally, simulation results show that the economic power dispatch and voltage regulation are achieved among VESS simultaneously, even in the presence of time-varying directed and unbalanced communication networks.

Wide‐bandgap (WBG) perovskite solar cells (PSCs) are acknowledged as promising candidates for tandem solar cells and building photovoltaics. It is well known that cesium‐based all‐inorganic halide WBG perovskites possess the comparable optoelectronic properties as the organic–inorganic counterparts, but exhibit superior thermal stability. Among them, CsPbIBr2 is considered a feasible material for tandem solar cells after balancing the bandgap and stability of the inorganic perovskite. However, CsPbIBr2 PSCs are often subjected to drastic interfacial charge recombination especially in carbon‐based device structure derived from the chemical bonding defects (i.e., uncoordinated Pb2+) naked on CsPbIBr2 soft lattice, which dramatically limits overall efficiency of CsPbIBr2 WBG PSCs. Herein, a trimethyl ammonium salt hexyltrimethylammonium bromide is presented for CsPbIBr2/carbon interfacial modification. Benefiting from the −N+(CH3)3 passivation effect and −C6H13 hydrophobic alkyl chain, the optimal device with highly smooth morphology and sufficient charge extraction exhibits a champion power conversion efficiency of 11.24% and improved long‐term stability with 99.7% and 79.7% efficiency retention under dry air atmosphere and continuous 85 °C thermal stress, indicating the valuable potential application of the lattice solidified CsPbIBr2 WBG PSCs.

The use of superconducting magnetic energy storage (SMES) has been widely reported in the literature to mitigate various load frequency control (LFC) issues in a multi-area power system. Most of these are thyristor based SMES, employing proportional type controllers. In this paper, a supervisory adaptive model predictive control scheme (AMPC) is proposed for a voltage source converter based relatively small rating SMES for LFC application. The reference power command for the SMES is derived from the AMPC in such a manner that it effectively handles the operational constraints of the SMES system. A representative first-order system emulating the inner loop dynamics of the SMES chopper system, is tuned offline via genetic algorithm (GA) and is used to derive dynamic constraints for the cost function in AMPC. The effectiveness of the scheme is demonstrated through simulations.

Experimental investigation into the effects of different pilot amounts of dimethyl ether (DME) on the performance and emission of a single-cylinder directinjection DME engine is conducted. The results show that a DME engine can operate at a wider range of speeds and loads at quasi-homogenous charge compression ignition (QHCCI) mode. The brake thermal efficiency increases while the exhaust temperature decreases. NOx emission decreases by about 30%–50% although there is a slight increase in HC and CO emissions. NOx, HC and CO emissions increase with an increase in the amount of DME pilot. QHCCI is a good way to increase thermal efficiency and decrease NOx emission.

Abstract Predicting and optimizing radiative thermal properties have been acknowledged as an efficient way to improve thermal insulation performance of fibrous materials with high porosity. Based on experimental investigation of infrared spectral of ultrafine fibrous insulations with diameters of 520–650 nm, a method of calculating radiative thermal properties was presented by combining Rosseland equation, Mie scattering theory, Beer’s law and Subtractive Kramers–Kronig (SKK) relation. To ensure the calculation correct the uniqueness analysis was performed for Poly(vinylidene fluoride) (PVDF) fibers, which indicated the valid fiber diameter was less than 1.06 μm. The calculated thermal radiative conductivities by using the method agreed well with the measured data. The effect of fiber diameter on the thermal properties of the fibrous insulations was also investigated to minimize the radiative thermal conductivity. The results indicated that the minimized radiative thermal conductivities by regulating fiber diameters could be approximately 25% smaller than those for experimental fiber diameters. The method of predicting and minimizing radiative thermal conductivities of fibrous insulations demonstrated in this paper could be of great advantage to thermal engineering applications aiming to reducing heat loss and saving energy.

Reactive power consumption of the modern power system is a very important problem that to solve well. Due to the line commutated current source converter-based (LCC) HVDC system is a multivariable and strong coupling nonlinear system, it is difficult to model reactive power consumption of receiving end of LCC-HVDC by traditional analysis method. This paper proposes the reactive power consumption model of receiving end of LCC-HVDC on account of the radial basis function (RBF) neural network. The model with the turn-off angle for input, with reactive power loss on the inverter side of LCC-HVDC for output, using 500kV, 1000MW LCC-HVDC system PSCAD simulation test data training RBF neural network and testing network generalization ability. The results indicate that the reactive power loss model on account of the RBF neural network has much faster convergence speed and higher convergence precision.

A template‐agent can affect defect formation as well as influence interface properties, due to the rapid growth of perovskite film from the solution. Herein, diethylammonium iodide (DAI) is used as an effective template‐agent to control the perovskite crystallization during preparation. It is found that a very small amount of DAI in chlorobenzene (CB) can slow down the perovskite growth of the CH3NH3PbI3 (MAPbI3) film with more large grain size and compacted crystal‐grains resulting in the lesser grain boundaries (GBs) in favor of carrier transport in perovskite solar cells (PSCs). Moreover, some redundant PbI2 can be digested to form DA2PbI4. One part of DA2PbI4 can form the sub‐grains with the composition of (DA2PbI4)0.2(PbI2)0.8 to passivate the GB defects, and other part can cover the surface to passivate the surface defects in large MAPbI3 grains. Using an optimized DAI concentration of 0.5 mg mL−1 in CB solution, the corrsponding MAPbI3 PSC achieves an increased power conversion efficiency of 20.31% with suppressed current–voltage hysteresis. This DAI passivation strategy provides a simple approach to effectively assist the grain‐growth for improved device performance.

Abstract The prime purpose of this work is to prepare a novel kind of Pickering interfacial solid catalysts for biodiesel production to meet the requirements of highly efficiency and environmental benign. To achieve this goal, the core–shell P[xSPA-yDABCO]@SiO2@Fe3O4 composite materials with a shell of photo-responsive and base catalytic sites were manufactured by means of layer-by-layer fabrication method. The modified materials, entirely characterized by transmission electron microscopy (TEM), scanning electron microscope (SEM), Fourier transform infrared (FT-IR) spectra, X-ray powder diffraction (XRD) and magnetization versus magnetic (VSM) techniques, demonstrated sufficient catalytic active sites and photo-responsive sites. Among all the so-prepared catalysts, P[3SPA-2DABCO]@SiO2@Fe3O4 performs extremely well and can stabilize soybean oil-in-methanol Pickering emulsion for 24 h, achieving a biodiesel yield up to 98.2% at a catalyst dosage of 5 wt% after the reaction time of 5 h at 60 °C. Furthermore, the double responsive solid catalyst can be readily separated from the mixture of reaction by an external magnet and UV irradiation, and still presented superior catalytic activity after 6 cycles.