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Second-generation bioethanol can be produced from various lignocellulosic biomasses such as wood, agricultural or forest residues. Lignocellulosic biomass is inexpensive, renewable and abundant source for bioethanol production. The conversion of lignocellulosic biomass to bioethanol could be a promising technology though the process has several challenges and limitations such as biomass transport and handling, and efficient pretreatment methods for total delignification of lignocellulosics. Proper pretreatment methods can increase concentrations of fermentable sugars after enzymatic saccharification, thereby improving the efficiency of the whole process. Conversion of glucose as well as xylose to bioethanol needs some new fermentation technologies to make the whole process inexpensive. The main goal of pretreatment is to increase the digestibility of maximum available sugars. Each pretreatment process has a specific effect on the cellulose, hemicellulose and lignin fraction; thus, different pretreatment methods and conditions should be chosen according to the process configuration selected for the subsequent hydrolysis and fermentation steps. The cost of ethanol production from lignocellulosic biomass in current technologies is relatively high. Additionally, low yield still remains as one of the main challenges. This paper reviews the various technologies for maximum conversion of cellulose and hemicelluloses fraction to ethanol, and it point outs several key properties that should be targeted for low cost and maximum yield.

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.

<abstract> <p>The rising proportion of inverter-based renewable energy sources in current power systems has reduced the rotational inertia of overall microgrid systems. This may cause high-frequency fluctuations in the system leading to system instability. Several initiatives have been suggested concerning inertia emulation based on other integrated external energy sources, such as energy storage systems, to combat the ever-declining issue of inertia. Hence, to deal with the aforementioned issue, we suggest the development of an optimal fractional sliding mode control (FSMC)-based frequency stabilization strategy for an industrial hybrid microgrid. An explicit state-space industrial microgrids model comprised of several coordinated energy sources along with loads, storage systems, photovoltaic and wind farms, is considered. In addition to this, the impact of electric vehicles and batteries with adequate control of the state of charge was investigated due to their short regulation times and this helps to balance the power supply and demand that in turn brings the minimization of the frequency deviations. The performance of the FSMC controller is enhanced by setting optimal parameters by employing the tuning strategy based on an iterative teaching-learning-based optimizer (ITLBO). To justify the efficacy of the proposed controller, the simulated results were obtained under several system conditions by using a vehicle simulator in a MATLAB/Simulink environment. The results reveal the enhanced performance of the ITLBO optimized fractional sliding mode control to effectively damp the frequency oscillations and retain the frequency stability with robustness, quick damping, and reliability under different system conditions.</p> </abstract>

Smart grid (SG) is an innovative technology which aims to make the conventional power grids capable enough to handle the ever increasing demands of power in an efficient manner. SG technology renders the electric distribution system with the capability of accumulating energy from various sources like wind, solar etc. But these sources have intermittency issues which can be handled in an effective manner with the coupling of electric vehicles (EVs) into the SGs. Thus, this paper presents a novel concept in the vehicle-to-grid (V2G) configuration. The primary objective of this paper to provide frequency support to grid by regulating the charging and discharging rates of EVs. These EVs are made to charge and discharge their respective energies at the charging stations (CSs) based on grid's overall requirements. Aggregators (AGs) at the CS level have been specially deployed to regulate EVs activities and maintain grid's stability. It has been verified through extensive simulations that EVs in V2G environment can stabilize the grid in terms of frequency if the coordination amongst the EVs is achieved through aggregators. The results obtained clearly depict that the controlled charging and discharging of EVs' battery can stabilize the grid in terms of frequency.

The most severe failures in power grids are often characterized as cascading failures where an initial event triggers consequent failures all along the grid often leading to blackouts. Upon identifying a failure and its cascade potential, timely control actions should be performed by the grid operators to mitigate the effect of the cascade. These actions have to be delivered to one or more control devices, creating a dependency between the power grid and its control network. This paper examines the dependency of the operation of the power grid on the control network. Different from literature studies on failure control, our dependency model captures the impact of networking parameters. We formulate an algorithmic model that describes the impact of this dependency on cascade control. Based on this model, we propose an efficient cascade control algorithm using load shedding with consideration of delays in the communication network for power grids. Finally, we evaluate the impact of the power-communication network dependency with uncontrolled grids, ideal/simple control grids and our proposed control scheme. The results demonstrate that the proposed algorithm can significantly reduce the failure of power lines while sustaining larger power demand for users.

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.