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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.

Abstract Previous studies have shown that titanium-based glazes containing silicon and calcium tend to crystallize into titanite. In this work, ZnO was found to be an effective additive to produce rutile but not titanite. Images of the micromorphology showed that the glaze doped with 8 wt% ZnO was UV–VIS–NIR of acicular rutile and granular silica. The refractive index of crystalline rutile is higher than that of crystalline titanite, which leads to higher reflectance in the near-infrared range. However, excessive rutile counteracted this improvement in the UV–VIS–NIR spectrum reflectance of solar energy because it decreased the visible spectrum reflectance. As a result, the best ratio of added ZnO was confirmed to be 2 wt%.

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.

Microwave-assisted alkali pre-treatment of wheat straw and its enzymatic hydrolysis were investigated and compared with the conventional alkali pre-treatment process. First, the effect of microwave power and pre-treatment time on the weight loss and composition of wheat straw was examined. The results show that the higher microwave power with shorter pre-treatment time and the lower microwave power with longer pre-treatment time had the same effect on the weight loss and composition at the same energy consumption. The comparison was then made between the effect of the microwave-assisted alkali pre-treatment and the conventional alkali one on the weight loss and composition of wheat straw. The wheat straw had a weight loss of 48·4% and a composition of cellulose 79·6%, lignin 5·7% and hemicellulose 7·8% after 25 min microwave-assisted alkali pre-treatment at 700 W, compared with a weight loss of 44·7% and a composition of cellulose 73·5%, lignin 7·2% and hemicellulose 11·2% after 60 min conventional alkali pre-treatment. The microwave-assisted alkali pre-treatment removed more lignin and hemicellulose from wheat straw with shorter pre-treatment time compared with the conventional alkali one. Finally, the enzymatic hydrolysis of pre-treated wheat straw (substrate concentration 50 g l−1, enzyme loading 20 mg g−1 substrate) was also investigated and the results indicate that the microwave-assisted alkali pre-treated wheat straw had higher hydrolysis rate, reducing sugar concentration and glucose content in the hydrolysate than the conventional alkali pre-treated one. Microwave-assisted alkali pre-treatment is a potential alternative of wheat straw pre-treatment for its enzymatic hydrolysis.

Interest in the highly efficient energy hub (EH) model has been growing despite the high computational requirements of planning for a multi-energy, multi-device operation. To address both the device size limitation and the multi-scenario issue, we propose a new solution methodology for solving the EH planning problem. In the method, the decision variables are device sizes. First, a dimension reduction technique is proposed to address the curse of dimensionality based on the correlation of unknown variables such as the capacities of different devices in an EH. Second, to avoid local convergence, a solution method called the variable-sized unimodal searching (VUS) approach is proposed to assure a global optimal planning scheme for the one-dimensional non-convex optimization model obtained from the preceding dimension reduction process. The case study indicates that the proposed approach has a higher computing efficiency than the Benders decomposition (BD) algorithm to deal with a scenario-based stochastic planning problem with a large number of scenarios. Thus, the effectiveness of the EH planning approach is verified.