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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.

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.

In this paper, a numerical method coupling moment method with circuit theory is used to analyze the influence of the grounding material's property on the performance of grounding grids. It can be seen that for a large grounding grid, when the soil resistivity is small, the influence of both resistivity and permeability of grounding material on the performance of grounding grids is great. If the frequency is not very high, the grounding material's resistivity can affect the performance of the grounding grid obviously. If the frequency is very low or very high, the effect of the grounding material's permeability on the performance of the grounding grid is small, while the effect is obvious at other frequency.

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.

Abstract Transient thermal behaviors of ultra-supercritical steam turbine control valves during the cold start warm-up process of steam turbine systems were comprehensively studied using conjugate heat transfer (CHT) simulation. The geometrical configurations and boundary conditions used in simulation were identical to the field setup in a thermal power plant. The simulated temperature variations were first validated using measurements by the flush-mounted thermocouples inside the solid valve bodies. The CHT simulation implementing the shear stress transport (SST) turbulence model demonstrated good agreement with the field data, and the overall numerical errors were below 10%; however, the numerical errors of the simulation, which used empirical heat transfer coefficients at the fluid–solid interfaces, reached 40%. The determined temperature differences between the cold valve bodies with the hot steam flow decreased significantly. Specifically, the temperature differences along the inner wall surfaces of the valve bodies decreased to less than 50 °C. Further investigation of the transient heat flux distributions and Nusselt number distributions confirmed that the unsteady flow behaviors, such as the alternating oscillations of the annular wall-attached jet, the central reverse flow and the intermediate shear layer instabilities, enhanced the fluid–solid heat convection process and thus contributed to the warming up of the solid valve bodies.

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.