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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.

Flue gas desulfurization (FGD) and selective catalytic reduction (SCR) technologies have been widely used to control the emissions of sulphur dioxide (SO2) and nitrogen oxides (NOX) from coal-fired power plants (CFPPs). Field measurements of emission characteristics of four conventional CFPPs indicated a significant increase in particulate ionic species, increasing PM2.5 emission with FGD and SCR installations. The mean concentrations of PM2.5 from all CFPPs tested were 3.79 ± 1.37 mg/m3 and 5.02 ± 1.73 mg/m3 at the FGD inlet and outlet, respectively, and the corresponding contributions of ionic species were 19.1 ± 7.7% and 38.2 ± 7.8%, respectively. The FGD was found to enhance the conversion of NH3 slip from the SCR to NH4+ in the PM2.5, together with the conversion of SO2 to SO42-, and increased the primary NH4+ and SO42- aerosol emissions by approximately 18.9 and 4.2 times, respectively. This adverse effect should be considered when updating the emission inventory of CFPPs and should draw the attention of policy-makers for future air pollution control.

Abstract Compared with other traditional energy sources, renewable energy, which results the less pollution and has numerous resources, is a significant factor in addressing the current issues of the serious environmental pollution and the resource depletion. Large-scale renewable energy integrated to the grid could bring change in both morphological structure and operation modes of energy transmission. Therefore, it is necessary to research the evolution mechanism of the future transmission network with a high proportion of the renewable energy. In this paper, an evolution framework of power system with high proportion of renewable energy is proposed. Firstly, a network equivalence and simplification based on power transfer distribution factors (PTDFs) is proposed, which can effectively simplify the decision-making process of evolution of large-scale power system. Then, an annual production simulation (8760 h) which takes into account renewable energy and load fluctuations is used to find out the bottleneck of the power grid. Based on the above methods, evolution strategy of power system with high proportion of renewable energy is studied for finding out optimal expansion strategy. A real power system of Zhejiang province is used as a test system. Test results demonstrate the feasibility of the proposed evolution framework.

To unlock the potential of flexible resources, a multi-time-scale economic scheduling strategy for the virtual power plant (VPP) to participate in the wholesale energy and reserve market considering large quantity of deferrable loads (DLs) aggregation and disaggregation is proposed in this paper. For the VPP multi-time-scale scheduling including day-ahead bidding and real-time operation, the following models are proposed, namely, DLs aggregation model based on clustering approach, economic scheduling model considering DLs aggregation, and DLs disaggregation model satisfying consumers’ requirements, respectively. The proposed strategy can realize the efficient management of massive DLs to reduce the energy management complexity and increase the overall economics with high computation efficiency, which indicate its promising application in the VPP economic scheduling.

Applicability problems of the widely-used aggregation-based representation of DFIG wind farms focus on the modelling “error and impact” in transient stability computation. In order to identify crucial LVRT behaviors of DFIGs, specifically in system-level analysis, control strategies including vector-orientation, closed-loop rotor-current regulation, and Crowbar protection are given a full consideration. It is proved that, the DFIG works with its electrical and mechanical dynamics decoupled, and the result is that the DFIG behaves as an ideal controlled-power source. The DFIG's power-modulation functions defined by the grid code distinguish it from the conventional synchronous generator, which also conveniently offers dominant dynamics for studying errors of aggregation. Next, errors caused by the coupling between distribution factors and LVRT behaviors of wind farms (but ignored in aggregation) are discussed. With some existing grid codes, these factors not only generate a distributive profile of residual voltages along the feeders, but more importantly nonuniform power responses among individual wind turbines. Impacts of aggregation's errors on transient stability of power systems are analyzed. The trend in the error and impact of aggregation is analytically surveyed in a rudimentary system, and further numerically verified in a multi-machine system.

Abstract In solid fuel (such as lignite) chemical looping combustion, solid fuels undergo pyrolysis and gasification. The volatiles from pyrolysis and the gasification product (CO/H 2 ) react with oxygen carriers. The gas conversion (to CO 2 /H 2 O) in the fuel reactor is a key point. However, char particles of different sizes and conversion ratios cause segregation in the fuel reactor, which influences the contact time between fuel gases and the carrier, thereby changing the gas conversion behavior. In order to gain information on obtaining a high gas conversion in the fuel reactor, this work focused on the effect of the char particle segregation on gas conversion. Different factors – the char particle size, the fluidizing gas velocity, and the oxygen carrier reactivity – were taken into account. Smaller char particles with low density would float on top of the fluidized bed, corresponding to a low gas conversion ( U mf in the Fe63Al bed) can reduce the segregation effect, resulting in a higher CO conversion. High reactivity carriers can convert CO completely although segregation exists, whereas low reactivity carriers exhibit the segregation effect and thus corresponds to a low CO conversion.

Abstract With increasing prosumers employed with flexible resources, advanced demand-side management has become of great importance. To this end, integrating demand-side flexible resources into electricity markets is a significant trend for smart energy systems. The continuous double auction (CDA) market is viewed as a promising P2P (peer to peer) market mechanism to enable interactions among demand side prosumers and consumers in distribution grids. To achieve optimal operations and maximize profits, prosumers in the electricity market must act as price makers to simultaneously optimize their operations and trading strategies. However, the CDA-based market is difficult to model explicitly because of its information-based clearing mechanism and the stochastic bidding behaviors of its participants. To facilitate prosumers actively participating in the CDA market, this paper proposes a novel prediction-integration strategy optimization (PISO) model. A surrogate market prediction model based on Extreme Learning Machine (ELM) is developed, which learns the interaction relationship between prosumer bidding actions and market responses from historical transaction data. Moreover, the prediction model can be conveniently transformed and integrated into the prosumer operation optimization model in the form of constraints. Therefore, prosumer operations and market trading strategies can be jointly optimized through the proposed approach, facilitating the integration of flexible resources into electricity markets. Numerical studies demonstrate the effectiveness of the proposed model by comparing with existing CDA trading strategies under various market conditions.

Abstract China has the advantage of learning from the mistakes made by nations that have developed their nuclear power energy system in the last century. Such mistakes encompass the lack of sustainable development of the nuclear energy and poor planning in the back-end of the nuclear fuel cycle. The present paper studies the evolution of a double strata cycle with fast reactor gradually replacing the LWR. It starts from 2035 and covers the historical development of nuclear energy in China to the year 2100. The paper studies the ADS impact on the NFC and estimates the number and the deploying schedule of the ADS reactors to limit the accumulation of minor actinides. The other aspects considered here are natural uranium resources, fuel utilization efficiency, proliferation and diversion risks, spent fuel production and overall materials flow. Additionally, perturbation calculations were performed to evaluate the impact of the uncertainty on key parameters. The results are discussed in view of the Chinese nuclear fuel cycle plan and their policy implications are thoroughly evaluated.

Abstract In the study, a three-dimensional CFD simulating model coupling with reasonable turbulent model and reduced chemical kinetic mechanism was established and validated. The auto-ignition development and knocking characteristics of a downsized spark-ignition (SI) gasoline rotary engine (RE) under different boosted conditions were numerically investigated. Results showed that as inlet pressure increased from 1.12 bar to 1.16 bar, the knocking intensity (KI) of the RE was enhanced gradually, and the knocking onset was advanced. However, with the further augment of inlet pressure, the KI did not further increase due to the larger heat dissipation loss caused by high turbulent kinetic energy in the long and narrow combustion chamber of the RE. This indicated that the structure of the downsized SI RE had a certain ability of knocking suppression when inlet pressure was sufficiently boosted. The KI of the RE was more serious in the trailing part of the combustion chamber as compared to other positions due to the unidirectional flow field, especially on both sides near the end cover in the trailing part of the combustion chamber. Therefore, it was concluded that strengthening the cooling of both sides near the end cover in the trailing part of the combustion chamber may be an effective way to reduce the KI of a downsized boosted SI RE. In addition, the KI is closely related to auto-ignition development processes, e.g. end-gas auto-ignition modes. Under the mass fraction of unburned mixtures at the moment of end-gas auto-ignition, the local KI caused directly by single hot-spot auto-ignition was higher than that caused by multiple hot-spots auto-ignitions, and the local KI caused by homogeneous auto-ignition was higher than that caused by multiple hot-spots auto-ignitions.