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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.

Abstract Bio-based polyurethane (BPU) foams were successfully prepared using hydrothermally liquefied wheat straw (WS) to substitute a mass fraction of up to 50% of polyols. Response surface methodology (RSM) based on central composite design (CCD) was employed to optimize four process parameters: NCO/OH molar ratio, loading of crosslinking agent (glycerol), loading of catalyst (a mixture of triethylene diamine, stannous octoate, and triethanolamine), and loading of blowing agent (water) for the maximum compression strength of the rigid BPU foams. With the quadratic orthogonal regression model, verified by experimentation, the maximum compression strength of approximately 180 kPa was obtained at the following optimal conditions: NCO/OH molar ratio of 1.24:1, glycerol addition of 12.11%, catalyst loading of 0.76%, and blowing agent addition of 1.31% in relation to the total mass of polyols. The BPU foam prepared at the optimal conditions exhibits good thermal conductivity (0.045 Wm−1K−1) and thermal stability, comparable to those of a reference foam prepared with 100% PPG400.

Abstract Canada has numerous climatic and geographical regions and the Canadian housing stock (CHS) is diversified in terms of vintage, geometry, construction materials, envelope, occupancy, energy sources and heating, ventilation and air conditioning system and equipment. Therefore, strategies to achieve net zero energy (NZE) status with the current stock of houses need to be devised considering the unique characteristics of the housing stock, the economic conditions and energy mix available in each region. Identifying and assessing pathways for converting existing houses to NZE buildings at the housing stock level is a complex and multifaceted problem and requires extensive analysis on the impact of energy efficiency and renewable/alternative energy technology retrofits on the energy use and GHG emissions of households. A techno-economic analysis of retrofitting renewable/alternative energy technologies in the CHS to reduce energy consumption and GHG emissions was conducted to develop strategies to achieve or approach NZE status for Canadian houses. The results indicate that substantial energy savings and GHG emission reductions are techno-economically feasible for the CHS through careful selection of retrofit options. While achieving large scale conversion of existing houses to NZEB is not feasible, approaching NZE status is a realistic goal for a large percentage of Canadian houses.

Cold-start (subzero startup) capability of polymer electrolyte fuel cell (PEFC) is of great importance. In this paper, the effects of the micro-porous-layer (MPL), varying start-up temperatures, start-up current density variation on the cold-start operation are investigated. We found PEFC with anode micro-porous-layer (AMPL), compared with one with CMPL, has higher possibility of successful cold start. By analyzing the anode and cathode pressure revolution of PEFC, the effect of MPL on the super cooled water removal and ice formation at different temperatures (-7,-10,-15 and-20℃) are discussed . In addition, we investigate the ice distribution in MEA through the X-ray device, by comparing the initial image when fuel cells shut down (before the produce the water) and final image after failed cold-start. We also explore the negative voltage phenomenon in the initial process of cold-start operation.

Chromium (Cr) exposure during gestation causes malformations in animal experiments. In this multicenter case-control study, we initially involved 130 orofacial clefts (OFCs) and 260 controls to assess the association between Cr concentration and risk for OFCs. Then, umbilical cord serum (49 vs. 119) and cord tissue (84 vs. 142) were used to validate the association between Cr and OFCs. We found that maternal serum Cr concentrations in OFC cases were significantly higher than those in controls. Compared with the lowest tertile of maternal serum Cr concentration, the highest tertile of Cr increased the risk for OFCs [OR = 2.14 (1.14-4.05)]. In the validation cohort of umbilical cord serum and tissue, higher concentrations of Cr were associated with increased risks for OFCs in a dose-dependent manner (all Ps for trends <0.05). Cr concentrations in maternal serum and cord serum showed a positive correlation. The Cr concentration in cord serum was inversely correlated with egg and milk consumption frequencies, and the Cr concentration in cord tissue was positively associated with indoor coal burning. In conclusion, prenatal Cr exposure is a risk factor for OFCs, and indoor coal burning during pregnancy may be one of the sources of Cr exposure.

Abstract To alleviate the shortage of natural gas resource and ease carbon emissions, a novel solar-driven combined cooling, heating and power (CCHP) system is designed and optimized using the genetic algorithm in the work. Different from the process of direct combustion in a conventional CCHP system, natural gas is firstly converted into syngas by a solar-driven natural gas reforming step, which is consumed in an efficient tri-generation system. Energy, economic and environmental evaluations on five office buildings in different climate zones in China are implemented to validate the advantages of the proposed system. Results show that the annual maximum primary energy saving, total cost saving, and CO2 emission reduction are 69.76%, 49.80%, and 71.55%, respectively. The system located in severe cold zones, where solar energy is abundant and building requires more heat load in whole year, achieves the highest benefits in comparison with separate systems. Furthermore, the sensitivities on the price fluctuations of electricity, natural gas and solar field to the system profits are investigated, which indicates that the influence of electricity price on the system performance is the most significant. Thus, a promising method for reducing the natural gas consumption and improving the utilization efficiency of solar energy is provided.

This work presents a new method to evaluate generation reserve margins in systems with renewable sources. In assessing the adequacy of generation reserve amounts, besides failures in generating units, their capacity intermittencies, unavailability, and capacity limits of the transmission system are duly considered. Risk indices are evaluated using quasi-sequential Monte Carlo simulation techniques. The cross-entropy method is used to treat rare events and also to identify critical equipment for operation in each scenario. The proposed method is applied to the original IEEE RTS system and to a modified configuration with insertion of wind power plants. A subsystem of the Brazilian interconnected network is also used to illustrate the practicality of the proposed method.

People’s attention of transformer condition assessment is increasing in recent year and data mining is applied in transformer condition assessment with its obvious superiority in dealing with complex data and finding potential problems. In this paper, the process of the research on applying data mining to transformer condition assessment is summarized. The research results of data processing methods such as rough set theory, principal component analysis and colony algorithm as well as the research result of transformer condition assessment such as vector machine, neural network, Bayes network, association rules and fuzzy theory are introduced, analyzed and compared in detail. The advise on the choice and quantification of figure, the employ of the pattern recognition method, the blend of multi-information and visualization display are given.