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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.

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.

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.

In this study, Myriophyllum elatinoides growth under different nitrogen (N) concentrations (2, 250, 300, 350 and 400 mg L-1) and changes in rhizosphere bacterial community structure were investigated. High N (>300 mg L-1) concentrations caused reduction in M. elatinoides biomass. Growth tended to stabilize at 49 days. N concentration in roots were higher than that in stems and leaves under high N conditions. TN and NH4+ removal efficiencies reached 84.0% and 87.2%, respectively, in M. elatinoides surface flow constructed wetlands (SFCWs). Rhizosphere bacterial diversity increased over time. Proteobacteria, Firmicutes, Cyanobacteria, and Bacteroidetes dominated at the phylum level. Genera Turicibacter, Allochromatium, and Methylocystis increased at low N (<300 mg L-1) concentrations, while Pseudomonas increased at high N concentrations over the experimental period. Redundancy analysis showed that pH was strongly correlated with changes in rhizosphere bacterial community structure. These findings helped to insight into N removal mechanism in M. elatinoides.

Societal relations in rural areas have entered into a new stage of adjustment over the past decade. However, the adjustment, which might bring about profound societal changes in countryside as well as in China as a whole, have not been paid much attention and very few studies have been conducted from the perspective of ecological resource crises. We use the case of a village as an example to show how water pollution, as one of the contributory factors, possibly affect the transition of clans and societal changes in Chinese villages. Through observation and interviews, we find that there is an apparent rise of “New Clanism” within clans, which gradually abandons the tradition of supremacy of clan interests and places family or individual interests at top priority. We also find that clan boundaries get increasingly obscure since the integrity of clans is undermined by the rise of new interest groups across clans, but the boundaries remain relatively clear due to the consistency (albeit incomplete) of clan interests. Some new clan élites and representatives of new interest groups get involved in village governance, which indicates that their goals have shifted from natural resources to social or political capital. The significance of our findings is that they provide not only a unique perspective for the interaction between society and resources, but also some new ideas for the future study of rural China at the environment-social interface.

The global energy demand is increasing due to climate changes and carbon usages. Accumulating evidences showed energy sources using offshore wind from the sea can be added to increase our consumption capacity in long term. In addition, building offshore wind farms can also be environmentally advantageous compared to onshore farms. The assessment of wind energy resources is crucial for the site selection of wind farms. Currently, short-term wind forecast models have been developed to predict the wind power generation. However, methods are needed to improve the forecasting accuracy for ever-changing weather data. So, we try to use deep learning methods to predict long-term wind energy for identifying potential offshore wind farms. The experimental results indicate that PredRNN++ prediction model designed from the spatiotemporal perspective is feasible to evaluate long-term wind energy resources and has better performance than traditional LSTM.

Abstract To further improve the cycle efficiency with the heat transfer curves between higher than 350 °C heat resource and the evaporating working medium of the Kalina cycle and to reduce the exhaust temperature of heat resource, the dual-pressure vaporization Kalina cycle for cascade utilization of high-to-mid grade heat resource is proposed. The optimization was conducted for parameters in this modified Kalina cycle such as concentrations of work solution and basic solution, evaporation dew point temperature. Under the conditions of inlet temperatures of heat resource and cooling water of respectively 400 °C and 25 °C and the constraints of proper heat transfer pinch point temperature differences, the maximum evaporation pressure not exceeds 20 MPa, the vapour quality at the turbine outlet is greater than 0.85 and the exhaust temperature of heat resource is not lower than 90 °C, the optimum parameters are obtained that the work and basic concentrations are 0.45 and 0.272 respectively, the dew point temperature of evaporation is 300 °C, and the corresponding power recovery efficiency of the dual-pressure vaporization Kalina cycle reaches 27%, which is 17% higher than that of the Kalina cycle with optimum parameters.

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.

In recent years, the energy consumption structure has been accelerating towards clean and low-carbon globally, and China has also set positive goals for new energy development, vigorously promoting the development and utilization of renewable energy, accelerating the implementation of renewable energy substitution actions, and focusing on improving the consumption capacity of new energy. However, due to the intermittent and unstable characteristics of renewable energy, it is difficult to meet the demands of the power load side in practical applications. Energy storage is an important link for the grid to efficiently accept new energy, which can significantly improve the consumption of new energy electricity such as wind and photovoltaics by the power grid, ensuring the safe and reliable operation of the grid system, but energy storage is a high-cost resource. Therefore, this paper focuses on the energy storage scenarios for a big data industrial park and studies the energy storage capacity allocation plan and business model of big data industrial park. Firstly, based on the characteristics of the big data industrial park, three energy storage application scenarios were designed, which are grid center, user center, and market center. On this basis, an optimal energy storage configuration model that maximizes total profits was established, and financial evaluation methods were used to analyze the corresponding business models. Finally, taking an actual big data industrial park as an example, the economic viability of energy storage configuration schemes under two scenarios was discussed, and an energy storage system construction plan was proposed to promote the zero-carbon target of the big data industrial park.