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Supercapacitors are power-type energy storage devices with high power density and long cycle life. However, the low energy density limits wider applications. In this paper, the working principle of supercapacitors was first introduced, and the characteristics of electrode materials needed and current progress were summarized. Then, the characteristics and related progress of aqueous, organic and ionic liquid electrolytes were introduced. Finally, the principle and the factors of energy density limitation of supercapacitors were pointed out, and the improvement methods were discussed from the aspects of electrode materials, electrolytes and the structure of supercapacitors, respectively.

The heat storage process of the high temperature thermal energy storage container using single-stage PCM for solar thermal power generation is numerically simulated. The change curves of the PCM's temperature and liquid rate over time during the single-stage thermal stage melting process and its cloud distributions of the liquid rate at different time have been obtained. Through the analysis of the curves and cloud figures, in the premise of ensuring the heat accumulation amount, three kinds of PCM cascade regenerative high temperature thermal energy storage container design schemes are derived and simulated on their regenerative performance. The result indicates that the cascade heat storage schemes greatly reduce the total heat storage time and the "dead zones" effects effectively on the performance of heat storage during the single-stage regenerative terminal stage, which can make the PCM liquid rate distribution to be more uniform. It provides the theoretical basis for the optimization design of the high temperature phase change thermal energy storage container for solar thermal power generation applications.

Designing novel harvesting system is necessary for collecting low-frequency renewable energy due to their environment dependent and intermittent characteristics. A strategy is proposed of using the multilevel feedback rectifying and BOOST/BUCK circuits, low power control chip and software control algorithm to tracking maximum power of input low-frequency energy(MPPT),meanwhile, supercapacitors are used as energy storage devices to replacing the batteries. Finally , use current transformer ,consequently, high efficiency to reach the effect of energy harvest, rectify and output. The results show that the maximum collecting efficiency is 91.16%, and that is 53.18% after integrating the input/output circuits for enhancing capability of driving loads.

At present,most of the improved?protocols based on LEACH (low energy adaptive clustering hierarchy) start from the aspect of cluster head election, cluster distribution, path selection, etc., but the redundancy between the data is not considered enough. This paper proposes an improved LEACH protocol combining relative information entropy (LEACH-CIE). Combined with the relative entropy model, this paper calculates the relative entropy value of the probability distribution of the data of the two adjacent cycles of the node, and then the relative entropy value is compared with the threshold to judge the redundancy of the data. By rejecting the transmission of redundant data, the purpose of reducing network energy consumption is achieved. In the data send stage, considering the communication distance, energy consumption ratio and other factors, a multi-hop forwarding routing considering its own residual energy (MFRCRE) is proposed, which can optimize node forwarding conditions and balance the energy consumption rate of each node in the network. Experimental results show that the method in this paper can effectively reduce and balance the energy consumption of network nodes to extend the network life cycle.

In view of the lack of sufficient computing, storage and communication network resources for computing equipment at the edge of inclement industrial environments, and the requirement for ms-level data processing response, the agile intelligent system named “SWIFT” was introduced from the general architecture, hardware and software composition and key technology. This system has been verified in wind power equipment, which improves the user′s ability to optimize wind turbine operation and maintenance detection, and reduces downtime loss and operation cost. At the same time, it also discussed that the agile intelligent cluster computing technology could be applied in not only intelligent operation and maintenance of single wind turbine，but also the intelligent operation of large-scale wind farm.

Based on the positive piezoelectric effect of piezoelectric ceramic(PZT), an energy harvesting device with cantilever beam is designed for the single pressing energy harvesting, which can be used for wireless switches and so on. The mathematical analysis of the response of PZT cantilever beam is carried out on the condition of single pressing. Through comparison of the performances, a cantilever beam is made of the selected P-81 piezoelectric plates which are superimposed on the both sides of the copper alloy sheet. Finite element analysis shows that the double piezoelectric plates connected in parallel can export higher power than that connected in series. However, the natural frequency has no relation with their connecting forms. It indicates that the connecting forms of the double piezoelectric plates have no effect on its strain. The harvester is simulated and tested under the single pressing condition with 10 kΩ resistance load. The calculated and tested maximum instantaneous powers are 28.93 mW and 27.46 mW, respectively. The energy harvesting circuit is designed according to the high impedance characteristic of the PZT at low frequency. A threshold switch circuit is used to control the discharge voltage. The experimental results show that the output electric energy of the energy harvesting circuit can reach 5.05 mJ. The energy can meet the requirements of driving LED, RF transmitter and so on.

In response to the national call for energy conservation and emission reduction, the energy saving and consumption reduction of base stations has become the top priority in today′s communications field, the energy-saving technologies of major operators will also become core competitiveness. This paper studies the Artificial Intelligence(AI)-based 5G base station energy-saving technology. Traditional base station energy-saving technologies are analyzed. The prospect of 5G base station energy-saving technology combined with AI technology is explored. The development direction of AI energy-saving technology has been deeply studied. The AI energy saving model and the AI-based collaborative energy saving scheme are highlighted.

As the number of users in cellular mobile networks increases, cellular network changed from the previous single architecture to heterogeneous architecture, base station energy consumption also showed an exponential growth. Energy harvesting is an promising technology to save on-grid power and reduce carbon emission in wireless communications. The cell selection in the HetNets is the critical factor of affecting network performance. In this paper, from the energy harvesting, energy cooperation, heterogeneous networks and other aspects, a cell selection algorithm is proposed, which can compromise energy harvesting, energy consumption, load balancing and other criteria. The simulation results show that this algorithm can reduce the base station′s demand for grid energy, increase the possibility of user selection, and satisfy the fairness and validity. It′s applicable to 5G green communications.

The volatility and randomness of wind speed have caused potential safety hazards to wind power grid integration. Improving wind speed forecasting is crucial to the stability of wind power systems and the development of wind energy. A novel short-term wind speed forecasting model (MI-RNN) was proposed based on mutual information (MI) and recursive neural network (RNN). In this model, the MI theory was introduced to select the optimal length of historical wind speed sequence (τ), and the method of using each τ step to forecast wind speed at the next time point was adopted to input the historical wind speed data into RNN for model training. The final wind speed forecasting result was output by the trained RNN model. Besides, the MI-RNN model was applied to the wind speed dataset collected from a wind farm and the forecasting accuracy was compared with that of the traditional wind forecasting methods. The results show that the MI-RNN model has achieved a higher forecasting accuracy compared with the commonly used wind farm wind speed forecasting methods, and can accurately forecast the future wind direction, which is expected to be applied to wind speed forecasting of wind farms with spatial dimensions.

Note: This report is CCICED "Energy efficiency and environmental protection economic policy" Task Force final report submitted to the CCICED 2009 annual meeting. Chinese and foreign experts concluded the research group developed to promote the environmental and economic policies of practical experience in the analysis of energy and environmental management and the foundation of the current status quo and problems of our country on to improve energy efficiency and environmental management as a starting point for research through economic means, propose relevant strategies and policy recommendations for the Chinese government. 按：本报告是国合会“能源效率和环境保护经济政策”课题组提交给国合会2009年年会的最终研究报告。课题组中外专家总结了发达国家推进环境经济政策的实践经验，在分析我国当前能源与环境管理现状和存在问题的基础上，以提高能源效率和环境管理水平为出发点，以经济手段为研究内容，为中国政府提出相关战略和政策建议。现摘编印发，以供参考。