Dongxiao Niu

Knowledge Mining Based on Environmental Simulation Applied to Wind Farm Power Forecasting

Considering the inherent variability and uncertainty of wind power generation, in this study, a self-organizing map (SOM) combined with rough set theory clustering technique (RST) is proposed to extract the relative knowledge and to choose the most similar history situation and efficient data for wind power forecasting with numerical weather prediction (NWP). Through integrating the SOM and RST methods to cluster the historical data into several classes, the approach could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy echo state network (ESN) trained by the class of the forecasting day that is adopted to forecast the wind power output accordingly. The developed methods are applied to a case of power forecasting in a wind farm located in northwest of China with wind power data from April 1, 2008, to May 6, 2009. In order to verify its effectiveness, the performance of the proposed method is compared with the traditional backpropagation neural network (BP). The results demonstrated that knowledge mining led to a promising improvement in the performance for wind farm power forecasting.

Environmental and Economic Optimization Model for Electric System Planning in Ningxia, China: Inexact Stochastic Risk-Aversion Programming Approach

The main goal of this paper is to provide a novel risk aversion model for long-term electric power system planning from the manager’s perspective with the consideration of various uncertainties. In the proposed method, interval parameter programming and two-stage stochastic programming are integrated to deal with the technical, economics, and policy uncertainties. Moreover, downside risk theory is introduced to balance the trade-off between the profit and risk according to the decision-maker’s risk aversion attitude. To verify the effectiveness and practical application of this approach, an inexact stochastic risk aversion model is developed for regional electric system planning and management in Ningxia Hui Autonomous Region, China. The series of solutions provide the decision-maker with the optimal investment strategy and operation management under different future emission reduction scenarios and risk-aversion levels. The results indicated that pollution control devices are still the main measures to achieve the current mitigation goal and the adjustment of generation structure would play an important role in the future cleaner electricity system with the stricter environmental policy. In addition, the model can be used for generating decision alternatives and helping decision-makers identify desired energy structure adjustment and pollutants/carbon mitigation abatement policies under various economic and system-reliability constraints.

A New Algorithm of Parameter Estimation for the Logistic Equation in Modeling CO2 Emissions from Fossil Fuel Combustion

CO2 emissions from fossil fuel combustion have been considered as the most important driving factor of global climate change. A complete understanding of the rules of CO2 emissions is warranted in modifying the climate change mitigation policy. The current paper advanced a new algorithm of parameter estimation for the logistic equation, which was used to simulate the trend of CO2 emissions from fossil fuel combustion. The differential equation of the transformed logistic equation was used as the beginning of the parameter estimation. A discretization method was then designed to input the observed samples. After minimizing the residual sum of squares and letting the summation of the residual be equal to 0, the estimated parameters were obtained. Finally, this parameter estimation algorithm was applied to the carbon emissions in China to examine the simulation precision. The error analysis indicators mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), maximal absolute percentage error (MaxAPE), and geometric mean relative absolute error (GMRAE) all showed that the new algorithm was better than the previous ones.

Assessment of power industry development in China: Possibilities and challenges

Chinas power industry is facing the issue of reducing carbon emissions, a particularly important matter to address during the industrial development. Based on the emission reduction status of power industries in China, the possibilities and the challenges of dealing with climate change for Chinese power industries are discussed in this paper by employing PEST (political, economic, social, technological)-strengths, weaknesses, opportunities, and threats analysis. The Tradable Green Certificate (TGC) system and the Carbon Emission Trading scheme for power industry development and environmental protection are analyzed as well. The results show that (1) the possibilities of developing power industries and addressing the climate change issue involve internal advantages (three strengths) and external chances (four opportunities); (2) the challenges for the Chinese power industry involve internal disadvantages (three weaknesses) and external unfavorable factors (four threats); and (3) both the TGC planning and the carbon emission scheme, as an efficient market-oriented strategic change, can jointly adjust the structure of power industries.Chinas power industry is facing the issue of reducing carbon emissions, a particularly important matter to address during the industrial development. Based on the emission reduction status of power industries in China, the possibilities and the challenges of dealing with climate change for Chinese power industries are discussed in this paper by employing PEST (political, economic, social, technological)-strengths, weaknesses, opportunities, and threats analysis. The Tradable Green Certificate (TGC) system and the Carbon Emission Trading scheme for power industry development and environmental protection are analyzed as well. The results show that (1) the possibilities of developing power industries and addressing the climate change issue involve internal advantages (three strengths) and external chances (four opportunities); (2) the challenges for the Chinese power industry involve internal disadvantages (three weaknesses) and external unfavorable factors (four threats); and (3) both the TGC planning and ...

Sensitivity and trend analysis of carbon emissions embodied in China's international trade with input-output technique and nonhomogeneous exponential growth model

This paper provides a quantitative analysis of the sensitivity, amount, and the development trend of carbon emissions embodied in Chinas international trade. With the input-output technique, nonhomogeneous exponential growth model, and carbon transmission-relative data, the following conclusions were drawn: (a) The total (direct and indirect) carbon intensity of each industrial sector was measured. Of all the 27 industrial sectors, Production and Supply of Electric Power and Heat Power ranks first. Because of the large consumption of electric power by nearly all the industrial sectors, encouraging the electric power sectors to utilize non-fossil energy (especially wind and photovoltaics), to improve the generation efficiency, and to import electric power overseas is crucial for decreasing the overall level of Chinas carbon intensity. (b) The amount of carbon transmission embodied in exports and imports of each industrial sector was also measured. Owing to its enormous international trade values, the sector of Manufacture of Electrical Machinery and Equipment ranks first, with absolute predominance in both exports and imports. Adjusting Chinas industrial policy to decrease the net export of this sector would significantly reduce the amount of net carbon transmission in the country. (c) The future net carbon transmission of each industrial sector was forecasted. Trend analysis indicates that changes in the overall international trade situation would cause the carbon transmission amount embodied in exports in China to become less than that embodied in imports since 2015.