A medium and long-term carbon emission forecasting method for provincial power grid
The development of low-carbon electric power industry is a key way to deal with the greenhouse effect and lower the PM2.5. When integrated into power grid, renewable energy can efficiently cut down the output of the thermal power and reduce the carbon emission of the grid significantly. However, it lacks a more comprehensive and systematic approach to forecast a medium and long-term carbon emission for provincial power grid. Thus, in this paper, life cycle assessment (LCA) method and gray prediction model are used to forecast the medium and long-term carbon emission. In addition, the carbon intensity of provincial power grid is evaluated based on historical data such as electricity consumption, gross domestic product (GDP), carbon emission coefficient. Taking Jiangxi power grid as the example, the results indicate that the proposed method is of good accuracy and the integration of renewable energy can effectively reduce the carbon emission and the carbon intensity.
Gray-Regression Variable Weight Combination Model for Load Forecasting
A gray model and regression model based middle and long term load forecasting method using variable weight combination model is proposed. In view of the shortcomings of grey prediction model is not very suitable for middle and long term load forecasting, the equivalent dimensions additional data processing technology is adopted to build the equivalent dimensions additional grey model to improve the model. At the same time, there are some characteristic within mid-long term load forecasting such as the long study time span, the complex factors with large uncertainty which have great influence on load forecasting, and the possible original error occurring in basic data of forecasting, the time-varying weight combinational prediction method is adopted to overcome the shortcomings of the fixed weight, it is more practical. The example results show that this model is applicable in the long-term load forecasting, and it has a high forecasting accuracy.
The impacts of carbon tax on energy intensity and economic growth – A dynamic evolution analysis on the case of China
This paper examines the impacts of carbon tax on energy intensity and economic growth in a novel four-dimensional energy-saving and emission-reduction system with carbon tax constraints. Based on Lyapunov exponents and bifurcation diagrams, the dynamic behavior of the system is analyzed. The quantitative coefficients of the actual system are identified by artificial neural network. A scenario study is undertaken by observing the dynamic evolution behavior of energy intensity and economic growth in reality. The concept of turning point of energy intensity in the four-dimensional dynamic system is put forward for the first time. By adjusting the correlation coefficients of the four-dimensional system, more effective methods being performed to steadily and diligently reduce energy intensity. Take for instance the situation in China, the problem of when and how to introduce carbon tax are settled within the framework of the four-dimensional dynamic system. The results show that, as the tax levy point of carbon tax grows larger, the energy intensity of the four-dimensional system could be controlled better. It is both important and necessary to note the inhibition effect of these changes on economic growth. The best time to levy carbon tax and the best tax levy point are achieved after a comprehensive analysis within the framework of the four-dimensional dynamic system. The more appropriate time carbon tax is started, the higher growth rate of carbon tax is adopted, the better corresponding policies and laws are made, the easier the carbon emissions could be controlled and the more energy intensity could be declined, so as to achieve the goal of reducing the carbon dioxide emissions and keeping proper energy intensity. Numerical simulations are carried out to demonstrate the results.