Electricity Demand Optimization And Forecasting Based On DSM Projects

With the rapid development of China’s science,technology and economy,electricity is becoming one of the most important forms of energy for its advantages of versatility, convenience and cleaness.As one of the largest comprehensive metropolises,Beijing’s power demand is growing rapidly,which results in the power grid becoming increasingly heavy burdened,the constantly increasing peak load and peak valley difference are specially highlighted,which leads to the operation of power grid to face great threats.In recent years,the electric power demand side management has proven to be a very efficient energy management measure to keep the balance of power supply and demand.Reasonable demand side management can not only effectively debase the power peak load and the peak valley difference,but also reduce carbon emissions.So it is very necessary for the in-depth analysis and research in the electric power demand side management and electric power demand forecasting.Firstly,the thesis used LMDI model to analyse the electric power demand of Beijing.The LMDI model resolved power consumption into economic scale effect, industrial structure effect and power intensity effect to analyze them separately.Analysis results indicate that the most effctive way to promote energy-saving and emission-reduction of Beijing is to improve the power eficiency.In the end,the thesis analyzed the technological methods of electric DSM in Beijing,including green lighting technology,cool storage air conditioning technology,motor technology and heat pump technology separately,and took DSM projects to show its good power saving effect and peak load transferring effect.Secondly,for the situation of a certain DSM lighting project,the thesis made different reform plans and build an electric power demand optimization model to optimize its power demand under the restriction of fund.Finally,according to the DSM projects statistics,the thesis made in-depth analysis on statistical data of the electrical characteristics of the energy saving projects,providing a basis for the subsequent electric power demand forecasting.Then according to the statistical data of energy-saving projects,the thesis used a bottom-up electric power forecasting method.According to the statistical data of the green lighting projects of energy saving,the regression analysis model was established,including multiple linear regression and regression with dummy variables analysis methods.According to discrete distribution data and small sample size of the statistics of air conditioning load projects,using grey neural network model to make electric power demand forecasting on them.In the end,the final plan was made to forecast the whole city’s power demand and energy saving potential with the entire statistics of the city.The results of this study provides references and basis for DSM projects of electric consumers,and for the drawing up of the power demand side management or energy-saving and emission-reduction targets of government departments.