Energy-conservation based generation dispatching is the revolutionary change in operation mode which could reduce energy consumption and pollutant emissions, promote power industry restructuring, and achieve sustainable development. Social benefit evaluation of the energy-conservation based generation dispatching under the new situation and environment has come into being as an important theoretical issue. A new scenario analysis based social benefit evaluating method, which is implemented by comparing the energy consumption and pollutant emissions of different scenarios defined by the key indicators, i.e., load factor and coal consumption structure, is proposed in the paper. Then the composition of social benefit is analyzed from the point of the dispatching mode and the coal consumption structure. The method proposed is of clear physical meaning. It is not only practical, but also applicable for the implementation of energy-conservation based generation dispatching in different phases and with different goals.

The variability characteristics of Guangdong daily power load from 2002 to 2004 and its connection to meteorological variables are analyzed with wavelet analysis and correlation analysis. Prediction equations are established using optimization subset regression. The results show that a linear increasing trend is very significant and seasonal change is obvious. The power load exhibits significant quasi-weekly (5 – 7 days) oscillation, quasi-by-weekly (10 – 20 days) oscillation and intraseasonal (30 – 60 days) oscillation. These oscillations are caused by atmospheric low frequency oscillation and public holidays. The variation of Guangdong daily power load is obviously in decrease on Sundays, shaping like a funnel during Chinese New Year in particular. The minimum is found at the first and second day and the power load gradually increases to normal level after the third day during the long vacation of Labor Day and National Day. Guangdong power load is the most sensitive to temperature, which is the main affecting factor, as in other areas in China. The power load also has relationship with other meteorological elements to some extent during different seasons. The maximum of power load in summer, minimum during Chinese New Year and variation during Labor Day and National Day are well fitted and predicted using the equation established by optimization subset regression and accounting for the effect of workdays and holidays.