中国风电的时空分布特征和发展趋势

利用1990-2005年中国和英国风电发展数据,对两国风电的时空分布特征和发展趋势进行了比较分析。据英国风电发展的阶段性,把中国风电发展划分为试验性发展、规模发展、过渡发展3个阶段,并预测到2007年步入大规模发展阶段。因此,未来几年,中国风电场数目和规模将会快速增长,并且由集中走向高密度集中分布。据对影响风电场省级时空分布的自然、市场和政策共8个选择因子的主成分分析和系统聚类分析结果,将中国风电场按省级划分为3个区域和8个亚区域,对聚类结果进行逐步判别回判,结果表明准确率达92.6%。

Studies on Characteristics of the Spatial-Temporal Distribution and Developmet Trend for Wind Farms in China

Wind energy,as a kind of reproducible resource without any pollution,has been developed fast all over the world in the past 20 years.Early of 2006,wind farms have been set up in 66 countries with an accumulated installed capacity up to 59.322GW,to which China has contributed 1.262GW.At present,there are 59 wind farms built in 15 provinces in China.It has shown that wind power is the most rapidly developing continuable energy resource in the whole world and in China,where the average rise ratio reached 28.6% and 42.8% in the past 10 years. According to the plan of China,wind power used for electricity will reach 30GW in 2020.Thus, study on location selection and spatial distribution of wind farms is becoming more and more important in China. Based on the pertinent data from 1991 to 2005 of both China and UK,the most interrelated countries in the developing process for wind power use compared with other four countries via correlation coefficients analysis,the evolution process of wind use could be divided into three comparative stages:examination stage,scale developing stage and transition developing stage,and future large scale developing stage.Compared with UK, it could be forecasted that the number of wind farms in China will increase fast,and the pattern of spatial distribution will change from convergence to cluster.With the methods of Principal Component Analysis(PCA)and Hierarchical Cluster Analysis(HCA)using eight variables about the nature,market and policy, checked with Discriminating Analysis(DA),wind energy development in China could be divided into three regions and eight sub-regions,which are scale developing region,steadily developing region and under-exploited region.The result indicated that the accuracy is 92.6% of the original grouped cases classified.This study is helpful for the further wind energy utilization in China.