长江流域资源与环境 >> 2017, Vol. 26 >> Issue (11): 1784-.doi: 10.11870/cjlyzyyhj201711007

• 区域可持续发展 • 上一篇    下一篇

武汉城市圈城市空间相互作用时空演变分析

俞  艳, 童  艳*, 胡珊珊, 柯圆圆   

  1. (武汉理工大学资源与环境工程学院,湖北 武汉 430070)
  • 出版日期:2017-11-20

APPLICATION OF CFSR DATA TO OFFSHORE WIND FARMS’ LONG TERM WIND ENERGY RESOURCE ASSESSMENT

ZHANG Shuang-yi1,2,3, HU Fei1, WANG Yi-qun3,ZHANG Ji-li3   

  1. (1. State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China; 3. China Three Gorges New Energy Co., Ltd, Beijing 100053, China)
  • Online:2017-11-20

摘要: 引入人口重心、GIS网络分析及时序全局主成分分析法对传统引力模型进行修正,由此测算城市潜力值,以定量测度2002~2013年武汉城市圈城市空间相互作用强度,并结合IDW空间插值法和CTCSTP算法揭示出其时空演变规律。结果表明:(1)武汉城市圈各城市与其他城市间相互作用总强度总体呈增长趋势,其中武汉市增幅远高于其他城市,其与孝感-鄂州-黄石形成显著的轴线式发展格局,其余城市始终为空间联系弱区地带。(2)武汉市辐射带动作用日趋强劲,辐射范围逐年扩大并呈现一定的“圈层式”分布,其余城市辐射带动作用较弱,仅孝感市略显对外辐射趋势。(3)整个区域的空间相互作用表现出显著的“西南部低、北部高、东部适中”的空间分异特征,且其重心由南向北转移明显,区域发展不均衡特性显著。

Abstract: Offshore wind power is an important resource in China’s thirteenth five-year plan of renewable energy development. Compared with near-shore wind farms, offshore wind farms have more complicated wind conditions, higher construction costs and lower risk bearing capacity. It is necessary to carry out long term wind energy resource assessment, which can provide offshore wind farms’ feasibility study and risk assessment. The NCEP (National Centers for Environmental Prediction)’s CFSR (Climate Forecast System Reanalysis) data have advantages of global coverage, high resolution and large marine data sources. In this study, the NCEP and CFSR data were applied to access the long term wind energy resource of the Xiangshui offshore wind farm in Jiangsu province. The results showed: (1) the mean deviation and root-mean-square-error of the comparisons between CFSR and CFSR 10m height wind speed were -0.2 m·s-1 and 1.9 m·s-1, respectively.(2) The correlation coefficients between CFSR data’s 10m height wind speed and the offshore mast data’s 90 m height wind speed were 0.359-0.619, which were improved by 40%~90% compared to the meteorological station’s correlation coefficients. CFSR data could improve the long term correction method’s accuracy and correction result’s reliability. (3) The recent 30-year wind data of the offshore mast’s 90 m height were obtained by application of CFSR data to long term correction, with the multi-annual mean wind speed of 6.9  m·s-1 and the standard deviation of 0.2  m·s-1 (2.9%). (4) The recent 30-years equivalent full load hours of the offshore wind farm were calculated by the wind energy resource assessment software WAsP, with the multi-year mean value of 2 423 h and the standard deviation of 112 h (4.6%). (5) By the evaluation by the normal distribution model, the minimum yearly mean wind speeds in the 25 (the lifetime of offshore wind farms), 10 and 5 years period were 6.6, 6.7, and 6.8 m·s-1, respectively; and the minimum equivalent full load hours were 2 227, 2 280, and 2 329 h, respectively. In conclusion, it is reasonable that applying the CFSR data on the offshore wind farms’ long term wind energy resource assessment.

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