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基于短期历史资料的风能预报中风速误差循环订正新方法

A New Cycle Correction Method for Wind Speed Error in Wind Energy Forecast Based on Short-term Historical Data

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【作者】 张铁军颜鹏程李照荣王有生李耀辉

【Author】 ZHANG Tiejun;YAN Pengcheng;LI Zhaorong;WANG Yousheng;LI Yaohui;College of Atmospheric Sciences,Lanzhou University;Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province/Key Laboratory of Arid Climatic Change and Reducing Disaster of China Meteorological Administration,Institute of Arid Meteorology,China Meteorological Administration;Meteorological Service Center of Gansu Province;

【机构】 兰州大学大气科学学院中国气象局兰州干旱气象研究所甘肃省干旱气候变化与减灾重点实验室/中国气象局干旱气候变化与减灾重点实验室甘肃省气象服务中心

【摘要】 风电场风能预报的准确性对于风力发电在并网过程中的稳定性有很大影响,提升风能预报水平能够有效减轻电网并网压力、降低经济运行成本。基于历史资料提出一种可快速更新的风速预报误差订正方法,该方法利用甘肃省风电功率预报系统的风速预报结果并结合实况资料对模拟风速的趋势、均值、方差进行订正,并应用于甘肃省内3个风电场(黑崖子、马昌山、南湫)的风速预报误差订正。结果表明:订正前风速的平均误差为23 m·s-1、订正后为12 m·s-1,误差率改善17%23%,本研究为风速误差订正提供了一个新思路和新方法。

【Abstract】 The accuracy of wind energy forecast of wind farm has a great influence on the stability of wind power generation in the process of grid-connected. Thus,to improve the level of wind energy forecast would reduce grid connection pressure effectively and reduce economic operation costs. Based on the historical data,a method to modify the wind speed forecast error was proposed in this paper. The method used the wind forecast results of wind power forecast in Gansu Province and combined the actual data to revise the trend,mean and variance of simulated wind speed. By applying the method in three wind farms including Heiyazi,Machangshan and Nanqiu,the results show that the average errors of corrected wind speed were 2-3 m·s-1 and 1-2 m·s-1 before and after revise,respectively,and the error rate was improved by 17%-23%,which reduced the wind speed forecast error effectively and raised the prediction level. This study provided a new idea and a new method for wind speed error correction.

【基金】 国家电网(河北)项目(SGTYHT/16-JS-198);国家自然科学基金(41375069,41405094,41605119);中国气象局兰州干旱气象研究所科研启动基金(KYS2016BSKY01)共同资助
  • 【文献出处】 干旱气象 ,Journal of Arid Meteorology , 编辑部邮箱 ,2017年06期
  • 【分类号】TM614
  • 【被引频次】6
  • 【下载频次】150
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