| 引用本文: | 罗 庆,晁 勤,王一波,罗建春,刘改倩.基于场景划分方法的风光出力耦合特性机理[J].电力自动化设备,2014,34(8): |
| LUO Qing,CHAO Qin,WANG Yibo,LUO Jianchun,LIU Gaiqian.Characteristics of wind-photovoltaic power output coupling based on scenario classification[J].Electric Power Automation Equipment,2014,34(8): |
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| 摘要: |
| 利用数据挖掘技术，根据风光出力特性采用不同场景数据划分方法对风光资源丰富地区的实际风电、光电出力数据进行场景划分，在划分的典型场景数据下对风光出力互补耦合特性进行分析，研究合成出力跟踪系统负荷的机制特性及提高出力预测精度问题，提出耦合度和跟踪负荷度计算方法。研究表明，风光互补合成出力耦合特性在一定程度上减小了出力的波动性，合成出力对系统负荷跟踪度达到了12.2 %，同时风光合成出力预测的耦合所得误差比单独的风、光电出力预测系统所得误差小。 |
| 关键词: 场景划分方法 风电 光伏 合成出力 耦合特性 跟踪负荷度 误差 预测 |
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| 基金项目:基金项目：国家国际科技合作专项资助项目（2013DFG61520）；国家自然科学基金资助项目（51267020）；教育部2012年高等学校博士学科点专项科研基金博导类联合资助课题（2012-6501110003） |
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| Characteristics of wind-photovoltaic power output coupling based on scenario classification |
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LUO Qing1,2, CHAO Qin1, WANG Yibo3, LUO Jianchun4, LIU Gaiqian5
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1.School of Electrical Engineering，Xinjiang University，Urumqi 830047，China;2.State Grid Electric Power Research Institute of Xinjiang Electric Power Company，Urumqi 830013，China;3.The Institute of Electrical Engineering of CAS，Beijing 100190，China;4.State Grid Wulong Power Supply Company，Chongqing 408500，China;5.Korla State Power Generation Company，Korla 836500，China
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| Abstract: |
| Based on the data mining technology，the actual wind and photovoltaic power outputs of rich-resource regions are classified by different scenario data classification methods according to their characteristics. Based on the typical scenario data classified，the coupling characteristics of the complementary wind and photovoltaic power outputs are analyzed. The mechanism of the coupled power output following the system load and the improvement of power output prediction precision are researched，and a method is proposed to calculate the coupling degree and load-following degree. Research shows that，the complementary coupling of wind and photovoltaic power outputs reduces the fluctuation of total power output in a certain degree，its load-following degree reaches 12.2 %，and its prediction error is less than that of either wind or photovoltaic power output prediction. |
| Key words: scenario classification method wind power photovoltaic power synthetic power coupling characteristics load-following degree errors forecasting |
