中国电机工程学报
中國電機工程學報
중국전궤공정학보
ZHONGGUO DIANJI GONGCHENG XUEBAO
2013年
34期
119-127
,共9页
丁华杰%宋永华%胡泽春%吴金城%范晓旭
丁華傑%宋永華%鬍澤春%吳金城%範曉旭
정화걸%송영화%호택춘%오금성%범효욱
风电场%功率特性%日前预测误差%概率分布%最大似然估计
風電場%功率特性%日前預測誤差%概率分佈%最大似然估計
풍전장%공솔특성%일전예측오차%개솔분포%최대사연고계
wind farm%power curve%day-ahead forecast error%probability distribution%maximum likelihood estimation
风电功率预测误差的分布对电力系统调度决策、备用安排等有着重要的影响。基于风电场日前功率预测“从风速到功率”的实际过程,提出了确定日前风电功率预测误差分布的分析方法。首先,使用最小二乘法拟合风电场的功率特性,分析风电场功率特性拟合误差及其对日前风电功率预测误差的影响;其次,通过模拟日前风速的预测误差,研究风速预测误差对日前风电功率预测误差的影响;最后,使用蒙特卡罗双层抽样技术模拟上述2种误差的共同作用,并使用最大似然估计技术,确定各风速区间对应的日前功率预测误差所服从的分布。基于实际风电场历史数据的算例分析结果表明,所提出的误差分析方法能够准确描述风电场在不同预测风速下的功率预测误差概率分布,并能够确定各分布的适用范围,可为优化调度方案的制定提供参考。
風電功率預測誤差的分佈對電力繫統調度決策、備用安排等有著重要的影響。基于風電場日前功率預測“從風速到功率”的實際過程,提齣瞭確定日前風電功率預測誤差分佈的分析方法。首先,使用最小二乘法擬閤風電場的功率特性,分析風電場功率特性擬閤誤差及其對日前風電功率預測誤差的影響;其次,通過模擬日前風速的預測誤差,研究風速預測誤差對日前風電功率預測誤差的影響;最後,使用矇特卡囉雙層抽樣技術模擬上述2種誤差的共同作用,併使用最大似然估計技術,確定各風速區間對應的日前功率預測誤差所服從的分佈。基于實際風電場歷史數據的算例分析結果錶明,所提齣的誤差分析方法能夠準確描述風電場在不同預測風速下的功率預測誤差概率分佈,併能夠確定各分佈的適用範圍,可為優化調度方案的製定提供參攷。
풍전공솔예측오차적분포대전력계통조도결책、비용안배등유착중요적영향。기우풍전장일전공솔예측“종풍속도공솔”적실제과정,제출료학정일전풍전공솔예측오차분포적분석방법。수선,사용최소이승법의합풍전장적공솔특성,분석풍전장공솔특성의합오차급기대일전풍전공솔예측오차적영향;기차,통과모의일전풍속적예측오차,연구풍속예측오차대일전풍전공솔예측오차적영향;최후,사용몽특잡라쌍층추양기술모의상술2충오차적공동작용,병사용최대사연고계기술,학정각풍속구간대응적일전공솔예측오차소복종적분포。기우실제풍전장역사수거적산례분석결과표명,소제출적오차분석방법능구준학묘술풍전장재불동예측풍속하적공솔예측오차개솔분포,병능구학정각분포적괄용범위,가위우화조도방안적제정제공삼고。
Distribution of wind power forecast error significantly affects the decision of dispatch and reserve etc. in power systems. Based on the ‘wind to power’ day-ahead forecast procedure of wind farms, this paper put forward an approach to determine the probability density function of day-ahead wind power forecast error of wind farms. Firstly, this paper studied the power curve of wind power with least square fitting method and analyzed the influences of power curve-fitting error on day-ahead wind power forecast. Then the fitted power curve was used to study how the wind speed forecast error affects the wind power forecast error in different speed intervals analytically and by simulation. Finally, Monte-Carlo two-stage sampling was used to simulate the common effect of the fitting error of power curve and wind speed forecast error. The distributions of day-ahead forecast errors corresponding to different speed intervals were determined with Maximum Likelihood Estimation. Case study results based on historical data of an actual wind farm show that the proposed method can precisely depict the wind power forecast error under different wind speed, determine the proper wind speed range for each probability distribution, and provide references for optimal dispatch.