电力系统自动化
電力繫統自動化
전력계통자동화
AUTOMATION OF ELECTRIC POWER SYSTEMS
2015年
16期
8-15
,共8页
赵唯嘉%张宁%康重庆%王跃峰%李鹏%马烁
趙唯嘉%張寧%康重慶%王躍峰%李鵬%馬爍
조유가%장저%강중경%왕약봉%리붕%마삭
光伏发电%Copula%点预测%概率性预测%条件预测误差%天气类型
光伏髮電%Copula%點預測%概率性預測%條件預測誤差%天氣類型
광복발전%Copula%점예측%개솔성예측%조건예측오차%천기류형
photovoltaic power generation%Copula%point forecast%probabilistic forecast%conditional forecast error%weather type
光伏发电出力的可预测性较低,相比点预测而言,光伏发电出力的概率性预测能够提供更多的信息,有利于电力系统的安全经济运行。提出了一种基于 Copula 理论的光伏发电出力的条件预测误差分布估计方法。采用 Copula 函数对光伏实际出力与点预测的联合概率分布进行建模,实现了任意点预测对应的光伏实际出力的条件概率分布的估计。针对天气状况,对光伏预测精度影响较大的实际情况,采用聚类的方法按天气类型将历史数据进行分类,针对每类天气类型的光伏预测误差分别进行建模以提高预测误差估计的准确度。以2014全球能源预测竞赛(GEFC 2014)中的光伏出力数据进行了实证分析,验证了所提出方法对光伏出力条件预测误差估计的有效性,结果表明提出的方法在校准性和锐度方面均优于常用的正态分布的预测误差估计方法。
光伏髮電齣力的可預測性較低,相比點預測而言,光伏髮電齣力的概率性預測能夠提供更多的信息,有利于電力繫統的安全經濟運行。提齣瞭一種基于 Copula 理論的光伏髮電齣力的條件預測誤差分佈估計方法。採用 Copula 函數對光伏實際齣力與點預測的聯閤概率分佈進行建模,實現瞭任意點預測對應的光伏實際齣力的條件概率分佈的估計。針對天氣狀況,對光伏預測精度影響較大的實際情況,採用聚類的方法按天氣類型將歷史數據進行分類,針對每類天氣類型的光伏預測誤差分彆進行建模以提高預測誤差估計的準確度。以2014全毬能源預測競賽(GEFC 2014)中的光伏齣力數據進行瞭實證分析,驗證瞭所提齣方法對光伏齣力條件預測誤差估計的有效性,結果錶明提齣的方法在校準性和銳度方麵均優于常用的正態分佈的預測誤差估計方法。
광복발전출력적가예측성교저,상비점예측이언,광복발전출력적개솔성예측능구제공경다적신식,유리우전력계통적안전경제운행。제출료일충기우 Copula 이론적광복발전출력적조건예측오차분포고계방법。채용 Copula 함수대광복실제출력여점예측적연합개솔분포진행건모,실현료임의점예측대응적광복실제출력적조건개솔분포적고계。침대천기상황,대광복예측정도영향교대적실제정황,채용취류적방법안천기류형장역사수거진행분류,침대매류천기류형적광복예측오차분별진행건모이제고예측오차고계적준학도。이2014전구능원예측경새(GEFC 2014)중적광복출력수거진행료실증분석,험증료소제출방법대광복출력조건예측오차고계적유효성,결과표명제출적방법재교준성화예도방면균우우상용적정태분포적예측오차고계방법。
Owing to the poor predictability of photovoltaic power,its probabilistic forecast provides more information about the underlying uncertainties compared with the traditional point forecast.This paper proposes a Copula theory based method to estimate the conditional forecasting error in photovoltaic power generation.The joint probability between the actual power output and its forecast is modeled using the Copula function.The conditional forecast error corresponding to each photovoltaic forecast level is then derived from this joint probability model.Considering the fact that weather has a strong impact on the accuracy of photovoltaic forecasting,cluster technique is used to divide the data according to weather types.A joint distribution model each is developed for the respective weather types to estimate the forecast error.Empirical study is carried out to validate the proposed model using the data from Global Energy Forecasting Competition 2014.The results show that the proposed method has improved both the calibration and sharpness of photovoltaic generation probabilistic forecast compared with the traditional normal-distribution-based probabilistic forecast method.