电力系统自动化
電力繫統自動化
전력계통자동화
AUTOMATION OF ELECTRIC POWER SYSTEMS
2014年
12期
46-53
,共8页
风力发电%无迹变换技术%Sigma点%概率最优潮流%内点法
風力髮電%無跡變換技術%Sigma點%概率最優潮流%內點法
풍력발전%무적변환기술%Sigma점%개솔최우조류%내점법
wind power generation%unscented transformation technique%Sigma points%probabilistic optimal power flow%interior point method
提出了一种基于无迹变换(UT)技术求解大规模风电场并网的电力系统概率最优潮流(POPF)的计算方法。利用UT技术将 POPF 问题转化为少量样本点的确定性最优潮流问题，然后采用现代内点法加以求解。考虑了风电场出力的随机性和节点负荷功率的不确定性，讨论了不同风电接入水平下利用该方法计算 POPF 模型中变量的概率特征参数的计算精度。IEEE 30节点、IEEE 57节点、IEEE 118节点标准系统和一个实际系统 S-1047的计算结果表明，与蒙特卡洛方法相比，基于 UT技术的POPF计算方法在保持误差很小的同时，计算效率可提高数十倍，且容易实现、易于处理随机变量相关性，为POPF问题的有效求解和应用提供了工具。
提齣瞭一種基于無跡變換(UT)技術求解大規模風電場併網的電力繫統概率最優潮流(POPF)的計算方法。利用UT技術將 POPF 問題轉化為少量樣本點的確定性最優潮流問題，然後採用現代內點法加以求解。攷慮瞭風電場齣力的隨機性和節點負荷功率的不確定性，討論瞭不同風電接入水平下利用該方法計算 POPF 模型中變量的概率特徵參數的計算精度。IEEE 30節點、IEEE 57節點、IEEE 118節點標準繫統和一箇實際繫統 S-1047的計算結果錶明，與矇特卡洛方法相比，基于 UT技術的POPF計算方法在保持誤差很小的同時，計算效率可提高數十倍，且容易實現、易于處理隨機變量相關性，為POPF問題的有效求解和應用提供瞭工具。
제출료일충기우무적변환(UT)기술구해대규모풍전장병망적전력계통개솔최우조류(POPF)적계산방법。이용UT기술장 POPF 문제전화위소량양본점적학정성최우조류문제，연후채용현대내점법가이구해。고필료풍전장출력적수궤성화절점부하공솔적불학정성，토론료불동풍전접입수평하이용해방법계산 POPF 모형중변량적개솔특정삼수적계산정도。IEEE 30절점、IEEE 57절점、IEEE 118절점표준계통화일개실제계통 S-1047적계산결과표명，여몽특잡락방법상비，기우 UT기술적POPF계산방법재보지오차흔소적동시，계산효솔가제고수십배，차용역실현、역우처리수궤변량상관성，위POPF문제적유효구해화응용제공료공구。
A computation method for probabilistic optimal power flow(POPF)for power systems with large-scale wind farms based on unscented transformation (UT) technique is proposed.UT technique is employed to transform the POPF problem into the deterministic OPF problem with a few samples to be solved with the interior point method.The randomness of the wind farms”output power and the uncertainty of the bus load power are considered,the calculation accuracy of the probability characteristic parameters of the variables in the model of POPF using this method under different wind power capacities in power systems is discussed.The numerical results of IEEE 30-bus,57-bus,118-bus standard systems and a real system S-1047 indicate that,compared with Monte Carlo (MC) method,the proposed approach for POPF based on UT technique can remarkably reduce the computing time while keeping a high accuracy.It is easy to implement and facilitates handling of the correlation of the random variables,while providing a powerful tool for the solution and practical application of the POPF problem.
