基于偏最小二乘多项式稀疏展开的含风电电力系统概率潮流计算

PROBABILISTIC FLOW CALCULATION OF POWER SYSTEM CONSIDERING WIND POWER BASED ON SPARSE POLYNOMIAL CHAOS EXPANSION WITH PARTIAL LEAST SQUARES METHOD

计及新型环保绿色能源如风电、光伏的不确定性,传统确定性潮流计算难以全面描述电力系统的运行情况。针对传统蒙特卡洛概率潮流算法计算量庞大的问题,结合偏最小二乘回归算法和多项式代理模型,提出一种偏最小二乘多项式疏展开的概率潮流算法。利用偏最小二乘回归算法的伪交叉验证误差自适应机制筛选出多项式展开式中的贡献度较大的多项式,得到多项式展开式的稀疏表达形式,可克服多项式展开概率潮流在输入变量较多时面临的维数灾难问题。在改进的IEEE-9,IEEE-30算例中进行仿真计算,并与传统方法作对比,验证了所提方法的有效性。

Considering the uncertainty of new environmental protection and green energy such as wind power or photovoltaic power generation,the traditional deterministic calculation method of power flows is difficult to comprehensively reflect the operation of the power system.For the computational amount of traditional Monte Carlo probability power flows algorithm,combining the partial least squares regression algorithm and the multi-class proxy model,this paper proposes a probability power flow algorithm for partial least squarer polynomial sparse expansion.Using the pseudo-cross-correction error adaptive mechanism of the bias minimum square regression algorithm,this paper contributes larger polynomial to the polynomial development,and obtains a sparse expression of the polynomial expansion,overcomes the dimensional disaster facing the multi-term expansion probability power flows of the input variable.The simulation calculation is carried out in an improved IEEE-9 and IEEE-30 examples and compared with the traditional method.The reslts verify the effectiveness of the method.