基于高斯函数-最大熵展开的风电并网系统概率潮流计算

Probabilistic load flow calculation based on Gaussian function-maximum entropy expansion for a wind power integration system

为有效计及风电出力随机性对电网运行状态的影响,在风电并网系统中提出一种基于高斯函数-最大熵原理的改进半不变量概率潮流计算方法。首先,以高斯函数为风速分布信息的载体,在此基础上采用改进反射核密度估计,建立计及风速有界性的风电出力概率模型,以便精确地求取描述风电出力随机性的各阶矩、半不变量等数字特征。然后,基于节点电压、支路功率等状态变量的数字特征,采用高斯函数改进最大熵模型进行状态变量的分布展开,由高斯函数的数量和性质来计及输入侧风速分布形状对输出侧状态变量分布的影响。同时将所提改进最大熵模型的约束由积分形式转为代数形式,提升计算效率。最后,以IEEE30节点系统对所提方法进行测试,结果证明了所提方法的有效性、准确性。

To effectively mitigate the impact of wind power output variability on grid operation,an improved cumulant probabilistic load flow calculation method using the Gaussian function-maximum entropy principle is proposed for a wind power integration system.First,taking the Gaussian function as the carrier of wind speed distribution information,a probabilistic model of wind power output accounting for the boundedness of wind speed is developed using an improved reflectance kernel density estimation.This model can accurately derive numerical characteristics such as moments and cumulants of each order describing the stochastic nature of the wind power output.Second,based on the numerical characteristics of state variables such as node voltage and branch power,the Gaussian function is used to improve the maximum entropy model into the distribution expansion of state variables.The influence of wind speed distribution shape of the input side on the state variables distribution of output side is taken into account by using the number and the feature of Gaussian function.Concurrently,the constraint form of the enhanced maximum entropy model is transformed to an algebraic from an integral,boosting computational efficiency.Finally,the proposed method is tested with the IEEE30-bus system,and the results demonstrate the effectiveness and accuracy of the proposed method.