基于改进级数展开的含风电电力系统概率潮流计算方法

Improved Series Expansion Based Probabilistic Load Flow Calculation for Power System With Wind Power

我国正在构建新能源占比较高的新型电力系统,新能源风电的大规模并网和电动汽车等随机用电负荷的接入加剧了电力系统的不确定性,也给常规的半不变量概率潮流分析方法带来了挑战。在概率潮流计算中提出了一种利用最大熵原理改进的C型Gram-Charlier(C-type Gram-Charlier,CGC)级数展开法,并根据风速的Weibull分布特性推导了双馈风机出力的概率,同时利用双馈风机无功出力特性修正了用以计算系统状态量的半不变量的雅可比矩阵。在分析含新能源的电力系统时,既提高了传统CGC法的计算精度,也保留了较高的计算效率。最后,利用含双馈风场的IEEE测试系统验证了上述方法的准确性和有效性。算例结果表明,综合考虑计算精度和效率,所提出的方法具有较明显的优势。

A novel power system with the new energy as the main body is being constructed in China.However,as wind power,electric vehicles and other new energy forms with strong randomness are integrated into the grid in large quantities,the uncertainty of the power system has increased and the traditional probabilistic load flow based on cumulants has also been challenged.Therefore,an improved C-type Gram-Charlier(CGC) series expansion using the maximum entropy principle was proposed for the probabilistic load flow calculation in the paper.Besides,the probability distributions of the wind power of the doubly fed induction generations were derived based on the Weibull distribution of the wind speed.Meanwhile,according to the wind reactive power output characteristic,the Jacobian matrix for calculating the cumulants of the system state variables was modified.Thus,the proposed method not only improves the accuracy of the CGC method but also retains the advantages of its high computational efficiency.Finally,the accuracy and effectiveness of the proposed method are verified in the IEEE test systems bearing a wind farm with doubly fed induction generations.Results reveal that considering the accuracy and efficiency,the improved CGC method based on the maximum entropy in this paper has obvious superiority.