结合M-Copula理论与半不变量的随机潮流方法

Probabilistic Load Flow Method Combining M-Copula Theory and Cumulants

随着光伏等可再生能源越来越多地并入电力系统,电力系统运行的随机性问题越来越突出。传统的确定性潮流将无法准确描述电力系统的实际运行状态,而随机潮流能充分考虑电力系统的各种随机波动,从而为电力系统的规划和运行提供指导。然而,新能源出力由于自然条件的时空关联性存在复杂的非线性相关性;为处理该非线性相关性,提出了一种基于M-Copula理论的半不变量随机潮流计算方法,将Copula理论与半不变量法结合起来,同时选择多个Copula函数模型来充分描述变量之间的复杂相关性。该方法既考虑了电力系统输入变量之间广泛存在的非线性相关性,又充分利用了半不变量法计算速度较快的优点,从而实现随机潮流的快速准确计算。以修改的IEEE 14系统为例,与蒙特卡洛方法进行的对比,仿真验证了所提方法的快速性与准确性。

With more and more renewable energy, such as photovoltaic and wind power, integrated into power systems, random problem of power system operation becomes increasingly prominent. Traditional deterministic power flow will not reflect actual operation status of power systems, while probabilistic load flow can fully consider various kinds of operating conditions of power systems and provide a guidance for power system planning and operation. However, nonlinear correlation with renewable energy is complex due to time and spatial coupling of natural conditions. In order to deal with this nonlinear correlation, a new cumulant method based on M-Copula theory combining cumulants for probabilistic load flow is proposed, and multiple Copula function models are chosen to describe complex correlation between random variables. The proposed method can deal with the nonlinear correlation with renewable energy and maintain fast computation speed of cumulant method. The fast computation speed and high accuracy of this algorithm is proved with case study in a modified IEEE 14 system by comparing with Monte Carlo simulations.