考虑风-光-储不确定性的新型电力系统概率潮流计算

随着我国“双碳”目标的提出，具有可再生性、环保性和经济性等优点的分布式电源大规模的接入交流配电网以实现电力系统的绿色低碳转型。以风电、光伏为代表的可再生能源出力具有极强的不确定性,同时储能电站的出力呈现出的随机性与波动性给配电网带来了极大的不确定性,对电力系统的稳定运行提出了挑战。因此文章提出了计及风-光-储不确定性的半不变量概率潮流算法，首先采用最优带宽核密度估计法建立储能电站输出功率概率模型，同时建立风光电站的概率分布模型。其次，通过去随机化Halton序列对变量进行采样，计算输入变量及输出变量等状态变量的半不变量。最后在改造后的IEEE-33节点测试系统上与蒙特卡洛模拟法进行仿真对比。研究表明，文章在具有不确定性的新型电力系统中用所提方法计算并分析潮流高效、稳定。

Probabilistic power flow calculation of new power system considering wind-light-storage uncertainty

With the proposal of China''s Dual-Carbon goal, distributed power sources with advantages such as renewability, environmental friendliness, and economy are widely integrated into the AC distribution network to achieve green and low-carbon transformation of the power system. The output of renewable energy, represented by wind power and photovoltaics, has strong uncertainty. At the same time, the randomness and volatility of the output of energy storage stations bring great uncertainty to the distribution network, posing challenges to the stable operation of the power system. Therefore, this article proposes a Cumulant method probabilistic power flow algorithm that takes into account the uncertainty of wind solar storage. Firstly, the optimal bandwidth kernel density estimation method is used to establish a probability model for the output power of energy storage power stations, and at the same time, a model of new power system is established. Secondly, by randomizing the Halton sequence to sample variables, the Cumulant methods of state variables such as input and output variables are calculated. Finally, a simulation comparison study was conducted between the modified IEEE-33 node testing system and Monte Carlo simulation method, indicating that the proposed method is computationally efficient, stable, and suitable for probabilistic power flow analysis of new energy power systems with high uncertainty.