With the proposal of dual-carbon goal in China, 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. Meanwhile, 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 paper 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 adopted to establish a probability model for the output power of energy storage power stations, as well as a probabilistic distribution model of wind power station. Secondly, the cumulants of state variables such as input and output variables are calculated by randomizing the Halton sequence to sample variables. Finally, a simulation comparison study is 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 novel power system with high uncertainty.