Sequential Power‐Based Holomorphic Embedding Probabilistic Power Flow Method

ABSTRACT

To address the reduced computational efficiency of probabilistic power flow (PPF) caused by source‐load uncertainty, as well as the difficulty in accurately characterising its temporal features and inter‐temporal correlations, this paper proposes a holomorphic embedding PPF method based on power sequential. First, a power‐sequential partitioning scheme is introduced to unify the minimum partitioning step. Fuzzy clustering and the probability area method are employed to establish an uncertainty model for nodal injected energy within the power sequential framework. Second, combining the probabilistic model with the holomorphic embedding linear power flow formulation yields an approach for calculating the probability distributions of electrical variables. The probabilistic identity mapping between injected power and electrical variables is further established to enable efficient PPF calculation. Finally, simulations on an actual power grid are conducted to verify the effectiveness of the proposed method.