An Accelerated Solution Method for Stochastic Optimal Power Flow

The large-scale integration of renewable energy introduces significant uncertainty challenges to the optimal dispatch of power systems. To fully account for the impact of the randomness in renewable generation, this paper proposes an efficient solution algorithm based on the Gaussian mixture model (GMM) and the unscented transformation (UT) method, aimed at solving the stochastic optimal power flow (S-OPF) problem. Specifically, this paper first constructs the S-OPF model that considers a series of chance constraints. Next, the GMM is used to fit random variables with arbitrary distributions and the UT algorithm approximates the probabilistic characteristics of input variables with a small number of sample points. A Gaussian component simplification strategy is then proposed to minimize the computational burden while retaining the maximum reconstruction accuracy. Finally, the GMM-UT-based output variable probabilistic reconstruction algorithm is embedded into a general solution framework. Case studies demonstrate the advantages of the proposed algorithm over existing approximation methods and its higher computational efficiency compared to the traditional Monte-Carlo method.