High-precision distribution network parameter identification by integrating scenario generation and probabilistic optimal power flow

To address the strong randomness and model inaccuracy in distribution network state perception under high renewable energy penetration, this study proposes a parameter identification method that integrates scenario generation and probabilistic optimal power flow. The method adopts an integrated “generation-optimization-identification” framework: first, multicollinearity reduction conditional variational auto-encoder (MCLRCVAE) and conditional generative adversarial network (CGAN) are jointly employed to generate source-load random scenarios with spatiotemporal correlation. Then, a consistency conditions-guided Bayesian deep neural network (CCGBDNN) performs physics-informed probabilistic optimal power flow analysis, and the probability distributions of operating parameters are reconstructed via the cumulant method and Gram-Charlier series. Experiments demonstrate that the phase angle root mean square error can be as low as 0.12°, the 500-node system converges in only 27 iterations, the power trend fitting coefficient of determination reaches 0.99, and the Kullback-Leibler divergence attains 0.05. The proposed method provides reliable technical support for the safe and economical operation of distribution systems with high proportions of renewable energy.