A Data-Driven Method for Probabilistic Prediction of Power Grid Critical Section Power from Probability Distributions of Renewable Energy

The probabilistic prediction of power grid critical section power is essential to the operational analysis and control of the power system. However, Monte Carlo Simulation (MCS) methods bring the heavy computational burden, which hardly meets the demand of practical application. The linearization assumption of the analytical methods brings inherent error in scenarios with high-dimensional input random variables. This article proposes a data-driven method for the complex nonlinear mapping from probability distributions of renewable energy to those of critical section power (called distributions-indistributions-out) with higher computational efficiency and accuracy. Compared with the existing methods, a larger number of probability distributions are considered in the proposed method by constructing probability distributions as features. Comparison cases on the IEEE 39-bus and 118-bus systems indicate that the proposed data-driven method has the better accuracy and robustness compared with the linear mapping of existing analytical methods.