Gaussian Process Learning-based Probabilistic Optimal Power Flow
This addresses uncertainty management in power systems, offering a more efficient solution for grid operators, but it is incremental as it builds on existing probabilistic methods with a new learning approach.
The paper tackles the problem of solving Probabilistic Optimal Power Flow under renewable and load uncertainties of arbitrary distribution by proposing a Gaussian Process Learning-based method, achieving reasonably accurate solutions compared to Monte-Carlo Simulations with much fewer samples and less elapsed time in tests on 14-bus and 30-bus systems.
In this letter, we present a novel Gaussian Process Learning-based Probabilistic Optimal Power Flow (GP-POPF) for solving POPF under renewable and load uncertainties of arbitrary distribution. The proposed method relies on a non-parametric Bayesian inference-based uncertainty propagation approach, called Gaussian Process (GP). We also suggest a new type of sensitivity called Subspace-wise Sensitivity, using observations on the interpretability of GP-POPF hyperparameters. The simulation results on 14-bus and 30-bus systems show that the proposed method provides reasonably accurate solutions when compared with Monte-Carlo Simulations (MCS) solutions at different levels of uncertain renewable penetration as well as load uncertainties, while requiring much less number of samples and elapsed time.