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 non-parametric Bayesian inference-based uncertainty propagation approach, called (GP). We also suggest new type sensitivity Subspace-wise Sensitivity, using observations the ...

An improved cross-entropy (CE) method assisted with a chaotic operator (CGSCE) is presented for solving the optimal power flow (OPF) problem. The introduction of helps to enhance exploration capability popular approach while global best solution preserved. To handle constraints in flow, an efficient constraint handling technique no parameter adjustment also introduced. tested on both IEEE-30 bu...

The optimal power flow is very important in the study of system, and it great significance for economic operation system to solve active output generator through flow. In order problem that traditional method sensitive initial state large iteration times, this paper applies holomorphic embedding By variables, forms constraints KKT conditions are obtained. series coefficients node voltage calcul...