Dimension-Adaptive Sparse Grid Interpolation for Uncertainty Quantification in Modern Power Systems: Probabilistic Power Flow

摘要: In this paper, the authors firstly present theoretical foundation of a state-of-the-art uncertainty quantification method, dimension-adaptive sparse grid interpolation (DASGI), for introducing it into applications probabilistic power flow (PPF), specifically as discussed herein. It is well-known that numerous sources are being brought present-day electrical grid, by large-scale integration renewable, thus volatile, generation, e.g., wind power, and unprecedented load behaviors. presence these added uncertainties, imperative to change traditional deterministic (DPF) calculation take them account in routine operation planning. However, PPF analysis still quite challenging due two features modern systems: high dimensionality stochastic interdependence. Both traditionally addressed Monte Carlo simulation (MCS) at cost cumbersome computation; paper instead, they tackled with joint application DASGI Copula theory (especially advantageous constructing nonlinear dependence among various sources), order accomplish dependent high-dimensional an accurate faster manner. Based on DASGI, its combination DPF also introduced systematically work. Finally, feasibility effectiveness methodology validated test results standard IEEE cases.