Regional Sensitivity Analysis for Maximum Static Transfer Capability of Power Systems by Dimension-Adaptive Sparse Grid Interpolation

Abstract

The maximum static transfer capability (MSTC) can be regarded as a measure of the power transmission ability of power systems in steady-state operation. These emerging uncertainty sources pose a difficult challenge for the efficient analysis and effective control of MSTC. To address this challenge, the dimension-adaptive sparse grid interpolation (DASGI) method is applied with modified boundaries of the collocation points to establish a surrogate model for the faster calculation of the MSTC based on AC optimal power flow (AC-OPF), thus accelerating its probabilistic analysis as well. Subsequently, the regional sensitivity analysis (RSA) is introduced in terms of contribution to the sample mean (CSM) and to the sample variance (CSV) to identify the critical regions of the uncertainty sources. Hence, an adjustment strategy is developed to increase the mean or decrease the variance of MSTC for secure and stable power transmission. Furthermore, the total CSM (TCSM) and total CSV (TCSV) are proposed as indicators to select the most influential random inputs as the preliminary step of RSA, which reduces the analytical burden. The effectiveness of this proposition is illustrated on the modified IEEE 118-bus system and the Polish 2383-bus power system, where the Monte Carlo simulation method, in combination with the AC-OPF-based MSTC model, provides an accuracy reference and a traditional sensitivity index provides the term for comparison.