Fast Stability Scanning for Future Grid Scenario Analysis

Abstract

Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the interseasonal and temporal variations in the renewable generation of a future grid scenario necessitates the use of computationally intensive time-series analysis. In this paper, we propose a framework for fast stability scanning of future grid scenarios using an improved feature selection and self-adaptive PSO-k-means clustering algorithm. To achieve the computational speedup, the stability analysis is performed only on small number of representative cluster centroids instead of on the full set of operating conditions. As a case study, we perform small-signal stability and steady-state voltage stability scanning of a simplified model of the Australian national electricity market with significant penetration of renewable generation. The simulation results show the effectiveness of the proposed approach. Compared to an exhaustive time series scanning, the proposed framework reduced the computational burden up to ten times, with an acceptable level of accuracy.