Fast Stability Scanning for Future Grid Scenario Analysis
This work addresses the problem of efficient grid stability analysis for power system planners, though it is incremental as it builds on existing clustering and optimization methods.
The paper tackles the computational burden of analyzing future grid scenarios with high renewable penetration by proposing a planning framework that uses feature selection and clustering to reduce the number of stability analyses needed. The result is a reduction in computational burden by up to ten times compared to exhaustive scanning, while maintaining acceptable accuracy.
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 inter-seasonal variations in renewable generation of a future grid scenario necessitates the use of computationally intensive time-series analysis. In this paper, we propose a planning framework for fast stability scanning of future grid scenarios using a novel feature selection algorithm and a novel self-adaptive PSO-k-means clustering algorithm. To achieve the computational speed-up, 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.