  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.
