  Power system planning problems become computationally intractable if one
accounts for all uncertain operating scenarios. Consequently, one selects a
subset of scenarios that are representative of likely/extreme operating
conditions, e.g. heavy summer, heavy winter, light summer, and so on. However,
such an approach may not be able to accurately capture the dependencies that
exist between renewable generation (RG) and system load in RG-rich power
systems. This paper proposes the use of fast dynamic time warping (FDTW) and
fuzzy c-means++ (FCM++) clustering to account for key statistical properties of
load and RG for scenario generation for power system planning problems. Case
studies using a U.S. power network, and comparison with existing scenario
generation techniques demonstrate the benefits of the proposed approach.
