  Traditional load analysis is facing challenges with the new electricity usage
patterns due to demand response as well as increasing deployment of distributed
generations, including photovoltaics (PV), electric vehicles (EV), and energy
storage systems (ESS). At the transmission system, despite of irregular load
behaviors at different areas, highly aggregated load shapes still share similar
characteristics. Load clustering is to discover such intrinsic patterns and
provide useful information to other load applications, such as load forecasting
and load modeling. This paper proposes an efficient submodular load clustering
method for transmission-level load areas. Robust principal component analysis
(R-PCA) firstly decomposes the annual load profiles into low-rank components
and sparse components to extract key features. A novel submodular cluster
center selection technique is then applied to determine the optimal cluster
centers through constructed similarity graph. Following the selection results,
load areas are efficiently assigned to different clusters for further load
analysis and applications. Numerical results obtained from PJM load demonstrate
the effectiveness of the proposed approach.
