  The rapid deployment of renewable generations such as photovoltaic (PV)
generations brings great challenges to the resiliency of existing power
systems. Because PV generations are volatile and typically invisible to the
power system operator, estimating the generation and characterizing the
uncertainty are in urgent need for operators to make insightful decisions. This
paper summarizes our recent results on energy disaggregation at the substation
level with Behind-the-Meter solar generation. We formulate the so-called
``partial label'' problem for energy disaggregation at substations, where the
aggregate measurements contain the total consumption of multiple loads, and the
existence of some loads is unknown. We develop two model-free disaggregation
approaches based on deterministic dictionary learning and Bayesian dictionary
learning, respectively. Unlike conventional methods which require fully
annotated training data of individual loads, our approaches can extract load
patterns given partially labeled aggregate data. Therefore, our partial label
formulation is more applicable in the real world. Compared with deterministic
dictionary learning, the Bayesian dictionary learning-based approach provides
the uncertainty measure for the disaggregation results, at the cost of
increased computational complexity. All the methods are validated by numerical
experiments.
