  A distribution system can flexibly adjust its substation-level power output
by aggregating its local distributed energy resources (DERs). Due to DER and
network constraints, characterizing the exact feasible power output region is
computationally intensive. Hence, existing results usually rely on unpractical
assumptions or suffer from conservativeness issues. Sampling-based data-driven
methods can potentially address these limitations. Still, existing works
usually exhibit computational inefficiency issues as they use a random sampling
approach, which carries little information from network physics and provides
few insights into the iterative search process. This letter proposes a novel
network-informed data-driven method to close this gap. A computationally
efficient data sampling approach is developed to obtain high-quality training
data, leveraging network information and legacy learning experience. Then, a
classifier is trained to estimate the feasible power output region with high
accuracy. Numerical studies based on a real-world Southern California Edison
network validate the performance of the proposed work.
