  Increasing penetration of distributed energy resources complicate operations
of electric power distribution systems by amplifying volatility of nodal power
injections. On the other hand, these resources can provide additional control
means to the distribution system operator (DSO). This paper takes the DSO
perspective and leverages a data-driven distributionally robust decision-making
framework to overcome the uncertainty of these injections and its impact on the
distribution system operations. We develop an AC OPF formulation for radial
distribution systems based on the LinDistFlow AC power flow approximation and
exploit distributionally robust optimization to immunize the optimized
decisions against uncertainty in the probabilistic models of forecast errors
obtained from the available observations. The model is reformulated to be
computationally tractable and tested on multiple IEEE distribution test
systems. We also release the code supplement that implements the proposed model
in Julia and can be used to reproduce our numerical results.
