Data-Driven Distributionally Robust Optimal Power Flow for Distribution Systems

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