Scalable Economic Dispatch for Smart Distribution Networks

We present a novel algorithm for economic dispatch in electric power grids. The method is inspired by statistical inference methods. Using discretized optimization variables, our algorithm finds the globally optimal, single time-step dispatch assignment for radial grids in linear time with respect to the number of network nodes. For such problems, the algorithm outperforms state-of-the-art mixed-integer scheduling, both in run-time and in the allowed complexity of component and line models. Moreover, the necessary computations can be performed in a distributed fashion, facilitating both practical implementation as well as information privacy. Our algorithm is thus optimally suited for the very large dispatch problems that will arise in future smart distribution grids with hosts of small, decentralized, and flexibly controllable prosumers, i.e., entities able to consume and produce electricity.