Urban Distribution Grid Topology Estimation via Group Lasso

The growing penetration of distributed energy resources (DERs) in urban areas raises multiple reliability issues. The topology reconstruction is a critical step to ensure the robustness of distribution grid operation. However, the bus connectivity and network topology reconstruction are hard in distribution grids. The reasons are that 1) the branches are challenging and expensive to monitor due to underground setup; 2) the inappropriate assumption of radial topology in many studies that urban grids are mesh. To address these drawbacks, we propose a new data-driven approach to reconstruct distribution grid topology by utilizing the newly available smart meter data. Specifically, a graphical model is built to model the probabilistic relationships among different voltage measurements. With proof, the bus connectivity and topology estimation problems are formulated as a linear regression problem with least absolute shrinkage on grouped variables (Group Lasso) to deal with meshed network structures. Simulation results show highly accurate estimation in IEEE standard distribution test systems with and without loops using real smart meter data.