Urban MV and LV Distribution Grid Topology Estimation via Group Lasso
This addresses reliability issues in urban distribution grids for grid operators, but it is incremental as it applies an existing regularization technique (group lasso) to a specific domain problem.
The paper tackles the challenge of estimating topology in urban medium-voltage (MV) and low-voltage (LV) distribution grids, which is difficult due to issues like underground lines and mesh structures, by proposing a data-driven method using historical smart meter measurements. The result is highly accurate topology estimation, demonstrated through simulations on eight networks with 22 configurations using PG&E data.
The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid. The topology estimation is a critical step to ensure the robustness of distribution grid operation. However, the bus connectivity and grid topology estimation are usually hard in distribution grids. For example, it is technically challenging and costly to monitor the bus connectivity in urban grids, e.g., underground lines. It is also inappropriate to use the radial topology assumption exclusively because the grids of metropolitan cities and regions with dense loads could be with many mesh structures. To resolve these drawbacks, we propose a data-driven topology estimation method for MV and LV distribution grids by only utilizing the historical smart meter measurements. Particularly, a probabilistic graphical model is utilized to capture the statistical dependencies amongst bus voltages. We prove that the bus connectivity and grid topology estimation problems, in radial and mesh structures, can be formulated as a linear regression with a least absolute shrinkage regularization on grouped variables (\textit{group lasso}). Simulations show highly accurate results in eight MV and LV distribution networks at different sizes and 22 topology configurations using PG\&E residential smart meter data.