Topology Identification and Parameters Estimation of LV Distribution
Networks using Open GIS Data
Abstract
The topology of low-voltage distribution networks (LVDNs) is crucial for
system analysis, e.g., distributed energy resources (DERs) integration,
network hosting capacity analysis, state estimation, and electric
vehicle charging management. However, it is frequently unavailable or
incomplete. We develop a data-driven topology identification approach
for DNs with a high proportion of underground cables. The proposed
approach exploits the fact that underground cables usually follow the
street pattern, thus relying on open street map (OSM) and smart meter
(SM) data. Three stages compose the proposed approach: In the first
stage, a hierarchical minimum spanning tree algorithm is proposed to
generate the initial topology with an accurate number of sub-branches
from the pre-processed OSM data and peak demand. In the second stage,
based on the limited SM data, the location of breakpoints in mesh
topology caused by circle roads is verified and reconstructed to
guarantee the radial structure of LVDNs. Finally, given multiple
incomplete SM datasets, three data-driven optimization models based on a
state estimation model are constructed to mitigate the error of cable
length induced by OSM data. The feasibility of the proposed topology
identification approach is verified on three actual LVDNs in the
Netherlands and multiple incomplete SM datasets.