Learning Connectivity and Higher-order Interactions in Radial Distribution Grids

TL;DR

This paper introduces a nonlinear topology identification algorithm for radial distribution grids that uncovers both direct connections and higher-order interactions, aiding real-time grid reconfiguration and optimization.

Contribution

It establishes a novel link between distribution flow equations and the self-driven graph Volterra model to identify complex grid interactions.

Findings

Effective in revealing grid topology and higher-order interactions

Demonstrated superior performance on real 47-bus data

Facilitates real-time grid optimization and fault management

Abstract

To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of buses, also known as higher-order interactions in recent literature, has not yet been addressed. The system operator can benefit from having this knowledge when re-configuring the grid in real time, to minimize power losses, balance loads, alleviate faults, or for scheduled maintenance. Establishing a connection between the celebrated exact distribution flow equations and the so-called self-driven graph Volterra model, this paper puts forth a nonlinear topology identification algorithm, that is able to reveal both the edge connections as well as their higher-order interactions. Preliminary numerical tests using real data on a 47-bus distribution grid showcase the merits of the proposed scheme relative to existing alternatives.