Structure- & Physics- Preserving Reductions of Power Grid Models

TL;DR

This paper introduces advanced graph reduction methods for power grid models that preserve physical and structural properties, enabling faster simulations and accurate dynamic modeling of large-scale power networks.

Contribution

It extends iterative Kron reduction using grid topology for reference node selection and proposes subnetwork aggregation techniques that maintain power-flow equivalence.

Findings

Reductions are either equivalent to Kron reduction or power-flow equivalent.

The methods enable dynamic modeling of swing equations in reduced networks.

Algorithms efficiently utilize hash-tables for reduction steps.

Abstract

The large size of multiscale, distribution and transmission, power grids hinder fast system-wide estimation and real-time control and optimization of operations. This paper studies graph reduction methods of power grids that are favorable for fast simulations and follow-up applications. While the classical Kron reduction has been successful in reduced order modeling of power grids with traditional, hierarchical design, the selection of reference nodes for the reduced model in a multiscale, distribution and transmission, network becomes ambiguous. In this work we extend the use of the iterative Kron reduction by utilizing the electric grid's graph topology for the selection of reference nodes, consistent with the design features of multiscale networks. Additionally, we propose further reductions by aggregation of coherent subnetworks of triangular meshes, based on the graph topology and network characteristics, in order to preserve currents and build another power-flow equivalent network. Our reductions are achieved through the use of iterative aggregation of sub-graphs that include general tree structures, lines, and triangles. Important features of our reduction algorithms include that: (i) the reductions are, either, equivalent to the Kron reduction, or otherwise produce a power-flow equivalent network; (ii) due to the former mentioned power-flow equivalence, the reduced network can model the dynamic of the swing equations for a lossless, inductive, steady state network; (iii) the algorithms efficiently utilize hash-tables to store the sequential reduction steps.