Online Energy Price Matrix Factorization for Power Grid Topology Tracking

TL;DR

This paper proposes a method to recover power grid topology by analyzing real-time energy prices derived from market data, using matrix factorization techniques to infer the grid's physical structure.

Contribution

It introduces a novel topology recovery approach based on online matrix factorization of price data, scalable to high-dimensional and streaming market environments.

Findings

Successfully recovers grid topology from market data

Scalable algorithms demonstrated on IEEE 30-bus system

Provides insights for market design and grid security

Abstract

Grid security and open markets are two major smart grid goals. Transparency of market data facilitates a competitive and efficient energy environment, yet it may also reveal critical physical system information. Recovering the grid topology based solely on publicly available market data is explored here. Real-time energy prices are calculated as the Lagrange multipliers of network-constrained economic dispatch; that is, via a linear program (LP) typically solved every 5 minutes. Granted the grid Laplacian is a parameter of this LP, one could infer such a topology-revealing matrix upon observing successive LP dual outcomes. The matrix of spatio-temporal prices is first shown to factor as the product of the inverse Laplacian times a sparse matrix. Leveraging results from sparse matrix decompositions, topology recovery schemes with complementary strengths are subsequently formulated. Solvers scalable to high-dimensional and streaming market data are devised. Numerical validation using real load data on the IEEE 30-bus grid provide useful input for current and future market designs.