Online Energy Price Matrix Factorization for Power Grid Topology Tracking
This addresses grid security and market transparency issues for energy systems, but it is incremental as it builds on existing sparse matrix decomposition methods.
The paper tackled the problem of recovering power grid topology from publicly available market data, specifically real-time energy prices, by formulating matrix factorization and sparse decomposition schemes, achieving scalable solvers validated on the IEEE 30-bus grid with real load data.
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.