  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.
