Grid topology identification using electricity prices

Vassilis Kekatos, Georgios B. Giannakis, Ross Baldick

Research output: Contribution to journalConference articlepeer-review

41 Scopus citations


The potential of recovering the topology of a grid using solely publicly available market data is explored here. In contemporary whole-sale electricity markets, real-time prices are typically determined by solving the network-constrained economic dispatch problem. Under a linear DC model, locational marginal prices (LMPs) correspond to the Lagrange multipliers of the linear program involved. The interesting observation here is that the matrix of spatiotemporally varying LMPs exhibits the following property: Once premultiplied by the weighted grid Laplacian, it yields a low-rank and sparse matrix. Leveraging this rich structure, a regularized maximum likelihood estimator (MLE) is developed to recover the grid Laplacian from the LMPs. The convex optimization problem formulated includes low rank-and sparsity-promoting regularizers, and it is solved using a scalable algorithm. Numerical tests on prices generated for the IEEE 14-bus benchmark provide encouraging topology recovery results.

Original languageEnglish (US)
Article number6939474
JournalIEEE Power and Energy Society General Meeting
Issue numberOctober
StatePublished - Oct 29 2014
Event2014 IEEE Power and Energy Society General Meeting - National Harbor, United States
Duration: Jul 27 2014Jul 31 2014


  • Nuclear norm regularization
  • alternating direction method of multipliers
  • compressed sensing
  • economic dispatch
  • graph Laplacian
  • locational marginal prices


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