NDLI: Grid topology identification using electricity prices

Content Provider	IEEE Xplore Digital Library
Author	Kekatos, V. Giannakis, G.B. Baldick, R.
Copyright Year	2014
Description	Author affiliation: Dept. of ECE, Univ. of Minnesota, Minneapolis, MN, USA (Kekatos, V.; Giannakis, G.B.) \|\| Dept. of ECE, Univ. of Texas at Austin, Austin, TX, USA (Baldick, R.)
Abstract	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.
Starting Page	1
Ending Page	5
File Size	579746
Page Count	5
File Format	PDF
ISBN	9781479964154
DOI	10.1109/PESGM.2014.6939474
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Transmission line matrix methods Topology Laplace equations Network topology Symmetric matrices Sparse matrices Real-time systems graph Laplacian Nuclear norm regularization compressed sensing alternating direction method of multipliers economic dispatch locational marginal prices
Content Type	Text
Resource Type	Article

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