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Reduced-Order Models of Static Power Grids Based on Spectral Clustering

EasyChair Preprint no. 10670

6 pagesDate: August 4, 2023


For large-scale interconnected power systems that cover large geographical areas, certain electrical studies are required so that appropriate decisions ensure system reliability and low cost. For such studies, it is often neither practical nor necessary to model in detail the entire power system, which is increasingly complex due to a more diverse range of grid assets to choose from in both short and long-term planning.
The goal of this paper is to present a methodology to reduce the order of large-scale power networks based on spectral graph theory given that current methods for static network reduction are not scalable. A brief analysis of some spectral clustering properties to determine which graph Laplacian matrix should be used and why is included. The analysis shows that the utilization of the normalized graph Laplacian is more advantageous for clustering purposes. Techniques are proposed to approximate cost functions for the aggregated generators. This is done via linear regression. The reduced-order model obtained with the proposed methodology has an accuracy above 94% and solves the scalability issue commonly present in other reduction methods. If the utilization of the reduced-order model is either constrained to load levels above mid-peak demand, or cost functions of aggregated units are approximated via a piece-wise quadratic approach, then the error distribution is in the order of 0.001.

Keyphrases: Aggregation of electrical components, function approximation, linear regression, Network Reduction, spectral graph theory, unit commitment and economic dispatch

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Mario Daniel Baquedano Aguilar and Sean Meyn and Arturo Bretas},
  title = {Reduced-Order Models of Static Power Grids Based on Spectral Clustering},
  howpublished = {EasyChair Preprint no. 10670},

  year = {EasyChair, 2023}}
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