Information geometry for model reduction of dynamic loads in power systems

Author(s):  
Clifford C. Youn ◽  
Andrija T. Saric ◽  
Mark K. Transtrum ◽  
Aleksandar M. Stankovic
Author(s):  
Diego A. Monroy-Ortiz ◽  
Sergio A. Dorado-Rojas ◽  
Eduardo Mojica-Nava ◽  
Sergio Rivera

Abstract This article presents a comparison between two different methods to perform model reduction of an Electrical Power System (EPS). The first is the well-known Kron Reduction Method (KRM) that is used to remove the interior nodes (also known as internal, passive, or load nodes) of an EPS. This method computes the Schur complement of the primitive admittance matrix of an EPS to obtain a reduced model that preserves the information of the system as seen from to the generation nodes. Since the primitive admittance matrix is equivalent to the Laplacian of a graph that represents the interconnections between the nodes of an EPS, this procedure is also significant from the perspective of graph theory. On the other hand, the second procedure based on Power Transfer Distribution Factors (PTDF) uses approximations of DC power flows to define regions to be reduced within the system. In this study, both techniques were applied to obtain reduced-order models of two test beds: a 14-node IEEE system and the Colombian power system (1116 buses), in order to test scalability. In analyzing the reduction of the test beds, the characteristics of each method were classified and compiled in order to know its advantages depending on the type of application. Finally, it was found that the PTDF technique is more robust in terms of the definition of power transfer in congestion zones, while the KRM method may be more accurate.


2019 ◽  
Vol 177 ◽  
pp. 106002
Author(s):  
Johnny Leung ◽  
Michel Kinnaert ◽  
Jean-Claude Maun ◽  
Fortunato Villella

2018 ◽  
Vol 33 (1) ◽  
pp. 440-450 ◽  
Author(s):  
Mark K. Transtrum ◽  
Andrija T. Saric ◽  
Aleksandar M. Stankovic

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 97863-97872
Author(s):  
Ning Tong ◽  
Zhihao Jiang ◽  
Lin Zhu ◽  
Yilu Liu

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