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Multi-attribute Partitioning of Power Networks Based on Electrical Distance

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  • Identifying coherent sub-graphs in networks is important in many applications. In power systems, large systems are divided into areas and zones to aid in planning and control applications. But not every partitioning is equally good for all applications; different applications have different goals, or attributes, against which solutions should be evaluated. This paper presents a hybrid method that combines a conventional graph partitioning algorithm with an evolutionary algorithm to partition a power network to optimize a multi-attribute objective function based on electrical distances, cluster sizes, the number of clusters, and cluster connectedness. Results for the IEEE RTS-96 show that clusters produced by this method can be used to identify buses with dynamically coherent voltage angles, without the need for dynamic simulation. Application of the method to the IEEE 118 bus and a 2383 bus case indicates that when a network is well partitioned into zones, intra-zone transactions have less impact on power flows outside of the zone; i.e., good partitioning reduces loop flows. This property is particularly useful for power system applications where ensuring deliverability is important, such as transmission planning or determination of synchronous reserve zones.
  • This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by IEEE-Institute of Electrical and Electronics Engineers and can be found at: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59. ©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
  • Keywords: Network clustering, Evolutionary algorithms, Power network partitioning, Electrical distance
  • Keywords: Network clustering, Evolutionary algorithms, Power network partitioning, Electrical distance
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  • Cotilla-Sanchez, E., Hines, P. D. H., Barrows, C., Blumsack, S., & Patel, M. (2013). Multi-attribute partitioning of power networks based on electrical distance. IEEE Transactions on Power Systems, 28(4), 4979-4987. doi:10.1109/TPWRS.2013.2263886
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  • 28
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  • 4
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  • This work was funded in part by PJM Applied Solutions, NSF ECCS award #0848247, and US DOE award #DE-OE0000447.
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