A Game Theory Approach to Vulnerability Analysis: Integrating Power Flows with Topological Analysis
Abstract
This paper presents a new framework for vulnerability analysis. Under this framework, we can identify the vulnerable components and the critical components of a power grid. Distinct from previous work, our model considers the interaction between the components of the power system, and models the dynamic evolving process of cascading failures. The impact of a component failure on the system is dynamically changing as the failure propagates. We analyze the vulnerability of a power grid using an optimization model based on game theory, and use linear programming method to solve it. Since instability is the reason of power outage, we use an instability index to measure the negative impact to the system. The results from this optimization problem suggest which component of the system is critical since its failure can most negatively impact the cyber-physical system.
Recommended Citation
M. X. Cheng et al., "A Game Theory Approach to Vulnerability Analysis: Integrating Power Flows with Topological Analysis," International Journal of Electrical Power and Energy Systems, vol. 82, pp. 29 - 36, Elsevier, Nov 2016.
The definitive version is available at https://doi.org/10.1016/j.ijepes.2016.02.045
Department(s)
Computer Science
Second Department
Electrical and Computer Engineering
Sponsor(s)
National Science Foundation (U.S.)
United States. Department of Energy
Keywords and Phrases
Electric load flow; Electric power transmission networks; Embedded systems; Game theory; Linear programming; Optimization; Plasma stability; Topology; Cascading failures; Component failures; Cyber physical systems (CPSs); Optimization modeling; Optimization problems; Power flows; Topological analysis; Vulnerability analysis; Outages; Instability
International Standard Serial Number (ISSN)
0142-0615
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Elsevier Ltd, All rights reserved.
Publication Date
01 Nov 2016
Comments
This research work is supported in part by US National Science Foundation under Grants ECCS-1307458, CNS-1537538, CNS1545063, and CMMI-1551448. Dr. Crow is supported by multiple NSF - United States and DOE - United States grants.