THE ANALYSIS OF INFORMATION IMPACTS IN COORDINATING DEFENSE AGAINST MALICIOUS ATTACKS FOR INTERCONNECTED POWER SYSTEMS

The heightened concern for possible physical or cyber attacks on electricity networks has received con- siderable attention recently. Coordination among the different system operators (SO) of interconnected power systems in developing effective measures against such attacks has become a critically important problem in power system security. This paper presents an analytic tool for the assessment of information impacts in the man- agement of the operational reliability after the onset of a malicious attack. The tool uses multi-agent modeling and casts the problem into a game theoretic context with the equilibrium of a fictitious play being used to analyze the impacts of various levels of information available to the SOs on the outcomes of the decision-making process to respond to the attack. We illustrate the capabilities of the proposed tool on a 34-bus test system that represents a three-area interconnection. We compare the impacts of different information scenarios in terms of the response formulations and discuss the salient aspects of the key findings.

[1]  John C. McDonald,et al.  Confronting the risks of terrorism: making the right decisions , 2004, Reliab. Eng. Syst. Saf..

[2]  Akira Namatame,et al.  Social learning in a society of decentralized agents , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[3]  Yoav Shoham,et al.  If multi-agent learning is the answer, what is the question? , 2007, Artif. Intell..

[4]  Nicholas R. Jennings,et al.  Socially Rational Agents , 1997 .

[5]  J. Arroyo,et al.  On the solution of the bilevel programming formulation of the terrorist threat problem , 2005, IEEE Transactions on Power Systems.

[6]  F.D. Galiana,et al.  A mixed-integer LP procedure for the analysis of electric grid security under disruptive threat , 2005, IEEE Transactions on Power Systems.

[7]  Jeff S. Shamma,et al.  Dynamic fictitious play, dynamic gradient play, and distributed convergence to Nash equilibria , 2005, IEEE Transactions on Automatic Control.

[8]  J. Salmeron,et al.  Analysis of electric grid security under terrorist threat , 2004, IEEE Transactions on Power Systems.

[9]  Christian Rehtanz,et al.  Autonomous Systems and Intelligent Agents in Power System Control and Operation (Power Systems) , 2003 .

[10]  Åke J. Holmgren,et al.  Evaluating Strategies for Defending Electric Power Networks Against Antagonistic Attacks , 2007, IEEE Transactions on Power Systems.

[11]  Craig Boutilier,et al.  The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems , 1998, AAAI/IAAI.

[12]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[13]  Nicholas R. Jennings,et al.  Learning when and how to coordinate , 2003, Web Intell. Agent Syst..

[14]  Fei Xue,et al.  The analysis of malicious threats to infrastructures: a conceptual approach based on multi-agent systems , 2007 .