DocumentCode :
1422842
Title :
Toward self-healing energy infrastructure systems
Author :
Amin, M.
Author_Institution :
EPRI, Palo Alto, CA, USA
Volume :
14
Issue :
1
fYear :
2001
fDate :
1/1/2001 12:00:00 AM
Firstpage :
20
Lastpage :
28
Abstract :
Virtually every crucial economic and social function depends on the secure, reliable operation of energy, telecommunications, transportation, financial, and other infrastructures. However, with increased benefit has come increased risk. As they have grown more complex to handle a variety of demands, these infrastructures have become more interdependent. This strong interdependence means that an action in one part of one infrastructure network can rapidly create global effects by cascading throughout the same network and even into other networks. Moreover, interdependence is only one of several characteristics that challenge the control and reliable operation of these networks. These characteristics, in turn, present unique challenges in modeling, prediction, simulation, cause-and-effect relationships, analysis, optimization, and control. Deregulation and economic factors and policies and human performance also affect these networks. The Complex Interactive Networks/Systems Initiative (GIN/SI) is a joint program by the Electric Power Research Institute (EPRI) and the US Department of Defense (DOD) that is addressing many of these issues. The goal of the 5-year, $30 million effort, which is part of the Government-Industry Collaborative University Research (GICUR) program, is to develop new tools and techniques that will enable large national infrastructures to self-heal in response to threats, material failures, and other destabilizers. Of particular interest is how to model enterprises at the appropriate level of complexity in critical infrastructure systems
Keywords :
power system reliability; Complex Interactive Networks/Systems Initiative; EPRI; Electric Power Research Institute; US Department of Defense; agent technology; cascading failures; critical infrastructure systems; destabilizers; material failures; multiagent systems; network reliability; network vulnerability; power grid; self-healing energy infrastructure systems; telecommunications; Analytical models; Economic forecasting; Humans; Power generation economics; Power system economics; Power system modeling; Predictive models; Telecommunication network reliability; Transportation; US Department of Defense;
fLanguage :
English
Journal_Title :
Computer Applications in Power, IEEE
Publisher :
ieee
ISSN :
0895-0156
Type :
jour
DOI :
10.1109/67.893351
Filename :
893351
Link To Document :
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