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CEIC-05-07

"Controlling Cascading Failures with Cooperative Autonomous Agents"

Paul Hines and Sarosh Talukdar

Abstract:
Cascading failures in electricity networks cause blackouts and blackouts often come with severe economic and social consequences. Cascading failures are typically initiated by a set of equipment outages that cause operating constraint violations. These initiating events can be triggered by naturally occurring events, such as a wind storm, or human intervention, such as a terrorist attack. When violations persist in a network they can trigger additional outages which in turn may cause further violations. This paper proposes a method for limiting the social costs of cascading failures by eliminating violations before a dependent outage occurs. This global problem is solved using a new application of distributed model predictive control. Specifically, our method is to create a network of autonomous agents, one at each bus of a power network. The task assigned to each agent is to solve the global control problem with limited communication abilities. Each agent builds a simplified model of the network based on locally available data and solves its local problem using model predictive control and cooperation. Through extensive simulations with IEEE test networks, we find that the autonomous agent design meets its goals with limited communication. Experiments also demonstrate that cooperation among software agents can vastly improve system performance. Finally, we discuss the relevance of this work to some current policy
issues.

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