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"Controlling Cascading Failures with Cooperative Autonomous Agents"

Paul Hines

Cascading failures in electricity networks cause blackouts, which often lead to severe economic and social consequences. Cascading failures are typically initiated by a set of equipment outages that cause operating constraint violations. 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.

While the principle contribution of this paper is the development of a new method for controlling cascading failures, several aspects of the included results are also relevant to contemporary policy problems. Firstly, this paper demonstrates that it is possible to perform some network control tasks without large-scale centralization. This property could be valuable in the US where centralization of control and regulatory functions has proved politically difficult. Secondly, this paper presents preliminary estimates of the benefits, costs, and risks associated with this technology. With some additional development, the methods will be useful for evaluating and comparing grid control technologies.

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