"Controlling Cascading Failures with Cooperative Autonomous
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
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