"Distributed Model Predictive
Control for Electric Grids"
Paul Hines, Dong Jia, and Sarosh Talukdar
Cascading failures cause blackouts with high social costs. A cascading
failure can be thought of as an alternating sequence of equipment
outages and constraint violations. We describe a network of fast-acting,
autonomous agents for shortening such sequences. The agents work by
eliminating violations before they can cause further outages. They make
their decisions with DMPC—a distributed adaptation of the Model
Predictive Control technique. Each agent has a suite of models,
specialized for its location in the grid. It uses these models to
predict what the other agents will do and how the grid will respond.
Each agent optimizes its decisions with respect to the predictions. In
tests on small grids, these prediction-based optima come close to the
true, global optima. In other words, the agents seem able to make good
decisions. Future work includes extending the tests to larger grids, and
augmenting DMPC with cooperation and automatic learning.
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