NCSAatIllinois
“Distributed State Estimation for Electric Power Systems” — Ariana Minot, Harvard University
Accurate state estimation is critical for robust and secure operation of the electric power grid under a high penetration of renewable energy. Recently there has been much interest in how to enhance state estimation through advanced sensor technology. Processing the new measurements generated by these sensors introduces additional computational demands, motivating the need for state estimation algorithms that execute in a distributed manner. We explore the use of high-performance computing for improving distributed state estimation algorithms in power systems. Blue Waters allows us to test our algorithms on large-scale, realistic systems. We present a new parallel state estimation algorithm using matrix-splitting techniques that exploits inherent sparse structure in power systems. The algorithm is written in terms of information exchanged between nodes in a graph and implemented in MPI. With Blue Waters, we can distribute our algorithm on thousands of processors, where each processor simulates a control area of the grid.
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