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Time-varying nonconvex optimization with application to OPF

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MESW03 - Closing workshop: Looking forward to 2050

Optimal power flow (OPF) problems are fundamental for power system operations.
They are nonconvex and, in future applications, time-varying. We present
a first-order proximal primal-dual algorithm and a second-order algorithm
for general time-varying nonconvex optimization and bound their tracking
performance. We incorporate real-time feedback in our algorithms for
applications to time-varying OPF problems, and illustrate their tracking
performance numerically.

(Joint work with Yujie Tang, Caltech, Emiliano Dall’Anese, U of Colorado,
Andrey Berstein, NREL )

This talk is part of the Isaac Newton Institute Seminar Series series.

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