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Real-time feedback optimization on the power flow manifold

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I will focus on online optimization of AC power systems in closed loop. In contrast to the conventional approach where an optimal power flow solution is computed offline and online controllers enforce these set-points, our objective is to design an adaptive feedback controller that steers the system robustly and in real time to the optimal operating point. Our methodological approach is based on online algorithms for manifold optimization that can be applied in feedback with real-time measurements and actuation. We treat the power flow equations as implicit constraints that are naturally enforced by the physics and hence give rise to the power flow manifold. Based on our theoretical results for this type of optimization problems, we propose a projected gradient descent scheme on the power flow manifold. In detailed simulation case studies we validate the performance of our algorithm and show that it reliably tracks the time-varying optimum of the underlying AC optimal power flow problem.

Co-authors: Adrian Hauswirth (ETH Zurich), Saverio Bolognani (ETH Zurich), Gabriela Hug (ETH Zurich)

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

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