  Alternating current optimal power flow (AC-OPF) is one of the fundamental
problems in power systems operation. AC-OPF is traditionally cast as a
constrained optimization problem that seeks optimal generation set points
whilst fulfilling a set of non-linear equality constraints -- the power flow
equations. With increasing penetration of renewable generation, grid operators
need to solve larger problems at shorter intervals. This motivates the research
interest in learning OPF solutions with neural networks, which have fast
inference time and is potentially scalable to large networks. The main
difficulty in solving the AC-OPF problem lies in dealing with this equality
constraint that has spurious roots, i.e. there are assignments of voltages that
fulfill the power flow equations that however are not physically realizable.
This property renders any method relying on projected-gradients brittle because
these non-physical roots can act as attractors. In this paper, we show
efficient strategies that circumvent this problem by differentiating through
the operations of a power flow solver that embeds the power flow equations into
a holomorphic function. The resulting learning-based approach is validated
experimentally on a 200-bus system and we show that, after training, the
learned agent produces optimized power flow solutions reliably and fast.
Specifically, we report a 12x increase in speed and a 40% increase in
robustness compared to a traditional solver. To the best of our knowledge, this
approach constitutes the first learning-based approach that successfully
respects the full non-linear AC-OPF equations.
