Frederik Diehl (fortiss)
Efficient control of power grids is important both for efficiently managing genera- tors and to prolong longevity of components. However, that problem is NP-hard and linear approximations are necessary. The deployment of machine learning methods is hampered by the need to guarantee solutions. We propose to use Graph Neural Networks (GNNs) to model a power grid and produce an initial solution used to warm-start the optimization. This allows us to achieve the best of both worlds: Fast convergence and guaranteed solutions. On a synthetic power grid modelling Texas, we achieve a mean speedup by a factor of 2.8. This allows us to dispense with linear approximation, leads to more efficient generator dispatch, and can potentially save hundreds of megatons of CO2 -equivalent.