TOWARDS PREDICTING DYNAMIC STABILITY OF POWER GRIDS WITH GRAPH NEURAL NETWORKS Anonymous authors Paper under double-blind review

Abstract

To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia and volatility in production. However, dynamic stability simulations are intractable and exceedingly expensive for large grids. Graph Neural Networks (GNNs) are a promising method to reduce the computational effort of analyzing dynamic stability of power grids. We provide new datasets of dynamic stability of synthetic power grids and find that GNNs are surprisingly effective at predicting highly non-linear targets from topological information only. We show that large GNNs trained on our rich dataset outperform GNNs from previous work, as well as several baseline models based on handcrafted features. Furthermore, we use GNNs to demonstrate the accurate identification of particularly vulnerable nodes in power grids, so called troublemakers. Lastly, we find that GNNs trained on small grids generate accurate predictions for on a large synthetic model of the Texan power grid, which illustrates the potential real-world applications of the presented approach.

1. INTRODUCTION

Adaption to and mitigation of climate change jointly influence the future of power grids: 1) Mitigation of climate change requires power grids to be carbon-neutral, with the bulk of power supplied by solar and wind generators. These are more decentralized and as opposed to conventional turbine generators they have no intrinsic ability to respond to power imbalances and frequency deviations. Furthermore, the production of renewables is more volatile. Renewable energies will have to start contributing to the dynamical stability of the system (Milano et al., 2018; Christensen et al., 2020) in the future, requiring a new understanding of the complex synchronisation dynamics of power grids. 2) A higher global mean temperature increases the likelihood as well as the intensity of extreme weather events such as hurricanes or heatwaves (Field et al., 2012; Pörtner et al., 2022) which result in great challenges to power grids. Building sustainable grids as well as increasing the resilience of existing power grids towards novel threats are challenging tasks on their own. Tackling climate change in the power grid sector calls for a solution to both at the same time and requires new methods to investigate aspects of dynamic stability. Power grids are complex networks, consisting of nodes that represent different producers and consumers, as well as edges that represent power lines and power transformers. In contrast to many other networks, the interaction of nodes through the edges is governed by physical equations, the power flow. Their emergent properties can be highly unintuitive. For example, the Braess paradox describes the phenomenon that adding lines to a power grid may reduce its stability ((Witthaut & Timme, 2012; Schäfer et al., 2022) ). Such effects can be non-local, i.e. the parts of the grid with decreased stability might be far away from the added line. Similarly, failures of a line in one part of the network can lead to overloads far away. Our work deals with the challenge of predicting the ability of the grid to dynamically recover after localized faults perturbed the system. Classically, the dynamical actors are connected to the highest voltage level, the transmission grid. Transmission grid operators routinely simulate potential faults in the current state of the power grid, to assess its real time dynamic resilience. The possible faults are called contingencies in this context. Even though such simulations do not explicitly model the lower voltage layers of the grid, they are already compute bound. Hence, not all contingencies of interest can be tested for. As distributed re-

