Charlotte Cambier van Nooten has started in the collaborative PhD position between Alliander and the Radboud University. This PhD position is part of the AI for Energy Grids lab, one of the national ICAI labs.
The N-1 principle
Given the problems on grid capacity and contingency, the complexity of the power grid is increasing. Grid operators must ensure that the power grid remains reliable, even when a power cable fails. This is called the 'n-1 principle': in case of a failure, electricity must be able to be rerouted through alternative paths without causing problems.
During such rerouting, the load on alternative routes increases. Therefore, it is crucial to test whether these routes can handle the extra load. This involves checking not only the capacity of the cables but also whether the voltage and current and network stability remain within safe limits. Until now, for optimal results, the grid operators relied on mathematical calculations that checked all possible rerouting paths one by one - a process that could take hours.
The new approach
The new technology, developed by researcher Charlotte Cambier van Nooten and colleagues, uses machine learning. They have developed a 'Graph Neural Network' (GNN) specifically adapted for power grids. This method views the entire network as a whole, rather than examining each route separately. Additionally, the method takes into account the properties of both the cables and the nodes in its calculations. The system learns to recognize patterns and works even for situations it has never encountered before.
