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Researchers at Radboud College have developed a brand new methodology to calculate the reliability of the ability grid. This new methodology, primarily based on Graph Neural Networks, just isn’t solely a thousand occasions quicker but additionally extra correct than present strategies. The outcomes of the brand new methodology have been published within the journal Utilized Vitality.
The n-1 precept
Given the issues with grid capability and contingency, the complexity of the power grid is rising. Grid operators should make sure that the ability grid stays dependable, even when an influence cable fails. That is known as the “n-1 principle”: In case of a failure, electrical energy should be capable of be rerouted by way of different paths with out inflicting issues.
Throughout such rerouting, the load on different routes will increase. Due to this fact, it’s essential to check whether or not these routes can deal with the additional load. This includes checking not solely the capability of the cables but additionally whether or not the voltage and present and community stability stay inside protected limits. Till now, for optimum outcomes, the grid operators relied on mathematical calculations that checked all doable rerouting paths one after the other—a course of that might take hours.
The brand new strategy
The brand new expertise, developed by researcher Charlotte Cambier van Nooten and colleagues, makes use of machine learning. They’ve developed a “Graph Neural Network” (GNN) particularly tailored for energy grids. This methodology views your complete community as an entire, moderately than analyzing every route individually. Moreover, the strategy takes into consideration the properties of each the cables and the nodes in its calculations. The system learns to acknowledge patterns and works even for conditions it has by no means encountered earlier than.
Charlotte Cambier van Nooten states, “When there’s a failure, you want to quickly know the best method to solve it. Our new method can do this in seconds. Moreover, our method is on average 5% more accurate than traditional methods.”
The tactic has been examined on the medium-voltage grid, a fancy cable community that delivers electrical energy between completely different substations. Grid operator Alliander has already begun implementing this new expertise.
Extra data:
Charlotte Cambier van Nooten et al, Graph neural networks for assessing the reliability of the medium-voltage grid, Utilized Vitality (2025). DOI: 10.1016/J.APENERGY.2025.125401
Offered by
Radboud University
Quotation:
New methodology enhances energy grid reliability evaluation (2025, February 24)
retrieved 24 February 2025
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