AI model could add more reliability to power grids

The new model from the University of Virginia is based on multi-fidelity graph neural networks (GNNs), a type of AI designed to improve power flow analysis, which is the process of ensuring electricity is distributed safely and efficiently across the grid.

The multi-fidelity approach allows the AI model to leverage large quantities of lower-quality data (low-fidelity) while still benefiting from smaller amounts of highly accurate data (high-fidelity). This dual-layered approach is said to enable faster model training while increasing the overall accuracy and reliability of the system.

By applying GNNs, the model can adapt to various grid configurations and is claimed to be robust to changes, such as power line failures. It helps address the longstanding ‘optimal power flow’ problem, determining how much power should be generated from different sources.

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As renewable energy sources introduce uncertainty in power generation and distributed generation systems, along with electrification increasing the uncertainty in demand, traditional grid management methods struggle to effectively handle these real-time variations. The new AI model integrates both detailed and simplified simulations to optimise solutions within seconds, improving grid performance, even under unpredictable conditions.