Improving network stability with machine learning

NESO VoltaVisor

A ML-powered voltage advice tool to improve voltage management for the National Energy System Operator and ensure network stability.

Background

National Energy System Operator (NESO) manages voltage fluctuations, caused by a number of factors including power demand, using simulations to determine which equipment should be in use. Exceeding safe operating thresholds can lead to equipment failure and network instability so it’s important to curb voltages. However, manual simulations are slow and inflexible, and cannot provide an immediate response. As part of our long-standing partnership, we built a proof of concept, to showcase how AI could help.

Solution

We used machine learning to forecast voltage support requirements to keep the system in check. This enables rapid assessment of optimum asset configurations to support the system during periods of voltage instability.

Impact

Optimising the voltage advice and presenting forecasts in an interactive dashboard has the potential to equip operators with the tools to make better real-time control decisions. If successful, this could reduce the cost of their actions and enhance network stability. Learn more about the project, funded by the Network Innovation Allowance (NIA) mechanism here.

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Revolutionising

“Supporting the development of NESO’s AI Centre of Excellence, by prioritising and implementing targeted use cases, is critical to enabling the system operator of the future, and meeting our Net Zero ambitions.”

Niko Louvranos, Business Development Director

Faculty

processes.