New Publication: Prediction of voltage distribution using deep learning and identified key smart meter locations

The energy landscape for the Low-Voltage (LV) networks is undergoing rapid changes. These changes are driven by the increased penetration of distributed Low Carbon Technologies, both on the generation side (i.e. adoption of micro-renewables) and demand side (i.e. electric vehicle charging). The previously passive ‘fit-and-forget’ approach to LV network management is becoming increasing inefficient to ensure its effective operation. A more agile approach to operation and planning is needed, that includes pro-active prediction and mitigation of risks to local sub-networks (such as risk of voltage deviations out of legal limits).

Our research findings on this challenge, in collaboration with colleagues at SP Energy NetworksHeriot-Watt UniversityDerryherk Ltd. and Smart Meter Systems, has just been published as a preproof in Elsevier Energy and AI and has open access via this link.

This research has supported knowledge and technology transfer into new BaU capabilities for SP Energy Networks, readying their LV operational and planning decision support systems for the challenges and opportunities presented by the energy transition.

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