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

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

Mokhtar, Maizura (Heriot-Watt University)
Robu, Valentin (TU Delft Algorithmics; Heriot-Watt University)
Flynn, David (Heriot-Watt University)
Higgins, Ciaran (Derryherk Ltd.)
Whyte, Jim (SP Energy Networks)
Loughran, Caroline (SP Energy Networks)
Fulton, Fiona (SP Energy Networks)

2021

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). The mass rollout of smart meters (SMs) and advances in metering infrastructure holds the promise for smarter network management. However, many of the proposed methods require full observability, yet the expectation of being able to collect complete, error free data from every smart meter is unrealistic in operational reality. Furthermore, the smart meter (SM) roll-out has encountered significant issues, with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks. Even with a comprehensive SM roll-out privacy restrictions, constrain data availability from meters. To address these issues, this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits. The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution, even without the use of the high granularity personal power demand data from individual customers.

Analytic methods in power networks
Big Data Analytics
Deep neural learning
Distribution network operation
Privacy-preserving data analysis
Smart meters
Voltage prediction

http://resolver.tudelft.nl/uuid:1b17a624-4f6b-477d-8b4d-f3f9379c0481

https://doi.org/10.1016/j.egyai.2021.100103

Energy and AI, 6, 1-10

Institutional Repository

journal article

© 2021 Maizura Mokhtar, Valentin Robu, David Flynn, Ciaran Higgins, Jim Whyte, Caroline Loughran, Fiona Fulton