Abstract

Increasing penetration of low carbon technologies in residential low voltage (LV) networks increases the need for modelling their state to preempt voltage issues. Due to the challenges in modelling vast numbers of feeders, LV network models are often simplified, incomplete, or even absent. The large-scale roll-out of smart meters (SMs), creates the opportunity for generating accurate LV network models at scale at low cost. In this paper, a methodology for voltage estimation in LV networks without an electrical model is proposed and tested across 127 real distribution feeders. The approach uses machine learning and historical active power and voltage data from SMs to predict voltage at a node of interest. This approach shows promising results, particularly in its capability to generalise to different loading scenarios and estimate voltage under higher electric vehicle and solar photovoltaic penetration levels.