Effect of electric vehicles, heat pumps, and solar panels on low-voltage feeders: Evidence from smart meter profiles

TL;DR

This study analyzes how electric vehicles, heat pumps, and solar panels impact low-voltage power feeders using a large dataset of smart meter profiles, revealing new load patterns and providing practical monitoring methods.

Contribution

It introduces a novel approach to model and analyze the impact of low-carbon technologies on low-voltage feeders using real-world smart meter data.

Findings

Heat pumps contribute 1.2 kW to peak load.

Electric vehicles contribute 1.4 kW to peak load.

Fast-charging EVs contribute 2.0 kW to peak load.

Abstract

Electric vehicles (EVs), heat pumps (HPs) and solar panels are low-carbon technologies (LCTs) that are being connected to the low-voltage grid (LVG) at a rapid pace. One of the main hurdles to understand their impact on the LVG is the lack of recent, large electricity consumption datasets, measured in real-world conditions. We investigated the contribution of LCTs to the size and timing of peaks on LV feeders by using a large dataset of 42,089 smart meter profiles of residential LVG customers. These profiles were measured in 2022 by Fluvius, the distribution system operator (DSO) of Flanders, Belgium. The dataset contains customers that proactively requested higher-resolution smart metering data, and hence is biased towards energy-interested people. LV feeders of different sizes were statistically modelled with a profile sampling approach. For feeders with 40 connections, we found a contribution to the feeder peak of 1.2 kW for a HP, 1.4 kW for an EV and 2.0 kW for an EV charging faster than 6.5 kW. A visual analysis of the feeder-level loads shows that the classical duck curve is replaced by a night-camel curve for feeders with only HPs and a night-dromedary curve for feeders with only EVs charging faster than 6.5 kW. Consumption patterns will continue to change as the energy transition is carried out, because of e.g. dynamic electricity tariffs or increased battery capacities. Our introduced methods are simple to implement, making it a useful tool for DSOs that have access to smart meter data to monitor changing consumption patterns.