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

TL;DR

This study addresses how residential low-carbon technologies (EVs, heat pumps, and PV) shape low-voltage feeders using a large 2022 smart-meter dataset (42,089 profiles) from Fluvius. It introduces a simple Monte Carlo profile-sampling method to model feeder loads across 10–250 connections, quantifying feeder peak contributions (approximately $1.2$ kW per HP, $1.4$ kW per EV, and $2.0$ kW for high-power EV charging) and examining the timing of peaks under different LCT mixes. Key findings include the replacement of the classic duck curve with night-camel or night-dromedary shapes, substantial simultaneity-factor dynamics with feeder size, and weather-driven shifts in peak timing, all of which inform DSO planning and tariff-design considerations. The work provides DSOs with a practical tool to monitor evolving consumption patterns and underscores the caveats of representativeness and modeling assumptions when projecting future grid impacts.

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.

Figures (30)

• Figure 1: Visualization of the main steps of our approach, for the case of comparing feeders with and without EVs. Step 1: all smart meter measurements are divided into subsets: connections with and without EV. Step 2: feeders are simulated for each subset, by summing the power consumption of a sample of profiles, and calculating the feeder peak and simultaneity factor. Step 3: the effect of adding EVs to LV feeders can be investigated by comparing the results for the different subsets.
• Figure 2: Histogram of the yearly consumption of all profiles in each subset. The subsets are shown in the legend, and the median is also shown.
• Figure 3: Histogram of the quarter-hour with the highest consumption (i.e., the consumption peak) over the full year, for all profiles in each subset. The subsets are shown in the legend, and the median is also shown.
• Figure 4: Full year of quarter-hour electricity consumption data (kW) for a connection with a heat pump. a) Time series for each day of the year, plotted together. The mean (blue), maximum (red) and minimum (green) value for each quarter hour of the year are also shown. b) Heat map of consumption time series (kW). Hour of day is on the x-axis and day of the year is on the y-axis. c) Histogram of quarter-hour power values. The heat pump shows modulating consumption, mostly at night during winter. Peak power of the heat pump is around 2.5 kW, as can be seen from the histogram and the profile plots. The power of the PV inverter is approximately 6 kVA.
• Figure 5: Full year of quarter-hour electricity consumption data (kW) for a connection with an EV. a) Time series for each day of the year, plotted together. The mean (blue), maximum (red) and minimum (green) value for each quarter hour of the year are also shown. b) Heat map of consumption time series (kW). Hour of day is on the x-axis and day of the year is on the y-axis. c) Histogram of quarter-hour power values. Right: histogram of quarter-hour power values. Charging occurs at 22 kW, mostly in the evening between 16h and 22h. PV inverter power is around 4.5 kVA.