Green neighbourhoods in low voltage networks: measuring impact of electric vehicles and photovoltaics on load profiles

TL;DR

This paper presents an agent-based model to evaluate how electric vehicles and photovoltaics influence load profiles in UK low voltage networks, aiding future infrastructure planning.

Contribution

It introduces a probabilistic agent-based model that estimates LCT adoption impacts on LV networks using real data and social influence factors.

Findings

Electric vehicle adoption significantly alters load profiles.

Combined EV and PV scenarios show complex load variations.

Bounds on model responses inform network planning decisions.

Abstract

In the near future various types of low-carbon technologies (LCTs) are expected to be widely employed throughout the United Kingdom. However, the effect that these technologies will have at a household level on the existing low voltage (LV) network is still an area of extensive research. We propose an agent based model that estimates the growth of LCTs within local neighbourhoods, where social influence is imposed. Real-life data from a LV network is used that comprises of many socially diverse neighbourhoods. Both electric vehicle uptake and the combined scenario of electric vehicle and photovoltaic adoption are investigated with this data. A probabilistic approach is outlined, which determines lower and upper bounds for the model response at every neighbourhood. This technique is used to assess the implications of modifying model assumptions and introducing new model features. Moreover, we discuss how the calculation of these bounds can inform future network planning decisions.