Long term individual load forecast under different electrical vehicles uptake scenarios

Abstract

More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences-that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at daytime , night-time, or a mix of both. The model is implemented on a neighbourhood in southeast England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected.

Predicting key features of a substation without monitoring

“…A DNO can use these smart meter profiles within a network modelling environment, and make planning and management decisions. For example, electric vehicles and solar panels can be added (see [17,18]) to the virtual network to inform decisions about reinforcements.…”

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

“…In [9] a simple agent-based model of EV uptake and their impact on local grid is given, based on governmental scenarios of future EV uptake for UK and a small pilot project where participants were incentivised to charge over night. As expected, having a variety of EV charging patterns helped to reduce the peaks as opposed when all the domestic charging happened after work or over night.…”

“…Using s to inform EV allocation leads to clusters of EVs forming around the initial seeds. This method is an adaptation of the algorithm proposed in [9], which was also applied to model EV uptake. There is an assumed link between increased neighbourhood diversity and a heavily populated feeder.…”

Electric vehicles and low-voltage grid: impact of uncontrolled demand side response

“…This situation will worsen on local networks if these new loads are clustered. Several recent studies have explored in detail the impact of EVs on the network [1,2,3,4,5,6]. Whilst these investigations do suggest that there are benefits of demand side response, uncontrolled applications of time of use tariffs or direct action mechanisms could be extremely detrimental to low voltage networks, which were designed with behavioural diversity taken into account.…”