Monte Carlo modelling for domestic car use patterns in United Kingdom

For the purposes of quantifying the potential impact of widespread electric vehicles charging on the UK's power distribution system, it is essential to obtain relevant statistical data on domestic vehicle usage. Since electric vehicle ownership is presently very limited, these data will inevitably be for conventional internal combustion engine vehicles, and in particular privately owned vehicles. This should not be an issue since the limited journey distances that will dealt with in this work could as easily be undertaken by an electric vehicle as a conventional vehicle. Particular attention is paid to the United Kingdom 2000 Time Use Survey as it contains detailed and valuable statistical information about household car use. This database has been analyzed to obtain detailed car use statistics, such as departure and arrival time, individual journey time, etc. This statistical information is then used to build up two Monte Carlo simulation models in order to reproduce weekday car driving patterns based on these probability distributions. The Monte Carlo methodology is a well-known technique for solving uncertainty problems. In this paper, key statistics of domestic car use are presented together with two different Monte Carlo simulation approaches the simulation results that have been analyzed to verify the results being consistent with the statistics extracted from the TUS data

Electric vehicle charging simulations on a real distribution network using real trial data

This paper presents the development of models of a real distribution network from Geographical Information System (GIS) data provided by the Distribution Network Operator (DNO) of an area that is likely to see significant electric vehicle (EV) uptake. Using UK Census data with building analysis from Ordnance Survey datasets, likely existing domestic load profiles and likely locations of EV charge points are established. 12 months of data from the 215-vehicle My Electric Avenue EV trial are used to simulate the temporal variation of EV charging for various levels of EV uptake and power flow studies are run to examine the probable impact of EV uptake on a real distribution network in a suburban residential area in Scotland. It is shown that several parts of the network are expected to be faced with severe issues when 70% of vehicles in the area are replaced by EVs. The method presented is general and can be applied to any distribution system for which data is obtained to provide valuable insight as to the network issues that are likely to arise as a result of the uptake of EVs

Characterization of electric vehicle fast charging forecourt demand

Not all Electric Vehicle (EV) charging in future will take place at drivers’ homes or on-street; at least some will take place at fast-charging ‘forecourts’ analogous to today’s petrol stations. This paper presents a Monte Carlo (MC)-based method for the characterization of the likely demand profile of EV fast charging forecourts based on activity profiles of existing petrol stations, derived from smartphone users’ anonymised positional data captured in the ‘Popular Times’ feature in Google Maps. Unlike most academic works on the subject to date which rely on vehicle users’ responses to surveys, these data represent individuals’ actual movement patterns rather than how they might recall or divulge them. Other inputs to the model are generated from probability distributions derived from EV statistics in the UK and existing academic work. A queuing model is developed to simulate busy periods at charging forecourts. The output from the model is a set of expected time series of electrical demand for an EV forecourt and statistical analysis of the variation in results. Finally, a method is presented for the probabilistic evaluation of the combined loading of an EV forecourt and existing demand; this could be used to assess the sufficiency of existing network capacity and the potential for innovative smart grid technologies to facilitate increasing penetration of EVs

Impact analysis of domestic building energy demand and electric vehicles charging on low voltage distribution networks

There are lots of worldwide attentions paid to the greenhouse gas (GHG) emissions, which can result in serious climate change issues. Hence, finding ways to save energy and GHG emissions become important. Moreover, the energy demand from the residential sector accounts for around 30% of the total energy demand, which shows that it can be a potential way to contribute to reducing GHG emissions. Furthermore, the electric vehicle (EV) is going to play an important role in reducing GHG emissions, however, with the growth of EVs in the community, the low voltage (LV) distribution network (DN) will be affected directly. Therefore, investigating reducing the energy demand from domestic dwellings and minimising the impacts of EVs charging on dwellings and DNs become significantly important. Firstly, the energy demand of a domestic dwelling is modelled in the EnergyPlus. Potential energy savings from building material, photovoltaic/thermal (PV/T) panels, LED lights and occupants’ behaviours are analysed and improving the energy efficiency is investigated. Then, coupling by EnergyPlus and Matlab through Building Control Virtual Test Bed (BCVTB) interface, the Dwelling-EV Integration Model (DEIM) is established as the foundation for impact analysis of EVs charging on the energy demand in the dwellings and DNs. An individual domestic dwelling is modelled. Then load-shifting method and the battery storage energy system (BSES) are used to reduce the peak power demand in the dwelling, which are proved to be feasible and be able to smooth the daily power demand profile. III Further, in order to solve the issues caused by EVs charging, such as voltage drop, power loss etc. on DN, the impacts of EVs charging on the LV DN are analysed based on a typical network, and the concept of dwelling’s micro-grid, consisting of the PV and a battery storage system, is proposed. The dwelling’s micro-grid is used to minimise the impacts of EVs charging, and it is proved to be useful for reducing the voltage drop, the voltage disqualification rate and the power loss. Finally, an ordered charging strategy (OCS) of EVs using the expected power is proposed to minimise unbalanced load and increasing unqualified voltage caused by EVs charging. Additionally, the OCS using the expected power is combined with the BSES to further reduce the impacts. This method not only reduces the capacity of BSES, makes the voltage of DN qualify, but also smoothes the daily power demand. It solves the voltage drop caused by random EVs charging and overcomes the disadvantage of the large deployment of EVs on the DN