Anush publishes paper on electric vehicle charging

Dr Anush Poghosyan has had a paper published in the prestigious publication, Applied Energy, on modelling the likely electricity demand due to electric vehicles. The paper looks at the rate of uptake of electric vehicles and how the charging behaviour will affect the local grid in terms of peak electricity demand. The paper is very relevant for DNO's who are trying to understand the changing demand patterns due to the introduction of new technologies and is part of the work in the £30 million Low Carbon Network fund project - Thames Valley Vision (TVV). The text of the paper can be accessed through the link on the right hand side.

The work focussed on developing an agent based simulation approach to explore the influence of electric vehicles on the electrical demand for the low voltage network. The simulation used as its base input three UK Government scenarios for the uptake of low carbon technologies in the UK, real data collected from the Thames Valley Vision (TVV) project roll out of smart meters and daily charging patterns from an electric vehicles trial.

The analysis and charts can be found in the attached paper. The main conclusions from the work are:

- If the existing cheap night time electricity rate is maintained there is a strong incentive for the consumer to charge their vehicle at night and this is likely to start at around midnight. This will move the peak energy demand to 1.3kWh per building at midnight. Current typical mean load consumption throughout the day is 300W.

- If the consumer is incentivised by a dynamic tariff to charge in a random manner then this may lead to a more even profile but it should be noted that this will still lead to a greater peak energy demand than currently observed.

- For special days such as Christmas the maximum peak demand from domestic buildings will still remain around midday which is typically around 0.55kWh peak per household. This will increase to 0.8kWh if vehicles are charged during this period.

Further details of the modelling can be accessed here:

 Anush (2015) Long term individual load forecast

 

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