(471k) Optimal Electric Vehicle Charging Schedule: Impact of User Travel Behaviour and Distributed Energy Resource Availability

The role that greenhouse gas (GHG) emissions play especially as it relates to the potential impacts and associated risks of climate change have been acknowledged in most parts of the world. In light of this, up to eleven countries (the United Kingdom inclusive) have set a net-zero emission target by 2050 of GHG emissions (Committee on Climate Change, 2019). In the United Kingdom, transport has been identified as the largest contributor to GHG. To this end, one of the scenarios outlined towards achieving the net-zero goal involves ‘extensive electrification, particularly of transportation and heating’ (Committee on Climate Change, 2019; Küfeoğlu & Khah Kok Hong, 2020), which directly involves a transition to electric-powered surface transport vehicles.

In the UK, there has been a growing adoption of Electric Vehicles (EVs). This is attributed to benefits from capital subsidies, lower fuel and vehicle taxation as well as the increasing cost-competitiveness of EVs compared with traditional internal combustion engine-type vehicles. This growth has its impact on the utility grid. Unregulated connections of EVs can result in a substantial increase in aggregate demand, impact power quality, or cause an outright destabilization of the grid (Ahmadian et al., 2020; Xiong et al., 2017). There are also benefits to the grid. EVs act as additional energy storage devices providing energy through vehicle-to-home (V2H) and/or vehicle-to-grid (V2G) services. This allows for peak load shaving, reduction in household energy costs and backup power supply during outages. What then becomes important is the need to provide an optimal scheduling strategy to ensure that these benefits are leveraged whilst minimising the negative impacts to the grid.

This work thus proposes an optimal EV charging schedule for households considering the availability of EVs, stationary battery energy storage systems and/or solar power generation. Given a number of EV types and properties providing V2H services, as well as a number of different classes of EV user travel behaviour, the proposed optimisation model obtains the day-to-day charging schedule. The schedule will ensure minimal costs based on available electricity tariffs and household energy demand data. Using real-world cases, the most cost-effective electricity tariff can be selected, with its associated charging schedule, for a specific household and/or EV user type based on the aforementioned considerations.

References

Ahmadian, A., Mohammadi-ivatloo, B., & Elkamel, A. (2020). Electric vehicles in energy systems (A. Ahmadian, B. Mohammadi-ivatloo, & A. Elkamel (eds.)). Springer International Publishing. pp. 183. https://doi.org/10.1007/978-3-030-34448-1

Committee on Climate Change. (2019). Net Zero: The UK’s contribution to stopping global warming (Issue May). https://www.theccc.org.uk/publication/net-zero-the-uks-contribution-to-s...

Küfeoğlu, S., & Khah Kok Hong, D. (2020). Emissions performance of electric vehicles: A case study from the United Kingdom. Applied Energy, 260, 114241. https://doi.org/10.1016/j.apenergy.2019.114241

Xiong, Y., Chu, C., Gadh, R., & Wang, B. (2017). Distributed optimal vehicle grid integration strategy with user behavior prediction. In A. Ahmadian, B. Mohammadi-ivatloo, & A. Elkamel (Eds.), 2017 IEEE Power & Energy Society General Meeting (Vols. 2018-Janua, pp. 1–5). IEEE. https://doi.org/10.1109/PESGM.2017.8274327