Battery electric vehicles : The diffusion in Sweden and the impact on the local power grid in Västerås
Sammanfattning: There are many ways to address the growing concerns of rising greenhouse gas emissions which contribute to the global warming phenomenon. One of the most pivotal aspects would be to seek an electrification of the transportation system, to eliminate on-road emissions. Trend forecasts regarding the uptake of battery electric vehicles show a continuous and exponential growth. This indicates that momentum is building with each year that passes and a lot of that has to do with technological innovation. Specifically, we are seeing progress in battery development, with cheaper production and a significantly increasing supply. Auto-manufacturers are pledging BEVs to their vehicle line-ups and the consensus seems to be that the future of transport is electric. Questions regarding impacts on power grids and distribution networks have been discussed extensively in the literature. Case studies have exemplified how an increased penetration of BEVs will affect the electricity consumption of a specific country or region. This paper aims to expand such research and investigate how a local power grid can experience challenges when the proliferation of BEVs makes up 30% and 50% of the total number of cars in traffic. To achieve this a power estimation model is proposed to calculate the electricity consumption of any given BEV and then see how peak hour electricity demand will be affected without any management of charging patterns. The exemplification of this study occurs through an examination of two residential area in the city of Västerås in Sweden. These areas are comprised of individual housing and apartment complexes. Assumptions made, regarding daily driving distances and charging patterns, are supported by a literature study. From prior studies consisting of travel surveys and GPS data, conclusions could be made about how the BEV will be used on a daily basis. This knowledge was the foundation for the calculations conducted in our model. The construction of the modelling cases occurred in the Excel environment. The achieved results show that uncontrolled charging, where individuals plug-in the BEV to the grid during peak hours of electricity demand, will significantly increase the peaks. Tests were also conducted for a more spread out charging pattern, where individuals charge the BEVs at different times during the day. These results yield a more manageable situation since even with a penetration level of 50% the distribution network could handle the extra load, without the need for expensive upgrades.
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