The increasing popularity of electric vehicles (EVs) is known to amplify the already present peaks in electricity demand. The possibility to remotely control and influence the charging of many EVs using the Internet of Things (IoT) via an aggregator has been proposed to optimize resource usage, to alleviate peak problems, and to exploit revenues that may be harnessed from fluctuating electricity prices. However, so far, the potential hinged on the acceptance of users, particularly the willingness to change their charging behavior. In this study, we develop an unobtrusive and easily implementable optimization method. Its effectiveness is tested on 360,000 charging sessions at public charging points in Amsterdam during the year 2015, providing a realistic assessment of the effects of optimization in terms of reduced costs, change in peak demand, and long occupancy of charging points. Based on the model, an average reduction of electricity costs between 20% and 30% can be achieved, depending on the day of the week. We also show that changing EV owner’s charging preferences such as starting earlier or later can benefit certain groups of EV drivers substantially and reduce electricity charging costs up to 35%.

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http://dx.doi.org/10.1007/978-3-030-32296-0_13 under the license http://www.springer.com/tdm
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Published on 31/12/19
Accepted on 31/12/19
Submitted on 31/12/19

Volume 2020, 2020
DOI: 10.1007/978-3-030-32296-0_13
Licence: Other

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