Conference paper Open Access

Flexible Charging of Electric Vehicles: Results of a large scale smart charging demonstration

Bons, Pieter; van den Hoed, Robert; Buatois, Aymeric; Geerts, Frank; Schuring, Friso


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        <foaf:name>Bons, Pieter</foaf:name>
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        <foaf:name>van den Hoed, Robert</foaf:name>
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        <foaf:name>Buatois, Aymeric</foaf:name>
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        <foaf:name>Geerts, Frank</foaf:name>
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        <foaf:name>Schuring, Friso</foaf:name>
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    <dct:title>Flexible Charging of Electric Vehicles: Results of a large scale smart charging demonstration</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
    <dcat:keyword>smart charging</dcat:keyword>
    <dcat:keyword>electric vehicle (EV)</dcat:keyword>
    <dcat:keyword>charging infrastructure</dcat:keyword>
    <dcat:keyword>energy transition</dcat:keyword>
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    <dct:description>&lt;p&gt;Flexible charging can be applied to avoid peak loads on the electricity grid by curbing demand of electric vehicle chargers as well as matching charging power with availability of sustainable energy. This paper presents results of a large-scale demonstration project &amp;ldquo;Flexpower&amp;rdquo; where time-dependent charging profiles are applied to 432 public charging stations in the city of Amsterdam between November 2019 and March 2020. The charging current on Flexpower stations is reduced during household peak consumption hours (18:00 &amp;ndash; 21:00), increased during the night-time, and dynamically linked to solar intensity levels during the day. The results show that the EV contribution to the grid peak load can be reduced by 1.2 kW per charging station with very limited user impact. The increased charging current during sunny conditions does not lead to a significantly higher energy transfer during the day because of lack of demand and technical limitations in the vehicles. A simulation model is presented based on empirical power measurements over a wide range of conditions combining the flexibility provided by simulations with the power of real-world data. The model was validated by comparing aggregated results to actual measurements and was used to evaluate the impact of different smart charging profiles in the Amsterdam context.&lt;/p&gt;</dct:description>
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