Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: We analyze the impacts of future scenarios of electric vehicles (EVs) on the German power system, drawing on different assumptions on the charging mode. We find that the impact on the load duration curve strongly differs between charging modes. In a fully user-driven mode, charging largely occurs during daytime and in the evening, when power demand is already high. User-driven charging may thus have to be restricted because of generation adequacy concerns. In contrast, cost-driven charging is carried out during night-time and at times of high PV availability. Using a novel model formulation that allows for simulating intermediate charging modes, we show that even a slight relaxation of fully user-driven charging results in much smoother load profiles. Further, cost-driven EV charging strongly increases the utilization of hard coal and lignite plants in 2030, whereas additional power in the user-driven mode is predominantly generated from natural gas and hard coal. Specific CO 2 emissions of EVs are substantially higher than those of the overall power system, and highest under cost-driven charging. Only in additional model runs, in which we link the introduction of EVs to a respective deployment of additional renewables, electric vehicles become largely CO 2 -neutral. Terms of use: Documents inJEL: Q42; R41; Q54;

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The quality of electricity system modelling heavily depends on the input data used. Although a lot of data is publicly available, it is often dispersed, tedious to process and partly contains errors. We argue that a central provision of input data for modelling has the character of a public good: it reduces overall societal costs for quantitative energy research as redundant work is avoided, and it improves transparency and reproducibility in electricity system modelling. This paper describes the Open Power System Data platform that aims at realising the efficiency and quality gains of centralised data provision by collecting, checking, processing, aggregating, documenting and publishing data required by most modellers. We conclude that the platform can provide substantial benefits to energy system analysis by raising efficiency of data pre-processing, providing a method for making data pre-processing for energy system modelling traceable, flexible and reproducible and improving the quality of original data published by data providers.