Abstract
Electrification offers an opportunity to decarbonize the transport sector, but it might also increase the need for flexibility options in the energy system, as the uncoordinated charging process of battery electric vehicles (BEV) can lead to a demand with high simultaneity. However, coordinating BEV charging by means of smart charging control can also offer substantial flexibility potential. This potential is limited by restrictions resulting from individual mobility behavior and preferences. It cannot be assumed that storage capacity will be available at times when the impact of additional flexibility potential is highest from a systemic point of view. Hence, it is important to determine the flexibility available per vehicle in high temporal (and spatial) resolution. Therefore, in this paper a Markov-Chain Monte Carlo simulation is carried out based on a vast empirical data set to quantify mobility profiles as accurately as possible and to subsequently derive charging load profiles. An hourly flexibility potential is derived and integrated as load shift potential into a linear optimization model for the simultaneous cost-optimal calculation of the dispatch of technology options and long-term capacity planning to meet a given electricity demand. It is shown that the costs induced by BEV charging are largely determined by the profile costs from the combination of the profiles of charging load and renewable generation, and not only by the additional energy and capacity demand. If the charging process can be flexibly controlled, the storage requirement can be reduced and generation from renewable energies can be better integrated.
Highlights
In order to achieve climate targets specified in international agreements, countries set emission reduction targets and action plans in all sectors [1]
In order to quantify the costs of integrating electric mobility into the electricity supply task and to show the possibilities offered by the flexibility potential, this paper focuses on profile costs as part of the system costs
By making the charging process of all battery electric vehicles (BEV) flexible, an additional flexibility option is available to the model, which can be used for cost-optimal demand coverage
Summary
In order to achieve climate targets specified in international agreements, countries set emission reduction targets and action plans in all sectors [1]. The strong expansion of renewable energies plays an important role for the target achievement, and the rise of the electrification of the heat supply and the mobility sector [2] which, leads to a higher electricity demand. The integration of technologies for sector integration, such as heat pumps or battery electric vehicles (BEV), and the use of direct load control, e.g., for charging vehicles, are options to provide flexibility and should be part of the solution. Their actual flexibility potential, is subordinated to given boundary conditions from reality
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