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Deadline differentiated pricing in practice: marketing EV charging in car parks

Electric vehicle charging is considered as a prime case of load flexibility in future smart grids. We examine a scenario where electric vehicles are charged in a car park with local photovoltaic (PV) generation. In this setting, temporal charging flexibility can be leveraged to increase utilization of local generation. To incentivize flexible loads we apply a deadline differentiated pricing scheme. Prices are set by the car park operator in a profit-maximizing manner in settings with varying PV capacity and costs of conventional generation. This allows us to assess the value of flexibility passed on to customers in form of discounts. Furthermore, we determine the minimum flexibility level qualifying for this discount. By and large, absolute price levels and flexibility discounts are mainly driven by the cost of conventional generation. On the other hand, the minimum flexibility requirements are affected by both the costs of conventional generation as well as the local PV capacity: higher costs of conventional generation as well as larger PV capacities will decrease this threshold.
- Karlsruhe Institute of Technology Germany
- University of Würzburg Germany
info:eu-repo/classification/ddc/330, 330, ddc:330, Economics
info:eu-repo/classification/ddc/330, 330, ddc:330, Economics
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).23 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
