Pricing of demand flexibility: Exploring the impact of Electric Vehicle customer diversity

Salah, Florian; Schuller, Alexander; Maurer, Manuel; Weinhardt, Christof

doi:10.1109/eem.2016.7521202

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…However, they do not evaluate the approach regarding the charging providers profit. In Reference [77], Salah et al evaluate a deterministic version of the optimization without consideration of uncertainties (it is assumed that all charging requirements during the planning horizon are known in advance).…”

Section: Flexibility In the Charging Durationmentioning

confidence: 99%

Dynamic Pricing for Electric Vehicle Charging—A Literature Review

Limmer

2019

Energies

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Time-varying pricing is seen as an appropriate means for unlocking the potential flexibility from electric vehicle users. This in turn facilitates the future integration of electric vehicles and renewable energy resources into the power grid. The most complex form of time-varying pricing is dynamic pricing. Its application to electric vehicle charging is receiving growing attention and an increasing number of different approaches can be found in the literature. This work aims at providing an overview and a categorization of the existing work in this growing field of research. Furthermore, user studies and the modeling of user preferences via utility functions are discussed.

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Section: Flexibility In the Charging Durationmentioning

confidence: 99%

Dynamic Pricing for Electric Vehicle Charging—A Literature Review

Limmer

2019

Energies

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“…Consequently, a substantial body of research 20 has focused on proposing demand response (DR) algorithms to coordinate EV charging and establish their benefits (a review of various DR algorithms for charging coordination is given in [2], [3], [4], and [5]). However, one of the main limitations of such proposed DR algorithms is their potentially unrealistic assumptions about the EV owner behavior (e.g., time of availability of EV, sojourn 25 times and the fraction of the sojourn time that is not spent for charging and is named idle time). To design an efficient and practical DR algorithm, it is necessary to accurately understand the flexibility stemming from EVs and how to influence it (through price based and incentive based schemes) to maximize DR benefits.…”

Section: Introductionmentioning

confidence: 99%

“…However, our work differs from the aforementioned papers: instead of focusing on the impact of EVs on the load curve, we char- [24] estimated the EV flexibility benefits for providing spinning reserve services through matrices expressed as operational costs, environmental benefits and reduced wind curtailment. Salah et al [25] used the parking data from a car park in southern Germany, which is mainly used for shopping and working. [27] evaluate to what extent the charging of EVs can be accommodated using RES in two sociodemographic groups: retired vs. employed people.…”

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confidence: 99%