Quantifying the Flexibility of Electric Vehicles in Germany and California—A Case Study

Michel Zade, Zhengjie You, Babu Kumaran Nalini, Peter Tzscheutschler and Ulrich Wagner
Additional contact information
Michel Zade: Chair of Energy Economy and Application Technology, TUM Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany
Zhengjie You: Chair of Energy Economy and Application Technology, TUM Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany
Babu Kumaran Nalini: Chair of Energy Economy and Application Technology, TUM Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany
Peter Tzscheutschler: Chair of Energy Economy and Application Technology, TUM Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany
Ulrich Wagner: Chair of Energy Economy and Application Technology, TUM Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany

Energies, 2020, vol. 13, issue 21, 1-21

Abstract: The adoption of electric vehicles is incentivized by governments around the world to decarbonize the mobility sector. Simultaneously, the continuously increasing amount of renewable energy sources and electric devices such as heat pumps and electric vehicles leads to congested grids. To meet this challenge, several forms of flexibility markets are currently being researched. So far, no analysis has calculated the actual flexibility potential of electric vehicles with different operating strategies, electricity tariffs and charging power levels while taking into account realistic user behavior. Therefore, this paper presents a detailed case study of the flexibility potential of electric vehicles for fixed and dynamic prices, for three charging power levels in consideration of Californian and German user behavior. The model developed uses vehicle and mobility data that is publicly available from field trials in the USA and Germany, cost-optimizes the charging process of the vehicles, and then calculates the flexibility of each electric vehicle for every 15 min. The results show that positive flexibility is mostly available during either the evening or early morning hours. Negative flexibility follows the periodic vehicle availability at home if the user chooses to charge the vehicle as late as possible. Increased charging power levels lead to increased amounts of flexibility. Future research will focus on the integration of stochastic forecasts for vehicle availability and electricity tariffs.

Keywords: charging strategy; optimization; electricity pricing; electric vehicle; flexibility; flexibility market; home energy management system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/21/5617/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/21/5617/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:21:p:5617-:d:435447

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().