Modeling and Analyzing Electric Vehicle Charging

Ove Andersen; Benjamin Bjerre Krogh; Christian Thomsen; Kristian Torp

doi:10.1109/MDM.2016.52

Modeling and Analyzing Electric Vehicle Charging

Ove Andersen, Benjamin Bjerre Krogh, Christian Thomsen, Kristian Torp

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

8 Citations (Scopus)

Abstract

The combined battery capacity in electric vehicles (EVs) is considered an integral part of balancing a smart power grid in the future. In addition, EVs can reduce the usage of fossil fuels in the transport sector because EVs can be charged using electricity from renewable energy sources, such as wind turbines. To both enable a smart grid and the use of renewable energy, it is essential to know when and where an EV is plugged into the power grid and what battery capacity is available. In this paper, we present a generic spatio-temporal data-warehouse model for storing detailed information on all aspects of charging EVs, including integration with the electricity prices from a spot market. The proposed data warehouse is fully implemented and currently contains 2.5 years of charging data from 176 EVs. We describe the date warehouse model and the implementation including complex operations such as spatially identifying charging station usage patterns. Further, we give examples of novel analyses, e.g., how the free battery capacity
in the fleet of EVs changes over the day and how users can save money by charging the EVs when the electricity price is the lowest.

Translated title of the contribution	Modellering og analysering af opladning af elektriske biler
Original language	English
Title of host publication	17th IEEE International Conference on Mobile Data Management - Porto, Portugal
Editors	Albert Bifet, NURIA OLIVER
Number of pages	8
Volume	1
Place of Publication	Porto
Publisher	IEEE Press
Publication date	13 Jun 2016
Pages	302–309
ISBN (Print)	978-1-5090-0884-1, 978-1-5090-0883-4
ISBN (Electronic)	2375-0324
DOIs	https://doi.org/10.1109/MDM.2016.52
Publication status	Published - 13 Jun 2016
Event	17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016 - Porto, Portugal Duration: 13 Jun 2016 → 16 Jun 2016

Conference

Conference	17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016
Country/Territory	Portugal
City	Porto
Period	13/06/2016 → 16/06/2016
Sponsor	et al., IEEE, INESC TEC, LIAAD, University of Porto, Uporto, Faculdade de Economia

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Cite this

@inproceedings{82c3b943e68049bbba8dd4b34d75d2d8,

title = "Modeling and Analyzing Electric Vehicle Charging",

abstract = "The combined battery capacity in electric vehicles (EVs) is considered an integral part of balancing a smart power grid in the future. In addition, EVs can reduce the usage of fossil fuels in the transport sector because EVs can be charged using electricity from renewable energy sources, such as wind turbines. To both enable a smart grid and the use of renewable energy, it is essential to know when and where an EV is plugged into the power grid and what battery capacity is available. In this paper, we present a generic spatio-temporal data-warehouse model for storing detailed information on all aspects of charging EVs, including integration with the electricity prices from a spot market. The proposed data warehouse is fully implemented and currently contains 2.5 years of charging data from 176 EVs. We describe the date warehouse model and the implementation including complex operations such as spatially identifying charging station usage patterns. Further, we give examples of novel analyses, e.g., how the free battery capacityin the fleet of EVs changes over the day and how users can save money by charging the EVs when the electricity price is the lowest.",

author = "Ove Andersen and Krogh, {Benjamin Bjerre} and Christian Thomsen and Kristian Torp",

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booktitle = "17th IEEE International Conference on Mobile Data Management - Porto, Portugal",

publisher = "IEEE Press",

note = "17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016 ; Conference date: 13-06-2016 Through 16-06-2016",

}

Andersen, O, Krogh, BB, Thomsen, C & Torp, K 2016, Modeling and Analyzing Electric Vehicle Charging. in A Bifet & NURIA OLIVER (eds), 17th IEEE International Conference on Mobile Data Management - Porto, Portugal. vol. 1, IEEE Press, Porto, pp. 302–309, 17th IEEE International Conference on Mobile Data Management, IEEE MDM 2016, Porto, Portugal, 13/06/2016. https://doi.org/10.1109/MDM.2016.52

Modeling and Analyzing Electric Vehicle Charging. / Andersen, Ove; Krogh, Benjamin Bjerre; Thomsen, Christian et al.
17th IEEE International Conference on Mobile Data Management - Porto, Portugal. ed. / Albert Bifet; NURIA OLIVER. Vol. 1 Porto: IEEE Press, 2016. p. 302–309.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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AB - The combined battery capacity in electric vehicles (EVs) is considered an integral part of balancing a smart power grid in the future. In addition, EVs can reduce the usage of fossil fuels in the transport sector because EVs can be charged using electricity from renewable energy sources, such as wind turbines. To both enable a smart grid and the use of renewable energy, it is essential to know when and where an EV is plugged into the power grid and what battery capacity is available. In this paper, we present a generic spatio-temporal data-warehouse model for storing detailed information on all aspects of charging EVs, including integration with the electricity prices from a spot market. The proposed data warehouse is fully implemented and currently contains 2.5 years of charging data from 176 EVs. We describe the date warehouse model and the implementation including complex operations such as spatially identifying charging station usage patterns. Further, we give examples of novel analyses, e.g., how the free battery capacityin the fleet of EVs changes over the day and how users can save money by charging the EVs when the electricity price is the lowest.

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