Location
260-092, Owen G. Glenn Building
Start Date
12-15-2014
Description
Renewable energy integration is accompanied by highly volatile energy generation, which urges energy suppliers to use costly countermeasures to prevent energy imbalances, grid instabilities and power outages. Therefore, using demand-side approaches to shift flexible demand over time is a promising opportunity. In the case of electric vehicles, research papers mostly discuss vehicle drivers’ individual charging strategies based upon pricing information. The objective of this paper is to quantify the aggregate economic benefit of an advanced metering approach wherein electric vehicle drivers simply provide information about the start of the next trip to the energy supplier, who can then optimize the charging strategy for all drivers based on this information. By using a quantitative model and a multi-agent simulation for evaluation, we analyze original data from Germany to conclude that advanced metering can enable significant savings. Finally, we present a pricing scheme that would incentivize the drivers to provide truthful information.
Recommended Citation
Fridgen, Gilbert; Mette, Philipp; and Thimmel, Markus, "The Value of Information Exchange in Electric Vehicle Charging" (2014). ICIS 2014 Proceedings. 4.
https://aisel.aisnet.org/icis2014/proceedings/ConferenceTheme/4
The Value of Information Exchange in Electric Vehicle Charging
260-092, Owen G. Glenn Building
Renewable energy integration is accompanied by highly volatile energy generation, which urges energy suppliers to use costly countermeasures to prevent energy imbalances, grid instabilities and power outages. Therefore, using demand-side approaches to shift flexible demand over time is a promising opportunity. In the case of electric vehicles, research papers mostly discuss vehicle drivers’ individual charging strategies based upon pricing information. The objective of this paper is to quantify the aggregate economic benefit of an advanced metering approach wherein electric vehicle drivers simply provide information about the start of the next trip to the energy supplier, who can then optimize the charging strategy for all drivers based on this information. By using a quantitative model and a multi-agent simulation for evaluation, we analyze original data from Germany to conclude that advanced metering can enable significant savings. Finally, we present a pricing scheme that would incentivize the drivers to provide truthful information.