We present a coordination mechanism that reduces peak demand coming from EV charging, supports grid stability and environmental sustainability. The proposed mechanism accounts for individual commuting preferences, as well as desired states of charge by certain deadlines, which can serve as a proxy for range anxiety. It can shape EV charging toward a desired profile, without violating individual preferences. Our mechanism mitigates herding, which is typical in populations where all agents receive the same price signals and make similar charging decisions. Furthermore, it assumes no prior knowledge about EV customers and therefore learns preferences and reactions to prices dynamically. We show through simulations that our mechanism induces a less volatile demand and lower peaks compared to currently used benchmarks.
|Original language||English (US)|
|Title of host publication||Proceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019|
|Editors||Tung X. Bui|
|Publisher||IEEE Computer Society|
|Number of pages||10|
|State||Published - 2019|
|Event||52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 - Maui, United States|
Duration: Jan 8 2019 → Jan 11 2019
|Name||Proceedings of the Annual Hawaii International Conference on System Sciences|
|Conference||52nd Annual Hawaii International Conference on System Sciences, HICSS 2019|
|Period||1/8/19 → 1/11/19|
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