@inproceedings{8507299,
  abstract     = {{The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for the power grid, especially for distribution system operators (DSOs). The demand represented by EVs can be significant, but on the other hand, sojourn times of EVs could be longer than the time required to charge their batteries to the desired level (e.g., to cover the next trip). The latter observation means that the electrical load from EVs is characterized by a certain level of flexibility, which could be exploited for example in demand response (DR) approaches (e.g., to balance generation from renewable energy sources). 

This paper analyzes two data sets, one from a charging-at-home field trial in Flanders (about 8.5k charging sessions) and another from a large-scale EV public charging pole deployment in The Netherlands (more than 1M sessions). We rigorously analyze the collected data and quantify aforementioned flexibility: (1) we characterize the EV charging behavior by clustering the arrival and departure time combinations, identifying three behaviors (charging near home, charging near work, and park to charge), (2) we fit statistical models for the sojourn time, and flexibility (i.e., non-charging idle time) for each type of observed behavior, and (3) quantify the the potential of DR exploitation as the maximal load that could be achieved by coordinating EV charging for a given time of day t, continuously until t vertical bar Delta}},
  author       = {{Develder, Chris and Sadeghianpourhamami, Nasrin and Strobbe, Matthias and Refa, N}},
  booktitle    = {{2016 IEEE INTERNATIONAL CONFERENCE ON SMART GRID COMMUNICATIONS (SMARTGRIDCOMM)}},
  isbn         = {{978-1-5090-4075-9}},
  issn         = {{2373-6836}},
  keywords     = {{IBCN,DATA-DRIVEN APPROACH,DEMAND,CONSUMPTION,APPLIANCES,MODEL}},
  language     = {{eng}},
  location     = {{Sydney, Australia}},
  pages        = {{1--6}},
  title        = {{Quantifying flexibility in EV charging as DR potential : analysis of two real-world data sets}},
  year         = {{2016}},
}

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