Online Menu-Based Scheduling of Electric Vehicle Charging

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Abstract
This paper addresses the problem of efficiently scheduling of EV charging requests in a single Charging Station (CS), also taking into consideration the inability of EV users to express their preferences in closed-form utility functions. We consider a paradigm where a menu of possible charging intervals, each associated with an urgency price, is generated online. By letting the EV users pick their most preferable menu option, the proposed algorithm commits on each EV’s charging completion time upon its arrival, achieves a near optimal total weighted charging completion time, and prevents the users from strategically misreporting their preferences, while offering a practical and implementable solution to the problem of EVs -charging station interaction. Our experimental evaluation demonstrates that our algorithmic approach performs nearly optimal in a variety of practically relevant scenarios.
Funding Information
  • National Technical University of Athens (NTUA) Basic Research Grant (PEBE 2020) “Algorithm Design through Learning Theory: Learning-Augmented and Data-Driven Online Algorithms (LEADAlgo)”

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