Renewable energy-combined scheduling for electric vehicle charging. (1st January 2014)

• Record Type: Journal Article
• Authors: Lee, Junghoon; Park, Gyung-Leen
• Is Part Of: International journal of intelligent information and database systems. Volume 8:Number 1(2014)
• Page Start: 1
• Page End: 13
• Publication Date: 2014-01-01
• Subjects: electric vehicle -- charging scheduler -- preemptive task model -- renewable energy -- peak load reduction; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

This paper designs a heuristic-based charging scheduler capable of integrating renewable energy for electric vehicles, aiming at reshaping power load induced from the large deployment of electric vehicles. Based on the power consumption profile as well as the preemptive charging task model which includes the time constraint on the completion time, a charging schedule is created as a form of time tables. Each entry indicates the source of power supply, namely, either regular power or renewable energy, and how much power is supplied to a vehicle. Basically, it assigns the charging operation to those slots having the smallest power load one at a time, taking different allocation orders according to slack, operation length, and per-slot power demand. Finally, the peaking task of the peaking slot is iteratively picked to assign renewable energy stored in the station battery. The performance measurement result shows that our scheme can reduce the peak load by up to 37.3% compared with the earliest allocation scheme for the given parameter set.