Fast scheduling policy for electric vehicle charging stations in smart transportation

Lee, Junghoon; Park, Gyung-Leen; Kim, Hye-Jin; Jeon, Hongbeom

doi:10.1145/2103380.2103403

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…To begin with, each charging operation is first modelled as a task as in our previous works (Lee et al, 2011b). Task T i can be specified the tuple of < A i , D i , U i >, and this model was inherited from the real-time task model (Derin and Ferrante, 2010).…”

Section: Scheduling Schemementioning

confidence: 99%

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Renewable energy-combined scheduling for electric vehicle charging

Lee

Park

2014

IJIIDS

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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.

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Section: Scheduling Schemementioning

confidence: 99%

“…Earliest scheduling is selected for the performance comparison as in Lee et al (2011b) and Derin and Ferrante (2010). The experiment sets the schedule length, namely, M, to 25 time units.…”

Section: Performance Measurementmentioning

confidence: 99%