An improved genetic algorithm with initial population strategy and self-adaptive member grouping
Computers and Structures
Design of a power scheduler based on the heuristic for preemptive appliances
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Design of an energy consumption scheduler based on genetic algorithms in the smart grid
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Fast scheduling policy for electric vehicle charging stations in smart transportation
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Power-Constrained actuator coordination for agricultural sensor networks
GPC'12 Proceedings of the 7th international conference on Advances in Grid and Pervasive Computing
Genetic algorithm-based demand response scheme for electric vehicle charging
International Journal of Intelligent Information and Database Systems
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This paper presents a design and evaluates the performance of an efficient charging scheduler for electric vehicles, aiming at reducing the peak load of a fast charging station while meeting the time constraint of all charging requests. Upon the task model consist of actuation time, operation length, deadline, and a consumption profile, the proposed scheduler fills the allocation table, by which the power controller turns on or off the electric connection switch to the vehicle on each time slot boundary. For the sake of combining the time-efficiency of heuristic-based approaches and the iterative evolution of genetic algorithms, the initial population is decided by a heuristic which selects necessary time slots having the lowest power load until the previous task allocation. Then, the regular genetic operations further improve the schedule, additionally creating a new chromosome only from the valid range. The performance measurement result obtained from a prototype implementation shows that our scheme can reduce the peak load for the given charging task sets by up to 4.9 %, compared with conventional schemes.