Genetic algorithm-based admission test forvehicle-to-grid electricity trade services

This paper designs and evaluates a vehicle-to-grid (v2g) electricity trader capable of selecting an appropriate subset out of a large number of electric vehicles (evs) which want to sell their energy to a microgrid. a genetic algorithm,tailored for this trade coordination,reduces the amount of unmet demand forecasted one day advance in the microgrid. each subset is encoded to an integer r vector whose element has either 1 or 0 according to whether the associated ev is included in the subset or not. the evaluation function estimates the fitness of a feasible solution,employing a fast heuristic-based unit scheduler. its lightweight-ness allows the genetic algorithm to calculate the fitness of the massive number of feasible subsets,each of which has a fixed number of evs. this admission test gives a chance for evs to contact with other microgrids when they are not accepted to the final trade schedule. the performance measurement result obtained from a prototype implementation reveals that the proposed scheme achieves up to 20.8% performance improvement over the random selection scheme in terms of unmet demand. moreover,the proposed scheme can efficiently cope with overload condition,that is,many evs are concentrated in a single microgrid,judging from its stable performance curve. © 2016 penerbit utm press. all rights reserved.