Abstract
In order to cope with the challenges brought by the large-scale Electric Vehicles (EVs) application to the power system dispatch, an dynamic economic emission dispatch model with the EVs is established. The vehicle to grid (V2G) power and conventional generator outputs of each dispatch period are set as the decision variables. The main optimization objectives are minimizing the total fuel cost and the pollution emission, so that the charging and discharging behavior of EVs is dynamically managed in the premise of meeting the demands of system energy and user travel. In this paper, the nondominated sorting genetic algorithm-II (NSGA-II) is used to solve such a model.
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Acknowledgments
This research is partially supported by National Natural Science Foundation of China (61673404 and 61473266), Scientific and Technological Projects of Henan (132102210521, 152102210153, 172102210601), Innovative Talents in Universities Support Project of Henan (16HASTIT033) and Key Scientific and Technological Research Projects of Education Department of Henan (17A470006).
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Qu, B., Qiao, B., Zhu, Y., Jiao, Y., Xiao, J., Wang, X. (2017). Using Multi-objective Evolutionary Algorithm to Solve Dynamic Environment and Economic Dispatch with EVs. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_4
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DOI: https://doi.org/10.1007/978-3-319-61833-3_4
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