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International Journal of Photoenergy
Volume 2015, Article ID 497697, 13 pages
Research Article

Coordinated Control of PV Generation and EVs Charging Based on Improved DECell Algorithm

1School of Electrical Engineering, Southeast University, Nanjing 210096, China
2Jiangsu Key Lab of Smart Grid Technology and Equipment, Zhenjiang 212009, China
3School of Urban Rail Transit, Changzhou University, Changzhou 213164, China

Received 25 September 2014; Revised 30 December 2014; Accepted 30 December 2014

Academic Editor: Zhixiong Guo

Copyright © 2015 Guo Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently, the coordination of EVs’ charging and renewable energy has become a hot research all around the globe. Considering the requirements of EV owner and the influence of the PV output fluctuation on the power grid, a three-objective optimization model was established by controlling the EVs charging power during charging process. By integrating the meshing method into differential evolution cellular (DECell) genetic algorithm, an improved differential evolution cellular (IDECell) genetic algorithm was presented to solve the multiobjective optimization model. Compared to the NSGA-II and DECell, the IDECell algorithm showed better performance in the convergence and uniform distribution. Furthermore, the IDECell algorithm was applied to obtain the Pareto front of nondominated solutions. Followed by the normalized sorting of the nondominated solutions, the optimal solution was chosen to arrive at the optimized coordinated control strategy of PV generation and EVs charging. Compared to typical charging pattern, the optimized charging pattern could reduce the fluctuations of PV generation output power, satisfy the demand of EVs charging quantity, and save the total charging cost.