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International Journal of Photoenergy - Volume 2017 2017, Article ID 1504857, 11 pages -

Research Article

School of Electrical Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China

Jiangsu Electric Power Research Institute, No. 1 Paweier, Nanjing 211103, China

Correspondence should be addressed to Qingshan Xu

Received 16 October 2016; Revised 15 January 2017; Accepted 22 January 2017; Published 6 April 2017

Academic Editor: Santolo Meo

Copyright © 2017 Shengjun Wu 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.


Photovoltaic- PV- based battery swapping stations BSSs utilize a typical integration of consumable renewable resources to supply power for electric vehicles EVs. The charging strategy of PV-based BSSs directly influences the availability, cost, and carbon emissions of the swapping service. This paper proposes an optimal charging strategy to improve the self-consumption of PV-generated power and service availability while considering forecast errors. First, we introduce the typical structure and operation model of PV-based BSSs. Second, three indexes are presented to evaluate operational performance. Then, a particle swarm optimization PSO algorithm is developed to calculate the optimal charging power and to minimize the charging cost for each time slot. The proposed charging strategy helps decrease the impact of forecast uncertainties on the availability of the battery swapping service. Finally, a day-ahead operation schedule, a real-time decision-making strategy, and the proposed PSO charging strategy for PV-based BSSs are simulated in a case study. The simulation results show that the proposed strategy can effectively improve the self-consumption of PV-generated power and reduce charging cost.

Author: Shengjun Wu, Qingshan Xu, Qun Li, Xiaodong Yuan, and Bing Chen



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