@inproceedings{d51e3d410aef4423b84d5e9c9c83e143,
title = "Charging/Discharging strategy for electric vehicles based on bi-level programming problem: San Francisco case study",
abstract = "The increasing market share of electric cles (EVs) leads to determine a proper strategy for charging/discharging EV batteries such that rewards of all agents including EV charging stations (EVCSs) and EV owners (EVOs) that participate in charging/discharging EV batteries are anteed. In this study, an economical and technical strategy developed. It focuses on finding proper EVCSs by EVOs determining optimal day-ahead electricity prices traded between all agents such that the rewards of EVCSs and EVOs are met multaneously. This optimal charging/discharging decision making and optimal day-ahead electricity prices are determined by level programming problem (BLPP). The outer level corresponds to the optimization problem of EVCSs and the inner level belongs to EVOs. Salp swarm optimization (SSO) algorithm is utilized to solve BLPP. Based on determination of minimum distance travelled by EVOs and optimal day-ahead electricity prices offered by EVCSs, the rewards of EVCSs and EVOs are analysed during charging/discharging period. For simulation purposes, case study based on San Francisco in US is presented to visualize and validate the modelling results. Six EVCSs are installed in Francisco for charging/discharging 247 EVs during 24 hours of typical day. Simulation results show that under implementing proposed charging/discharging strategy, the total cost of EVOs decreases by 17.8% and total revenue of EVCSs increases 18.2%, in comparison with not considering the proposed strategy.",
keywords = "Bi-level programming problem, charging/discharging decision, electric vehicles, electricity pricing",
author = "{Bagheri Tookanlou}, M. and M. Marzband and J. Kyyra and {Al Sumaiti}, A. and {Al. Hosani}, K.",
note = "Funding Information: ACKNOWLEDGMENT The work was supported in part by the PGR scholarship at Northumbria university. In addition, the project was partly supported by Khalifa university, Abu Dhabi, United Arab Emirates under awards No. kkjrc-2019-trans 2. REFERENCES [1] M. Latifi, A. Rastegarnia, A. Khalili, and S. Sanei, “Agent-based de-centralized optimal charging strategy for plug-in electric vehicles,”IEEE Trans. Ind. Electron., vol. 66, pp. 3668-3680, 2019. [2] Z. Wei, Y. Li, Y. Zhang, and L. Cai, “Intelligent parking garage EV charging scheduling considering battery charging characteristic,”IEEE Trans. Ind. Electron., vol. 65, pp. 2806-2816, 2018. [3] M. Honarmand, A. Zakarizadeh, and Sh. Jadid, “Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-grid concept and battery condition,” Energy, vol. 65, pp. 572-579, 2014. [4] W. Su and M.Y. Chow, “Performance evaluation of an eda-based large-scale plug-in hybrid electric vehicle charging algorithm,” IEEE Trans. Smart Grid, vol. 3, pp. 308-315, 2011. [5] J. Jannati and D. Nazarpour, “Optimal performance of electric vehicles parking lot considering environmental issue,” J. Clean. Prod., vol. 206, pp. 1073-1088, 2019. [6] Ch. G. Hoehne and M. V. Chester, “Optimizing plug-in electric vehicle and vehicle-to-grid charge scheduling to minimize carbon emissions,” Energy, vol. 115, pp. 646-657, 2016. [7] M. Shamshirband, J. Salehi, and F.S. Gazijahani, “Decentralized trading of plug-in electric vehicle aggregation agents for optimal energy man-agement of smart renewable penetrated microgrids with the aim of CO2 emission reduction,” J. Clean. Prod., vol. 200, pp. 622-640, 2018. [8] S. Pelletier, O. Jabali, and G. Laporte, “Charge scheduling for electric freight vehicles,” Trasport Res. B, vol. 115, pp. 246-269, 2018. [9] Zh. Wei, Y. Li, and L. Cai, “Electric Vehicle Charging Scheme for a Park-and-Charge System Considering Battery Degradation Costs,” IEEE Trans. Intell. Veh., vol. 3, pp. 361-373, 2018. [10] W. Su and M. Y. Chow, “Performance evaluation of an EDA-based large-scale plug-in hybrid electric vehicle charging algorithm,” IEEE Trans. Smart Grid, vol. 3, pp. 308-315, 2012. [11] V. del Razo and H.A Jacobsen, “Smart charging schedules for highway travel with electric vehicles,” IEEE Trans. Transport. Electrific., vol. 2, pp. 160-173, 2016. [12] S.G. Tichi and M.M. Ardehali and M.E. Nazari, “Examination of energy price policies in Iran for optimal configuration of CHPand CCHP systems based on particle swarm optimization algorithm,” Energy Policy, vol. 38, pp. 6240-6250, 2010. [13] Zh. Liu and F. Wen and G. Ledwich, “Optimal Planning of Electric-Vehicle Charging Stations in Distribution Systems,” IEEE Trans. Power Del., vol. 28, pp. 102-110, 2012. [14] S. Mirjalili and A. H.Gandomi and S.Z. Mirjalili and Sh. Saremi, H. Faris, S.M. Mirjalili, “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems,”ADV ENG SOFTW, vol. 114, pp. 163-191, 2017. [15] California ISO, available at: http://www.oasis.caiso.com. Publisher Copyright: {\textcopyright} 2020 IEEE.; 14th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020 ; Conference date: 08-07-2020 Through 10-07-2020",
year = "2020",
month = jul,
doi = "10.1109/CPE-POWERENG48600.2020.9161485",
language = "British English",
series = "Proceedings - 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "24--29",
booktitle = "Proceedings - 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2020",
address = "United States",
}