Abstract
Large-scale electric grid connection of electric vehicles will affect the reliable and economic operation of power grid. Meanwhile, the traditional time-of-use price is relatively fixed and not flexible. Aiming at the above problems, the paper comprehensively considers the time-of-use price, load variation and users’ willingness to charge, and proposes a multi-objective coordinated charging strategy for electric vehicles (EVs) considering dynamic time-of-use electricity price. Firstly, the EVs charging behavior characteristics are analyzed using the measured EVs charging data of a public charging station in Hunan Province, and the dynamic time-of-use price model is given. Secondly, an EV orderly charging optimization model is constructed, which is aimed at minimum peak-valley difference, minimum daily load variance, minimum user charging cost of distribution network. Then, a Genetic Simulated Annealing Particle Swarm Optimization (GSAPSO) algorithm based on adaptive inertial weight is proposed. Finally, the rationality and effectiveness of the proposed strategy are verified by simulation of typical scenarios. The simulation results show that the coordinated charging strategy solved by dynamic time-of-use price and GSAPSO algorithm can better reduce the peak-valley difference, the daily load variance, and the charging cost of users. As the responsiveness of electric vehicles participating in the coordinated charging strategy increases, the optimization effect is more obvious.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, Z.: Research on interactive technology of orderly charging and vehicle network for electric vehicles under dual carbon goals. Electr. Power Eng. Technol. 40(05), 1 (2021). (in Chinese)
Pradhan, P., Ahmad, I., Habibi, D., et al.: Reducing the impacts of electric vehicle charging on power distribution transformers. IEEE Access 8, 210183–210193 (2020)
Jahangir, H., Gougheri, S.S., Vatandoust, B., et al.: Plug-in electric vehicle behavior modeling in energy market: a novel deep learning-based approach with clustering technique. IEEE Trans. Smart Grid 11(6), 4738–4748 (2020)
Gan, L., Chen, X., Yu, K., Zheng, J., Du, W.: A probabilistic evaluation method of household EVs dispatching potential considering users’ multiple travel needs. IEEE Trans. Ind. Appl. 56(5), 5858–5867 (2020)
Guo, J., Wen, F.: Impact of electric vehicle charging on power system and relevant countermeasures. Electr. Power Autom. Equipment 35(06), 1–9 (2015). (in Chinese)
Al-Ogaili, A.S., Juhana, T., Rahmat, N.A., et al.: Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: challenges and recommendations. IEEE Access 7(1), 128353–128371 (2019)
Habib, S., Khan, M.M., Abbas, F., et al.: A comprehensive study of implemented international standards, technical challenges, impacts and prospects for electric vehicles. IEEE Access 6, 13866–13890 (2018)
Su, H., Liang, Z.: Orderly charging control based on peak-valley electricity tariffs for household electric vehicles of residential quarter. Electr. Power Autom. Equipment 35(06), 17–22 (2015). (in Chinese)
Chen, L., Pan, Z., Yu, T., et al: Real-time optimal dispatch of large-scale electric vehicles based on dynamic non-cooperative game theory. Autom. Electr. Power Syst. 43(24), 32–40+66 (2019). (in Chinese)
Yu, N., Yu, F., Huang, D., et al.: Multi-agent system based charging and discharging of electric vehicles distributed coordination dispatch strategy. Power Syst. Prot. Control 47(5), 1–9 (2019). (in Chinese)
Shen, G., Chen, G., Zhao, Y., et al.: Orderly charging optimization strategy of an electric vehicles based on double objective hierarchical optimization and TOPSIS ranking. Power Syst. Prot. Control 49(11), 115–123 (2021). (in Chinese)
Chen, K., Ma, Z., Zhou, S., et al.: Charging control strategy for electric vehicles based on two-stage multi-target optimization. Power Syst. Prot. Control 48(1), 65–72 (2020). (in Chinese)
Li, J., Yang, X., Zhang, L., et al.: Research on hierarchical control strategy of orderly charging of large-scale electric vehicles. Electr. Measur. Instrum. 58(5), 168–175 (2021). (in Chinese)
Wu, J., Qiu, X., Pan, Y., et al.: Research on orderly charging strategy of electric vehicle based on improved chicken swarm optimization. Electr. Measur. Instrum. 56(9), 97–103 (2019). (in Chinese)
Acknowledgements
This research is supported by the International Science and Technology Cooperation Program of China (2018YFE0125300), the Excellent Innovation Youth Program of Changsha (KQ2009037), and the National Nature Science Foundation of China (52061130217).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Beijing Paike Culture Commu. Co., Ltd.
About this paper
Cite this paper
Hou, Z., Li, Y., Guo, Y., Qiao, X., Zhang, Z., Cao, Y. (2023). Multi-objective Coordinated Charging Strategy for Electric Vehicles Considering Dynamic Time-of-Use Electricity Price. In: Dong, X., Yang, Q., Ma, W. (eds) The proceedings of the 10th Frontier Academic Forum of Electrical Engineering (FAFEE2022). FAFEE 2022. Lecture Notes in Electrical Engineering, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-99-3408-9_30
Download citation
DOI: https://doi.org/10.1007/978-981-99-3408-9_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3407-2
Online ISBN: 978-981-99-3408-9
eBook Packages: EnergyEnergy (R0)