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Scheduling strategy of electric vehicle charging considering different requirements of grid and users

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  • Yin, WanJun
  • Ming, ZhengFeng
  • Wen, Tao

Abstract

With the increasing penetration of electric vehicles(EVs) that has enormous scheduling potentials, if it can fully tap its dispatching potential, it can help the power grid improve system operation efficiency, on the user side, it can save charging costs and improve user satisfaction. In view of this, this paper designs a charging scheduling method for EVs that meets the actual situation, which can not only choose the optimization target, but also the control strategy. Firstly,a dynamic multi-objective optimization scheme with more reasonable optimization objectives in each time period is constructed, so that the optimization objectives can be changed according to the actual situation; then, the orderly charging control is realized by adjusting the charging start time strategy or the variable charging power strategy, which not only prolongs the battery life, but also helps to smooth the grid load fluctuations. Thirdly, to solve the problem, an improved multi-objective PSO(particle swarm optimization) algorithm is designed, the improved algorithm uses the maximum and minimum fitness function based on the dynamic crowding distance and the amount of change, and optimizes the inertia weight coefficient and the learning factor to improve the performance of the algorithm. Finally, the effectiveness of the model is verified by numerical examples.

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  • Yin, WanJun & Ming, ZhengFeng & Wen, Tao, 2021. "Scheduling strategy of electric vehicle charging considering different requirements of grid and users," Energy, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:energy:v:232:y:2021:i:c:s0360544221013669
    DOI: 10.1016/j.energy.2021.121118
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    3. Wang, Kang & Wang, Haixin & Yang, Zihao & Feng, Jiawei & Li, Yanzhen & Yang, Junyou & Chen, Zhe, 2023. "A transfer learning method for electric vehicles charging strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 343(C).
    4. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
    5. Yin, WanJun & Qin, Xuan & Huang, ZhiZhong, 2022. "Optimal dispatching of large-scale electric vehicles into grid based on improved second-order cone," Energy, Elsevier, vol. 254(PB).
    6. Qiwei Yang & Yantai Huang & Qiangqiang Zhang & Jinjiang Zhang, 2023. "A Bi-Level Optimization and Scheduling Strategy for Charging Stations Considering Battery Degradation," Energies, MDPI, vol. 16(13), pages 1-15, June.
    7. Signer, Tim & Baumgartner, Nora & Ruppert, Manuel & Sandmeier, Thorben & Fichtner, Wolf, 2024. "Modeling V2G spot market trading: The impact of charging tariffs on economic viability," Energy Policy, Elsevier, vol. 189(C).
    8. Yin, Wanjun & Ji, Jianbo & Qin, Xuan, 2023. "Study on optimal configuration of EV charging stations based on second-order cone," Energy, Elsevier, vol. 284(C).
    9. Müller, Mathias & Blume, Yannic & Reinhard, Janis, 2022. "Impact of behind-the-meter optimised bidirectional electric vehicles on the distribution grid load," Energy, Elsevier, vol. 255(C).
    10. Aree Wangsupphaphol & Surachai Chaitusaney, 2022. "Subsidizing Residential Low Priority Smart Charging: A Power Management Strategy for Electric Vehicle in Thailand," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    11. Powell, Siobhan & Vianna Cezar, Gustavo & Apostolaki-Iosifidou, Elpiniki & Rajagopal, Ram, 2022. "Large-scale scenarios of electric vehicle charging with a data-driven model of control," Energy, Elsevier, vol. 248(C).
    12. Du, Wenyi & Ma, Juan & Yin, Wanjun, 2023. "Orderly charging strategy of electric vehicle based on improved PSO algorithm," Energy, Elsevier, vol. 271(C).
    13. Aixin Yang & Guiqing Zhang & Chenlu Tian & Wei Peng & Yechun Liu, 2024. "Charging Behavior Portrait of Electric Vehicle Users Based on Fuzzy C-Means Clustering Algorithm," Energies, MDPI, vol. 17(7), pages 1-27, March.
    14. Li, Xinyu & Cao, Yue & Yan, Fei & Li, Yuzhe & Zhao, Wanlin & Wang, Yue, 2022. "Towards user-friendly energy supplement service considering battery degradation cost," Energy, Elsevier, vol. 249(C).

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