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Orderly charging strategy for electric vehicles based on multi-level adjustability

1School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China

2International Joint Laboratory of Integrated Energy Equipment and Integration in Jiangsu Province, Nanjing, China

3State Grid Jiangsu Electric Vehicle Service Co., China

Received: 25 Dec 2023; Revised: 21 Jan 2024; Accepted: 12 Feb 2024; Available online: 18 Feb 2024; Published: 1 Mar 2024.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2024 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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The development of electric vehicles (EVs) is one of the essential ways to reduce environmental pollution. With the rapid growth in EVs, an orderly charging strategy based on multi-level adjustable charging power is proposed to address the problem of increasing peak-to-valley difference due to disorderly charging in different scenarios. Based on the information of multi-level adjustable charging power, information about staying in the residential area, and charging demands of EVs, this research designs a centralized charging mode with complete information under the centralized scenario and a decentralized charging mode with incomplete information under the decentralized scenario. This research takes the minimization of peak-to-valley difference in the residential area as the objective function and considers that the charging pile can have the function of multi-level adjustable charging power to support these two scenarios. Two charging modes of the charging pile are designed, and orderly charging model of EVs in the residential area is constructed. EVs can select charging time and charging power by using Bluetooth or code scanning in the charging pile. This research aims to design two orderly charging modes to effectively implement peak shaving and valley filling while ensuring the charging demand of EVs. This research uses the CPLEX solver in MATLAB to solve the objective. The simulation results show that EVs can reasonably select the multi-level adjustable charging power under different scenarios and provide a reference for engineering related to orderly charging. Strategy 4, proposed in this research, has the lowest peak-to-valley difference of the four strategies. The peak-to-valley difference is only 87 kW under the centralized scenario, and the peak-to-valley difference is 282 kW under the decentralized scenario.

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Keywords: Electric vehicles; Orderly charging; Multi-level adjustable charging

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  1. Ahsan, F., Dana, N. H., Sarker, S. K., Li, L., Muyeen, S. M., Ali, M. F., Das, P. (2023). Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review. Protection and Control of Modern Power Systems, 8(3), 1-42.
  2. Al Zishan, A., Haji, M. M., & Ardakanian, O. (2021). Adaptive congestion control for electric vehicle charging in the smart grid. IEEE Transactions on Smart Grid, 12(3), 2439-2449.
  3. Alilou, M., Tousi, B., & Shayeghi, H. (2020). Home energy management in a residential smart micro grid under stochastic penetration of solar panels and electric vehicles. Solar Energy, 212, 6-18.
  4. Ayyadi, S., Bilil, H., & Maaroufi, M. (2019). Optimal charging of Electric Vehicles in residential area. Sustainable Energy, 19, 100240.
  5. Du, W., Ma, J., & Yin, W. (2023). Orderly charging strategy of electric vehicle based on improved PSO algorithm. Energy, 271, 127088.
  6. Duan, X., Hu, Z., Cui, Y., Guo, Z., Cao, X., Ding, N., & Zhang, Y. (2018). Optimal charging and discharging strategy for electric vehicles in large timescales. Power System Technology, 42(12), 4037-4044.
  7. Ehsani, M., Singh, K. V., Bansal, H. O., & Mehrjardi, R. T. (2021). State of the art and trends in electric and hybrid electric vehicles. Proceedings of the IEEE, 109(6), 967-984.
  8. Wu, F., Yang, J., Zhan, X., Liao, S., & Xu, J. (2020). The online charging and discharging scheduling potential of electric vehicles considering the uncertain responses of users. IEEE Transactions on Power Systems, 36(3), 1794-1806.
  9. Fachrizal, R., & Munkhammar, J. (2020). Improved photovoltaic self-consumption in residential buildings with distributed and centralized smart charging of electric vehicles. Energies, 13(5), 1153.
  10. Faddel, S., & Mohammed, O. A. (2018). Automated distributed electric vehicle controller for residential demand side management. IEEE Transactions on Industry Applications, 55(1), 16-25.
  11. Fan, S., Liu, J., Wu, Q., Cui, M., Zhou, H., & He, G. (2020). Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm. Applied Energy, 277, 115583.
  12. Gao, S., Li, H., Jurasz, J., & Dai, R. (2021). Optimal charging of electric vehicle aggregations participating in energy and ancillary service markets. IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 3(2), 270-278.
  13. Gong, L., Cao, W., Liu, K., & Zhao, J. (2020). Optimal charging strategy for electric vehicles in residential charging station under dynamic spike pricing policy. Sustainable Cities and Society, 63, 102474.
  14. Hou, H., Xue, M., Xu, Y., Zhao, Z., Deng, X., Xu, T., & Cui, R. (2020). Multi-objective economic dispatch of a microgrid considering electric vehicle and transferable load. Applied Energy, 262, 114489.
  15. Hu, Y., Zhang, M., Wang, K., & Wang, D. (2022). Optimization of orderly charging strategy of electric vehicle based on improved alternating direction method of multipliers. Journal of Energy Storage, 55, 105483.
  16. Huang, Y., Guo, C., Wang, L., Bao, Y., Dai, S., & Ding, Q. (2015). A cluster-based dispatch strategy for electric vehicles considering user satisfaction. Automation of Electric Power Systems, 39(17), 183-191.
  17. Hou, H., Tang, J., Wang, Y., X., Wang, F., & Hu, P. (2022). Long-time-scale charging and discharging scheduling of electric vehicles under joint price and incentive demand response. Automation of Electric Power Systems, 46(15), 46-55.
  18. Jian, L., Zheng, Y., & Shao, Z. (2017). High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles. Applied Energy, 186, 46-55.
  19. Knez, M., Zevnik, G. K., & Obrecht, M. (2019). A review of available chargers for electric vehicles: United States of America, European Union, and Asia. Renewable and Sustainable Energy Reviews, 109, 284-293.
  20. Luo, Y., Zhu, T., Wan, S., Zhang, S., & Li, K. (2016). Optimal charging scheduling for large-scale EV (electric vehicle) deployment based on the interaction of the smart-grid and intelligent-transport systems. Energy, 97, 359-368.
  21. Moghaddam, Z., Ahmad, I., Habibi, D., & Phung, Q. V. (2017). Smart charging strategy for electric vehicle charging stations. IEEE Transactions on Transportation Electrification, 4(1), 76-88.
  22. Naqash, M. T., Aburamadan, M. H., Harireche, O., AlKassem, A., & Farooq, Q. U. (2021). The potential of wind energy and design implications on wind farms in Saudi Arabia. International Journal of Renewable Energy Development, 10(4), 839.
  23. Nguyen, T. T. M., Khoa, P. N. D., & Huynh, N. A. (2022). Electrical Energy Management According to Pricing Policy: A Case in Vietnam. International Journal of Renewable Energy Development, 11(3), 851-862.
  24. Pournaras, E., Jung, S., Yadhunathan, S., Zhang, H., & Fang, X. (2019). Socio-technical smart grid optimization via decentralized charge control of electric vehicles. Applied Soft Computing, 82, 105573.
  25. Qin, M., Yang, Y., Zhao, X., Xu, Q., & Yuan, L. (2023). Low-carbon economic multi-objective dispatch of integrated energy system considering the price fluctuation of natural gas and carbon emission accounting. Protection and Control of Modern Power Systems, 8(4), 1-18.
  26. Renzhou, L., Bai, X., Peijie, L. I., Dai, J., & Lin, S. (2016). Decentralized charging control of electric vehicles based on alternate direction method of multiplier. Automation of Electric Power Systems.
  27. Sanguesa, J. A., Torres-Sanz, V., Garrido, P., Martinez, F. J., & Marquez-Barja, J. M. (2021). A review on electric vehicles: Technologies and challenges. Smart Cities, 4(1), 372-404.
  28. Sharma, P., & Naidu, R. C. (2023). Optimization techniques for grid-connected PV with retired EV batteries in centralized charging station with challenges and future possibilities: A review. Ain Shams Engineering Journal, 14(7), 101985.
  29. Tao, Y., Huang, M., Chen, Y., & Yang, L. (2020). Orderly charging strategy of battery electric vehicle driven by real-world driving data. Energy, 193, 116806.
  30. Tippichai, A., Teungchai, K., & Fukuda, A. (2023). Energy demand modeling for low carbon cities in Thailand: A case study of Nakhon Ratchasima province. International Journal of Renewable Energy Development, 12(4).
  31. Turker, H., & Bacha, S. (2018). Optimal minimization of plug-in electric vehicle charging cost with vehicle-to-home and vehicle-to-grid concepts. IEEE Transactions on Vehicular Technology, 67(11), 10281-10292.
  32. Wang, N., Li, B., Duan, Y., & Jia, S. (2021). A multi-energy scheduling strategy for orderly charging and discharging of electric vehicles based on multi-objective particle swarm optimization. Sustainable Energy Technologies and Assessments, 44, 101037.
  33. Wang, X., Sun, C., Wang, R., & Wei, T. (2020a). Two-stage optimal scheduling strategy for large-scale electric vehicles. IEEE Access, 8, 13821-13832.
  34. Wang, Y., Chen, J., Ma, X., Hou, X., Zheng, K., & Chen, W. (2020b). Interactive scheduling strategy between electric vehicles and power grid based on group optimization. Electric Power Automation Equipment, 40, 77-85.
  35. Wang, Y., Wang, F. H., Hou, X. Z., Sun, H., Zhu, B., & Liu, G. (2018). Random access control strategy of charging for household electric vehicle in residential area. J. Automation of Electric Power Systems, 42, 53-58.
  36. Yang, B., Wang, L., Liao, C., & Ji, L. (2015). Distributed coordinated charging control system model for large-scale electric vehicles. Automation of Electric Power Systems, 39(20), 41-46.
  37. Yin, W., Ming, Z., & Wen, T. (2021). Scheduling strategy of electric vehicle charging considering different requirements of grid and users. Energy, 232, 121118.
  38. Zhang, F., Yang, Q., & An, D. (2020). CDDPG: A deep-reinforcement-learning-based approach for electric vehicle charging control. IEEE Internet of Things Journal, 8(5), 3075-3087.
  39. Zhang, Q., Hu, Y., Tan, W., Li, C., & Ding, Z. (2020). Dynamic time-of-use pricing strategy for electric vehicle charging considering user satisfaction degree. Applied Sciences, 10(9), 3247.
  40. Zheng, Y., Shang, Y., Shao, Z., & Jian, L. (2018). A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid. Applied Energy, 217, 1-13.
  41. Z Zhou, B., Yao, F., Littler, T., & Zhang, H. (2016). An electric vehicle dispatch module for demand-side energy participation. Applied Energy, 177, 464-474.
  42. Zhou, K., Cheng, L., Wen, L., Lu, X., & Ding, T. (2020). A coordinated charging scheduling method for electric vehicles considering different charging demands. Energy, 213, 118882.

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