آمار

تعداد نشریات26
تعداد شماره‌ها404
تعداد مقالات3,551
تعداد مشاهده مقاله5,509,044
تعداد دریافت فایل اصل مقاله3,769,239
Kamona, S.M.H., Abbas, H.A., Ibrahim, A.A., Mohammed, N.Q., Ali, A.A., Mohammed, B.A., Hamza, M.S.. (1402). A Comparative Study on Charging Time of Electric Vehicles Optimization Using Cuckoo Search and Particle Swarm Optimization Methods. سامانه مدیریت نشریات علمی, 11(Special Issue), 63-68. doi: 10.22098/joape.2023.13832.2066
S.M.H. Kamona; H.A. Abbas; A.A. Ibrahim; N.Q. Mohammed; A.A. Ali; B.A. Mohammed; M.S. Hamza. "A Comparative Study on Charging Time of Electric Vehicles Optimization Using Cuckoo Search and Particle Swarm Optimization Methods". سامانه مدیریت نشریات علمی, 11, Special Issue, 1402, 63-68. doi: 10.22098/joape.2023.13832.2066
Kamona, S.M.H., Abbas, H.A., Ibrahim, A.A., Mohammed, N.Q., Ali, A.A., Mohammed, B.A., Hamza, M.S.. (1402). 'A Comparative Study on Charging Time of Electric Vehicles Optimization Using Cuckoo Search and Particle Swarm Optimization Methods', سامانه مدیریت نشریات علمی, 11(Special Issue), pp. 63-68. doi: 10.22098/joape.2023.13832.2066
Kamona, S.M.H., Abbas, H.A., Ibrahim, A.A., Mohammed, N.Q., Ali, A.A., Mohammed, B.A., Hamza, M.S.. A Comparative Study on Charging Time of Electric Vehicles Optimization Using Cuckoo Search and Particle Swarm Optimization Methods. سامانه مدیریت نشریات علمی, 1402; 11(Special Issue): 63-68. doi: 10.22098/joape.2023.13832.2066

A Comparative Study on Charging Time of Electric Vehicles Optimization Using Cuckoo Search and Particle Swarm Optimization Methods

Journal of Operation and Automation in Power Engineering
دوره 11، Special Issue، اسفند 2023، صفحه 63-68    اصل مقاله (694.44 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.13832.2066
نویسندگان
S.M.H. Kamona* 1؛ H.A. Abbas2؛ A.A. Ibrahim3؛ N.Q. Mohammed4؛ A.A. Ali5؛ B.A. Mohammed6؛ M.S. Hamza7
1Department of Medical Laboratory Technics, Al-Manara College for Medical Sciences, Amarah, Iraq
2College of Technical Engineering, National University of Science and Technology, Dhi Qar, Iraq
3Department of Medical Engineering, Mazaya University College, Iraq
4Department of Medical Engineering, Al-Nisour University College, Iraq
5College of Petroleum Engineering, Al-Ayen University, Thi-Qar, Iraq
6Department of Medical Engineering, Al-Hadi University College Baghdad, Iraq
7Department of Medical Engineering, Al-Esraa University College, Baghdad, Iraq
چکیده
The implementation of electric vehicles for this specific purpose could potentially cause an impact on the load on the network. From one standpoint, it is more advantageous to initiate the charging process of electric vehicle batteries as soon as they are connected to the grid, in order to guarantee sufficient charge levels in the event of unforeseen events. The current investigation showcases an innovative algorithm specifically engineered for the smart grid, wherein the principal aim is to approximate the time needed to fully charge electric vehicles. The algorithm being evaluated prioritizes the decrease in both the unfulfilled energy demand and the daily load profile standard deviation. The algorithm has been purposefully designed to regulate and supervise the charging process in an efficient manner. The algorithm incorporates various elements pertaining to the anticipated conduct of specific electric vehicles, such as their projected arrival and departure times, as well as their initial charge status upon arrival. In situations involving a substantial quantity of automobiles, statistical techniques are applied to decrease the number of variables, thereby diminishing the algorithm's computational time. The optimization technique implemented in this research is inspired by natural phenomena and is founded upon the cuckoo orphan search pattern. The proposed algorithm and the PSO algorithm were implemented in order to simulate the 34-bus IEEE standard radio distribution network. Upon comparing the outcomes derived from the analysis, it was discovered that the implementation of the CS algorithm led to a substantial decrease in peak load by 33% in comparison to the situation in which no optimization was executed. Furthermore, the CS algorithm accomplished a 27% reduction in peak load, which was superior to the PSO algorithm.
کلیدواژه‌ها
Electric vehicles؛ optimization؛ particle swarm optimization؛ cuckoo search algorithm؛ load demand
مراجع
  1. Sharma, A. K. Panwar, and M. Tripathi, “Storage technologies for electric vehicles,” J. Traffic Transp. Eng., vol. 7, no. 3, pp. 340–361, 2020.
  2. A. Sanguesa, V. Torres-Sanz, P. Garrido, F. J. Martinez, and J. M. Marquez-Barja, “A review on electric vehicles: Technologies and challenges,” Smart Cities, vol. 4, no. 1, pp. 372–404, 2021.
  3. Goel, R. Sharma, and A. K. Rathore, “A review on barrier and challenges of electric vehicle in india and vehicle to grid optimisation,” Transp. Eng., vol. 4, p. 100057, 2021.
  4. Verma, S. Mishra, A. Gaur, S. Chowdhury, S. Mohapatra, G. Dwivedi, and P. Verma, “A comprehensive review on energy storage in hybrid electric vehicle,” J. Traffic Transp. Eng., vol. 8, no. 5, pp. 621–637, 2021.
  5. S. Mastoi, S. Zhuang, H. M. Munir, M. Haris, M. Hassan, M. Usman, S. S. H. Bukhari, and J.-S. Ro, “An in-depth analysis of electric vehicle charging station infrastructure, policy implications, and future trends,” Energy Rep., vol. 8, pp. 11504–11529, 2022.
  6. Ali and M. Naushad, “Insights on electric vehicle adoption: Does attitude play a mediating role?,” Innovative Mark., vol. 18, no. 1, pp. 104–116, 2022.
  7. Huang, Y. Lin, F. Liu, M. K. Lim, and L. Li, “Battery recycling policies for boosting electric vehicle adoption: evidence from a choice experimental survey,” Clean Technol. Environ. Policy, vol. 24, no. 8, pp. 2607–2620, 2022.
  8. H. Langbroek, M. Cebecauer, J. Malmsten, J. P. Franklin, Y. O. Susilo, and P. Georén, “Electric vehicle rental and electric vehicle adoption,” Res. Transp. Econ., vol. 73, pp. 72–82, 2019.
  9. Kim, J. Lee, and C. Lee, “Does driving range of electric vehicles influence electric vehicle adoption?,” Sustainability, vol. 9, no. 10, p. 1783, 2017.
  10. Maybury, P. Corcoran, and L. Cipcigan, “Mathematical modelling of electric vehicle adoption: A systematic literature review,” Transp. Res. Part D Transp. Environ., vol. 107, p. 103278, 2022.
  11. Salmani, A. Rezazadeh, and M. Sedighizadeh, “Robust stochastic blockchain model for peer-to-peer energy trading among charging stations of electric vehicles,” J. Oper. Autom. Power Eng., vol. 12, no. 1, pp. 54–68, 2024.
  12. Dejamkhooy and A. Ahmadpour, “Torque ripple reduction of the position sensor-less switched reluctance motors applied in the electrical vehicles,” J. Oper. Autom. Power Eng., vol. 11, no. 4, pp. 258–267, 2023.
  13. Cheshme-Khavar, A. Abdolahi, F. Gazijahani, N. Kalantari, and J. Guerrero, “Short-term scheduling of cryogenic energy storage systems in microgrids considering chp-thermal-heatonly units and plug-in electric vehicles,” J. Oper. Autom. Power Eng., 2023.
  14. Potdar, S. Batool, and A. Krishna, “Risks and challenges of adopting electric vehicles in smart cities,” Smart Cities: Dev. Governance Frameworks, pp. 207–240, 2018.
  15. Tang, G. Liu, and Q. Pan, “A review on representative swarm intelligence algorithms for solving optimization problems: Applications and trends,” IEEE/CAA J. Autom. Sin., vol. 8, no. 10, pp. 1627–1643, 2021.
  16. Rajamoorthy, G. Arunachalam, P. Kasinathan, Devendiran, P. Ahmadi, S. Pandiyan, S. Muthusamy, H. Panchal, H. A. Kazem, and P. Sharma, “A novel intelligent transport system charging scheduling for electric vehicles using grey wolf optimizer and sail fish optimization algorithms,” Energy Sources Part A, vol. 44, no. 2, pp. 3555– 3575, 2022.
  17. M. Martinez, X. Hu, D. Cao, E. Velenis, B. Gao, and M. Wellers, “Energy management in plug-in hybrid electric vehicles: Recent progress and a connected vehicles perspective,” IEEE Trans. Veh. Technol., vol. 66, no. 6, pp. 4534–4549, 2016.
  18. Cao, X. Chen, R. Qiu, and S. Hou, “Electric vehicle industry sustainable development with a stakeholder engagement system,” Technol. Soc., vol. 67, p. 101771, 2021.
  19. -W. Chang, “An indispensable role in promoting the electric vehicle industry: An empirical test to explore the integration framework of electric vehicle charger and electric vehicle purchase behavior,” Transp. Res. Part A Policy Pract., vol. 176, p. 103824, 2023.
  20. Hussain, Y.-S. Kim, S. Thakur, and J. G. Breslin, “Optimization of waiting time for electric vehicles using a fuzzy inference system,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, pp. 15396–15407, 2022.
  21. Yang and P. Chen, “Optimization of charging schedule for battery electric vehicles using dc fast charging stations,” IFAC-PapersOnLine, vol. 54, no. 20, pp. 418–423, 2021.
  22. Ullah, K. Liu, T. Yamamoto, M. Shafiullah, and A. Jamal, “Grey wolf optimizer-based machine learning algorithm to predict electric vehicle charging duration time,” Transp. Lett., vol. 15, no. 8, pp. 889–906, 2023.
  23. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. AlTashi, M. A. Summakieh, and S. Mirjalili, “Particle swarm optimization: A comprehensive survey,” IEEE Access, vol. 10, pp. 10031–10061, 2022.
  24. Li, S. Miao, X. Luo, and J. Wang, “Optimization scheduling model based on source-load-energy storage coordination in power systems,” in 2016 22nd Int. Conf. Autom. Comput. (ICAC), pp. 120–125, IEEE, 2016.
  25. Liu, Z. Deng, H. Wang, X. Zheng, X. Fu, and F. Wang, “Classified particle swarm optimization based algorithm for cooperative localization,” in China Satell. Navig. Conf. (CSNC) 2020 Proc.: Volume III, pp. 405–414, Springer, 2020.
  26. R. Kaloop, D. Kumar, F. Zarzoura, B. Roy, and W. Hu, “A wavelet-particle swarm optimization-extreme learning machine hybrid modeling for significant wave height prediction,” Ocean Eng., vol. 213, p. 107777, 2020.
  27. Mareli and B. Twala, “An adaptive cuckoo search algorithm for optimisation,” Appl. Comput. Inf., vol. 14, no. 2, pp. 107–115, 2018.
  28. Jamil, T. A. Alghamdi, Z. A. Khan, S. Javaid, A. Haseeb, Z. Wadud, and N. Javaid, “An innovative home energy management model with coordination among appliances using game theory,” Sustainability, vol. 11, no. 22, p. 6287, 2019.
  29. Huang, Z. Xie, and X. Huang, “Fault location of distribution network base on improved cuckoo search algorithm,” IEEE Access, vol. 8, pp. 2272–2283, 2019.
آمار
تعداد مشاهده مقاله: 322
تعداد دریافت فایل اصل مقاله: 357


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب