آمار

تعداد نشریات26
تعداد شماره‌ها306
تعداد مقالات2,644
تعداد مشاهده مقاله3,971,880
تعداد دریافت فایل اصل مقاله2,676,819
Usmanovich, B.A., Kinanah, T.M.H., Al-Mansor, A.H.O., Al-Majdi, K., Hlail, S.H., Lafta, D.A., Zaboun, A.R.T., Abbas, J.K.. (1403). The Optimal Parking Lot for Electrical Vehicle: An Assessment Based on Artificial Immune System Algorithm. سامانه مدیریت نشریات علمی, 11(Special Issue (Open)), -. doi: 10.22098/joape.2024.13834.2068
B.A. Usmanovich; T.M.H. Kinanah; A.H.O. Al-Mansor; K. Al-Majdi; S.H. Hlail; D.A. Lafta; A.R.T. Zaboun; J.K. Abbas. "The Optimal Parking Lot for Electrical Vehicle: An Assessment Based on Artificial Immune System Algorithm". سامانه مدیریت نشریات علمی, 11, Special Issue (Open), 1403, -. doi: 10.22098/joape.2024.13834.2068
Usmanovich, B.A., Kinanah, T.M.H., Al-Mansor, A.H.O., Al-Majdi, K., Hlail, S.H., Lafta, D.A., Zaboun, A.R.T., Abbas, J.K.. (1403). 'The Optimal Parking Lot for Electrical Vehicle: An Assessment Based on Artificial Immune System Algorithm', سامانه مدیریت نشریات علمی, 11(Special Issue (Open)), pp. -. doi: 10.22098/joape.2024.13834.2068
Usmanovich, B.A., Kinanah, T.M.H., Al-Mansor, A.H.O., Al-Majdi, K., Hlail, S.H., Lafta, D.A., Zaboun, A.R.T., Abbas, J.K.. The Optimal Parking Lot for Electrical Vehicle: An Assessment Based on Artificial Immune System Algorithm. سامانه مدیریت نشریات علمی, 1403; 11(Special Issue (Open)): -. doi: 10.22098/joape.2024.13834.2068

The Optimal Parking Lot for Electrical Vehicle: An Assessment Based on Artificial Immune System Algorithm

Journal of Operation and Automation in Power Engineering
دوره 11، Special Issue (Open)، خرداد 2023    اصل مقاله (1.04 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2024.13834.2068
نویسندگان
B.A. Usmanovich* 1؛ T.M.H. Kinanah2؛ A.H.O. Al-Mansor3؛ K. Al-Majdi4؛ S.H. Hlail5؛ D.A. Lafta6؛ A.R.T. Zaboun7؛ J.K. Abbas8
1DSc in Economics, Professor, Department of Management Ferghana Polytechnic Institute, Ferghana, Uzbekistan.
2Department of Biomedical Engineering, Mazaya University College, Iraq.
3Department of Optical Techniques, Al-Zahrawi University College, Karbala, Iraq.
4Department of Biomedical Engineering, Ashur University College, Baghdad, Iraq.
5College of Technical Engineering, National University of Science and Technology, Dhi Qar, Iraq.
6College of Petroleum Engineering, Al-Ayen University, Thi-Qar , Iraq.
7Department of Biomedical Engineering, Al-Esraa University College, Baghdad, Iraq.
8Department of Biomedical Engineering, AL-Nisour University College, Baghdad, Iraq.
چکیده
The optimum location of electric vehicle (EV) parking lots is critical in distribution network design for lowering costs, boosting revenues, and enhancing dependability. However, conventional distribution network schedulers were not designed with these variables in mind. Furthermore, the increased use of EVs for environmental reasons mandates the planning of EV parking spaces. As a result, distribution network designers must examine network technical difficulties, design approaches, and changing consumer needs. The placement of dispersed manufacturing resources and EV parking without sufficient planning and ideal location leads in economic challenges for investors and technical concerns for the network. As a result, future distribution networks should prioritize the ideal placement of EV parking lots and distributed production resources in order to maximize network capabilities and meet the needs of companies and power applications in the digital society. According to the findings, the rate of EV parking installations is very high. When power consumers remain connected to the grid during peak hours, distribution businesses benefit significantly, and the overall voltage profile improves. Variations in electric vehicle (EV) battery capacity, power cost, EV adoption, and the weighting coefficients required for optimization will all have different outcomes. It is critical to precisely determine the battery capacity of electric vehicles (EVs) as well as the efficiency of inverters in order to produce more accurate results. According to the findings, increasing the number of parking lot for EVs in a network enhances the benefit from minimizing losses, and providing peak load significantly. So that using 2 parking lot for EVs in a network can increase the overall profit to 129%.
کلیدواژه‌ها
Electric vehicles؛ optimization؛ parking lot؛ artificial immune system algorithm؛ optimal location
مراجع
  1. Zheng, Z. Shao, X. Lei, Y. Shi, and L. Jian, “The economic analysis of electric vehicle aggregators participating in energy and regulation markets considering battery degradation,” J. Energy Storage, vol. 45, p. 103770, 2022.
  2. Englberger, K. A. Gamra, B. Tepe, M. Schreiber, A. Jossen, and H. Hesse, “Electric vehicle multi-use: Optimizing multiple value streams using mobile storage systems in a vehicle-to-grid context,” Appl. Energy, vol. 304, p. 117862, 2021.
  3. S. A. M. K. Abad, P. Javidan, M. Baghdadi, and N. Mehrdadi, “Green synthesis of pd@ biochar using the extract and biochar of corn-husk wastes for electrochemical cr (vi) reduction in plating wastewater,” J. Environ. Chem. Eng., vol. 11, no. 3, p. 109911, 2023.
  4. Al-Aloosi, Y. Alaiwi, and H. Hamzah, “Thermal performance analysis in a parabolic trough solar collector with a novel design of inserted fins,” Case Stud. Therm. Eng., vol. 49, p. 103378, 2023.
  5. Alaiwi and A. Mutlu, “Modelling, simulation and implementation of autonomous unmanned quadrotor,” Mach. Technol. Mater., vol. 12, no. 8, pp. 320–325, 2018.
  6. 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., vol. 12, no. 3, pp. 233–244, 2024.
  7. 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.
  8. 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.
  9. 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.
  10. Tehranian, “Can machine learning catch economic recessions using economic and market sentiments?,” ArXiv Prepr. ArXiv:2308.16200, 2023.
  11. Tehranian, “Monetary policy & stock market,” ArXiv Prepr. ArXiv:2305.13930, 2023.
  12. 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.
  13. Sharma, A. K. Panwar, and M. Tripathi, “Storage technologies for electric vehicles,” J. Traffic Transp. Eng., vol. 7, no. 3, pp. 340–361, 2020.
  14. 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.
  15. Ali and M. Naushad, “Insights on electric vehicle adoption: Does attitude play a mediating role?,” Innovative Marketing, vol. 18, no. 1, pp. 104–116, 2022.
  16. 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.
  17. Kim, J. Lee, and C. Lee, “Does driving range of electric vehicles influence electric vehicle adoption?,” Sustainability, vol. 9, no. 10, p. 1783, 2017.
  18. 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.
  19. Yu, “Impacts assessment of phev charge profiles on generation expansion using national energy modeling system,” in 2008 IEEE Power Energy Soc. Gen. Meet.-Convers. Delivery Electr. Energy 21st Century, pp. 1–5, IEEE, 2008.
  20. Clement-Nyns, E. Haesen, and J. Driesen, “The impact of charging plug-in hybrid electric vehicles on a residential distribution grid,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 371–380, 2009.
  21. F. Marzoghi, S. Bahramara, F. Adabi, and S. Nojavan, “Interval multi-objective optimization of hydrogen storage based intelligent parking lot of electric vehicles under peak demand management,” J. Energy Storage, vol. 27, p. 101123, 2020.
  22. S. Kopplin, B. M. Brand, and Y. Reichenberger, “Consumer acceptance of shared e-scooters for urban and short-distance mobility,” Transp. Res. Part D Transp. Environ., vol. 91, p. 102680, 2021.
  23. Vinet and A. Zhedanov, “A missingfamily of classical orthogonal polynomials,” J. Phys. A: Math. Theor., vol. 44, no. 8, p. 085201, 2011.
  24. Saeidi, S. Enjedani, E. A. Behineh, K. Tehranian, and S. Jazayerifar, “Factors affecting public transportation use during pandemic: An integrated approach of technology acceptance model and theory of planned behavior,” Tehnicˇki Glasnik, vol. 18, no. 3, pp. 1–12, 2023.
  25. Grigg, P. Wong, P. Albrecht, R. Allan, M. Bhavaraju, R. Billinton, Q. Chen, C. Fong, S. Haddad, S. Kuruganty, et al., “The ieee reliability test system-1996. a report prepared by the reliability test system task force of the application of probability methods subcommittee,” IEEE Trans. Power Syst., vol. 14, no. 3, pp. 1010–1020, 1999.
  26. -H. Lo and N. Ansari, “Decentralized controls and communications for autonomous distribution networks in smart grid,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 66–77, 2012.
  27. Nagarajan, S. K. Mahalingam, J. Kandasamy, and S. Gurusamy, “Application of artificial immune system algorithm for simultaneous minimisation of multiple objectives in single row facility layout problems,” Int. J. Ind. Syst. Eng., vol. 36, no. 1, pp. 49–72, 2020.
  28. -q. Li, Z.-m. Liu, C. Li, and Z.-x. Zheng, “Improved artificial immune system algorithm for type-2 fuzzy flexible job shop scheduling problem,” IEEE Trans. Fuzzy Syst., vol. 29, no. 11, pp. 3234–3248, 2020.
  29. Liu and T.-P. Chung, “A modified immunoglobulin-based artificial immune system algorithm for solving the permutation flow shop scheduling problem,” J. Ind. Prod. Eng., vol. 34, no. 7, pp. 542–550, 2017.
  30. A. Raj, D. Ravindran, M. Saravanan, and T. Prabaharan, “Simultaneous scheduling of machines and tools in multimachine flexible manufacturing systems using artificial immune system algorithm,” Int. J. Comput. Integr. Manuf., vol. 27, no. 5, pp. 401–414, 2014.
آمار
تعداد مشاهده مقاله: 70
تعداد دریافت فایل اصل مقاله: 63


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب