ОПРЕДЕЛЕНИЕ РАЦИОНАЛЬНЫХ ПАРАМЕТРОВ СЕТИ ЗАРЯДНЫХ СТАНЦИЙ ЭЛЕКТРОМОБИЛЕЙ

##plugins.themes.bootstrap3.article.sidebar##

Просмотров аннотации: 53
Загрузок PDF: 17
pdf (rus)
Опубликован: авг 27, 2024
DOI: https://doi.org/10.52928/2070-1616-2024-50-2-78-85
Ключевые слова:
transport systems, electric vehicles, charging stations, sparrow search algorithm транспортные системы, электромобили, зарядные станции, алгоритм «поиска по воробьям»
Выпуск
№ 2 (2024)
Раздел
Транспорт

##plugins.themes.bootstrap3.article.main##

ДУ СИЧЖОУ
Белорусский национальный технический университет, Минск
А. О. ЛОБАШОВ
Белорусский национальный технический университет, Минск
Д. В. КАПСКИЙ
Белорусский национальный технический университет, Минск

Аннотация

Исследуется проблема определения рациональных параметров сети зарядных станций электромобилей. Проанализированы основные факторы, влияющие на размещение зарядных станций электромобилей. Разработана многоцелевая модель выбора места размещения зарядных станции. Предложена алгоритмическая модель для совершенствования метода «поиска по воробьям», который используется в качестве основы решения задачи определения рациональных параметров сети зарядных станций электромобилей.

##plugins.themes.bootstrap3.article.details##

Как цитировать
СИЧЖОУ, Д., ЛОБАШОВ, А. О., & КАПСКИЙ, Д. В. (2024). ОПРЕДЕЛЕНИЕ РАЦИОНАЛЬНЫХ ПАРАМЕТРОВ СЕТИ ЗАРЯДНЫХ СТАНЦИЙ ЭЛЕКТРОМОБИЛЕЙ. Вестник Полоцкого государственного университета. Серия B. Промышленность. Прикладные науки, (2), 78-85. https://doi.org/10.52928/2070-1616-2024-50-2-78-85
Лицензия Creative Commons

Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.

Биографии авторов

А. О. ЛОБАШОВ, Белорусский национальный технический университет, Минск

д-р техн. наук, проф.

Д. В. КАПСКИЙ, Белорусский национальный технический университет, Минск

д-р техн. наук, проф.

Crossref
Scopus
Google Scholar
Europe PMC

Библиографические ссылки

Zhao, F., Liu, X., Zhang, H. & Liu, Z. (2022). Automobile Industry under China’s Carbon Peaking and Carbon Neutrality Goals: Challenges, Opportunities, and Coping Strategies. Journal of Advanced Transportation, 2022(1), 1–13. DOI: 10.1155/2022/5834707

Martins, L.S., Guimarães, L.F., Junior, A.B.B., Tenório, J.A.S. & Espinosa, D.C.R. (2021). Electric Car Battery: An Overview on Global Demand, Recycling and Future Approaches Towards Sustainability. Journal of Environmental Management, (295), 113091. DOI: 10.1016/j.jenvman.2021.113091

Cao, L. (2020). Research on the Development and Application of Charging Piles Based on the Development of New Energy Vehicles. IOP Conference Series: Earth and Environmental Science, 565(1), 012001. DOI: 10.1088/1755-1315/565/1/012001

Pal, A., Bhattacharya, A. & Chakraborty, AK. (2021). Allocation of Electric Vehicle Charging Station Considering Uncertainties. Sustainable Energy, Grids and Networks, (25), 100422. DOI: 10.1016/j.segan.2020.100422

Bae, S. & Kwasinski, A. (2012). Spatial and Temporal Model of Electric Vehicle Charging Demand. IEEE Transactions on Smart Grid, 3(1), 394–403. DOI: 10.1109/TSG.2011.2159278

Xu, H. & Huang, Х. (2020). A Multi-Objective Coordinated Charging and Discharging Strategy for Electric Vehicles Based on Stackelberg Game. Energy and Power Engineering, 12(04), 63. DOI:10.4236/epe.2020.124B007

Mehrjerdi, H. & Hemmati, R. (2020). Stochastic model for electric vehicle charging station integrated with wind energy. Sustainable Energy Technologies and Assessments, (37), 157–177. DOI: 10.1016/j.seta.2019.100577

Liu, Z., Xiao, Z., Wu, Y., Hui, H., Tao, X., Qingyong, Z. & Changjun, X. (2020). Integrated optimal dispatching strategy considering power generation and consumption interaction. IEEE Access, (9), 1338–1349.

Wu, W., Lin, Y. & Liu, R. (2020). Online EV charge scheduling based on time-of-use pricing and peak load minimization: Properties and efficient algorithms. IEEE Transactions on Intelligent Transportation Systems, 23(1), 572–586. DOI: 10.1109/TITS.2020.3014088

Liu, J., Lin, G., Huang, S. & Zhou, Ya. (2020). Optimal EV charging scheduling by considering the limited number of chargers. IEEE Transactions on Transportation Electrification, 7(3), 1112–1122. DOI: 10.1109/TTE.2020.3033995

Fernandez, G.S., Krishnasamy, V., Kuppusamy, S., Jagabar, S. Ali, Ziad, M. Ali, El-Shahat, A. & Abdel Aleem, Shady H.E. (2020). Optimal dynamic scheduling of electric vehicles in a parking lot using particle swarm optimization and shuffled frog leaping algorithm. Energies, 13(23), 6384. DOI: 10.3390/en13236384

Abdullah-Al-Nahid, S., Khan, T.A., Taseen, M.A. & Taskin, J. (2022). A novel consumer-friendly electric vehicle charging scheme with vehicle to grid provision supported by genetic algorithm based optimization. Journal of Energy Storage, (50), 104655. DOI:10.1016/j.est.2022.104655

Rahmani-Andebili, M., Bonamente, M. & Miller, J.A. (2020). Mobility Analysis of Plug-in Electric Vehicles in San Francisco Applying Monte Carlo Markov Chain. IEEE Kansas Power and Energy Conference (KPEC) [Manhattan, KS, USA (13–14 July 2020], 1–6. DOI: 10.1109/KPEC47870.2020.9167668

Betancur, D., Duarte, L.F., Revollo, J., Restrepo, C., Diez, A.E., Isaac, I.A. … Gonzalez, J.W. (2021). Methodology to evaluate the impact of electric vehicles on electrical networks using Monte Carlo. Energies, 14(5), 1300. DOI: 10.3390/en14051300

Anand, M.P., Bagen, B. & Rajapakse, A. (2020). Probabilistic reliability evaluation of distribution systems considering the spatial and temporal distribution of electric vehicles. International Journal of Electrical Power & Energy Systems, (117). 105609. DOI: 10.1016/j.ijepes.2019.105609

Meng, Z. & Pan, J.S. (2018). QUasi-Affine TRansformation Evolution with External ARchive (QUATRE-EAR): an enhanced structure for differential evolution. Knowledge-Based Systems, (155), 35–53. DOI: 10.1016/j.knosys.2018.04.034

Mirjalili, S. & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, (95), 51–67. DOI: 10.1016/j.advengsoft.2016.01.008

Du, S., Zhang, J., Wang, Y. & Li, Z. (2024). Integration of Computer Vision and IOT Into an Automatic Driving Assistance System for “Electric Vehicles”. IEEE Transactions on Industrial Informatics, 20(3), 4765–4772.

Kathiroli, P. & Selvadurai, K. (2022). Energy efficient cluster head selection using improved Sparrow Search Algorithm in Wireless Sensor Networks. Journal of King Saud University-Computer and Information Sciences, 34(10), 8564–8575. DOI: 10.1016/j.jksuci.2021.08.031

Zhang, G. & Zhang, E. (2021). A random opposition-based sparrow search algorithm for Path Planning Problem. Artificial Intelligence: First CAAI International Conference [CICAI 2021, Hangzhou, China, June 5–6, 2021], Part II 1, 408–418. DOI: 10.1007/978-3-030-93049-3_34

Yang, X.S. & Deb, S. (2010). Engineering optimization by cuckoo search. J. Math. Modell. Numer. Optim, 1(4), 330–343. DOI: 10.48550/arXiv.1005.2908

Ghamami, M., Nie, Y. & Zockaie, A. (2016). Planning charging infrastructure for plug-in electric vehicles in city centers. International Journal of Sustainable Transport, (10), 343–353. DOI: 10.1080/15568318.2014.937840

Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Syst, (96), 120–133.

Heidari, A.A., Mirjalili, S., Faris, H. & Mafarja, M. (2019). Harris hawks optimization: Algorithm and applications. Future Generat. Comput. Syst., (97), 849–872. DOI: 10.1016/j.future.2019.02.028

Jain, M., Singh, V. & Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm Evoluti. Comput., (44), 148–175. DOI: 10.1016/j.swevo.2018.02.013

Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M. & Tavakkoli-Moghaddam, R. (2020). Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft Comput, (24), 14637–14665. DOI: 10.1007/s00500-020-04812-z

Gimenez-Gaydou, D.A., Ribeiro, A.N., Gutierrez, J. & Antunes, A.P. (2016). Optimal location of battery electric vehicle charging stations in urban areas: A new approach. International Journal of Sustainable Transport, (10), 393–405. DOI: 10.1080/15568318.2014.961620

Braik, M., Sheta, A. & Al-Hiary, H. (2021). A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm. Neural Comput. Appli., (33), 2515–2547. DOI: 10.1007/s00521-020-05145-6

Abualigah, L., Yousri, D., Abd, Elaziz M., Ewees, A.A., Al-qaness, Mohamed A.A. & Gandomi, Amir H. (2021). Aquila optimizer: a novel metaheuristic optimization algorithm. Comput. Indust. Engin., (157), 107250. DOI:10.1016/j.cie.2021.107250

Braik, M.S. (2021). Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems. Expert Syst. Appl., (174), 114685. DOI:10.1016/j.eswa.2021.114685

Yang, Z., Deng, L., Wang, Y. & Liu, J. (2021). Aptenodytes forsteri optimization: Algorithm and applications. Knowledge-Based Syst., (232), 107483. DOI:10.1016/j.knosys.2021.107483

Frade, I., Ribeiro, A., Goncalves, G. & Antunes, A. (2011). Optimal Location of Charging Stations for Electric Vehicles in a Neighborhood in Lisbon, Portugal. Transportation Research Record, (2), 91–98. DOI:10.3141/2252-12

Du, Sizhuo, Kapski, D.V. & Lobashov, A.O. (2024). Analiz preimucshestv i nedostatkov vospolneniya energii dlya zaryadnych stanciy elctromobiley. Sovremenye technologii v transportnoy otrasly: electron. sb. st. Mezdunar. nauch.-techn. konf. [Novopolotsk, 25-26 apr. 2024 g.], 122–124. (In Russ.)

Рекомендуемые статьи автора (авторов)

<< < 1 2 3