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Transformer performance enhancement by optimized charging strategy for electric vehicles

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Transformer efficiency and regulation, are to be maintained at maximum and minimum respectively by optimal loading, control, and compensation. Charging of electric vehicles at random charging stations will result in uncertain loading on the distribution transformer. The efficiency reduces and regulation increases as a consequence of this loading. In this work, a novel optimization strategy is proposed to map electric vehicles to a charging station, that is optimal with respect to the physical distance, traveling time, charging cost, the effect on transformer efficiency and regulation. Consumer and utility factors are considered for mapping electric vehicles to charging stations. An Internet of Things platform is used to fetch the dynamic location of electric vehicles. The dynamic locations are fed to a binary optimization problem to find an optimal routing table that maps electric vehicles to a charging station. A comparative study is carried out, with and without optimization, to validate the proposed methodology.
Rocznik
Strony
37--56
Opis fizyczny
Bibliogr. 21 poz., rys., tab., wz.
Bibliografia
  • [1] Bakhshinejad A., Tavakoli A., Moghaddam M.M., Modeling and simultaneous management of electric vehicle penetration and demand response to improve distribution network performance, Electrical Engineering, vol. 103, no. 1, pp. 325–340 (2021), DOI: 10.1007/s00202-020-01083-7.
  • [2] Jiao F., Deng Y., Li D., Wei B., Yue C., Cheng M., Zhang Y., Zhang J., A self-scheduling strategy of virtual power plant with electric vehicles considering margin indexes, Archives of Electrical Engineering, vol. 69, no. 4, pp. 907–920 (2020), DOI: 10.24425/aee.2020.134638.
  • [3] Tuchnitz F., Ebell N., Schlund J., Pruckner M., Development and evaluation of a smart charging strategy for an electric vehicle fleet based on reinforcement learning, Applied Energy, vol. 285, no. 1, p. 116382 (2021), DOI: 10.1016/j.apenergy.2020.116382.
  • [4] Powell S., Kara E.C., Sevlian R., Cezar G.V., Kiliccote S., Rajagopal R.,Controlled workplace charging of electric vehicles: The impact of rate schedules on transformer aging, Applied Energy, vol. 276, p. 115352 (2020), DOI: 10.1016/j.apenergy.2020.115352.
  • [5] Xu H., Xia X., Liang W., Zhang Lei., Dong G., Yan Y., Yu B., Ouyang Fan., Zhu W., Liu H., Optimal charging of large-scale electric vehicles over extended time scales, Electrical Engineering, vol. 102, bno. 1, pp. 461–469 (2020), DOI: 10.1007/s00202-019-00887-6.
  • [6] Suresh V., Janik P., Jasinski M., Metaheuristic approach to optimal power flow using mixed integer distributed ant colony optimization, Archives of Electrical Engineering, vol. 69, no. 2, pp. 335–348 (2020), DOI: 10.24425/aee.2020.133029.
  • [7] Liu H., Qu J., Yang S., Li Y., Intelligent optimal dispatching of active distribution network using modified flower pollination algorithm, Archives of Electrical Engineering, vol. 69, no. 1, pp. 159–174 (2020), DOI: 10.24425/aee.2020.131765.
  • [8] Savari G.F., Krishnasamy V., Sugavanam V., Vakesan K., Optimal charging scheduling of electric vehicles in micro grids using priority algorithms and particle swarm optimization, Mobile Networks and Applications, vol. 24, no. 6, pp. 1835–1847 (2019), DOI: 10.1007/s11036-019-01380-x.
  • [9] Al-Ogaili A.S., Tengku H., Tengku J. R., Nur A. R., Agileswari K.M., Marayati B. F., Mohammad H., Mahammad A., Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations, IEEE Access, vol. 7, pp. 128353–128371 (2019), DOI: 10.1109/ACCESS.2019.2939595.
  • [10] Suyono H., Rahman M.T., Mokhlis H., Othman M., Illias H.A., Mohamad H., Optimal scheduling of plug-in electric vehicle charging including time-of-use tariff to minimize cost and system stress, Energies, vol. 12, no. 8, pp. 17–21 (2019), DOI: 10.3390/en12081500.
  • [11] Ki Y., Kim B.I., Ko Y. M., Jeong H., Koo J., Charging scheduling problem of an M-to-N electric vehicle charger, Applied Mathematical Modelling, vol. 64, pp. 603–614 (2018), DOI: 10.1016/j.apm.2018.07.060.
  • [12] Rahimi K., Davoudi M., Electric vehicles for improving resilience of distribution systems, Sustainable Cities and Society, vol. 36, no. 1, pp. 246–256 (2018), DOI: 10.1016/j.scs.2017.10.006.
  • [13] Zhang Z., Shi H., Zhu R., Zhao H., Zhu Y., Research on electric vehicle charging load prediction and charging mode optimization, Archives of Electrical Engineering, vol. 70, no. 2, pp. 399–414 (2021), DOI: 10.24425/aee.2021.136992.
  • [14] Bharati G. R., Paudyal S., Coordinated control of distribution grid and electric vehicle loads, Electric Power Systems Research, vol. 140, pp. 761–768 (2016), DOI: 10.1016/j.epsr.2016.05.031.
  • [15] Carli R., Dotoli M., A distributed control algorithm for optimal charging of electric vehicle fleets with congestion management, IFAC-PapersOnLine, vol. 51, no. 9, pp. 373–378 (2018), DOI: 10.1016/j.ifacol.2018.07.061.
  • [16] Sedighizadeh M., Shaghaghi-shahr G., Esmaili M., Aghamohammadi M.R., Optimal distribution feeder reconfiguration and generation scheduling for microgrid day-ahead operation in the presence of electric vehicles considering uncertainties, Journal of Energy Storage, vol. 21, no. 1, pp. 58–71 (2019), DOI: 10.1016/j.est.2018.11.009
  • [17] Zhang H., Tang L., Yang C., Lan S., Locating electric vehicle charging stations with service capacity using the improved whale optimization algorithm, Advanced Engineering Informatics, vol. 41, no. 5, p. 100901 (2019), DOI: 10.1016/j.aei.2019.02.006.
  • [18] Kotsalos K., Miranda I., Silva N., Leite H., A horizon optimization control framework for the coordinated operation of multiple distributed energy resources in low voltage distribution networks, Energies, vol. 12, no. 6, pp. 1–27 (2019), DOI: 10.3390/en12061182.
  • [19] Godina R., Rodrigues E. M. G., Paterakis N. G., Erdinc O., Catalão J. P.S., Innovative impact assessment of electric vehicles charging loads on distribution transformers using real data, Energy Conversion and Management, vol. 120, pp. 206–216 (2016), DOI: 10.1016/j.enconman.2016.04.087.
  • [20] Venkataswamy R., Joseph T.M., Optimal charging strategy for spatially distributed electric vehicles in power system by remote analyser, Lecture Notes in Networks and Systems, vol. 80, pp. 400–412 (2020), DOI: 10.1007/978-3-030-23162-0_36.
  • [21] Usman M., Knapen L., Yasar A.U.H., Bellemans T., Janssens D., Wets G., Optimal recharging framework and simulation for electric vehicle fleet, Future Generation Computer Systems, vol. 107, pp. 745–757 (2020), DOI: 10.1016/j.future.2017.04.037
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1f3be031-4bb8-4a26-8eb1-f919ee72f783
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