جایابی بهینه ایستگاه‌های شارژ و دشارژ خودروهای‌الکتریکی متصل به شبکه توزیع انرژی الکتریکی در حضور منابع انرژی تجدیدپذیر با درنظرگفتن برنامه‌های پاسخگویی تقاضا مبتنی بر قیمت
محورهای موضوعی : مهندسی برق قدرت
مجید فرجامی پور
1
(دانشکده مهندسي برق، دانشگاه صنعتی شاهرود، شاهرود، ايران)
مجتبی شیوایی
2
(دانشکده مهندسي برق، دانشگاه صنعتی شاهرود، شاهرود، ايران)
کلید واژه: ایستگاه های شارژ و دشارژ, خودروی الکتریکی, الگوریتم ژنتیک بهبود یافته, برنامه‌های پاسخگویی تقاضا مبتنی بر قیمت, منابع تجدیدپذیر انرژی,
چکیده مقاله :
در دنیای صنعتی امروز، توسعه صنعت خودروهای الکتریکی، سبب شده است تا منابع ذخیره‌ساز انرژی نقش بسزایی در بهره‌برداری کارآمد از شبکه‌های توزیع انرژی الکتریکی داشته باشد. بر این اساس، جایابی بهینه ایستگاه‌های شارژ و دشارژ مرتبط با خودروهای الکتریکی به عنوان یک چالش فنی پیش‌روی بهره‌برداران شبکه قرار گرفته است. لذا، در این مقاله، با رویکردی جدید، مسئله جایابی ایستگاه‌های شارژ و دشارژ مرتبط با خودروهای الکتریکی در حضور منابع انرژی تجدیدپذیر مدل‌سازی می‌گردد. در مدل پیشنهادی، اهداف فنی شامل کمینه‌سازی تلفات و افت ولتاژ و همچنین، اهداف اقتصادی شامل کمینه‌سازی هزینه توان خریداری شده از شبکه و بیشینه‌سازی سود حاصل از فروش توان به شبکه توسط خودروهای الکتریکی در نظرگرفته می‌شوند. علاوه بر این، محدودیت‌های موجود در ظرفیت ایستگاه‌های شارژ، مجموع توان قابل شارژ و دشارژ در هر لحظه نیز به عنوان قیود مدل پیشنهادی لحاظ می‌گردد. از طرفی، در این مقاله، برای مدیریت بار در سمت مصرف‌کنندگان، راهبرد پیک‌سایی منحنی بار براساس برنامه‌های پاسخگویی تقاضا مبتنی بر قیمت اعمال می‌شود. مسئله غیرخطی مصرف توان و مشارکت خودروهای الکتریکی در تأمین توان شبکه، مختلط با اعداد صحیح که با استفاده از الگوریتم ژنتیک بهبودیافته حل شده و نتایج بدست آمده با الگوریتم اجتماع ذرات مقایسه می‌گردند. شبیه‌سازی مدل پیشنهادی توسط نرم‌افزار MATLAB و بر روی شبکه 69 گره استاندارد انجام شد و نتایج نشان‌دهنده اثربخشی و سودمندی آن است.
In today’s industrial world, the development of electric vehicles has made energy storage resources play a significant role in the efficient operation of electrical distribution networks. On this basis, the optimal placement of charging and discharging stations associated with electric vehicles has emerged as a technical challenge for network operators. In this paper, therefore, with a new perspective, the placement problem of charging and discharging stations pertaining to electric vehicles in the presence of renewable energy sources is modeled. In the proposed model, technical objectives including minimization of losses and voltage drop, as well as economic objectives comprising minimization of the cost of power purchased from the network and maximization of the profit derived from selling power to the network by electric vehicles are tajen into account. In addition to this, existing limitations at the capacity of charging stations and the total power that can be charged and discharged at any moment are applied as constraints of the proposed model. The model also considers a peak-shaving strategy for load management on the consumer-side according to price-based demand response programs. The nonlinear power consumption problem and the participation of electric vehicles in providing network power are formulated as a mixed-integer problem and are solved using an improved genetic algorithm, as well as the results obtained are compared with those determined by a particle swarm optimization algorithm. The simulation of the proposed model is conducted using MATLAB software on a standard 69-node network, and the calculated results demonstrate its effectiveness and profitableness.
[1] M. N. Azghandi, A. Shojaei, S. Toosi, and H. Lotfi, “Optimal reconfiguration of distribution network feeders considering electrical vehicles and distributed generators,” Evolutionary Intelligence, vol. 16, Feb. 2023, doi: 10.1007/s12065-021-00641-7.
[2] A. F. Guven, S. B. Akbasak, “DC fast charging station modeling and control for electric vehicles,” Karadeniz Fen Bilimleri Dergisi, vol. 11, Dec. 2021, doi: 10.31466/kfbd.986040.
[3] O. Lugovoy, S. Gao, J. Gao and K. Jiang, “Feasibility study of China’s electric power sector transition to zero emissions by 2050,” Energy Economics, vol. 96, Apr. 2021, doi: 10.1016/j.eneco.2021.105176.
[4] N. Bhusal, M. Abdelmalak, M. Kamruzzaman, and M. Benidris, “Power system resilience: Current practices, challenges, and future directions,” IEEE Access, vol. 8, Jan. 2021, doi: 10.1109/ACCESS.2020.2968586.
[5] K. Clement-Nyns, E. Hn, and J. Driesen, “The impact of charging plug-in hybrid electric vehicles on a residential distribution grid,” IEEE Transactions on Power Systems, vol. 25, Feb. 2010, doi: 10.1109/TPWRS.2009.2036481.
[6] M. H. Moradi, S. R. Tousi, and S. M. Hosseinian, “Optimal siting and sizing of renewable energy sources and charging stations simultaneously based on Differential Evolution algorithm, ” International Journal of Electrical Power & Energy Systems, vol. 73, Dec. 2020, doi: 10.1016/j.ijepes.2015.06.029.
[7] K. Subbaramaiah, P. Sujatha, “Optimal DG unit placement in distribution networks by multiobjective whale optimization algorithm & its techno-economic analysis,” Electric Power Systems Research, vol. 214, Jan. 2023, doi: 10.1016/j.epsr.2022.108869.
[8] M. Shivaie, M. Kiani-Moghaddam, and P. D. Weinsier, “Resilience-based tri-level framework for simultaneous transmission and substation expansion planning considering extreme weather-related events,” IET Generation, Transmission & Distribution, vol. 14, Aug. 2020, doi: 10.1049/iet-gtd.2019.1512.
[9] M. Aman, G. B. Jasmon, H. Mokhlis, and A. H. A. Bakar, “Optimal placement and sizing of a dg based on a new power stability index and line losses,” International Journal of Electrical Power & Energy Systems, vol. 43, Dec. 2012, doi: 10.1016/j.ijepes.2012.05.053.
[10] M. Zand, A. Nasab, A. Hatami, M. Kargar, and H. R. Chamorro, “Using adaptive fuzzy logic for intelligent energy management in hybrid vehicles,” Iranian Conference on Electrical Engineering, Tabriz, Iran, Aug. 2020, doi: 10.1109/ICEE50131.2020.9260941.
[11] M. Jiang, J. Wang, Y. Han, and Q. Zhao, “Coordination dispatch of electric vehicles charging/discharging and renewable energy resources power in microgrid,” Procedia Computer Science, vol. 107, Apr. 2017, doi: 10.1016/j.procs.2017.03.072.
[12] M. H. Moradi, M. Abedini, S. M. R. Tousi, and S. M. Hosseinian, “Optimal siting and sizing of renewable energy sources and charging stations simultaneously based on Differential Evolution algorithm,” International Journal of Electrical Power & Energy Systems, vol. 73, pp. 1015-1024, Dec. 2015, doi: 10.1016/j.ijepes.2015.06.029.
[13] J. A. Martín García and A. J. Gil Mena, “Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm,” International Journal of Electrical Power & Energy Systems, vol. 50, pp. 65-75, Sep. 2013, doi: 10.1016/j.ijepes.2013.02.023.
[14] M. H. Moradi, M. Abedini, “A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems,” International Journal of Electrical Power & Energy Systems, vol. 34, no. 1, pp. 66-74, Jan. 2012, doi: 10.1016/j.ijepes.2011.08.023.
[15] A. Najafi Tari, M. S. Sepasian, and M. Tourandaz Kenari, “Resilience assessment and improvement of distribution networks against extreme weather events,” International Journal of Electrical Power & Energy Systems, vol. 125, p. 106414, Feb. 2021, doi: 10.1016/j.ijepes.2020.106414.
[16] J. Aghaei, E. Bagheri, A. Heidari, G. J. Osório, M. Shafie-Khah, J. M. Lujano-Rojas, J. P. S. Catalão “Investigation of smart distribution network response to operation performance of plug-in hybrid electric vehicles,” Innovative Smart Grid Technologies Conference Europe, Turin, Italy, Jan. 2018, doi: 10.1109/ISGTEurope.2017.8260304.
[17] A. Shahbazi, J. Aghaei, S. Pirouzi, T. Niknam, M. Shafie-khah, and J. P. S. Catalão, “Effects of resilience-oriented design on distribution networks operation planning,” Electric Power Systems Research, vol. 191, p. 106902, Feb. 2021, doi: 10.1016/j.epsr.2020.106902.
[18] M. H. K. Tushar, C. Assi, M. Maier, and M. F. Uddin, “Smart microgrids: Optimal joint scheduling for electric vehicles and home appliances,” IEEE Transactions on Smart Grid, vol. 5, Jan. 2014, doi: 10.1109/TSG.2013.2290894.
[19] P. Aliasghari, B. Mohammadi-Ivatloo, M. Alipour, M. Abapour, and K. Zare, “Optimal scheduling of plug-in electric vehicles and renewable micro-grid in energy and reserve markets considering demand response program,” Journal of Cleaner Production, vol. 186, pp. 293-303, Jun. 2018, doi: 10.1016/j.jclepro.2018.03.058.
[20] M. J. Shahriary, I. Goroohi-Sardou, “Power system probabilistic scheduling with electric vehicles considering renewable energy sources uncertainties,” Computational Intelligence in Electrical Engineering, vol. 11, Nov. 2020, doi: 10.22108/isee.2019.118470.1264.
[21] A. Kavousi-Fard, T. Niknam, and M. Fotuhi-Firuzabad, “Stochastic Reconfiguration and Optimal Coordination of V2G Plug-in Electric Vehicles Considering Correlated Wind Power Generation,” IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 822-830, Jul. 2015, doi: 10.1109/TSTE.2015.2409814.
[22] H. Hashemi-Dezaki, M. Hamzeh, H. Askarian-Abyaneh, and H. Haeri-Khiavi, “Risk management of smart grids based on managed charging of PHEVs and vehicle-to-grid strategy using Monte Carlo simulation,” Energy Conversion and Management, vol. 100, pp. 262-276, Aug. 2015, doi: 10.1016/j.enconman.2015.05.015.
[23] S. B. Peterson, J. F. Whitacre, and J. Apt, “The economics of using plug-in hybrid electric vehicle battery packs for grid storage,” Journal of Power Sources, vol. 195, no. 8, pp. 2377-2384, Apr. 2010, doi: 10.1016/j.jpowsour.2009.09.070.
[24] M. Ahmadi, S. H. Hosseini, “Optimal location and operation planning of charging and discharging stations of electric vehicles using metaheuristic algorithms,” Iranian Electric Industry Journal Quality & Productivity, vol. 10, Sep. 2021, doi: 20.1001.1.23222344.1400.10.3.8.7.
[25] M. Shivaie, M. T. Ameli, M. S. Sepasian, P. D. Weinsier, and V. Vahidinasab, “A multistage framework for reliability-based distribution expansion planning considering distributed generations by a self-adaptive global-based harmony search algorithm,” Reliability Engineering & System Safety, vol. 139, pp. 68-81, Jul. 2015, doi: 10.1016/j.ress.2015.03.001.
[26] M. Kühnbach, A. Bekk, and A. Weidlich, “Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany,” Energy Strategy Reviews, vol. 34, p. 100609, Mar. 2021, doi: 10.1016/j.esr.2020.100609.
