ارائه یک مدل برنامه‌ریزی بهینه برای ایستگاه‌های شارژ سریع خودروهای برقی در یک شبکه توزیع کم آلاینده با هدف بهبود پارامترهای فنی و اقتصادی
محورهای موضوعی : مهندسی برق قدرت
موید محسنی
1
(شرکت سهامی برق منطقه ای خوزستان، اهواز، ایران)
علیرضا نیکنام کومله
2
(گروه مهندسی برق، دانشگاه صنعتی امیر کبیر، تهران، ایران)
جواد ابراهیمی
3
(اداره آموزش و پرورش استان اصفهان، مدیریت ناحیه 4، اصفهان، ایران)
مهیار عباسی
4
(گروه مهندسی برق، دانشکده مهندسی ، دانشگاه اراک، ایران)
محمود جورابیان
5
(گروه مهندسی برق، دانشکده مهندسی، دانشگاه شهید چمران اهواز، اهواز، ایران)
کلید واژه: برنامه ریزی بهینه, ایستگاه شارژ خودروی برقی, شارژ گسسته, پروفیل بار, توسعه شبکه توزیع ,
چکیده مقاله :
یکی از بزرگترین و مهم‌ترین دلایل حذف خودروهای بنزینی و دیزلی از بازارهای جهانی خودرو، میزان بالای آلایندگی آنهاست که موجب شده بسیاری از شهرهای بزرگ و صنعتی در جهان با معضل آلودگی همیشگی هوا مواجه شوند و شهروندان ساکن در آنها نیز با مشکلات متعددی مواجه شوند. اما استفاده از خودروهای برقی این معضلات متعدد را نداشته و باعث استقبال روز افزون ازخودروهای برقی شده است. در اکثر مقالات نرخ شارژ پیوسته در نظر گرفته شده است حال آنکه ایستگاه‌های شارژ با نرخ گسسته کار می‌کنند و اکثراً از نوع تک نرخی هستند. در این مقاله مدل ریاضی برای شارژ خودروهای برقی با نرخ گسسته بیان شده است که در آن منفعت مشترکین خودروی برقی، منفعت واحد هماهنگ ساز شارژ و منفعت بهره بردار شبکه به طور همزمان برآورده می‌شود. سپس مساله جلوگیری از قطع و وصل‌های بالا که منجر به آسیب رسانی به ایستگاه شارژ می‌شود در قالب ریاضی بیان شده و به مساله اعمال می‌گردد. در نهایت مدل برنامه ریزی توسعه ایستگاه شارژ نرخ گسسته در شبکه به عنوان نوآوری مطرح و توسط روش برنامه‌ریزی غیرخطی آمیخته با عدد صحیح بهینه می‌شود. نتایج نشان می‌دهد که نرخ شارژ گسسته خودروی برقی در عین سادگی می تواند فواید متعددی را از دیدگاه مسطح سازی پروفیل بار، تامین توان مورد نیاز مشترکین، و رعایت قیود امنیت شبکه را به همراه داشته باشد. اما عدم توسعه شبکه توزیع منجر به جلوگیری از رشد سطح نفوذ خودروهای برقی در شبکه خواهد شد. همچنین حضور این ایستگاه‌ها و برنامه‌ریزی بهینه آنها باعث کاهش میزان انتشار آلاینده‌ها در محیط زیست می‌شود.
One of the biggest and most important reasons for the removal fossil cars from the global car markets is their pollution, which has caused air pollution. But the use of electric cars does not have these many problems and has led to the increasing popularity of electric cars. In most of the articles, the charging rate is considered continuous, while the charging stations work with a discrete rate and are mostly of single rate type. In this paper, the mathematical model for electric vehicles charging with a discrete rate is stated, in which the benefit of the electric vehicle consumers, the benefit of the charging coordinating unit, and the benefit of the network operator are met equally. Then, the problem of preventing high disconnections and connections that lead to damage to the charging station is expressed in mathematical form and applied to the problem. Finally, the development planning model of the discrete rate charging station in the network is proposed as an innovation and optimized by the mixed integer non-linear programming method. The results show that the electric vehicle's discrete charging rate, while simple, can bring many benefits from the point of view of flattening the load profile, providing the power required by the consumers, and meeting the network security restrictions. But the lack of development of the distribution network will prevent the growth of penetration of electric vehicles in the network. Also, the presence of these stations and their optimal planning will reduce the emission of pollutants in the environment.
[1] A. Ahmad, Z. A. Khan, M. Saad Alam, and S. Khateeb, “A Review of the Electric Vehicle Charging Techniques, Standards, Progression and Evolution of EV Technologies in Germany,” Smart Science, vol. 6, no. 1. 2018. doi: 10.1080/23080477.2017.1420132.
[2] H. Shareef, M. M. Islam, and A. Mohamed, “A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles,” Renewable and Sustainable Energy Reviews, vol. 64. 2016. doi: 10.1016/j.rser.2016.06.033.
[3] G. Binetti, A. Davoudi, D. Naso, B. Turchiano, and F. L. Lewis, “Scalable Real-Time Electric Vehicles Charging with Discrete Charging Rates,” IEEE Trans Smart Grid, vol. 6, no. 5, 2015, doi: 10.1109/TSG.2015.2396772.
[4] Z. Ma, D. S. Callaway, and I. A. Hiskens, “Decentralized charging control of large populations of plug-in electric vehicles,” IEEE Transactions on Control Systems Technology, vol. 21, no. 1, 2013, doi: 10.1109/TCST.2011.2174059.
[5] O. Sundström and C. Binding, “Flexible charging optimization for electric vehicles considering distribution grid constraints,” IEEE Trans Smart Grid, vol. 3, no. 1, 2012, doi: 10.1109/TSG.2011.2168431.
[6] V. Aravinthan and W. Jewell, “Controlled electric vehicle charging for mitigating impacts on distribution assets,” IEEE Trans Smart Grid, vol. 6, no. 2, 2015, doi: 10.1109/TSG.2015.2389875.
[7] N. Chen, C. W. Tan, and T. Q. S. Quek, “Electric vehicle charging in smart grid: Optimality and valley-filling algorithms,” IEEE Journal on Selected Topics in Signal Processing, vol. 8, no. 6, 2014, doi: 10.1109/JSTSP.2014.2334275.
[8] S. Vandael, B. Claessens, M. Hommelberg, T. Holvoet, and G. Deconinck, “A scalable three-step approach for demand side management of plug-in hybrid vehicles,” IEEE Trans Smart Grid, vol. 4, no. 2, 2013, doi: 10.1109/TSG.2012.2213847.
[9] W. Tang, S. Bi, and Y. J. A. Zhang, “Online coordinated charging decision algorithm for electric vehicles without future information,” IEEE Trans Smart Grid, vol. 5, no. 6, 2014, doi: 10.1109/TSG.2014.2346925.
[10] H. Zhang, S. J. Moura, Z. Hu, and Y. Song, “PEV Fast-Charging Station Siting and Sizing on Coupled Transportation and Power Networks,” IEEE Trans Smart Grid, vol. 9, no. 4, 2018, doi: 10.1109/TSG.2016.2614939.
[11] H. Zhang, Z. Hu, Z. Xu, and Y. Song, “An Integrated Planning Framework for Different Types of PEV Charging Facilities in Urban Area,” IEEE Trans Smart Grid, vol. 7, no. 5, 2016, doi: 10.1109/TSG.2015.2436069.
[12] H. Zhang, Z. Hu, Z. Xu, and Y. Song, “Optimal Planning of PEV Charging Station with Single Output Multiple Cables Charging Spots,” IEEE Trans Smart Grid, vol. 8, no. 5, 2017, doi: 10.1109/TSG.2016.2517026.
[13] P. Sadeghi-Barzani, A. Rajabi-Ghahnavieh, and H. Kazemi-Karegar, “Optimal fast charging station placing and sizing,” Appl Energy, vol. 125, 2014, doi: 10.1016/j.apenergy.2014.03.077.
[14] W. Yao, C. Y. Chung, F. Wen, M. Qin, and Y. Xue, “Scenario-based comprehensive expansion planning for distribution systems considering integration of plug-in electric vehicles,” IEEE Transactions on Power Systems, vol. 31, no. 1, 2016, doi: 10.1109/TPWRS.2015.2403311.
[15] B. Zhou, F. Yao, T. Littler, and H. Zhang, “An electric vehicle dispatch module for demand-side energy participation,” Appl Energy, vol. 177, 2016, doi: 10.1016/j.apenergy.2016.05.120.
[16] Q. Cui, Y. Weng, and C. W. Tan, “Electric Vehicle Charging Station Placement Method for Urban Areas,” IEEE Trans Smart Grid, vol. 10, no. 6, 2019, doi: 10.1109/TSG.2019.2907262.
[17] M. H. Amini, M. P. Moghaddam, and O. Karabasoglu, “Simultaneous allocation of electric vehicles’ parking lots and distributed renewable resources in smart power distribution networks,” Sustain Cities Soc, vol. 28, 2017, doi: 10.1016/j.scs.2016.10.006.
[18] A. Nasri, A. Abdollahi, M. Rashidinejad, and M. Hadi Amini, “Probabilistic-possibilistic model for a parking lot in the smart distribution network expansion planning,” IET Generation, Transmission and Distribution, vol. 12, no. 13, 2018, doi: 10.1049/iet-gtd.2018.0366.
[19] H. Saboori, R. Hemmati, and V. Abbasi, “Multistage distribution network expansion planning considering the emerging energy storage systems,” Energy Convers Manag, vol. 105, 2015, doi: 10.1016/j.enconman.2015.08.055.
[20] M. Moradijoz, M. Parsa Moghaddam, and M. R. Haghifam, “A flexible distribution system expansion planning model: A dynamic Bi-level approach,” IEEE Trans Smart Grid, vol. 9, no. 6, 2018, doi: 10.1109/TSG.2017.2697917.
[21] A. Ehsan and Q. Yang, “Active distribution system reinforcement planning with EV charging stations - Part I: Uncertainty modeling and problem formulation,” IEEE Trans Sustain Energy, vol. 11, no. 2, 2020, doi: 10.1109/TSTE.2019.2915338.
[22] P. M. De Quevedo, G. Munoz-Delgado, and J. Contreras, “Impact of Electric Vehicles on the Expansion Planning of Distribution Systems Considering Renewable Energy, Storage, and Charging Stations,” IEEE Trans Smart Grid, vol. 10, no. 1, 2019, doi: 10.1109/TSG.2017.2752303.
[23] J. Aghaei, A. E. Nezhad, A. Rabiee, and E. Rahimi, “Contribution of Plug-in Hybrid Electric Vehicles in power system uncertainty management,” Renewable and Sustainable Energy Reviews, vol. 59. 2016. doi: 10.1016/j.rser.2015.12.207.
[24] B. Sun, Z. Huang, X. Tan, and D. H. K. Tsang, “Optimal scheduling for electric vehicle charging with discrete charging levels in distribution grid,” IEEE Trans Smart Grid, vol. 9, no. 2, 2018, doi: 10.1109/TSG.2016.2558585.
[25] A. Arezooye Araghi, A. Ahmarinejad, M. Alizadeh, and M. Babaei, “Optimizing Energy and Ancillary Services Markets in Transmission and Distribution Networks Through a Two-Stage Optimal Framework Considering Flexible Loads, Electric Vehicles, and Storage Systems,” Technovations of Electrical Engineering in Green Energy System, vol. 2, no. 4, pp. 38–64, 2024, doi: 10.30486/teeges.2023.1986699.1074.
[26] J. Ebrahimi, M. Abedini, M. M. Rezaei, and M. Nasri, “Optimum design of a multi-form energy in the presence of electric vehicle charging station and renewable resources considering uncertainty,” Sustainable Energy, Grids and Networks, vol. 23, 2020, doi: 10.1016/j.segan.2020.100375.
[27] Q. Yang, S. Sun, S. Deng, Q. Zhao, and M. Zhou, “Optimal Sizing of PEV Fast Charging Stations with Markovian Demand Characterization,” IEEE Trans Smart Grid, vol. 10, no. 4, 2019, doi: 10.1109/TSG.2018.2860783.
[28] P. Khademi Astaneh and H. Sheikh Shahrokh Dehkordi, “Integrated Optimal Active and Reactive Power Planning in Smart Microgrids with Possibility of One-Hour Islanding,” Technovations of Electrical Engineering in Green Energy System, vol. 2, no. 2, pp. 36–50, 2023, doi: 10.30486/teeges.2023.1979749.1063.
[29] M. Emadi, H. R. Massrur, E. Rokrok, and A. Samanfar, “A Comprehensive Framework for Optimal Stochastic Operating of Energy Hubs Integrated with Responsive Cooling, Thermal and Electrical Loads, and Ice Storage System by an Improved Self-Adaptive Slime Mold Optimization Algorithm,” Technovations of Electrical Engineering in Green Energy System, vol. 2, no. 1, pp. 77–95, 2023, doi: 10.30486/teeges.2022.1969195.1043.