برنامه‌ریزی توسعه ایستگاه‌های شارژ سریع با در نظر گرفتن گسترش چند مرحله‌ای شبکه توزیع شهری توسط روش برنامه‌ریزی خطی
محورهای موضوعی : مهندسی برق قدرت
موید محسنی
1
,
عطیه گل محمدی
2
,
محمد امین بهرامیان
3
,
رضا محمدی نیک
4
,
وحید دواتگران
5
1 - شرکت سهامی برق منطقه¬ای خوزستان، اهواز، ایران
2 - دانشکده فنی و مهندسی، گروه مهندسی برق، دانشگاه آزاد اسلامی، واحد تهران مرکز، تهران، ایران
3 - گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه اراک، اراک، ایران
4 - مجتمع دانشگاهی بمجتمع دانشگاهی برق و کامپیوتر، دانشگاه صنعتی مالک اشتر، تهران، ایرانرق و کامپیوتر، دانشگاه صنعتی مالک اشتر، تهران، ایران
5 - گروه مهندسی برق، دانشگاه فنی و حرفه¬ای، تهران، ایران
کلید واژه: ایستگاه شارژ سریع, توسعه شبکه, خودروی برقی, شبکه توزیع, منابع تجدیدپذیر,
چکیده مقاله :
در سال‌های اخیر گرمایش کره زمین و تغییرات اقلیمی یکی از بحث برانگیزترین و پرچالش‌ترین زمینه‌های مسائل برای دولت‌ها، شرکت‌ها و کنوانسیون‌های بین المللی بوده است. که عامل اصلی این گرمایش ابتدا روند صعودی افزایش جمعیت در چند دهه اخیر و سپس افزایش استفاده از سوخت‌های فسیلی برای تولید انرژی و حمل و نقل بوده است. لذا، اکثر دولت‌ها در برنامه‌ریزی‌های کلان خود الزام به حذف خودروهایی با سوخت فسیلی و جایگزینی آن‌ها با خودروهای برقی را دارند. از این رو، با افزایش استفاده از خودروهای برقی در شهرها، بحث برنامه‌ریزی برای مصرف توان الکتریکی توسط آن‌ها و جانمایی صحیح آن‌ها تعداد و مکان آن‌ها به مسأله مهمی برای بهره‌برداران و طراحان تبدیل شده است. در این مقاله به منظور برنامه‌ریزی توسعه شبکه توزیع در حضور ایستگاه‌های شارژ خودروی برقی و منابع تولید تجدیدپذیر یک روش حل مبتنی بر برنامه‌ریزی خطی عدد صحیح استفاده شده است. تابع هدف این مسئله سعی بر کاهش هزینه‌های احداث شبکه، پست‌ها، ایستگاه‌های شارژ، احداث منابع تجدیدپذیر، بانک‌های خازنی و خرید برق از شبکه در چشم‌انداز بلند مدت دارد. روش پیشنهادی روی یک شبکه 18 شینه در سه سناریو اجرا شده که نتایج نشان می‌دهد حضور ایستگاه‌های شارژ خودرو برقی در شبکه تا حدی می‌تواند سودمند باشد و باعث افزایش قابلیت اطمینان و کاهش هزینه‌های شبکه گردد.
In recent years, global warming and climate change have been one of the most controversial and challenging issues for governments, companies and international conventions. The main cause of this warming is the upward trend of population growth in the last few decades and then the increase in the use of fossil fuels for energy production and transportation. Therefore, most of the governments in their grand plans are required to remove fossil fuel vehicles and replace them with electric vehicles. Therefore, with the increase in the use of electric vehicles in cities, the discussion of planning for electric power consumption by them and their correct placement, their number and location has become an important issue for operators and designers. In this article, in order to plan the development of the distribution network in the presence of electric vehicle charging stations and renewable production sources, a solution method based on integer linear programming is used. The purpose of this problem is to reduce the costs of building the network, substations, charging stations, building renewable resources, capacitor banks and buying electricity from the network in the long term. The proposed method has been implemented on an 18-bus network in three scenarios, and the results show that the presence of electric vehicle charging stations in the network can be beneficial to some extent and increase reliability and reduce network costs.
[1] F. Shahnia, A. Arefi, and G. Ledwich, Electric distribution network planning. 2018.
[2] S. S. Tanwar and D. K. Khatod, ‘A review on distribution network expansion planning’, in 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015, 2016. doi: 10.1109/INDICON.2015.7443851.
[3] A. R. Jordehi, ‘How to deal with uncertainties in electric power systems? A review’, Renewable and Sustainable Energy Reviews, vol. 96. 2018. doi: 10.1016/j.rser.2018.07.056.
[4] J. Haas et al., ‘Challenges and trends of energy storage expansion planning for flexibility provision in low-carbon power systems – a review’, Renewable and Sustainable Energy Reviews, vol. 80. 2017. doi: 10.1016/j.rser.2017.05.201.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] F. Eldali, S. Suryanarayanan, and M. E. Samper, ‘Risk-adjusted cost ratios for quantifying improvements in wind power forecasting’, in 2019 IEEE Milan PowerTech, PowerTech 2019, 2019. doi: 10.1109/PTC.2019.8810537.
[10] Y. Xiang, W. Yang, J. Liu, and F. Li, ‘Multi-objective distribution network expansion incorporating electric vehicle charging stations’, Energies (Basel), vol. 9, no. 11, 2016, doi: 10.3390/en9110909.
[11] 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.
[12] S. Zolfaghari Moghaddam, ‘Generation and transmission expansion planning with high penetration of wind farms considering spatial distribution of wind speed’, International Journal of Electrical Power and Energy Systems, vol. 106, 2019, doi: 10.1016/j.ijepes.2018.10.007.
[13] J. M. Home-Ortiz, O. D. Melgar-Dominguez, M. Pourakbari-Kasmaei, and J. R. S. Mantovani, ‘A stochastic mixed-integer convex programming model for long-term distribution system expansion planning considering greenhouse gas emission mitigation’, International Journal of Electrical Power and Energy Systems, vol. 108, 2019, doi: 10.1016/j.ijepes.2018.12.042.
[14] B. Jeddi, V. Vahidinasab, P. Ramezanpour, J. Aghaei, M. Shafie-khah, and J. P. S. Catalão, ‘Robust optimization framework for dynamic distributed energy resources planning in distribution networks’, International Journal of Electrical Power and Energy Systems, vol. 110, 2019, doi: 10.1016/j.ijepes.2019.03.026.
[15] M. Jooshaki, A. Abbaspour, M. Fotuhi-Firuzabad, H. Farzin, M. Moeini-Aghtaie, and M. Lehtonen, ‘A milp model for incorporating reliability indices in distribution system expansion planning’, IEEE Transactions on Power Systems, vol. 34, no. 3, 2019, doi: 10.1109/TPWRS.2019.2892625.
[16] J. Haas, W. Nowak, and R. Palma-Behnke, ‘Multi-objective planning of energy storage technologies for a fully renewable system: Implications for the main stakeholders in Chile’, Energy Policy, vol. 126, 2019, doi: 10.1016/j.enpol.2018.11.034.
[17] M. Moradijoz, M. P. Moghaddam, and M. R. Haghifam, ‘A flexible active distribution system expansion planning model: A risk-based approach’, Energy, vol. 145, 2018, doi: 10.1016/j.energy.2017.12.160.
[18] J. M. H. Ortiz, M. Pourakbari-Kasmaei, J. López, and J. R. S. Mantovani, ‘A stochastic mixed-integer conic programming model for distribution system expansion planning considering wind generation’, Energy Systems, vol. 9, no. 3, 2018, doi: 10.1007/s12667-018-0282-z.
[19] C. Rathore and R. Roy, ‘Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning’, International Journal of Electrical Power and Energy Systems, vol. 75, 2016, doi: 10.1016/j.ijepes.2015.07.040.
[20] M. Wu, L. Kou, X. Hou, Y. Ji, B. Xu, and H. Gao, ‘A bi-level robust planning model for active distribution networks considering uncertainties of renewable energies’, International Journal of Electrical Power and Energy Systems, vol. 105, 2019, doi: 10.1016/j.ijepes.2018.09.032.
[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] F. Ugranlı, ‘Analysis of renewable generation’s integration using multi-objective fashion for multistage distribution network expansion planning’, International Journal of Electrical Power and Energy Systems, vol. 106, 2019, doi: 10.1016/j.ijepes.2018.10.002.
[23] S. Xie et al., ‘Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network’, Appl Energy, vol. 219, 2018, doi: 10.1016/j.apenergy.2018.03.023.
[24] 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.
[25] 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.
[26] S. Qaeini, M. S. Nazar, M. Yousefian, A. Heidari, M. Shafie-Khah, and J. P. S. Catalaõ, ‘Optimal expansion planning of active distribution system considering coordinated bidding of downward active microgrids and demand response providers’, IET Renewable Power Generation, vol. 13, no. 8, 2019, doi: 10.1049/iet-rpg.2018.6006.
[27] G. Muñoz-Delgado, J. Contreras, and J. M. Arroyo, ‘Distribution System Expansion Planning Considering Non-Utility-Owned DG and an Independent Distribution System Operator’, IEEE Transactions on Power Systems, vol. 34, no. 4, 2019, doi: 10.1109/TPWRS.2019.2897869.
[28] A. Bagheri, H. Monsef, and H. Lesani, ‘Integrated distribution network expansion planning incorporating distributed generation considering uncertainties, reliability, and operational conditions’, International Journal of Electrical Power and Energy Systems, vol. 73, 2015, doi: 10.1016/j.ijepes.2015.03.010.
[29] N. Amjady, A. Attarha, S. Dehghan, and A. J. Conejo, ‘Adaptive Robust Expansion Planning for a Distribution Network with DERs’, IEEE Transactions on Power Systems, vol. 33, no. 2, 2018, doi: 10.1109/TPWRS.2017.2741443.
[30] A. Tabares, J. F. Franco, M. Lavorato, and M. J. Rider, ‘Multistage Long-Term Expansion Planning of Electrical Distribution Systems Considering Multiple Alternatives’, IEEE Transactions on Power Systems, vol. 31, no. 3, 2016, doi: 10.1109/TPWRS.2015.2448942.
[31] S. M. Sadeghi, M. Daryalal, and M. Abasi, ‘Two-stage planning of synchronous distributed generations in distribution network considering protection coordination index and optimal operation situation’, IET Renewable Power Generation, vol. 16, no. 11, 2022, doi: 10.1049/rpg2.12526.
[32] M. Abasi, M. F. Nezhadnaeini, M. Karimi, and N. Yousefi, ‘A novel metaheuristic approach to solve unit commitment problem in the presence of wind farms’, Revue Roumaine des Sciences Techniques Serie Electrotechnique et Energetique, vol. 60, no. 3, 2015.
[33] J. Ebrahimi and M. Abasi, ‘Design of a Power Management Strategy in Smart Distribution Networks with Wind Turbines and EV Charging Stations to Reduce Loss, Improve Voltage Profile, and Increase Hosting Capacity of the Network’, Journal of Green Energy Research and Innovation, vol. 1, no. 1, pp. 1–15, Mar. 2024, doi: 10.61186/jgeri.1.1.1.
[34] B. Boroomandnasab and M. H. Zolfaghari, ‘Optimization CIGS/CIGS Tandem Solar Cells by Adjusting Layer Thickness Using Silvaco-Tcad’, Journal of Green Energy Research and Innovation, vol. 1, no. 1, pp. 48–54, Mar. 2024, doi: 10.61186/jgeri.1.1.48.
[35] B. Arandian, ‘Utilizing Hybrid Sine Cosine Shuffled Frog Leaping Algorithm for Optimal Energy Management in the Residential building with Renewable Energy Resources and Corresponding Uncertainties’, Journal of Green Energy Research and Innovation, vol. 1, no. 1, pp. 66–79, Mar. 2024, doi: 10.61186/jgeri.1.1.65.
[36] J. Ebrahimi, M. Abedini, and M. M. Rezaei, ‘Optimal scheduling of distributed generations in microgrids for reducing system peak load based on load shifting’, Sustainable Energy, Grids and Networks, vol. 23, 2020, doi: 10.1016/j.segan.2020.100368.
[37] M. Abasi, A. T. Farsani, A. Rohani, and M. A. Shiran, ‘Improving Differential Relay Performance during Cross-Country Fault Using a Fuzzy Logic-based Control Algorithm’, in 2019 IEEE 5th Conference on Knowledge Based Engineering and Innovation, KBEI 2019, 2019. doi: 10.1109/KBEI.2019.8734991.
[38] S. Darvish Kermani, M. Fayazi, J. Barati, and M. Joorabian, ‘Percentage of Islanding and Peninsulating Detection in Large Microgrids with Renewable Energy Resources with Multiple Connection Points to Different Grids’, Journal of Green Energy Research and Innovation, vol. 1, no. 2, pp. 1–14, Jun. 2024, doi: 10.61186/jgeri.1.2.1.
[39] 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.