بازآرایی شبکه توزیع همزمان با تعیین مکان و ظرفیت بهینه میکروتوربین ها با لحاظ مسائل فنی به کمک الگوریتم بهینه سازی چندهدفه وال و تکنیک فازی
اسماعیل خلیل زاده
1
(
گروه مهندسی برق، واحد ارسنجان، دانشگاه آزاد اسلامی، ارسنجان، ایران
)
محمد امین صالحی بالاشهر
2
(
گروه مهندسی برق، واحد بندرعباس، دانشگاه آزاد اسلامی، بندرعباس، ایران
)
کلید واژه: الگوریتم بهینه¬سازی وال, بازآرایی چند¬هدفه شبکه توزیع, بهینه میکروتوربین, تکنیک فازی, کاهش جریان خطا,
چکیده مقاله :
شبکههای برق به دلیل عوامل متعددی از جمله فرسودگی قطعات الکتریکی مورد استفاده در سیستمهای کنونی، معرفی فناوریهای نوآورانه در منابع انرژی، بخشهای اطلاعات و ارتباطات، محدودیت سوخت‌های فسیلی و افزایش فشار برای انطباق با الزامات زیست محیطی دستخوش تغییرات و بازسازیهای قابل توجهی هستند. در این مقاله، مساله بازآرایی چندهدفه شبکههای توزیع (DNR) در حضور میکروتوربینها مطالعه شده است. در مقالات قبلی بیشتر توابع هدفی شامل کاهش تلفات و بهبود پروفیل ولتاژ مدنظر قرار گرفتهاند. در این مقاله، علاوه بر کاهش تلفات و بهبود پروفیل ولتاژ، مکان و ظرفیت بهینه میکروتوربینها و همچنین کاهش جریان خطای خطوط شبکه به کمتر از جریان خطای نامی کلیدهای قدرت به صورت همزمان، بررسی شدهاند. در این مقاله، از الگوریتم بهینهسازی وال (WOA) برای حل مساله بازآرایی چندهدفه شبکه توزیع استفاده شده است. در ادامه، مقادیر توابع هدف با استفاده از توابع عضویت فازی، نرمالیزه شدهاند. از آنجایی که مجموعهای از پاسخهای کاندید به کمک الگوریتم بهینهسازی چندهدفه وال ایجاد میشود، از تکنیک فازی برای یافتن بهینهترین جواب در میان جوابهای پارتو بهدست آمده، استفاده شده است. روش پیشنهادی روی سیستم تست 33 باسه IEEE پیادهسازی شده است. نتایج شبیهسازی نشان میدهند روش پیشنهادی در بهبود سه تابع هدف در نظر گرفته شده به صورت همزمان، عملکرد مؤثری داشته و منجر به کاهش تلفات و بهبود پروفیل ولتاژ شده است. همچنین به صورت همزمان، جریان خطای عبوری از خطوط، کمتر از مقدار قابل تحمل کلیدهای قدرت شبکه شده است.
چکیده انگلیسی :
Electricity networks are undergoing significant changes and restorations due to several factors, including the aging of electrical components used in current systems, the introduction of innovative technologies in energy sources, information and communication sectors, limitation of fossil fuels, and increasing pressure to comply with environmental requirements. In this paper, to solve the multi-objective problem of distribution network reconfiguration (DNR) in the presence of microturbines, the Whale optimization algorithm (WOA) has been proposed. In this field, the power losses and voltage profile improvement are the two most used objective functions in the literature. In addition to the mentioned objective functions, this paper also considers the optimal generation capacity of microturbines and reducing the fault current of the network lines to less than the rated fault current of the power switches. In this paper, the values of different objective functions are normalized by the fuzzy method. Since a set of candidate responses is created using Whale multi-objective algorithm, the Fuzzy technique is used to determine the most optimal solution among the Pareto-optimal solutions. The proposed algorithm is implemented on IEEE 33-bus test system. The simulation results show the efficiency of the proposed algorithm in improving the considered objective functions. The proposed method, by establishing a suitable fit between different objective functions has introduced a more efficient structure with lower losses and voltage profile improvement, as well as the fault current passing through the lines less than the tolerable amount of the network power switches.
K. Subbaramaiah, and P. Sujatha, “Optimal DG unit placement in distribution networks by multi-objective whale optimization algorithm & its techno-economic analysis,” Electric Power Systems Research, vol. 214, pp. 869-884, Jan. 2023, doi: 10.1016/j. epsr.2022.108869.
[2] M. Y. Shih, A. Conde, C. Ángeles-Camacho, E. Fernández, Z. Leonowicz, F. Lezama, and J. Chan, “A two stage fault current limiter and directional overcurrent relay optimization for adaptive protection resetting using differential evolution multi-objective algorithm in presence of distributed generation,” Electric Power Systems Research, vol. 190, pp. 844-859, Jan. 2021, doi: 10.1016/j. epsr.2020.106844.
[3] O. Eo, A. To, O. Ik, and A. I. Oo, “Optimal sitting and sizing of shunt capacitor for real power loss reduction on radial distribution system using firefly algorithm: A case study of Nigerian system,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 45, no. 2, pp. 5776-5788, 2023, doi: 10.1080/15567036.2019.1673507.
[4] M. T. Mouwafi, M. T. El-Sehiemy, and A. A. Abou El-Ela, “A two-stage method for optimal placement of distributed generation units and capacitors in distribution systems,” Applied Energy, vol. 307, pp. 118-135, Feb. 2022, doi: 10.1016/j. apenergy.2021.118188.
[5] M. Amini, A. Jalilian, and , M. R. P. Behbahani, “A new method for evaluation of harmonic distortion in reconfiguration of distribution network,” International Transactions on Electrical Energy Systems, vol. 30, no. 12, pp. 1237-1255, March. 2020, doi: 10.1002/2050-7038.12370.
[6] A. Mishra, M. Tripathy, and P. Ray, “A survey on different techniques for distribution network reconfiguration,” Journal of Engineering Research, vol. 12, no. 1, pp. 173-181, March. 2024, doi: 10.1016/j. jer.2023.09.001.
[7] S. Kamel, M. Khasanov, F. Jurado, A. Kurbanov, H. M. Zawbaa, and M. A. Alathbah, “Simultaneously distributed generation allocation and network reconfiguration in distribution network considering different loading levels,” IEEE Access, vol. 12, pp. 625-736, 2023, doi: 10.1109/access.2023.3319456.
[8] M. R. Behbahani, A. Jalilian, A. Bahmanyar, and D. Ernst, “Comprehensive Review on Static and Dynamic Distribution Network Reconfiguration Methodologies,” IEEE Access, vol. 12, pp. 874-991, 2024, doi: 10.1109/access.2024.3350207.
[9] M. Mahdavi, K. E. K. Schmitt, and F. Jurado, “Robust distribution network reconfiguration in the presence of distributed generation under uncertainty in demand and load variations,” IEEE Transactions on Power Delivery, vol. 38, no. 5, pp. 3480-3495, 2023, doi: 10.1109/tpwrd.2023.3277816.
[10] G. Abbas, Z. Wu, and A. Ali, “Multi objective multi period optimal site and size of distributed generation along with network reconfiguration,” IET Renewable Power Generation, vol. 11, no. 2, pp. 223-235, 2024, doi: 10.1049/rpg2.12949.
[11] O. Badran, and J. Jallad, “Multi-objective decision approach integrated with Load ability and weight factor analysis for reconfiguration with DG sizing and allocation including tap changer,” Arabian Journal for Science and Engineering, vol. 48, no. 5, pp. 6797-6818, May. 2023, doi: 10.1007/s13369-022-07557-z.
[12] M. R. Behbahani, and A. Jalilian, “Reconfiguration of distribution network for improving power quality indexes with flexible lexicography method,” Electric Power Systems Research, vol. 230, pp. 172-189, May. 2024, doi: 10.1016/j. epsr.2024.110172.
[13] A. Bagheri, M. Bagheri, and A. Lorestani, “Optimal reconfiguration and DG integration in distribution networks considering switching actions costs using tabu search algorithm,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 7837-7856, July. 2021, doi: 10.1007/s12652-020-02511-z.
[14] P. Zare, I. F. Davoudkhani, R. Mohajery, R. Zare, H. Ghadimi, and M. Ebtehaj, “Multi-objective coordinated optimal allocation of distributed generation and D-STATCOM in electrical distribution networks using Ebola optimization search algorithm,” 8th International Conference on Technology and Energy Management (ICTEM), pp. 1-7, 2023, doi: 10.1109/ictem56862.2023.10084121.
[15] A. M. Helmi, R. Carli, M. Dotoli, and H. S. Ramadan, “Efficient and sustainable reconfiguration of distribution networks via metaheuristic optimization,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 1, pp. 82-98, May. 2021, doi: 10.1109/tase.2021.3072862.
[16] I. Oladeji, R. Zamora, and T. T. Lie, “Security constrained optimal placement of renewable energy sources distributed generation for modern grid operations,” Sustainable Energy, Grids and Networks, vol. 32, pp. 897-911, Dec. 2022, doi: 10.1016/j.segan.2022.100897.
[17] M. C. V. Suresh, and J. B. Edward, “A hybrid algorithm based optimal placement of DG units for loss reduction in the distribution system,” Applied Soft Computing, vol. 91, pp. 191-211, Jun. 2020, doi: 10.1016/j.asoc.2020.106191.
[18] R. Sellami, S. H. E. R. Farooq, and N. E. J. I. Rafik, “An improved MOPSO algorithm for optimal sizing & placement of distributed generation: A case study of the Tunisian offshore distribution network (ASHTART),” Energy Reports, vol. 8, pp. 6960-6975, Nov. 2022, doi: 10.1016/j.egyr.2020.05.049.
[19] W. Haider, S. J. U. Hassan, A. Mehdi, A. Hussain, G. O. M. Adjayeng, and C. H. Kim, “Voltage profile enhancement and loss minimization using optimal placement and sizing of distributed generation in reconfigured network,” Machines, vol. 9, no. 1, pp. 20-37, Jan. 2021, doi: 10.3390/machines9010020.
[20] P. Agrawal, N. Kanwar, N. Gupta, K. R. Niazi, and A. Swarnkar, “Resiliency in active distribution systems via network reconfiguration,” Sustainable Energy, Grids and Networks, vol. 26, pp. 434-452, Jun. 2021, doi: 10.1016/j.sgean.2021.100434.
[21] A. S. Chaitra, and H. R. Sudarshana Reddy, “Improving Reliability in Distribution Systems through Optimal Allocation of Distributed Generators,” Network Reconfiguration and Capacitor Placement. SN Computer Science, vol. 5, no. 5, pp. 1-12, April. 2024, doi: 10.1007/s42979-024-02753-0.
[22] M. I. Pathan, M. Al-Muhaini, and S. Z. Djokic, “Optimal reconfiguration and supply restoration of distribution networks with hybrid microgrids,” Electric Power Systems Research, vol. 187, pp. 458-573, Oct. 2020, doi: 10.1016/j.epsr.2020.106458.
[23] A. Uniyal, and S. Sarangi, “Optimal network reconfiguration and DG allocation using adaptive modified whale optimization algorithm considering probabilistic load flow,” Electric Power Systems Research, vol. 192, pp. 909-927, March. 2021, doi: 10.1016/j.epsr.2020.106909.
[24] H. S. Ramadan, and A. M. Helmi, “Optimal reconfiguration for vulnerable radial smart grids under uncertain operating conditions,” Computers & Electrical Engineering, vol. 93, pp. 310-327, July. 2021, doi: 10.1016/j.compeleceng.2021.103710.
[25] S. Mirjalili, and A. Lewis, “The whale optimization algorithm,” Advances in engineering software, vol. 95, pp. 51-67, May. 2016, doi: 10.1016/j.advengsoft.2016.01.008.