ارائه یک رویکرد ترکیبی جدید برای مسئله چندهدفه تجدید آرایش شبکه توزیع با در نظر گرفتن اهمیت قابلیت اطمینان

الموضوعات :

بنیامین کتانچی ¹ ( گروه مهندسی برق- واحد نیشابور، دانشگاه آزاد اسلامی، نیشابور، ایران )
علی اصغر شجاعی ² ( گروه مهندسی برق- واحد نیشابور، دانشگاه آزاد اسلامی، نیشابور، ایران )
مهدی یعقوبی ³ ( گروه مهندسی برق- واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران )

تاريخ الإرسال : 26 الخميس , صفر, 1444 تاريخ التأكيد : 27 الجمعة , جمادى الثانية, 1444 تاريخ الإصدار : 19 الخميس , جمادى الأولى, 1446

الکلمات المفتاحية: قابلیت اطمینان, تجدید آرایش فیدرهای شبکه, بهینه‌سازی اجتماع ذرات, واحدهای تولیدپراکنده,

ملخص المقالة :

تجدید آرایش فیدرهای شبکه توزیع یک مسئله بهینه سازی در سیستم قدرت است که با تغییر وضعیت سوئیچینگ در شبکه توزیع برای برآورده کردن توابع هدف خاصی انجام می شود. بررسی مطالعات نشان می‌دهد که اغلب تلفات توان و انحراف ولتاژ باس ها به‌عنوان توابع هدف در حل مسئله تجدید آرایش فیدرهای شبکه توزیع در نظر گرفته ‌شده است. با این ‌حال توابع هدف جدیدتر نظیر قابلیت اطمینان کمتر مورد توجه قرار گرفته‌است. در این مقاله، برای حل مسئله چندهدفه تجدید آرایش شبکه توزیع، از شاخص انرژی توزیع نشده به‌عنوان تابع قابلیت اطمینان، همراه با تلفات توان و تعداد سوئیچینگ در حضور واحدهای تولید پراکنده استفاده ‌شده است. تجدید آرایش فیدر های توزیع به طور ذاتی مساله پیچیده ای است، در نظر گرفتن تاثیر منابع تولیدپراکنده در شبکه توزیع مساله را پیچیده تر از قبل می کند، به همین منظور از یک روش تکاملی مبتنی بر ترکیب روش‌های اجتماع ذرات و بهبود یافته جهش قورباغه برای حل مسئله بهینه‌سازی غیرخطی استفاده شده است. سیستم های 33 و 70 باسه نیز برای سنجش اثربخشی الگوریتم ترکیبی پیشنهادی مورد استفاده قرار گرفته‌اند و همچنین نتایج روش پیشنهادی با نتایج سایر روش های تکاملی مقایسه می شود.

المصادر:

[1] R. Borjali-Navesi, D. Nazarpour-Akbari, R. Ghanizadeh, P. Alemi, "Coordination of switchable capacitor banks and dynamic network reconfiguration for the improvement of distribution network operation integrated with renewable energy resources", Journal of Intelligent Procedures in Electrical Technology, vol. 12, no. 48, pp. 43-59, March 2022 (dor: 20.1001.1.23223871.1400.12.48.2.2).

[2] A.B. Morton, I.M. Mareels, "An efficient brute-force solution to the network reconfiguration problem", IEEE Trans. on Power Delivery, vol. 15, no. 3, pp. 996-1000, July 2000 (doi: 10.11591/ijeecs.v14.i2.ppab-cd).

[3] D.S. Rani, N. Subrahmanyam, M. Sydulu, "Multi-objective invasive weed optimization–an application to optimal network reconfiguration in radial distribution systems", International Journal of Electrical Power and Energy Systems, vol. 73, pp. 932-942, Dec. 2015 (doi: 10.1016/j.ijepes.2015.06.020).

[4] T.T. Nguyen, "Optimization of distribution network configuration with multi objective function based on improved cuckoo search algorithm", Bulletin of Electrical Engineering and Informatics, vol. 9, no. 4, pp. 1685-1693, Aug. 2020 (doi: 10.11591/eei.v9i4.1886).

[5] F. Alonso, D. Oliveira, A.Z. Souza, "Artificial immune systems optimization approach for multiobjective distribution system reconfiguration", IEEE Trans. on Power Systems, vol. 30, no. 2, pp. 840-847, Mar. 2015 (doi: 10.1109/ISGT-LA.2015.7381120).

[6] V. Roberge, M. Tarbouchi, F. Okou, "New encoding based on the minimum spanning tree for distribution feeder reconfiguration using a genetic algorithm", Proceeding of the IEEE/ICEIT, Tangiers, Morocco, May 2016 (doi: 10.1109/EITech.2016.7519574).

[7] A. Roosta, H.-R. Eskandari, M. H. Khooban, "Optimization of radial unbalanced distribution networks in the presence of distribution generation units by network reconfiguration using harmony search algorithm", Neural Computing and Applications, vol. 31, pp. 7095-7109, Nov. 2019 (doi: 10.1515/ijeeps-2021-0093).

[8] A. Landeros, S. Koziel, M. F. Abdel-Fattah, "Distribution network reconfiguration using feasibility-preserving evolutionary optimization", Journal of Modern Power Systems, vol. 7, no. 3, pp. 589–598, May 2019 (doi: 10.1155/2020/2353901).

[9] R. Pegado, Z. Naupari, Y. Molina, C. Castillo, "Radial distribution network reconfiguration for power losses reduction based on improved selective BPSO", Electric Power Systems Research, vol. 169, pp. 206–213, Jan. 2019 (doi: 10.1016/j.epsr.2018.12.030).

[10] H. Lotfi, R. Ghazi, M.B. Naghibi Sistani, "Multi-objective distribution feeder reconfiguration considering reliability in the presence of distributed generators", Electric Power Components and Systems, vol. 50, no. 8, pp. 426-442, Nov. 2022 (doi:10.1080/15325008.2022.2134509).

[11] J. Siahbalaee, N. Rezanejad, G.B. Gharehpetian, "Reconfiguration and DG sizing and placement using improved shuffled frog leaping algorithm", Electric Power Components and Systems, vol. 47, no. 16-17, pp. 1475–1414, Jan. 2020 (doi: 10.1080/15325008.2019.1689449).

[12] V. Fathi, H. Seyedi, B.M. Ivatloo, "Reconfiguration of distribution systems in the presence of distributed generation considering protective constraints and uncertainties", International Transactions on Electrical Energy Systems, vol. 30, no. 5, Article Number: e12346, Feb. 2020 (doi: 10.1002/2050-7038.12346).

[13] S.R. Tuladhar, J.G. Singh, W. Ongsakul, "Multi-objective approach for distribution network reconfiguration with optimal DG power factor using NSPSO", IET Generation, Transmission and Distribution, vol. 10, no. 12, pp. 2842-2851, Sept. 2016 (doi: 10.1049/iet-gtd.2015.0587).

[14] M.R. Narimani, A. Azizivahed, R. Azizipanah, M. Javidsharifi, "Enhanced gravitational search algorithm for multi-objective distribution feeder reconfiguration considering reliability, loss and operational cost", IET Generation Transmission and Distribution, vol, 8, no.1 pp. 55-69, Jan. 2014 (doi: 10.1049/iet gtd.2013.0117).

[15] A. Azizivahed, H. Narimani, E. Naderi, M. Fathi, M.R. Narimani, "A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration", Energy, vol. 138, no. 1, pp. 355-373, July 2017 (doi: 10.101­6/j.en­ergy.2017.07.102).

[16] A. Kavousi-Fard, T. Niknam, "Optimal distribution feeder reconfiguration for reliability improvement consi­de­ring uncertainty", IEEE Trans. on Power Delivery, vol. 29, no. 3, pp. 1344-1353, Dec. 2013 (doi: 10.1109/T­PWR­D.2013.2292951).

[17] A. Zidan, E.F. El-Saadany, "Distribution system reconfiguration for energy loss reduction considering the variability of load and local renewable generation", Energy, vol. 59, pp. 698-707, July 2013 (doi: 10.1016/j.en­ergy.20­13.06.061).

[18] R. Srinivasa Rao, K. Ravindra,  K. Satish , S.V.L. Narasimham, "Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation", IEEE Trans. on Power Systems, vol. 28, no. 1, pp. 317-325, May 2012 (doi: 10.1109/TPWRS.2012.2197227).

[19] A. Kavousi-Fard, T. Niknam, "Multi-objective probabilistic distribution feeder reconfiguration considering wind power plants", International Journal of Electrical Power and Energy Systems, vol. 55, pp. 680-691, Nov. 2013 (doi: 10.1016/j.ijepes.2013.10.028).

[20] A. Kavousi-Fard, T. Niknam, M.H. Khooban, ''Intelligent stochastic framework to solve the reconfiguration problem from the reliability view'', IET Science, Measurement and Technology, vol. 8, no. 5, pp. 245-259, Sept. 2014 (doi: 10.1049/iet-smt.2013.0106).

[21] E. Mahboubi-Moghaddam, M.R. Narimani, M.H. Khoban, A. Azizivahed, M. Javidsharifi, "Multi-objective distribution feeder reconfiguration to improve transient stability, and minimize power loss and operation cost using an enhanced evolutionary algorithm at the presence of distributed generations", International Journal of Electrical Power and Energy Systems, vol. 76, pp. 35-43, Oct. 2015 (doi: 10.1016/j.iepes.2015.09.007).

[22] S. Heang, V. Vai, P. Hem, D. Eam, L. You, S. Eng, "Optimal network reconfiguration with DGs  placement and sizing in a distribution system using hybrid SOE and GA", Proceeding of the IEEE/ECTI-CON, pp. 1-4, Prachuap Khiri Khan, Thailand, May. 2022 (doi: 10.1109/ECTI-CON54298.2022.9795530).

[23] J. Dias Santos, F. Marques, L.P.G. Negrete, G.A.A. Brigatto, J.M. López-Lezama, N. Muñoz-Galeano, "A novel solution method for the distribution network reconfiguration problem based on a search mechanism enhancement of the improved harmony search algorithm", Energies, vol. 15, no. 6, Article Number: 2083, 2022 (doi: 10.3390/en15062083).

[24] R. Fathi, B. Tousi, S. Galvani, "Allocation of renewable resources with radial distribution network reconfi­gura­tion using improved salp swarm algorithm", Applied Soft Computing, vol. 132, pp. 109828, Dec. 2022 (doi: 10.1016/j.asoc.2022.109828).

[25] M.R. Babu, C.V. Kumar, S. Anitha, "Simultaneous Reconfiguration and Optimal Capacitor Placement Using Adaptive Whale Optimization Algorithm for Radial Distribution System", Journal of Electrical Engineering and Technology, vol. 16, no. 1, pp. 181–190, Jan. 2021 (doi: 10.1007/s42835-020-00593-5).

[26] A. N. Hussain, W.K. Shakir Al-Jubori, H.F. Kadom, "Hybrid design of optimal capacitor placement and reconfiguration for performance improvement in a radial distribution system", Journal of Engineering, vol. 2019, pp. 1–15, Dec. 2019 (doi: 10.1155/2019/1696347).

[27] A. Shaheen, A. Elsayed, A. Ginidi, R. El-Sehiemy, E. Elattar, "Reconfiguration of electrical distribution network-based DG and capacitors allocations using artificial ecosystem optimizer: Practical case study", Alexandria Engineering Journal, vol. 61, no. 8, pp. 6105-6118, Aug. 2022 (doi: 10.1016/j.aej.2021.11.035).

[28] M.M. Sayed, M.Y. Mahdy, S.H.A. Aleem, H.K. Youssef, T.A. Boghdady, "Simultaneous distribution network reconfiguration and optimal allocation of renewable-based distributed generators and shunt capacitors under uncertain conditions", Energies, vol. 15, no. 6, pp. 2299, Mar. 2022 (doi: 10.3390/en15124244).

[29] H. Lotfi, R. Ghazi, M.B. Naghibi-Sistani, "Multi-objective dynamic distribution feeder reconfiguration along with capacitor allocation using a new hybrid evolutionary algorithm", Energy Systems, vol, 11, no. 3, pp. 779-809, Aug. 2020 (doi: 10.29252/ieijqp.8.3.22).

[30] H. Lotfi, A. A. Shojaei., "A dynamic model for multi-objective feeder reconfiguration in distribution network considering demand response program", Energy Systems, pp. 1-30, Mar. 2022, (doi: 10.1007/s12667-022-00507-6).

[31] H. Lotfi, R. Ghazi, M.B. Naghibi-Sistani, "Providing a novel approach for dynamic feeder reconfiguration considering importance of reliability and grid's security", Journal of Intelligent Procedures in Electrical Technology, vol. 10, no. 40, pp. 13-22, Aug. 2020 (dor: 20.1001.1.23223871.1398.10.40.2.2).

[32] H. Hasanshahi, M. Nafar, M. Simab, "Operation of micro-grid for provide clean energy constrained to system optimal reliability", Journal of Intelligent Procedures in Electrical Technology, vol. 13, no. 50, pp. 133-148, Sept. 2022 (dor: 20.1001.1.23223871.1401.13.50.9.0).

[33] T.M. Shami, A.A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M.A. Summakieh, S. Mirjalili, "Particle swarm optimization: A comprehensive survey", IEEE Access, vol. 10, pp. 10031-10061, Jan. 2022 (doi: 10.1109/AC­CES­S.2022.3142859).

[34] H. Lotfi, M. Samadi, A. Dadpour, "Optimal capacitor placement and sizing in radial distribution system using an improved particle swarm optimization algorithm", Proceeding of the IEEE/EPDC, Karaj, Iran, April 2016 (doi: 10.1109/EPDC.2016.7514799).

[35] M. Eusuff, K. Lansey, F. Pasha, "Shuffled frog-leaping algorithm a memetic meta-heuristic for discrete optimization", Engineering Optimization, vol. 38, no. 2, pp.129-154, Jan. 2007 (doi: 10.1080/0305215­05003­84­759).

[36] J.G. Digalakis, K.G. Margaritis, "On benchmarking functions for genetic algorithms", International Journal of Computer Mathematics, vol. 77, no. 4, pp. 481-506, Mar 2007 (doi: 10.1080/00207160108805080).

[37] H. Lotfi, R. Ghazi, "Optimal participation of demand response aggregators in reconfigurable distribution system considering photovoltaic and storage units", Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 2, pp. 2233–2223, Feb. 2021 (doi: 10.1007/s12652-020-02322-2).

[38] D. Das, "Reconfiguration of distribution system using fuzzy multi-objective approach", International Journal of Electrical Power and Energy Systems, vol. 28, no. 5, pp. 331-338, June 2006 (doi: 10.1016/j.ijepes.200­5.0­8.0­18).

[39] T. Niknam, "An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective distribution feeder reconfiguration", Energy Conversion and Management, vol. 50, no. 8, pp. 2074-2082, Aug. 2009 (doi: 10.1016/j.enconman.2009.03.029).