پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 705 |
| تعداد مقالات | 10,146 |
| تعداد مشاهده مقاله | 71,451,118 |
| تعداد دریافت فایل اصل مقاله | 63,174,121 |
تخصیص بهینه منابع تجدیدپذیر در شبکههای توزیع با در نظر گرفتن عدم قطعیت بر اساس تئوری تصمیمگیری شکاف اطلاعاتی با استفاده از الگوریتم اجتماع سالپ بهبودیافته | ||
| مدل سازی در مهندسی | ||
| دوره 20، شماره 68، فروردین 1401، صفحه 207-223 اصل مقاله (985.92 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2021.23075.2078 | ||
| نویسندگان | ||
| رحیم فتحی1؛ بهروز طوسی* 2؛ سجاد گلوانی2 | ||
| 1برق قدرت، دانشکده فنی، دانشگاه ارومیه, ارومیه - ایران | ||
| 2گروه مهندسی برق قدرت- دانشکده برق کامپیوتر-دانشگاه ارومیه | ||
| تاریخ دریافت: 23 فروردین 1400، تاریخ بازنگری: 13 تیر 1400، تاریخ پذیرش: 24 شهریور 1400 | ||
| چکیده | ||
| در این مقاله تخصیص بهینه منابع تجدیدپذیر با هدف کمینهسازی هزینه تلفات توان و هزینه بهبود قابلیت اطمینان و با در نظر گرفتن عدم قطعیت تولید و مصرف بر اساس روش جدید تئوری تصمیمگیری شکاف اطلاعاتی (IGDT) ارائه شده است. متغیرهای تصمیمگیری شامل مکان، ظرفیت نصب و ضریب قدرت و همچنین شعاع عدم قطعیت تولید منابع تجدیدپذیر و بار شبکه با استفاده از الگوریتم اجتماع سالپ بهبودیافته (ISSA) بصورت بهینه تعیین شده است. در روش ISSA، عملکرد روش اجتماع سالپ سنتی (SSA) برای بهبود سرعت و دقت همگرایی با استفاده از عملگرهای روش دیفرانسیلی تکاملی (DE) بهبود یافته است. مساله پیشنهادی با دو روش قطعی و روش مبتنی بر IGDT با راهبرد ریسک گریز بر روی شبکه توزیع 33 شینه استاندارد IEEE پیادهسازی شده است. نتایج به دست آمده نشان می دهد که برای شبکه 33 شینه برای توربین بادی با افزایش 20% بودجه عدم قطعیت، بار شبکه 61/7 % افزایش یافته و تولید توربین بادی به مقدار 06/44% کاهش یافته است. همچنین نسبت به حالت قطعی مقدار هزینه تلفات و هزینه قابلیت اطمینان به ترتیب 87/20 و 58/4 درصد افزایش یافته و سود مالی شبکه نیز 33/6% کاهش یافته است. | ||
| کلیدواژهها | ||
| الگوریتم اجتماع سالپ؛ تخصیص بهینه منابع انرژی تجدیدپذیر؛ تئوری تصمیمگیری شکاف اطلاعاتی؛ دیفرانسیل تکاملی؛ عدم قطعیت؛ قابلیت اطمینان | ||
| عنوان مقاله [English] | ||
| Optimal Allocation of Renewable Resources in Distribution Networks Considering Uncertainty Based on Info-Gap Decision Theory Using Improved Salp Swarn Algorithm | ||
| نویسندگان [English] | ||
| Rahim Fathi1؛ Behrouz Tousi2؛ sadjad galvani2 | ||
| 1Electrical Faculty. Urmia University. Urmia. Iran | ||
| 2Department of electrical power engineering, faculty of electrical engineering, urmia university | ||
| چکیده [English] | ||
| In this paper, the optimal allocation of renewable energy resources is presented with the aim of minimizing cost of power losses and reliability improvement considering generation and load uncertainty using new approach named information gap decision theory (IGDT). Decision variables include location, size and power factor of renewable resources, also the maximum uncertainty radius of generation and load using improved salp swarm algorithm (ISSA). In the ISSA method, the performance of traditional salp swarm algorithm is improved to increase convergence speed and accuracy using evolutionary differential operators. The problem is implemented as deterministic and IGDT-based methods on 33 bus-IEEE networks with risk aversion. In the wind turbine scenario, the results showed that for the 33 bus network in the deterministic method that is increased by 20% in IGDT, the network load is increased by 7.61% and wind turbine generation is decreased by 44.06%. Also, compared to the deterministic method, the losses cost and reliability cost increased by 20.87% and 4.58%, respectively and the net saving is decreased by 6.33%. | ||
| کلیدواژهها [English] | ||
| Salp Swarm Algorithm, Optimal Allocation of Renewable Resources, Info-Gap Decision Theory, Differential Evolutionary, Uuncertainty, Reliability | ||
| مراجع | ||
|
[1] سید محمد مهدی میر طلائی و گلناز تاجمیر، " طراحی، مدلسازی و ساخت یک مبدل Z-Source بهره بالا برای کاربرد در اتصال منابع انرژی تجدیدپذیر به شبکه برق "، نشریه مدلسازی در مهندسی، دوره 16، شماره 53، زمستان 1397، صفحه 221- 229. [2] A. Schweer, " Impact of increasing contribution of dispersed generation on the power system", Cigre working group 37.23, editor, Cigre Technical Brochure 137, September 1999. [3] M. J. Hadidian-Moghaddam, S. Arabi-Nowdeh, M. Bigdeli, and D. Azizian, "A multi-objective optimal sizing and siting of distributed generation using ant lion optimization technique", Ain shams engineering journal, Vol. 9, No. 4, December 2018, pp. 2101-2109. [4] R. Billinton, and R.N. Allan, Reliability assessment of large electric power systems, Springer Science and Business Media, 2012. [5] علی رضا ابراهیمی، عباس دیدبان و رضا کی پور، " استراتژی کنترلی نوین در سیستمهای انرژی ترکیبی بادی-خورشیدی برمبنای تعیین محدودههای بهینه شارژ و دشارژ باتریها در بازههای زمانی مختلف "، نشریه مدلسازی در مهندسی، دوره 16، شماره 55، زمستان 1397، صفحه 163- 173. [6] سید قاسم میربابایی رکنی، مسعود رادمهر و علیرضا ذکریازاده، " مدلسازی مدیریت منابع انرژی پراکنده در ریزشبکه با استفاده از روش توزیع شده"، نشریه مدلسازی در مهندسی، دوره 17، شماره 57، تابستان 1398، صفحه 241- 252. [7] R. H. A. Zubo, G. Mokryani, H. S. Rajamani, J. Aghaei, T. Niknam, and P. Pillai, "Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: A review", Renewable and Sustainable Energy Reviews, Vol. 72, May 2017, pp. 1177-1198. [8] A. Naderipour, S. A. Nowdeh, P. B. Saftjani, Z. Abdul-Malek, M. W. B. Mustafa, H. Kamyab, and I. F. Davoudkhani, "Deterministic and probabilistic multi-objective placement and sizing of wind renewable energy sources using improved spotted hyena optimizer ", Journal of Cleaner Production, Vol. 286, Vol. 1, March 2021, p.124941. [9] A. Soroudi, "Distribution network planning under uncertainty considering dg units", PhD thesis, 2013, University College Dublin, 10.13140/2.1 .4406 .1129. [10] R. Mafakheri, P. Sheikhahmadi, and S. Bahramara, "A two-level model for the participation of microgrids in energy and reserve markets using hybrid stochastic-IGDT approach",International Journal of Electrical Power and Energy Systems, Vol. 119, July 2020, p.105977. [11] A. Ahmadi, A. Esmaeel Nezhad, P. Siano, B. Hredzak, and S. Saha, "Information-Gap Decision Theory for Robust Security-Constrained Unit Commitment of Joint Renewable Energy and Gridable Vehicles", IEEE Transactions on Industrial Informatics, Vol. 16, No. 5, May 2020, pp. 3064-3075. [12] A. Safaei, B. Vahidi, and H. Askarian-abyaneh, "A two-step optimization algorithm for wind turbine generator placement considering maximum allowable capacity ", Renewable energy, Vol. 92, July 2016, pp. 75-82. [13] H. S. Ramadan, A. F. Bendary, and S. Nagy, "Particle swarm optimization algorithm for capacitor allocation problem in distribution systems with wind turbine generators", International Journal of Electrical Power and Energy Systems, Vol. 84 January 2017, pp. 143-152. [14] A. Selim, S. Kamel, L.S. Nasrat, and F. Jurado, "Voltage Stability Assessment of Radial Distribution Systems Including Optimal Allocation of Distributed Generators", International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 6, No. 1, February 2020, pp. 32-40. [15] E. S. Ali, S. M. Abd Elazim, and A. Y. Abdelaziz, "Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations ", Renewable Energy, Vol. 101, February 2017, pp. 1311-1324. [16] S. Arabi Nowdeh, I. Faraji Davoudkhani, M. J. Hadidian Moghaddam, E. Seifi Najmi, A. Y. Abdelaziz, A. Ahmadi, S. E. Razavi, and F. H. Gandoman, "Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method ", Applied Soft Computing, Vol. 77, April 2019, pp. 761-779. [17] K. S. Sambaiah, and T. Jayabarathi, "Optimal allocation of renewable distributed generation and capacitor banks in distribution systems using salp swarm algorithm", International journal of renewable energy research, Vol. 9, No.1 March 2019, pp. 96-107. [18] S. S. Parihar, and N. Malik, "Optimal allocation of renewable DGs in a radial distribution system based on new voltage stability index", International Transactions on Electrical Energy Systems, Vol. 30, No. 4, November 2020. [19] M. Daneshvar, M. Abapour, B. Mohammadi, and S. Asadi, "Impact of optimal DG placement and sizing on power reliability and voltage profile of radial distribution networks", Majlesi Journal of Electrical Engineering, Vol. 13, No.2, June 2019, pp. 91-102 [20] A. Hussain, V. H. Bui and H. M. Kim, "Robust optimization-based scheduling of multi-microgrids considering uncertainties", Energies, Vol. 9, April 2019, pp. 761-779. [21] C. Peng, P. Xie, L. Pan, and R. Yu, "Flexible Robust Optimization Dispatch for Hybrid Wind/Photovoltaic/Hydro/Thermal Power System", IEEE Transactions on Smart Grid, Vol. 7, No. 2, March 2016, pp. 751-762. [22] D. K. Khatod, V. Pant and J. Sharma, "Evolutionary programming based optimal placement of renewable distributed generators", IEEE Transactions on Power Systems, Vol. 28, No. 2, May 2013, pp. 683-695. [23] A. Soroudi, and M. Ehsan, "A possibilistic-probabilistic tool for evaluating the impact of stochastic renewable and controllable power generation on energy losses in distribution networks—a case study", Renewable and Sustainable Energy Reviews, Vol. 15, No. 1, January 2011, pp. 794-800. [24] S. S. Tanwar, and D. K. Khatod, "Techno-economic and environmental approach for optimal placement and sizing of renewable DGs in distribution system ", Energy, Vol. 127, May 2017, pp. 52-67. [25] A. Khazali, N. Rezaei, A. Ahmadi, and B. Hredzak,"Information gap decision theory based preventive/corrective voltage control for smart power systems with high wind penetration ", IEEE Transactions on Industrial Informatics, Vol. 14, No. 10, October 2018, pp. 4385-4394. [26] A. Najafi-Ghalelou, S. Nojavan, and K. Zare, "Heating and power hub models for robust performance of smart building using information gap decision theory", International Journal of Electrical Power and Energy Systems, Vol. 98, June 2018, pp. 23-35. [27] S. M. Moghaddas-Tafreshi, M. Jafari, S. Mohseni, and S. A. Kelly, "Optimal operation of an energy hub considering the uncertainty associated with the power consumption of plug-in hybrid electric vehicles using information gap decision theory", International Journal of Electrical Power and Energy Systems, Vol. 112, November 2019, pp. 92-108. [28] A. Naderipour, Z. Abdul-Malek, Z. A. Noorden, I. F. Davoudkhani, S. A. Nowdeh, H. Kamyab, S. Chelliapan, and S. M. S. Ghiasi, "Carrier wave optimization for multi-level photovoltaic system to improvement of power quality in industrial environments based on Salp swarm algorithm", Environmental Technology and Innovation, Vol. 21, February 2021, p.101197. [29] M. Tubishat, N. Idris, L. Shuib, M. A. Abushariah, and S. Mirjalili "Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection", Expert Systems with Applications, Vol. 145, May 2020, p.113122. [30] K. Gholami, and M. H. Parvaneh, "A mutated salp swarm algorithm for optimum allocation of active and reactive power sources in radial distribution systems", Applied Soft Computing, Vol. 85, December 2019, p.105833. [31] S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, " Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems", Advances in Engineering Software, Vol. 114, December 2017, pp. 163-191. [32] P. P. Biswas, P. N. Suganthan, G. Wu, and G. A. Amaratunga,"Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm", Renewable Energy, Vol. 132, March 2019, pp. 425-438. [33] S. Dhabal, R. Chakrabarti, N. S. Mishra, and P. Venkateswaran,"An improved image denoising technique using differential evolution-based salp swarm algorithm", Soft Computing, Vol. 190, August 2020, pp. 1941-1961. [34] M. Majidi, B. Mohammadi-Ivatloo, and A. Soroudi, "Application of information gap decision theory in practical energy problems: A comprehensive review", Applied Energy, Vol. 249, September 2019, pp. 157-165. [35] M. E. Baran, and F. F. Wu, "Network reconfiguration in distribution systems for loss reduction and load balancing ", IEEE Power Engineering Review, Vol. 9, No. 4, April 1989, pp. 101-102. | ||
|
آمار تعداد مشاهده مقاله: 1,005 تعداد دریافت فایل اصل مقاله: 1,042 |
||