تعیین بهینه ظرفیت باتری‌ها در بهره‌برداری اقتصادی از ریزشبکه با استفاده از الگوریتم جستجوی فاخته

نوع مقاله: مقاله پژوهشی

نویسندگان

1 گروه مهندسی برق ، واحد بندرعباس، دانشگاه آزاد اسلامی ، بندرعباس ، ایران

2 گروه مهندسی برق، واحد گچساران، دانشگاه آزاد اسلامی ، گچساران ، ایران

چکیده

امروزه با افزایش میزان تقاضا برای انرژی الکتریکی استفاده از منابع تولید پراکنده و به ویژه منابع تجدید پذیر روز به روز در حال رشد می‌باشد که این منابع در غالب ریزشبکه‌ها توان مورد نیاز سیستم را تامین می‌کنند. بهره‌برداری از منابع تجدیدپذیر معمولاً با عدم‌قطعیت همراه می‌باشد. از این رو، در این مقاله در ابتدا آرایش بهینه تولید منابع تولید پراکنده موجود در سیستم در دو مد جزیره‌ای و اتصال به شبکه تعیین می‌گردند. از آنجایکه بهره‌برداری از ریزشبکه در حضور منابع تولید پراکنده و قیود مختلف، یک مسئله بهینه‌سازی با قیود متعدد می‌باشد در این مطالعه از الگوریتم جستجوی فاخته که یک الگوریتم فراابتکاری با سرعت همگرایی بالا می‌باشد به منظور حل مسئله بهینه‌سازی و تعیین آرایش تولید واحدها استفاده شده است. به منظور کاهش تأثیر عدم‌قطعیت توان خروجی سیستم فتوولتاییک از ذخیره‌سازی انرژی استفاده شده است که در این مقاله ظرفیت بهینه آن متناسب با شرایط بهره‌برداری تعیین می‌گردد. الگوریتم پیشنهادی برای تعیین ظرفیت بهینه باتری در یک ریزشبکه نمونه پیاده‌سازی شده است. نتایج بدست آمده نشان دهنده کارآیی روش پیشنهادی برای تعیین آرایش بهینه منابع تولید پراکنده و ظرفیت بهینه باتری دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Determination of Optimal Battery Capacity in Economic Operation of Micro Grid by Cuckoo Search Algorithm

نویسندگان [English]

  • sana ansari 1
  • Sirus Mohammadi 2
1 Department of electrical engineering, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas , Iran
2 Department of electrical engineering , Gachsaran Branch, Islamic Azad University, Gachsaran , Iran
چکیده [English]

The current demand in the power system has led to increased usage of the Distributed Generation (DG) and renewable resources. The renewable resources can efficiently supply the loads in the micro grids. The output power generation of renewable energy resources is unpredictable. Hence, in this paper the optimal generation dispatch of the DGs in micro grids in both grid-connected and islanded modes is determined. Since the operation of the micro grid in presence of DGs and various constraints is a complicated optimization problem, in this paper a meta-heuristic Cuckoo search (CS) algorithm with high convergence speed is used. In order to reduce the uncertainty of the output power of photovoltaic system the energy storage system is implemented and the optimal capacity of the storage is determined based on operation conditions. The proposed algorithm for determining the optimal capacity of the battery in a sample micro grid is applied. The results show the effectiveness of the proposed method for determining the optimal dispatch of the DGs and capacity of the energy storage system.

کلیدواژه‌ها [English]

  • Micro grid
  • Cuckoo search algorithm
  • energy storage system
  • Economic dispatch
  • Photovoltaic system
[1] S. R. Sivarasu, E.C. Sekaran, P. Karthik, "Development of renewable energy based microgrid project implementations for residential consumers in India  Scope, challenges and possibilities", Renewable and Sustainable Energy Reviews,Vol . 50:256–269, Oct. 2015 (doi:10.1016/j.rser.2015.04.118).

 [2] S .Soleymani, M. E .Mosayebian, S. Mohammadi, "A combination method for modeling wind power plants in power systems reliability evaluation", Computers and Electrical Engineering,Vol.41, pp.28-39, Jan. 2015 (doi:10.1016/j.compeleceng.2014.12.005).

[3] P. Basak, S. Chowdhury ,S. H. Dey, S. P. Chowdhury, "A literature review on integration of distributed energy resources in the perspective of control,protection and stability of microgrid",Renewable and Sustainable Energy Reviews, Vol. 16, No. 8, pp. 5545–5556, Oct. 2012 (doi:10.1016/j.rser.2012.05.043).

[4] T. S. Ustun,C.Ozansoy, A. Zayegh, "Recent developments in microgrids and example cases around the world—A review", Renewable and Sustainable Energy Reviews,Vol.15, No. 8, pp. 4030–4041, Oct. 2011 (doi:10.1016/j.rser.2011.07.033).

[5] F. Li, M. Wu, Y. He, X. Chen, "Optimal control in microgrid using multi-agent reinforcement learning", ISA Transactions, Vol. 51, No. 6, pp. 743-51, Nov. 2012 (doi:10.1016/j.isatra.2012.06.010).

[6] E. Foruzan, L. K. Soh, S. Asgarpoor, "Reinforcement learning approach for optimal distributed energy management in a microgrid",IEEE Transactions on Power Systems, Vol. 33, No. 15, pp. 5749 – 5758, Sep. 2018 (doi: 10.1109/TPWRS.2018.2823641).

[7]K. M. Kelly-Pitou, A. Ostroski, B. Contino, B. Grainger, A. Kwasinski, G. Reed ,"Microgrids and resilience: using a systems approach to achieve climate adaptation and mitigation goals", The Electricity Journal, Vol. 30, No. 10, pp. 23-31, Dec. 2017 (doi:10.1016/j.tej.2017.11.008).

[8]J. Silvente, L. G. Papageorgiou, V. Dua, "Optimal management of microgrids under uncertainty using scenario reduction", Computer Aided Chemical Engineering, Vol. 40, pp. 2257-2262, 2017 (doi:10.1016/B978-0-444-63965-3.50378-0).

[9] S. R. Gampa, D. Das, "Optimum placement and sizing of DGs considering average hourly variations of load", International Journal of Electrical Power and Energy Systems, Vol. 66, pp. 25-40, March 2015 (doi:10.1016/j.ijepes.2014.10.047).

[10] S. Mazzola, M. Astolfi, E. Macchi, "A detailed model for the optimal management of a multigood microgrid", Applied Energy, Vol. 154, pp. 862–873, Sep. 2015 (doi:10.1016/j.apenergy.2015.05.078).

[11] J. S. Kumar, S. C. Raja, J. J. D. Nesamalar, P. Venkatesh, "Optimizing renewable based generations in AC/DC microgrid system using hybrid Nelder-Mead – Cuckoo Search algorithm", Energy, Vol. 158, pp. 204-215, Sep. 2018 (doi:10.1016/j.energy.2018.06.029).

[12] K. S. El-Bidairi, H. D..Nguyen, S. D. G. Jayasinghe, T. S. Mahmoud, I. Penesis,"A hybrid energy management and battery size optimization for standalone microgrids: A case study for Flinders Island, Australia", Energy Conversion and Management, Vol 175, pp. 192-212, Nov. 2018 (doi:10.1016/j.enconman.2018.08.076).

[13]Y. Zhang, A. Lundblad, P. E. Campana, F. Benavente, J. Yan, "Battery sizing and rulebasedoperation of grid-connected photovoltaic-battery system: a case studyin Sweden", Energy Conversion and Management,Vol. 133, pp. 249-63, Feb. 2017 (doi:10.1016/j.enconman.2016.11.060).

[14] L. Benyekhlef, L. Benasla, A. Belmadani, R. Mostefa, "Cuckoo search algorithm for solving economic power dispatch problem with consideration of facts devices", UPB Scientific Bulletin, Series C: Electrical Engineering, Vol. 79, No. 1, pp. 43-54, 2017.

[15] T. Yuvaraj, K. Ravi, "Multi-objective simultaneous DG and DSTATCOM allocation in radial distribution networks using cuckoo searching algorithm", Alexandria Engineering Journal,Vol. 57, No. 4, pp. 2729-2742, Dec. 2018 (doi:0.1016/j.aej.2018.01.001).

[16] Y. Lim, H.M. Kim, "Strategic bidding using reinforcement learning for load shedding in microgrids", Computers and Electrical Engineering, Vol. 40, No. 5, pp. 1439–1446, July 2014 (doi:10.1016/j.compeleceng.2013.12.013).

[17] S. X. Chen, H. B. Gooi, M. Q. Wang, "Sizing of energy storage for microgrids", IEEE Trans. on Smart Grid, Vol. 3, No. 1, pp. 142 –151, March 2012 (doi:10.1109/TSG.2011.2160745).

[18] C. Tao, D. Shanxu, C. Changsong, "Forecasting power output forgrid-connected photovoltaic power system without using solar radiationmeasurement", Proceeding of the IEEE/PEDG, pp.773–777, Hefei, China, June 2010  (doi:10.1109/PEDG.2010.5545754).

[19] O. Deperlioglu, U. Kose, "An educational tool for artificial neural networks", Computers and Electrical Engineering, Vol. 37, No. 3, pp. 392–402, May 2011 (doi:10.1016/j.compeleceng.2011.03.010).

[20] A. Qazia, H. Fayazb ,A. Wadib, R. G. Raja, N. A. Rahimb, W. A. Khan, "The artificial neural network for solar radiation prediction and designing solar systems: A systematic literature review",Journal of Cleaner Production, Vol. 104, pp. 1-12, Oct. 2015 (doi:10.1016/j.jclepro.2015.04.041).

[21] J. Ramos, A. Andreas, "University of Texas Panamerican (UTPA): Solar Radiation Lab (SRL); Edinburg, Texas (Data)", NREL Report No. DA 5500-56514. [Online] (doi:10.5439/1052555,2013).

[22] A. P. Agalgaonkar, C. V. Dobariya, M. G. Kanabar, S. A. Khaparde, S. V. Kulkarni, “Optimal sizing of distributed generators in microgrid”, Proceeding of the IEEE/POWERIm  pp. 901–908, New Delhi, India, April 2006 (doi:10.1109/POWERI.2006.1632627).

[23] W. Buaklee, K. Hongesombut, "Optimal DG allocation in a smart distribution grid using cuckoo search algorithm", Proceeding of the IEEE/ECTICON, Krabi, pp.1 – 6, May 2013 (doi: 10.1109/ECTICon.2013.6559624).

[24] X. S. Yang, S. Deb, "Engineering optimisation by cuckoo search", International Journal of Mathematical Modelling and Numerical Optimisation, Vol. 1,  pp. 330-343, May 2010 (doi:10.1504/IJMMNO.2010.035430).

[25] C. Chen, S. Duan, T. Cai, B. Liu, G. Hu, "Optimal allocation and economic analysis of energy storage system in microgrids", IEEE Trans. on Power Electronics, Vol. 26, No. 10, pp. 2762 –2773, Oct. 2011 (doi:10.1109/TPEL.2011.2116808).

[26] U. S. Energy Information Administration., "Weekly U.S. regular conventional retail gasolineprices (Dollarsper Gallon)", [Online], Available:http://www.eia.gov/petroleum/gasdiesel,2013.

[27] APX, "Day-ahead market – historical data", [Online]. Available: ftp://ftp.apxgroup.com, 2013.

[28] A. AAkhil,G.Huff, A.B. Currier, B.C. Kaun, D. M. Rastler, S. B. Chen, A. L. Cotter, D. T. Bradshaw,W. D. Gauntlett, "DOE/EPRI 2013 Electricity Storage Handbook in Collaboration with NRECA.SANDIAREPORT", [Online], Available:http://www.sandia.gov/ess/handbook.php, July 2013.