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جلد 4 شماره 3 صفحات 9-1 |
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Arasteh H, Mirsaeedi H. Parallel GA-PSO Algorithm to Solve the Unit Commitment Problem. sjis 2022; 4 (3) :1-9
URL: http://sjis.srpub.org/article-5-171-fa.html
URL: http://sjis.srpub.org/article-5-171-fa.html
آراسته حمیدرضا، میرسعیدی حامد. الگوریتم موازی GA-PSO برای حل مسئله تعهد واحد. نشریه مطالعات بین رشته ای. 1401; 4 (3) :1-9
گروه تحقیقات بهره برداری و برنامه ریزی سیستم های قدرت، پژوهشکده نیرو، تهران، ایران.
چکیده: (621 مشاهده)
مسئله تعهد واحد (UC) همواره به عنوان یکی از فعالیت های اصلی مورد توجه برنامه ریزان سیستم بوده است. با توجه به ماهیت غیر خطی و پیچیده UC، رویکردهای بهینه سازی متفاوتی برای حل مسئله ارائه شده است. در سال‌های اخیر، الگوریتم‌های فراابتکاری به دلیل کارایی آنها برای بهینه‌سازی مسائل پیچیده مورد توجه قرار گرفته‌اند. این مقاله مفاهیم دو الگوریتم، یعنی بهینه‌سازی ازدحام ذرات (PSO) و الگوریتم ژنتیک (GA) را به صورت موازی ترکیب می‌کند و یک روش ترکیبی GA-PSO را برای بهینه‌سازی مسئله تعهد واحد (UC) پیشنهاد می‌کند. نتایج شبیه‌سازی، اثربخشی و مزایای روش پیشنهادی را در مقایسه با روش‌های جداگانه توجیه کرده است.
نوع مطالعه: پژوهشي |
موضوع مقاله:
مهندسی کنترل و سیستم
دریافت: 1401/1/24 | ویرایش نهایی: 1401/3/21 | پذیرش: 1401/4/4 | انتشار: 1401/5/3
دریافت: 1401/1/24 | ویرایش نهایی: 1401/3/21 | پذیرش: 1401/4/4 | انتشار: 1401/5/3
فهرست منابع
1. Guzmán-Feria JS, Castro LM, Tovar-Hernández JH, González-Cabrera N, Gutiérrez-Alcaraz G. Unit commitment for multi-terminal VSC-connected AC systems including BESS facilities with energy time-shifting strategy. Int J Elect Power Energ Syst. 2022; 134: 107367. [DOI:10.1016/j.ijepes.2021.107367]
2. Huo Y, Bouffard F, Joós G. Integrating learning and explicit model predictive control for unit commitment in microgrids Appl Energ. 2022; 306: Part A, 118026. [DOI:10.1016/j.apenergy.2021.118026]
3. Tehzeeb-ul-Hassan H, Ahmad A. Profit based unit commitment and economic dispatch of IPPs with new technique. Elect Power Energ Syst. 2013; 44: 880-888. [DOI:10.1016/j.ijepes.2012.08.039]
4. Hou W, Hou L, Zhao S, Liu W. A hybrid data-driven robust optimization approach for unit commitment considering volatile wind power. Elect Power Syst Res. 2022; 205: 107758. [DOI:10.1016/j.epsr.2021.107758]
5. Abdi H. Profit-based unit commitment problem: A review of models, methods, challenges, and future directions. Renew Sustain Energ Rev. 2021; 138: 110504. [DOI:10.1016/j.rser.2020.110504]
6. Morales-Espa˜naa G, Lorca Á, de Weerdt MM. Robust unit commitment with dispatchable wind power. Elect Power Syst Res. 2018; 155: 58-66. [DOI:10.1016/j.epsr.2017.10.002]
7. Abujarad SY, Mustafa MW, Jamian JJ. Recent approaches of unit commitment in the presence of intermittent renewable energy resources: A review. Renew Sustain Energ Rev. 2017; 70: 215-223. [DOI:10.1016/j.rser.2016.11.246]
8. Wu YK, Chang HY, Chang SM. Analysis and comparison for the unit commitment problem in a large-scale power system by using three meta-heuristic algorithms. Energ Proc. 2017; 141: 423-427. [DOI:10.1016/j.egypro.2017.11.054]
9. Senjyu T, Shimabukuro K, Uezato K, Funabashi TA. Fast technique for unit commitment problem by extended priority list. IEEE Trans Power Syst. 2003; 18(2): 882-888. [DOI:10.1109/TPWRS.2003.811000]
10. Snyder Jr WL, Powell Jr HD, Rayburn JC. Dynamic programming approach to unit commitment. IEEE Trans Power App Syst. 1987; PAS-2: 339-350. [DOI:10.1109/TPWRS.1987.4335130]
11. Rong A, Hakonen H, Lahdelma R. A dynamic regrouping based sequential dynamic programming algorithm for unit commitment of combined heat and power systems. Energ Conv Manag. 2009; 50: 1108-1115. [DOI:10.1016/j.enconman.2008.12.003]
12. Zhuang F, Galiana FD. Toward a more rigorous and practical unit commitment by lagrangian relaxation. IEEE Trans Power Syst. 3(2): 763-770. [DOI:10.1109/59.192933]
13. Ongskul W, Petcharaks N. Unit commitment by enhanced adaptive Lagrangian relaxation. IEEE Trans Power Syst. 2004; 19(1): 620-628. [DOI:10.1109/TPWRS.2003.820707]
14. Cohen A, Yoshimura M. A branch-and-bound algorithm for unit commitment. IEEE Trans Power App Syst. 1983; PAS-102(2): 444-451. [DOI:10.1109/TPAS.1983.317714]
15. Anand H, Narang N, Dhillon JS. Multi-objective combined heat and power unit commitment using particle swarm optimization. Energ. 2019; 172: 794-807. [DOI:10.1016/j.energy.2019.01.155]
16. Simopoulos DN, Kavatza SD, Vournas CD. Unit commitment by an enhanced simulated annealing algorithm. IEEE Trans Power Syst. 2006; 21(1): 193-201. [DOI:10.1109/TPWRS.2005.860922]
17. Jeong Y-W, Park J-B, Shin J-R, Lee KY. A thermal unit commitment approach using an improved quantum evolutionary algorithm. Elect Power Compon Syst. 2009; 37(7): 770-786. [DOI:10.1080/15325000902762331]
18. Sasaki H, Watanabe M, Yokoyama R. A solution method of unit commitment by artificial neural networks. IEEE Trans Power Syst. 1992; 7(3): 974-981. [DOI:10.1109/59.207310]
19. Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw. 2014; 69: 46-61. [DOI:10.1016/j.advengsoft.2013.12.007]
20. Kamboj VK. A novel hybrid PSO-GWO approach for unit commitment problem. Neural Comp Appl. 2015; 13: Article in Press. [DOI:10.1007/s00521-015-1962-4]
21. Panwar LK, Reddy S, Verma A, Panigrahi BK, Kumar R. Binary grey wolf optimizer for large scale unit commitment problem. Swarm Evol Comput. 2018; 38: 251-266. [DOI:10.1016/j.swevo.2017.08.002]
22. Amini MH, Kargarian A, Karabasoglu O. ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Elect Power Syst Res. 2016; 140: 378-390. [DOI:10.1016/j.epsr.2016.06.003]
23. Inostroza JC, Hinojosa VH. Short-term scheduling solved with a particle swarm optimizer. IET Generat Transm Distrib. 2011; 5: 1091-1104. [DOI:10.1049/iet-gtd.2011.0117]
24. Ouyang Z, Shahidehpour SM. An intelligent dynamic programming for unit commitment application. IEEE Trans Power Syst. 1991; 6: 1203-1209. [DOI:10.1109/59.119267]
25. Scuzziato MR, Finardi EC, Frangioni A. Solving stochastic hydrothermal unit commitment with a new primal recovery technique based on Lagrangian solutions. Int J Elect Power Energ Syst. 2021; 127: 106661. [DOI:10.1016/j.ijepes.2020.106661]
26. Hong YY, Chen YY. Placement of power quality monitors using enhanced genetic algorithm and wavelet transform. IET Generat Transm Distrib. 2011; 5: 461-466. [DOI:10.1049/iet-gtd.2010.0397]
27. Håberg M. Fundamentals and recent developments in stochastic unit commitment. Int J Elect Power Energ Syst. 2019; 109: 38-48. [DOI:10.1016/j.ijepes.2019.01.037]
28. Arasteh HR, Parsa Moghaddam M, Sheikh-El-Eslami MK, Abdollahi A. Integrating commercial demand response resources with unit commitment. Elect Power Energ Syst. 2013; 51: 153-161. [DOI:10.1016/j.ijepes.2013.02.015]
29. Saber AY, Chakraborty S, Abdur Razzak SM, Senjyu T. Optimization of economic load dispatch of higher order general cost polynomials and its sensitivity using modified particle swarm optimization. Elect Power Syst Res. 2009; 79: 98-106. [DOI:10.1016/j.epsr.2008.05.017]
30. Subba Reddy GV, Ganesh V, Rao CS. Implementation of genetic algorithm based additive and divisive clustering techniques for unit commitment. Energ Proc. 2017; 117: 493-500. [DOI:10.1016/j.egypro.2017.05.175]
31. Vanithasri M, Balamurugan R, Lakshminarasimman L. Radial movement optimization (RMO) technique for solving unit commitment problem in power systems. J Elect Syst Inform Tech. 2017.
32. Yu X, Zhang X. Unit commitment using Lagrangian relaxation and particle swarm optimization. Elect Power Energ Syst. 2014; 61: 510-522. [DOI:10.1016/j.ijepes.2014.03.061]
33. Anand H, Narang N, Dhillon JS. Profit based unit commitment using hybrid optimization technique. Energ. 2018; 148: 701-715. [DOI:10.1016/j.energy.2018.01.138]
34. Shukla A, Singh SN. Advanced three-stage pseudo-inspired weight-improved crazy particle swarm optimization for unit commitment problem. Energ. 2016; 96: 23-36. [DOI:10.1016/j.energy.2015.12.046]
35. Kumar VS, Mohan MR. Solution to security constrained unit commitment problem using genetic algorithm. Elect Power Energ Syst. 2010; 32: 117-125. [DOI:10.1016/j.ijepes.2009.06.019]
36. Holland JH. Outline for a logical theory of adaptive systems. J Anoc Comput Mach. 1962; 297-314. [DOI:10.1145/321127.321128]
37. Goldberg DE. Genetic algorithms in search, optimization, and machine learning. Addison-Wesly, 1989.
38. Davis L. Handbook of genetic algorithms. Van Nostrand Reinhold, 1991.
39. Kennedy J, Eberhart R. Particle swarm optimization. Proc 4th IEEE Int Conf Neural Network. 1995; 1942-1948.
40. Hu W, Yen GG. Adaptive multiobjective particle swarm optimization basedon parallel cell coordinate system. IEEE Trans Evol Comput. 2015; 19: 1-18. [DOI:10.1109/TEVC.2013.2296151]
41. Arasteh H, Sepasian MS, Vahidinasab V, Siano P. SoS-based multiobjective distribution system expansion planning. Elect Power Syst Res. 2016; 141: 392-406. [DOI:10.1016/j.epsr.2016.08.016]
42. Arasteh H, Vahidinasab V, Sepasian MS, Aghaei J. Stochastic system of systems architecture for adaptive expansion of smart distribution grids. IEEE Trans Ind Informat. 2018; doi: 10.1109/TII.2018.2808268 [DOI:10.1109/TII.2018.2808268]
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