الگوريتم PSO در پخش بار اقتصادي و پخش آلودگي براي توابع هزينه ناصاف با وجود تلفات خطوط انتقال و محدوديت‌هاي عملي سيستم
محورهای موضوعی : مهندسی برق و کامپیوتررحمتالله هوشمند 1 * , معين پرستگاري 2
1 - دانشگاه اصفهان
2 - دانشگاه اصفهان
کلید واژه: الگوريتم PSOبهينه‌سازي تابع هزينه ناصافپخش بار آلايندگیپخش بار اقتصادي,
چکیده مقاله :
يکي از مسائل مهم در بهره‌برداری از سيستمهاي قدرت پخش بار اقتصادی دقیق و مبتنی بر واقعیت می‌باشد. به‌این منظور در اين مقاله پخش بار اقتصادي به‌وسیله الگوريتم پيشنهادي PSO انجام می‌پذیرد. برای نزدیک‌شدن شرایط مسئله پخش بار اقتصادی به شرایط واقعی تابع هزينه مصرف سوخت نیروگاه‌های سيستم قدرت به‌صورت ناصاف در نظر گرفته مي‌شود. از طرف دیگر کاهش میزان آلودگي ناشی از نیروگاه‌ها نيز به‌عنوان جزيي از اهداف مسأله در نظر گرفته شده و به‌همین علت هم‌زمان با پخش بار اقتصادي پخش آلودگی نیز انجام می‌شود. از طرفی براي انجام پخش بار اقتصادي واقعي و بهينه باید محدوديت‌هاي نقطه کار سيستم و تلفات شبکه نیز در روند بهينه‌سازي مد نظر قرار گيرد که اين محدودیت‌ها در الگوريتم پيشنهادي در نظر گرفته شده است. در انتها نتايج روش پيشنهادي با ديگر روش‌ها (از قبيل روش جست و جوي تابو، الگوريتم ژنتيک و شبکه‌هاي عصبي) مقايسه شده است، در نتیجه خصوصيات و مزاياي واقعي اين روش مشخص‌تر مي‌گردد. همچنين نتايج شبيه‌سازي نشان مي‌دهد که روش PSO يک روش سریع با دقت قابل قبول مي‌باشد.
Precise and practical based economic dispatch is one of the most important problems in power systems. Thus, this paper proposes usage of particle swarm optimization (PSO) algorithm for solving economic dispatch problem. In this study real constraints of economic dispatch problem are considered. For this purpose, it has been considered that the fuel cost function is a non-smooth one. On the other hand, reduction of the pollutants that is emitted from fossil fuel power plants is one of the goals of the optimization problem, so that we fulfill economic and emission dispatch at the same time for solving practical and optimum economic dispatch problem with consideration of many constraints in the operating point and transmission losses, these constraints are included in the proposed method. Finally, simulation results of the proposed method for economic dispatch are compared with those of the other methods such as tabu search, genetic algorithm, and artificial neural network. The results clearly show that the proposed method gives global optimum and fast solution compared to the other methods.
[1] Y. H. Hou, L. J. Lu, X. Y. Xiong, and Y. W. Wu, "Economic dispatch of power systems based on the modified particle swarm optimization algorithm," in Proc. IEEE Conf. on Transmission and Distribution for Asia and Pacific Region, vol. 1, pp. 1-6, 2005.
[2] A. I. Selvakumar and K. Thanushkodi, "A new particle swarm optimization solution to nonconvex economic dispatch problems," IEEE Trans. on Power System, vol. 22, no. 1, pp. 42-51, Feb. 2007.
[3] C. H. Chen and S. N. Yeh, "Particle swarm optimization for economic power dispatch with valve - point effects," in Proc. IEEE Conf. and Exposition on Transmission & Distribution, pp. 1-5, Jun. 2006.
[4] J. B. Park, Y. W. Jeong, W. N. Lee, and J. R. Shin, "An improved particle swarm optimization for economic dispatch problems with non - smooth cost functions," in Proc. IEEE Conf. and Exposition on Transmission & Distribution, pp. 1-7, Jun. 2006.
[5] S. Khamsawang, C. Boonseng, and S. Pothiya, "Solving the economic dispatch problem with Tabu search algorithm," in Proc. IEEE Int. Conf. on Industrial Technology, vol. 1, pp. 274-278, Dec. 2002.
[6] J. B. Park, K. S. Lee, J. R. Shin, and K. Y. Lee, "A particle swarm optimization for economic dispatch with nonsmooth cost functions," IEEE Trans. on Power Systems, vol. 20, no. 1, pp. 34-42, Feb. 2005.
[7] A. I. S. Kumar, K. Dhanushkodi1, J. Kumar, and C. K. C. Paul, "Particle swarm optimization solution to emission and economic dispatch problem," in Proc. Conf. on Convergent Technologies for Asia-Pacific Region, vol. 1, pp. 435-439, Oct. 2003.
[8] J. W. Lamont and E. V. Obessis, "Emission dispatch models and algorithms for the 1990's," IEEE Trans. on Power Systems, vol. 10, no. 2, pp. 941-947, May 1995.
[9] T. Thakur, K. Sem, S. Saini, and S. Sharma, "A particle swarm optimization solution to NO2 and SO2 emissions for environmentally constrained economic dispatch problem," in Proc. IEEE/PES Transmission & Distribution Conf. and Exposition, pp. 1-5, 15-18 Aug. 2006.
[10] و. ولن برگ، توليد، بهره‌برداري و كنترل در سيستمهاي قدرت، ترجمه حسین سيفي، انتشارات دانشگاه تربيت مدرس، 1984.
[11] D. C. Walters and G. B. Sheble, "Genetic algorithm solution of economic dispatch with valve point loading," IEEE Trans. Power System, vol. 8, no. 3, pp. 1325-1332, Aug. 1993.
[12] T. Yalcinoz and M. J. Short, "Neural networks approach for solving economic dispatch problem with transmission capacity constraints," IEEE Trans. on Power System, vol. 13, no. 2, pp. 307-313, May 1998.
[13] A. Bakirtzis, V. Petridis, and S. Kazarlis, "Genetic algorithm solution to the economic dispatch problem," IEE Proceedings Inst. Elect. Eng. -Gen., Trans. Dist., vol. 141, no. 4, pp. 377-382, Jul. 1994.
[14] Z. L. Gaing, "Particle swarm optimization to solving the economic dispatch considering the generator constraints," IEEE Trans. on Power System, vol. 18, no. 3, pp. 1187-1195, Aug. 2003.
[15] A. El - Gallad, M. El - Hawary, A. Sallam, and A. Kalas, "Paticle swarm optimizer for constrained economic dispatch with prohibited operating zones," in Proc. Conf on Electrical and Computer Engineering, vol. 1, pp. 78-81, May 2002.
[16] R. K. Pancholi and K. S. Swarup, "Particle swarm optimization for security constrained economic dispatch," in Proc. Conf. on Intelligent Sensing and Information Processing, ICISIP’04, pp. 7-12, 2004.
[17] K. Y. Lee and J. B. Park, "Application of particle swarm optimization to economic dispatch problem: advantages and disadvantages," in Proc. IEEE Conf. on Power Systems and Exposition, PSCE '06, vol. 1, pp. 188-192, Oct. 2006.
[18] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. of IEEE Int. Conf. on Neural Netwroks, vol. 4, pp. 1942-1948, Perth, Australia, 1995.