بازآرایی سیستم توزیع در محيط بازار برق با حضور منابع توليد پراكنده
محورهای موضوعی : مهندسی برق و کامپیوتربهداد آرنديان 1 , رحمت‌الله هوشمند 2 * , مهدی قلی‌پور 3
1 - دانشگاه اصفهان
2 - دانشگاه اصفهان
3 - دانشگاه اصفهان
کلید واژه: بازآرایی تجدید ساختار منابع تولید پراکنده,
چکیده مقاله :
شرکت‌های توزیع خصوصی (DISCO) سعی دارند با کاهش تلفات شبکه‌های خود، هزینه کمتری بابت خرید انرژی از بازار صرف نمایند و بازآرایی بهینه یکی از ارزان‌ترین روش‌ها جهت رسیدن به این هدف می‌باشد. در این مقاله، روش جدیدی در کاهش هزینه فعالیت یک DISCO از طریق کاهش میزان تلفات شبکه و کنترل توان تولیدی منابع تولید پراکنده ارائه می‌گردد. به دلیل فعال‌بودن بازار، قیمت‌ها مقدار ثابتی ندارند لذا این مسأله در سطوح مختلف بارگذاری شبکه با قیمت‌های متفاوت مورد بررسی قرار گرفته است. به دلیل وجود پارامترهای مختلف، بهینه‌سازی مذکور از مسائل پیچیده بهینه‌سازی محسوب می‌گردد و بنابراین روشی بر مبنای الگوریتم جهش قورباغه بهبودیافته که تاکنون جهت مسائل گسسته بهینه‌سازی به ویژه بازآرایی استفاده نگردیده است، پیشنهاد شده تا مسأله بهینه‌سازی به نحو مطلوبی صورت گیرد. همچنین اثرات حضور منابع تولید پراکنده بر نحوه پخش بار و مدل‌سازی تابع هدف مسأله در نظر گرفته شده است. نتایج شبیه‌سازی روش ارائه‌شده بر روی شبکه‌های استاندارد 33شینه IEEE و 69شینه IEEE، قابلیت روش پیشنهادی را در کاهش هزینه‌های فعالیت DISCO در محیط بازار برق و کاهش تلفات نشان می‌دهد. همچنین توانایی روش پیشنهادی نسبت به دیگر روش‌ها نیز نشان داده شده است.
Distribution system companies (DISCOs) can reduce their cost by reconfiguration as the economic way for loss reduction. This paper presents a new method for reducing DISCO costs in deregulated environment by loss reduction and power generation control of Distributed Generators (DGs). Because of changing the price of energy in this environment, different network load levels with different prices were considered. This complex optimization problem is solved by a new method based on shuffled frog leaping algorithm (SFLA). Also, influence of DG presence on objective function and load flow is considered. The proposed method is applied to IEEE 33-bus and 69-bus test systems to decrease the activity cost of DISCO in deregulated environment and its capability relative to other methods is shown.
[1] A. C. Gallardo, L. G. Santander, and J. E. Pezao, "Greedy reconfiguration algorithms for medium voltage distribution networks," IEEE Trans. on Power Delivery, vol. 24, no. 1, pp. 328-337, Jan. 2009.
[2] J. S. Savier and D. Das, "Loss allocation to consumers before and after reconfiguration of radial distribution networks," Electric Power and Energy Sys., vol. 33, no. 3, pp. 540-549, Mar. 2011.
[3] L. W. de Oloveria, et al., "Optimal reconfiguration and capacitor allocation in radial distribution systems for energy losses minimization," Electric Power and Energy Sys., vol. 32, no. 8, pp. 840-848, Oct. 2010.
[4] S. P. Singh, G. S. Raju, and G. K. Roa, "A heuristic method for feeder reconfiguration and service restoration in distribution networks," Electric Power and Energy Systems, vol. 31, nos. 7-8, pp. 309-314, Sep. 2009.
[5] J. Zhua, et al., "A rule based comprehensive approach for reconfiguration of electrical distribution network," Electric Power Systems Research, vol. 79, no. 2, pp. 311-315, Feb. 2009.
[6] G. K. V. Raju and P. R. Bijwe, "An efficient algorithm for minimum loss reconfiguration of distribution system based on sensitivity and heuristics," IEEE Trans. on Power Sys., vol. 23, no. 3, pp. 1280-1287, Aug. 2008.
[7] H. Braz and B. Souza, "Distribution network reconfiguration using genetic algorithms with sequential encoding: subtractive and additive approaches," IEEE Trans. on Power Sys., vol. 26, no. 2, pp. 582-593, May 2011.
[8] N. Gupta, A. Swarnkar, K. R. Niazi, and R. C. Bansal, "Multi objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework," IET Gener. Transm. Distrib., vol. 4, no. 12, pp. 1288-1298, Dec. 2010.
[9] W. Wu and M. Tsai, "Application of enhanced integer coded particle swarm optimization for distribution system feeder reconfiguration," IEEE Trans. on Power Sys., vol. 26, no. 3, pp. 1591-1599, Aug. 2011.
[10] T. Niknam, E. Azadfarsani, and M. Jabbari, "A new hybrid evolutionary algorithm based on new fuzzy adaptive PSO and NM algorithms for distribution feeder reconfiguration," Energy Conversion and Management, vol. 54, no. 1, pp. 7-16, Feb. 2012.
[11] A. Swarnkar, N. Gupta, and K. R. Niazi, "Adapted ant colony optimization for efficient reconfiguration of balanced and unbalanced distribution systems for loss minimization," Swarm and Evolutionary Computation, vol. 1, no. 3, pp. 129-137, Sep. 2011.
[12] A. Saffar, R. Hooshmand, and A. Khodabakhshian, "A new fuzzy optimal reconfiguration of distribution systems for loss reduction and load balancing using ant colony search-based algorithm," Applied Soft Computing, vol. 11, no. 5, pp. 4021-4028, Jul. 2011.
[13] M. Mashhour, M. A. Golkar, and S. M. Moghaddas-Tafreshi, "Extending market activities for a distribution company in hourly-ahead energy and reserve markets - Part I: problem formulation," Energy Conversion and Management, vol. 52, no. 1, pp. 477-486, Jan. 2011
[14] A. Ebrahimi Milani and M. R. Haghifam, "An evolutionary approach for optimal time interval determination in distribution network reconfiguration under variable load," Mathematical and Computer Modelling, vol. 57, nos. 1-2, pp. 68-77, Jan. 2011.
[15] L. L. Pfitscher, D. P. Bernardon, L. N. Canha, V. F. Montagner, V. J. Garcia, and A. R. Abaide, "Intelligent system for automatic reconfiguration of distribution network in real time," Electric Power Systems Research, vol. 97, pp. 84-92, Apr. 2013.
[16] S. Chandramohan, N. Atturulu, R. P. Kumudini Devi, and B. Venkatesh, "Operating cost minimization of a radial distribution system in a deregulated electricity market through reconfiguration using NSGA method," Electrical Power and Energy Systems, vol. 32, no. 2, pp. 126-132, Feb. 2010.
[17] N. Rugthaicharoencheep and S. Sirisumrannukul, "Feeder reconfiguration with dispatchable distributed generators in distribution system by tabu search," in Proc. 44th Int. Universities Power Engineering Conf., UPEC, 5 pp., 1-4 Sep. 2009.
[18] M. M. Eusuff, K. Lansey, and F. Pasha, "Shuffled frog-leaping algorithm: a mimetic meta-heuristic for discrete optimization," Engineering Optimization, vol. 38, no. 2, pp. 129-154, Mar. 2006.
[19] S. K. Goswami and S. K. Basu, "A new algorithm for the reconfiguration of distribution feeders for loss minimization," IEEE Trans. on Power Delivery, vol. 7, no. 3, pp. 1484-1491, Jul. 1992.
[20] F. V. Gomes, S. Carneiro, J. L. Pereira, P. A. N. Garcia, and L. R. Araujo, "A new heuristic reconfiguration algorithm for large distribution systems," IEEE Trans. on Power Sys., vol. 20, no. 3, pp. 1373-1378, Aug. 2005.
[21] T. Niknam, J. Olamaie, and R. Khorshidi, "A hybrid fuzzy algorithm for multiobjective distribution feeder reconfiguration," World Applied Sciences J., vol. 4, no. 2, pp. 308-315, 2008.