افزایش نفوذ منابع تولید پراکنده توسط جایابی هم‌زمان منابع تولید پراکنده و سیستم‌های ذخیره‌ساز انرژی در شبکه‌های توزیع
محورهای موضوعی : مهندسی برق و کامپیوترناصر بیابانی 1 , مریم رمضانی 2 , حمید فلقی 3
1 - دانشگاه بیرجند
2 - دانشگاه بیرجند
3 - دانشگاه بیرجند
کلید واژه: افزایش نفوذ منابع تولید پراکنده الگوریتم ژنتیک جایابی هم‌زمان سیستم‌های ذخیره‌ساز انرژی,
چکیده مقاله :
منابع تولید پراکنده افزون بر مزایای بسیار زیادی که برای سیستم قدرت به همراه دارند، دارای معایبی نیز هستند. افزایش نفوذ این تجهیزات در سیستم قدرت، در کنار اثرات مطلوبی چون کاهش توان دریافتی از شبکه بالادست، می‌تواند به اضافه بار در زمان‌های کم‌باری سیستم منجر شود. از این رو در مطالعات اخیر، تلاش‌های فراوانی برای رفع موانع افزایش نفوذ این منابع صورت گرفته است. استفاده از سیستم‌های ذخیره‌ساز انرژی یکی از روش‌هایی است که با جلوگیری از عیوب ممکن منابع تولید پراکنده، می‌تواند باعث افزایش نفوذ این منابع در سیستم‌های قدرت شود. سیستم‌های ذخیره‌ساز انرژی با ذخیره‌سازی انرژی در ساعات کم‌باری و تحویل آن به شبکه در ساعات پیک، می‌توانند افزون بر کاهش تلفات شبکه توزیع، نفوذ منابع تولید پراکنده را نیز افزایش دهند. در این مقاله پس از تشریح مسایل مکانیابی منابع تولید پراکنده و سیستم‌های ذخیره‌ساز انرژی، جایابی هم‌زمان منابع تولید پراکنده و سیستم‌های ذخیره‌ساز انرژی با هدف کاهش تلفات شبکه توزیع ارائه شده است. نتایج به دست آمده با استفاده ازالگوریتم ژنتیک نشان می‌دهد مکانیابی هم‌زمان منابع تولید پراکنده و سیستم‌های ذخیره‌ساز انرژی نسبت به جایابی جداگانه آنها، می‌تواند نفوذ منابع تولید پراکنده را افزایش داده و تلفات شبکه توزیع را به مقدار بیشتری کاهش دهد.
In addition to the great benefits of distributed generation (DG) resources to power systems, there are some disadvantages. In spite of some benefits like decrease of received power from transmission grid, increment of DGs penetration can lead to voltage increase in distribution networks during off peak. Hence the recent efforts have been made to handle problems to increase the penetration of these resources. The use of energy storage systems (ESSs) is one of the ways to prevent defects may arise from use of DGs in power systems and can help to increase penetration of DGs in power systems. ESSs can save energy during off peak and deliver it to the network in peak hours; hence, these equipment can reduce power losses and prevent voltage deviation during off peak by increasing load due to ESS charging. In this paper, first DG allocation and ESS placement are introduced then simultaneous placement of DG and ESS to reduce power losses in the distribution network are described. The proposed models are solved using genetic algorithm as optimization tool. The obtained results show that simultaneous placement could increase DG penetration compared to the separate allocation of these devices and pro
[1] W. El-Khattam, Y. G. Hegazy, and M. M. A. Salama, "An integrated distributed generation optimization model for distribution system planning," IEEE Trans. Power Systems, vol. 20, no. 2, pp. 1158-1165, May 2005.
[2] A. Silvestri, S. Berizzi, and S. Buonanno, "Distributed generation planning using genetic algorithms," in Proc. IEEE Int. Conf. Electric Power Eng., PowerTech'99, Budapest, Hungry, 29 Sep.-2 Aug.1999.
[3] C. Wang and M. H. Nehrir, "Analytical approaches for optimal placement of distributed generation sources in power systems," IEEE Trans. on Power Systems, vol. 19, no. 4, pp. 2068-2076, Nov. 2004.
[4] J. O. Kim, S. W. Nam, S. K. Park, and C. Singh, "Dispersed generation planning using improved hereford ranch algorithm," Electric Power System Research, vol. 47, no. 1, pp. 47-55, Oct. 1998.
[5] A. Silvestri, S. Berizzi, and S. Buonanno, "Distributed generation planning using genetic algorithms," in Proc. IEEE Int. Conf. Electric Power Eng., PowerTech'99, Budapest, Hungry, 29 Sep.-2 Aug.1999.
[6] P. Phonrattanasak, "Optimal placement of DG using multiobjective particle swarm optimization," in Proc. 2nd. Int. Conf. Mech. and Elec. Technology, pp. 342-346, Singapore, 10-12 Sep. 2010.
[7] L. F. Ochoa, A. Padilha-Feltrin, and G. Harrison, "Time-series-based maximization of distributed wind power generation integration," IEEE Trans. Energy Conversion, vol. 23, no. 3, pp. 968-974, Sep. 2008.
[8] G. Harrison, A. Piccolo, P. Siano, and A. Robin Wallace, "Hybrid GA and OPF evaluation of network capacity for distributed generation connections," Electric Power Systems Research, vol. 78, no. 3, pp. 392-398, Mar. 2008.
[9] G. P. Harrison, A. Piccolo, P. Siano, and A. Robin Wallace, "Exploring the tradeoffs between incentives for distributed generation developers and DNOs," IEEE Trans. Power Systems, vol. 22, no. 2, pp. 821-828, May 2007.
[10] G. P. Harrison and A. Robin Wallace, "Maximising distributed generation capacity in deregulated markets," in Proc. IEEE Transmission and Distribution Conf. and Expo., vol. 2, pp. 524-530, 7-12 Sep. 2003.
[11] L. F. Ochoa, C. J. Dent, and G. P. Harrison, "Distribution network capacity assessment: variable DG and active networks," IEEE Trans. Power Systems, vol. 25, no. 1, pp. 87-95, Feb. 2010.
[12] T. Griffin, K. Tomsovic, D. Secrest, and A. Law, "Placement of dispersed generation systems for reduced losses," in Proc. 33rd Annu. Hawaii Int. Conf. Systems Sciences, vol. 4, 9 pp., Maui, US, 4-7 Jan Jan. 2000.
[13] K. A. Brekken, A. Yokochi, A. V. Jouanne, Z. Z. Yen, H. M. Hapke, and D. A. Halamay, "Optimal energy storage sizing and control for wind power applications," IEEE Trans. on Sustainable Energy, vol. 2, no. 1, pp. 69-77, Jan. 2011.
[14] H. T. Le and T. Q. Nguyen, "Sizing energy storage systems for wind power firming: an analytical approach and a cost-benefit analysis," in Proc. IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, 8 pp., Pittsburgh, US, 20-24 Jul. 2008.
[15] Y. M. Atwa and E. F. El - Saadany, "Optimal allocation of ESS in distribution systems with a high penetration of wind energy," IEEE Trans. on Power Systems, vol. 25, no. 4, pp. 1815-1822, Nov. 2010.
[16] H. L. Willis, "Analytical methods and rules of thumb for modeling DG distribution interaction," in IEEE Proc. of the Power Engineering Society Summer Meeting, vol. 3, pp. 1643-1644, Jul. 2000.
[17] International Energy Agency, Distributed Generation in Liberalzed Electricity Market, 26 Jun. 2002.
[18] EPRI-DoE Handbook of Energy Storage for Trans. and Distribution Applications, EPRI Product 1001834, Dec. 2003.
[19] F. Diaz-Gonzaleza, A. Sumpera, O. Gomis--Bellmunta, and R. Villafafila-Robles, "A review of energy storage technologies for wind power applications," Renewable and Sustainable Energy Reviews, vol. 16, no. 4, pp. 2154-2171, May 2012.
[20] T. M. Masaud, K. Lee, and P. K. Sen, "An overview of energy storage technologies in electric power systems: what is the future?" in Proc. North American Power Symp., NAPS, 6 pp., 26-28 Sep. 2010.
[21] A. Nourai, "Larg-scale electricity storage technologies for energy management," in Proc. IEEE Power Engineering Society Summer Meeting, vol. 1, pp. 310-315, 25-25 Jul. 2002.
[22] K. C. Divya and J. Ostergaard, "Battery energy storage technology for power systems - an overview," Electric Power Systems Research, vol. 79, no. 4, pp. 511-520, Apr. 2009.
[23] K. L. Huang, X. Li, S. Liu, N. Tan, and L. Chen, "Research progress of vanadium redox flow battery for energy storage in China," Renewable Energy, vol. 33, no. 2, pp. 186-192, Feb. 2008.
[24] P. Poonpun and W. T. Jewell, "Analysis of the cost per kilowatt hour to store electricity," IEEE Trans. on Energy Convers., vol. 23, no. 2, pp. 529-534, Jun. 2008.
[25] D. Shirmohammadi, H. W. Hong, A. Semlyen, and G. X. Luo, "A compensation - based power flow method for weakly meshed distribution and transmission network," IEEE Trans. on Power Systems, vol. 3, no. 2, pp. 753-762, May 1988.
[26] R. Ranjan and D. Das, "Simple and efficient computer algorithm to solve radial distribution networks," Electric Power Components and Systems, vol. 31, no. 1, pp. 95-107, 2003.
[27] T. Boehme, A. Robin Wallace, and G. P. Harrison, "Applying time series to power flow analysis in networks with high wind penetration," IEEE Trans. on Power Systems, vol. 22, no. 3, pp. 951-957, Aug. 2007.
