روشی جدید در ارزيابي قابليت اطمينان عرضه در میکروگریدهای صنعتي با در نظر گيري رشد بار و عدم قطعيت منابع تجديدپذير
محورهای موضوعی : مهندسی برق و کامپیوترصادق رحیمی تاکامی 1 , رحمت‌الله هوشمند 2 , امین خدابخشیان 3 , سيدمصطفي نصرت‌آبادي 4
1 - دانشگاه اصفهان
2 - دانشگاه اصفهان
3 - مهندسی
4 - دانشگاه اصفهان
کلید واژه: توليد پراکنده (DG) ارزيابي قابليت اطمينان شبکه‌هاي صنعتي مونت کارلو ترتيبي,
چکیده مقاله :
وجود منابع تولید پراکنده در میکروگریدهای صنعتی، تأثیر زیادی در پارامترهای قابلیت اطمینان این شبکه‌ها دارد. لذا در این مقاله، ارزیابی قابلیت اطمینان میکروگریدهای صنعتی با استفاده از یک شاخص ترکیبی پیشنهادی در حضور منابع تولید پراکنده و بار پاسخگو ارائه می‌گردد. این روش ارزیابی قابلیت اطمینان بر مبنای مونت کارلوی ترتیبی با توجه به بار زمانی موجود می‌باشد. در این مقاله از تولیدات تجدیدپذیر پرکاربرد نیروگاه بادی و مولد فتوولتائیک استفاده می‌شود. با توجه به آن که توان خروجی این نوع DGها به متغیرهای تصادفی سرعت باد و میزان تابش خورشید بستگی دارد لذا جهت تعیین میزان توان خروجی آنها در هر ساعت برای هر کدام از آنها تعدادی سناریو در نظر گرفته شده است. با توجه به تعداد زیاد سناریوهای ایجادشده از روش کاهش سناریو بر مبنای دو شرط مبتنی بر توان تولیدی آنها و میزان بارها استفاده می‌شود. همچنین شاخص ترکیبی جدید بیانگر میزان تغییرات شاخص‌های SAIFI، SAIDI و EENS به ازای هر KW از DG نصب شده است. با توجه به رشد بارهای صنعتی در میکروگریدها، یک دوره مطالعه ده‌ساله در دو حالت عملکرد جزیره‌ای و اتصال به شبکه برنامه‌ریزی می‌شود. در حالت جزیره‌ای از مفهوم بار پاسخگو نیز استفاده می‌شود. برای نشان‌دادن کارایی الگوریتم پیشنهادی، این روش بر روی شبکه استاندارد 2IEEE-RBTS BUS در حضور منابع DG اعمال گردیده و نتایج در حالات مختلف بررسی گردید.
Distributed Generation (DG) resources can effect a lot on the reliability parameters in industrial microgrids. So, reliability evaluation of industrial microgrids is presented in this paper using a proposed composite index in the presence of DG resources and demand response (DR). This procedure of the reliability assessment is based on sequential Monte Carlo method with respect to the time varying load model. In this paper, wind and photovoltaic generations those are useful renewable generations are used. Since, the output power of these DGs depends on wind speed and solar radiation that are stochastic variables, therefore a number of scenarios have been considered in order to determine the output power per hour for each of them. According to the large number of generated scenarios, scenario reduction method is used based on two conditions that consist of power generation of DGs and load. Here the new composite index represents changes in the SAIFI, SAIDI and EENS indices per each KW of installed DGs. With considering to industrial load growth in the microgrid, a ten-year period is studied and the scheduling is performed in both islanding and grid connected operational modes. The concept of DR is also used in the islanding operational mode. To demonstrate the effectiveness of the proposed method, the approach is applied on a standard IEEE RBTS BUS2 system in the presence of DG resources and the results in different conditions are achieved.
[1] R. M. Ciric and N. L. J. Rajakovic, "A new composite index of reliability of supply in the industrial systems," Electrical Power and Energy Systems, vol. 44, no. 1, pp. 824-831, Jan. 2013.
[2] H. Garg, M. Rani, and S. P. Sharma, "An approach for analyzing the reliability of industrial systems using soft-computing based technique," Expert Systems with Applications, vol. 41, no. 2, pp. 489-501, Feb. 2014.
[3] A. A. Alkuhayli, "Reliability evaluation of distribution systems containing renewable distributed generations," in Proc. North American Power Symp., NAPS'12, 6 pp., 9-11 Sep.. 2012.
[4] A. R. Abul'Wafa and A. T. M. Taha, "Reliability evaluation of distribution systems under μ grid-tied and islanded μ grid modes using Monte Carlo simulation," Smart Grid and Renewable Energy, vol. 5, no. 3, pp. 52-62, Mar. 2014.
[5] Y. M. Atwa and E. F. El-Saadany, "Reliability evaluation for distribution system with renewable distributed generation during islanded mode of operation," IEEE Trans. on Power Systems, vol. 24, no. 2, pp. 572-581, Mar. 2009.
[6] P. Paliwal, N. P. Patidar, and R. K. Nema, "A novel method for reliability assessment of autonomous PV-wind-storage system using probabilistic storage model," International J. of Electrical Power & Energy Systems, vol. 55, pp. 692-703, Feb. 2014. [7] K. Chaiamarit and S. Nuchprayoon, "Modeling of renewable energy resources for generation reliability evaluation," Renewable and Sustainable Energy Reviews, vol. 26, pp. 34-41, Oct. 2013.
[8] C. L. T. Borges, "An overview of reliability models and methods for distribution systems with renewable energy distributed generation," Renewable and Sustainable Energy Reviews, vol. 16, no. 6, pp. 4008-4015, Aug. 2012.
[9] J. K. Lee, I. S. Bae, H. S. Jung, and J. O. Kim, "Evaluating reliability of distributed generation with analytical techniques," in Proc. IEEE Russia Power Tech., 6 pp., 27-30 Jun. 2005.
[10] I. S. Bae and J. O. Kim, "Reliability evaluation of distributed generation based on operation mode," IEEE Trans. on Power Systems, vol. 22, no. 2, pp. 785-790, Apr. 2007.
[11] A. A. Chowdhury, L. Bertling, B. P. Glover, and G. E. Haringa, "A Monte Carlo simulation model for multi-area generation reliability evaluation," in Proc. Int. Conf. on Probabilistic Methods Applied to Power Systems, 6 pp., 11-15 Jun. 2006.
[12] R. A. Bakkiyaraj and N. Kumarappan, "Evaluation of composite reliability indices based on non-sequential Monte Carlo simulation and particle swarm optimization," in Proc. IEEE Congress on Evolutionary Computation, CEC'10, 5 pp., 18-23 Jul. 2010.
[13] X. Zhang, Z. Bie, and G. Li, "Reliability assessment of distribution networks with distributed generations using Monte Carlo method," in Proc. of Int. Conf. on Smart Grid and Clean Energy Technologies, ICSGCE'11, vol. 12, pp. 278-286, 2011.
[14] L. B. Fang and J. D. Cai, "Reliability assessment of microgrid using sequential Monte Carlo simulation," J. of Electronic Science and Technology, vol. 9, no. 1, pp. 31-34, Jan. 2011.
[15] L. N. Kishore and E. Fernandez, "Reliability well-being assessment of PV-wind hybrid system using Monte Carlo simulation," in Proc. Int. Conf. on Emerging Trends in Electrical and Computer Technology, ICETECT'11, pp. 63-68, Mar. 2011.
[16] R. A. Bakkiyaraj and N. Kumarappan, "Optimal reliability planning for a composite electric power system based on Monte Carlo simulation using particle swarm optimization," International J. of Electrical Power & Energy Systems, vol. 47, pp. 109-116, May 2013.
[17] T. Nippert and L. Oprea, "Reliability analysis of an industrial power system with on-site generation," in Proc. 20th Int.l Conf. on Electricity Distribution, 4 pp., 8-11 Jun. 2009.
[18] M. E. Khodayar, M. Barati, and M. Shahidehpour, "Integration of high reliability distribution system in microgrid operation," IEEE Trans. on Smart Grid, vol. 3, no. 4, pp. 1997-2006, Sep. 2012.
[19] Y. K. Lin, "Using minimal cuts to evaluate the system reliability of a stochastic-flow network with failures at nodes and arcs," Reliability Engineering & System, vol. 75, no. 1, pp. 41-46, Sep. 2002.
[20] A. Volkanovski, M. Cepin, and B. Mavko, "Application of the fault tree analysis for assessment of power system reliability," Reliability Engineering & System Safety, vol. 94, no. 3, pp. 1116-1127, Jun. 2009.
[21] R. Billinton and R. N. Allan, Reliability Evaluation of Engineering Systems, New York: Plenum Press, pp. 155-173, 1992.
[22] M. Al-Muhaini and G. T. Heydt, "Evaluating future power distribution system reliability including distributed generation," IEEE Trans. on Power Delivery, vol. 28, no. 4, pp. 2264-2272, Sep. 2013.
[23] C. L. T. Borges, "An overview of reliability models and methods for distribution systems with renewable energy distributed generation," Renewable and Sustainable Energy Reviews, vol. 16, no. 6, pp. 4008-4015, Aug. 2012.
[24] F. J. Ruiz-Rodriguez, M. Gomez-Gonzalez, and F. Jurado, "Reliability optimization of an electric power system by biomass fuelled gas engine," International J. of Electrical Power & Energy Systems, vol. 61, pp. 81-89, Oct. 2014.
[25] C. L. T. Borges and R. J. Pinto, "Small hydro power plants energy availability modeling for generation reliability evaluation," IEEE Trans. on Power Systems, vol. 23, no. 3, pp. 1125-1135, Jul. 2008.
[26] R. E. Brown and L. A. A. Freeman, "Analyzing the reliability impact of distributed generation," in Proc. Power Engineering Society Summer Meeting, vol. 2, pp. 1013-1018, Jul. 2001.
[27] L. Huishi, S. Jian, and L. Sige, "Reliability evaluation of distribution system containing microgrid," in Proc. 2010 China Int. Conf. on Electricity Distribution, CICED'10, 7 pp., 13-16 Sep. 2010.
[28] R. Yousefian and H. Monsef, "DG-allocation based on reliability indices by means of monte carlo simulation and AHP," in Proc. 10th Int. Conf. on Environment and Electrical Engineering, EEEIC'11, 4 pp., 8-11 May 2011.
[29] R. Khairil and J. Slobodan, "Reliability and availability modeling of uninterruptible power supply (UPS) systems using Monte-Carlo simulation," in Proc. 35th Int. Telecommunications Energy Conf. Smart Power and Efficiency, INTELEC'13, 6 pp., 13-17 Oct. 2013.
[30] M. Z. Ming, W. Jianhong, W. Jingjing, and T. Kuo, "Impact of intermittent wind distributed generation on the reliability of distribution system," in Proc. Asia-Pacific Power and Energy Engineering Conf., APPEEC'10, 6 pp., 28-31 Mar. 2010.
[31] IEEE Std. 1366-2012: IEEE Guide for Electric Power Distribution Reliability Indices, pp. 1-43, 2012.
[32] N. Hatziargyriou, "Microgrids," IEEE Power and Energy Magazine, vol. 5, no. 4, pp. 78-94, Jul./Aug. 2007.
[33] N. Hatziargyriou, Microgrids Architectures and Control, John Wiley and Sons Ltd. p. 4. 2014.
[34] R. N. Allan, R. Billinton, I. Sjarief, L. Goel, and K. S. So, "A reliability test system for educational purposes - basic distribution system data and results," IEEE Trans. on Power Systems, vol. 6, no. 2, pp. 813-820, Aug. 1991.
