استفاده از تئوری تصمیم گیری مبتنی بر شکاف اطلاعاتی برای حل مسئله پخش بهینه توان مقید به پایداری ولتاژ در حضور مزارع بادی

نویسندگان

1 عضو هیئت علمی دانشگاه زنجان

2 دانشجوی کارشناسی ارشد دانشگاه زنجان

چکیده

چکیده: در این مقاله به مطالعه و بررسی مسئله پخش بار بهینه مقید به پایداری ولتاژ در حضور عدم­قطعیت موجود در تولید توان از مزارع بادی‌ پرداخته ‌شده است. با توجه به غیر­قطعی بودن تولید توان مزارع بادی، روشی پیشنهاد می‌شود که به ­ازای یک حد مجاز برای افزایش هزینه نسبت به مقدار پایه، بالاترین میزان از عدم­قطعیت برای تولید توان از مزارع بادی را مشخص کند. این میزان عدم­قطعیت با در نظر گرفتن مقدار مشخصی از حاشیه بارگذاری شبکه تعیین می­شود. لازم به ­ذکر است که حاشیه بارگذاری مهم­ترین شاخص ارزیابی پایداری ولتاژ است که فاصله نقطه کار فعلی شبکه را از نقطه فروپاشی ولتاژ تعیین می­کند. بدین­منظور از روش تئـوری تصمیم­گیـری مبتنـی بـر شکـاف اطـلاعـاتی برای مدیریت ارتباط بین عدم­قطعیت در تولید توان از مزارع بادی و حد بارپذیری شبکه استفاده می‌شود. مدل ارائه‌شده بر روی شبکه  39 و ۱۱۸ شینه IEEE و در محیط نرم‌افزار بهینه‌سازی GAMS پیاده‌سازی شده است. به‌منظور ارزیابی کارایی مدل ارائه‌شده، نتایج حاصل از آن با نتایج به‌دست‌آمده از روش شبیه‌سازی مونت‌کارلو مقایسه شده‌اند. نتایج شبیه‌سازی‌، نشان می­دهد که رهیافت ارائه‌شده، یک رهیافت بهینه و مقاوم برای لحاظ نمودن همزمان عدم­قطعیت و پایداری ولتاژ در مسئله پخش بار بهینه است.

کلیدواژه‌ها


عنوان مقاله [English]

Application of Information Gap Decision Theory for Solution of Voltage Stability Constrained Optimal Power Flow in the Presence of Wind Farms

چکیده [English]

Abstract: This paper deals with the Voltage Stability Constrained Optimal Power Flow (VSC-OPF) problem in the presence of wind power generation uncertainty. Due to the uncertain nature of wind power generation, an approach is proposed that determines the maximum uncertainty of wind power generation for give a percentage of total cost increase. This maximunm uncertainty is determined in a way that a desired loading margin (LM), is satisfied. It is worth to note that LM is the most important measure of voltage stability which reflects the distance from the current operating point to the voltage collapse point. For this aim, Information Gap Decision Theory (IGDT) is utilized to handle the uncertainty of wind power generation and voltage stability in the proposed VSC-OPF model. The proposed model is implemented on the IEEE 39 and 118-bus standard test systems, and solved by General Algebraic Modeling System (GAMS) optimization software. In order to evaluate the effectiveness of the proposed methodology for uncertainty handling, the results obtained by IGDT technique are compared with Monte Carlo Simulations (MCS). The simulation results imply that the uncertainty radius and the desired LM have an inverse relationship, such that for a given percentage of cost increase, the radius of uncertainty decreses with respect to the increase of the desired LM.

کلیدواژه‌ها [English]

  • Keywords: Optimal power flow (OPF)
  • wind power generation
  • information gap decision theory (IGDT)
  • uncertainty
  • voltage stability
  • loading margin (LM)
[1] A. Rabiee, A. Soroudi, B. Mohammadi-Ivatloo and M. Parniani, “Corrective voltage control scheme considering demand response and stochastic wind power,” IEEE Transactions on Power Systems, vol. 29, no. 6, pp. 2965-2973, 2014.
[2] A. Rabiee, A. Soroudi, and A. Keane, “Information gap decision theory based OPF with HVDC connected wind farms,” IEEE Transactions on Power Systems, vol. 30, no. 6, pp. 3396-3406, 2015.
[3] M. J. Hossain, H. R. Pota, M. A. Mahmud, and R. Ramos, “Investigation of the impacts of large-scale wind power penetration on the angle and voltage stability of power systems,” IEEE Systems Journal, vol. 6, no. 1, pp. 76-84, 2012.
[4] C. M. Affonso, L. C. Da Silva, F. G. Lima, and S. Soares, “MW and MVar management on supply and demand side for meeting voltage stability margin criteria,” IEEE Transactions on Power Systems, vol. 19, no. 3, pp. 1538-1545, 2004.
[5] E. Vittal, M. O'Malley, and A. Keane, “A steady-state voltage stability analysis of power systems with high penetrations of wind,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 433-442, 2010.
[6] W. Rosehart, C. Canizares, and V. Quintana, “Multiobjective optimal power flows to evaluate voltage security costs in power networks,”  IEEE Transactions on Power Systems, vol. 18, no. 2, pp. 578-587, 2003.
[7] فرید کربلایی، شهریار عباسی و حسین صابری، «محاسبه سریع و دقیق حاشیه پایداری ولتاژ با تقریب منحنی PV،» مجلـه مهندسـی بـرق دانشـگاه تبریـز، دوره 44، شماره 3، صفحه 33-40، 1393.
[8] A. Rabiee, and M. Parniani, “Voltage security constrained multi-period optimal reactive power flowusing benders and optimality condition decompositions,” IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 696-708, 2013.
[9] V. Kumar, K. K. Reddy, and D. Thukaram, “Coordination of reactive power in grid-connected wind farms for voltage stability enhancement,” IEEE Transactions on Power Systems, vol. 29, no. 5, pp. 2381-2390, 2014.
[10] A. Rabiee, M. Parvania, M. Vanouni, M. Parniani, and M. Fotuhi-Firuzabad, “Comprehensive control framework for ensuring loading margin of power systems considering demand-side participation,” IET Generation, Transmission & Distribution, vol. 6, no. 12, pp. 1189-1201, 2012.
[11] R. Al Abri, E. F. El-Saadany, and Y. M. Atwa, “Optimal placement and sizing method to improve the voltage stability margin in a distribution system using distributed generation,” IEEE Transactions on Power Systems, vol. 28, no. 1, pp. 326-334, 2013.
[12] A. Soroudi, B. Mohammadi-Ivatloo, and A. Rabiee, “Energy hub management with intermittent wind power,” Green Energy and Technology, ed., Springer, pp. 413-438, 2014.
[13] A. Rabiee, and A. Soroudi, “Stochastic multiperiod OPF model of power systems with HVDC-connected intermittent wind power generation,” IEEE Transactions on Power Delivery, vol. 29, no. 1, pp. 336-344, 2014.
[14] E. M. Constantinescu, V. M. Zavala, M. Rocklin, S. Lee, and M. Anitescu, “A computational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation,” IEEE Transactions on Power Systems, vol. 26, no 1, pp. 431-441, 2011.
[15] J. Wang, M. Shahidehpour, and Z. Li, “Security-constrained unit commitment with volatile windpower generation,” IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 1319-1327, 2008.
[16] B. Ayyub, “Applied research in uncertainty modeling and analysis,” Springer Science & Business Media, vol. 20, 2007.
[17] امیرحسین زارع نیستانک، رحمت­ا... هوشمند و معین پرستگاری، «بهره‌برداری بهینه از نیروگاه‌های بادی با استفاده از نیروگاه‌های تلمبه‌ای- ذخیره‌ای به‌منظور کاهش عدم­قطعیت در عملکرد آنان در بازار برق،» مجلـه مهندسـی بـرق دانشـگاه تبریـز، جلد 41، شماره 2، صفحه 52-59، ١٣۹۱.
[18] A. Soroudi, and T. Amraee, “Decision making under uncertainty in energy systems: state of theart,” Renewable and Sustainable Energy Reviews, vol. 28, pp. 376-384, 2013.
[19] A. Soroudi, and M. Ehsan, “IGDT based robust decision making tool for DNOs in load procurement under severe uncertainty,” IEEE Transactions on Smart Grid, vol. 4, no. 2, pp. 886-895, 2013.
[20] K. Zare, M. P. Moghaddam, and M. K. Sheikh-el-Eslami, “Electricity procurement for large consumers based on Information Gap Decision Theory,” Energy Policy, vol. 38, no. 1, pp. 234-242, 2010.
[21] B. Mohammadi-Ivatloo, H. Zareipour, N. Amjady, and M. Ehsan, “Application of information-gap decision theory to risk-constrained self-scheduling of GenCos,” IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1093-1102, 2013.
[22] M. P. Cheong, D. Berleant, and G. Sheblé, “Information gap decision theory as a tool for strategic bidding in competitive electricity markets,” IEEE International Conference on Power Systems, pp. 421-426, 2004.
[23] K. Zare, M. P. Moghaddam, and M. K. Sheykh-el-Eslami, “Demand bidding construction for a large consumer through a hybrid IGDT-probability methodology,” Energy, vol. 35, no. 7, pp. 2999-3007, 2010.
[24] K. Zare, M. P. Moghaddam, and M. K. Sheykh-el-Eslami, “Electricity procurement for large consumers based on information gap decision theory,” Energy Policy, vol. 38, pp. 234-242, 2010.
[25] S. Nojavan, K. Zare, and M. A. Ashpazi, “A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market,” International Journal of Electrical Power & Energy Systems, vol. 69, pp. 335-343, 2015.
[26] M. Moradi-Dalvand, B. Mohammadi-Ivatloo, N. Amjady,  H. Zareipour, and A. Mazhab-Jafari, “Self-scheduling of a wind producer based on Information Gap Decision Theory,” Energy, vol. 81,  pp. 588-600, 2015.
[27] Y. Ben-Haim, Info-gap Decision Theory: Decisions under Severe Uncertainty, Academic Press, 2006.
[28] R. J. Avalos, C. A. Ca˜nizares, F. Milano, and A. J. Conejo, “Equivalency of continuation and optimization methods to determine saddle-node andlimit-induced bifurcations in power systems,” IEEE Transactions on Circuits and Systems-I, vol. 56, no. 1, pp. 210-223, 2009.
[29] GAMS, A User Guide, New York, NY, USA, 2008.