منابع تولید پراکنده تجدید پذیر با لحاظ عدم قطعیت
محورهای موضوعی : علوم زیست محیطیمهدی دستخوان 1 , علی دوست رستمی زاده 2
1 - دانشگاه فنی و حرفه ای پسران یاسوج
2 - دانشگاه فنی و حرفه ای پسران یاسوج
کلید واژه: منابع تولید, محیط‌زیست, تجدید پذیر , دستگاه‌های قدرت, فتوولتاییک,
چکیده مقاله :
امروزه بیشتر انرژی برق موردنیاز کشورها با استفاده از سوخت‌های فسیلی تأمین می‌گردد. با توجه به این امر که انرژی‌های فسیلی تجدید ناپذیر می‌باشند و آلودگی‌های زیست‌محیطی را به همراه خواهند داشت، استفاده از منابع تجدید پذیر برای تأمین انرژی الکتریسیته امری ضروری می‌باشد. در بسیاری از مطالعات انجام شده تابه‌حال در این زمینه، رفتار تصادفی بار و منابع انرژی نو در نظر گرفته نشده است که این امر باعث می‌شود نتایج حاصله با این فرض دارای دقت کافی نباشند و به دلیل عدم بررسی دقیق، معمولاً در این طراحی‌ها، مقادیر به‌دست‌آمده بیش از مقدار موردنیاز می‌باشند که این امر موجب می‌گردد هزینه سیستم بیشتر شود. نتایج شبیه‌سازی شاخص ایجادشده را نشان می‌دهد. باید توجه داشت که شبیه‌سازی‌های مونت‌کارلو نتایج قطعی تولید نمی‌کنند، بلکه نتایجی که این شبیه‌سازی‌ها فراهم می‌کنند، همراه با توزیع‌های احتمالی می‌باشد. این روش پیشنهادی از این ویژگی استفاده می‌کند تا یکی از نواقص اساسی مدل‌های توضیح داده‌شده در مقالات قبلی، برطرف شود (اغلب این مقالات تنها به اثرات PHEVs بدون در نظر گرفتن احتمالات رخ دادن چنین اثراتی پرداخته‌اند). تحقیق انجام شده در این مقاله، عملکرد دستگاه‌های قدرت را تحت نفوذ بالای PHEVs و الکتریسیته فتوولتاییک تحلیل می‌کند و روش‌های جدیدی را برای هموار ساختن یکپارچه‌سازی این دو تکنولوژی در شبکه‌های کنونی ارائه می‌کند.
Today, most of the electrical energy needed by countries is provided by using fossil fuels. Due to the fact that fossil energies are non-renewable and will bring environmental pollution, it is necessary to use renewable sources to provide electricity. In many studies conducted so far in this field, the random behavior of the load and new energy sources have not been taken into account, which makes the results obtained with this assumption not have enough accuracy, and due to the lack of careful examination, usually in these designs, the values The obtained are more than the required amount, which causes the cost of the system to increase. It shows the simulation results of the created index. It should be noted that Monte Carlo simulations do not produce definitive results, but the results that these simulations provide are associated with probability distributions. This proposed method uses this feature to overcome one of the basic shortcomings of the models described in previous papers (most of these papers only dealt with the effects of PHEVs without considering the probability of such effects occurring). The research carried out in this paper analyzes the performance of power devices under the high penetration of PHEVs and photovoltaic electricity and presents new methods to facilitate the integration of these two technologies in current networks.
1) W. A. Omran, M. Kazerani, and M. M. A. Salama, "A Clustering-Based Method for Quantifying the Effects of Large On-Grid PV Systems," Power Delivery, IEEE Transactions on, vol. 25, pp. 2617-2625, 2010.
2) B. H. Chowdhury, "Effect of central station photovoltaic plant on power system security," in Photovoltaic Specialists Conference, 1990., Conference Record of the Twenty First IEEE, 1990, pp. 831-835.
3) F. A. Viawan, F. Vuinovich, and A. Sannino, "Probabilistic approach to the design of photovoltaic distributed generation in low voltage feeder," in Probabilistic Methods Applied to Power Systems, 2006. (PMAPS 2006). International Conference on, 2006, pp. 1-7.
4) [4] I. Abouzahr and R. Ramakumar, "An approach to assess the performance of utility-interactive photovoltaic systems," IEEE Transactions on Energy Conversion, vol. 8, pp. 145-153, 1993.
5) L. Shaobo, H. Minxiao, F. Ruixiang, and H. Xiaodong, "Configuration of energy storage system for distribution network with high penetration of PV," in Renewable Power Generation (RPG 2011), IET Conference on, 2011, pp. 1-6.
6) R. Xu and D. C. Wunsch, Clustering: Wiley-IEEE Press, 2009.
7) E. I. Ortiz-Rivera and F. Peng, "Analytical model for a photovoltaic module using the electrical characteristics provided by the manufacturer data sheet," in Power Electronics Specialists Conference, 2005. (PESC '05). IEEE 36th, 2005, pp. 2087-2091.
8) H. S. Rauschenbach, Solar cell array design handbook: The principles and technology of photovoltaic energy conversion: Van Nostrand Reinhold Ltd., 1980.
9) [9] S. Liu and R. A. Dougal, "Dynamic multiphysics model for solar array," Energy Conversion, IEEE Transactions on, vol. 17, pp. 285-294, 2002.
10) J. Bishop, "Computer simulation of the effects of electrical mismatches in photovoltaic cell interconnection circuits," Solar cells, vol. 25, pp. 73-89, 1988.
11) [] M. Wolf and H. Rauschenbach, "Series resistance effects on solar cell measurements," Advanced energy conversion, vol. 3, pp. 455-479, 1963.
12) D. Picault, B. Raison, S. Bacha, J. De La Casa, and J. Aguilera, "Forecasting photovoltaic array power production subject to mismatch losses," Solar energy, vol. 84, pp. 1301-1309, 2010.
13) M. Veerachary, "PSIM circuit-oriented simulator model for the nonlinear photovoltaic sources," IEEE transactions on aerospace and electronic systems, vol. 42, pp. 735-740, 2006.
14) W. Xiao, W. G. Dunford, and A. Capel, "A novel modeling method for photovoltaic cells," in Power Electronics Specialists Conference, 2004. (PESC 04). 2004 IEEE 35th Annual, 2004, pp. 1950-1956.
15) M. Ropp, M. Begovic, and A. Rohatgi, "Determination of the curvature derating factor for the Georgia Tech Aquatic Center photovoltaic array," in Photovoltaic Specialists Conference, 1997., Conference Record of the Twenty-Sixth IEEE, 1997, pp. 1297-1300.
16) J. V. Paatero and P. D. Lund, "Effects of large-scale photovoltaic power integration on electricity distribution networks," Renewable Energy, vol. 32, pp. 216-234, 2007.
17) W. A. Omran, M. Kazerani, and M. M. A. Salama, "A study of the impacts of power fluctuations generated from large PV systems," in Sustainable Alternative Energy (SAE), 2009 IEEE PES/IAS Conference on, 2009, pp. 1-6.
18) R. Perez, J. Doty, B. Bailey, and R. Stewart, "Experimental evaluation of a photovoltaic simulation program," Solar energy, vol. 52, pp. 359-365, 1994.
19) T. Ishikawa, "Grid-connected photovoltaic power systems: survey of inverter and related protection equipments," International Energy Agency, 2002.
20) F. Loxsom and P. Durongkaveroj, "Estimating the performance of a photovoltaic pumping system," Solar energy, vol. 52, pp. 215-219, 1994.
21) L. I. Smith, "A tutorial on principal components analysis," Cornell University, USA, vol. 51, p. 52, 2002.
22) S. Wold, K. Esbensen, and P. Geladi, "Principal component analysis," Chemometrics and intelligent laboratory systems, vol. 2, pp. 37-52, 1987.
23) H. Abdi and L. J. Williams, "Principal component analysis," Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, pp. 433-459, 2010.
24) K. Ferenc, L. Csaba, and B. Attila, "Cluster validity measurement techniques," in 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, Madrid, Spain, 2005, pp. 388-393.
25) J. C. Dunn, "A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters," Journal of Cybernetics, vol. 3, pp. 32-57, January 1973.
26) D. L. Davies and D. W. Bouldin, "A cluster separation measure," Pattern Analysis and Machine Intelligence, IEEE Transactions on, pp. 224-227, 1979.
27) S. Conti and S. Raiti, "Probabilistic load flow for distribution networks with photovoltaic generators Part 1: Theoretical concepts and models," in Clean Electrical Power, 2007. (ICCEP '07). International Conference on, 2007, pp. 132-136.
28) M. Shukla and G. Radman, "Optimal power flow using probabilistic load model," in System Theory, 2005. (SSST '05). Proceedings of the Thirty-Seventh Southeastern Symposium on, 2005, pp. 439-442.
29) Z. Tianshu, S. Wanxing, S. Xiaohui, M. Xiaoli, and S. Changkai, "Probabilistic modelling and simulation of stochastic load for power system studies," in Computer Modelling and Simulation (UKSim), 2013 UKSim 15th International Conference on, 2013, pp. 519-524.
30) P. Chen, B. Bak-Jensen, and Z. Chen, "Probabilistic load models for simulating the impact of load management," in Power & Energy Society General Meeting, 2009. IEEE, 2009, pp. 1-8.
31) ] S. W. Heunis and R. Herman, "A probabilistic model for residential consumer loads," Power Systems, IEEE Transactions on, vol. 17, pp. 621-625, 2002.
32) Z. Darabi and M. Ferdowsi, "Aggregated impact of plug-in hybrid electric vehicles on electricity demand profile," Sustainable Energy, IEEE Transactions on, vol. 2, pp. 501-508, 2011.
33) (May 2014). About NHTS transferability statistics Available: http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/subject_areas/national_household_travel_survey/about
