یک روش ترکیبی پیش بینی احتمالاتی بلندمدت بار خالص شبکه با در نظر گرفتن اثر توان تولید‌شده توسط منابع انرژی تجدیدپذیر در شبكه‌هاي هوشمند
الموضوعات :محسن جهان تیغ 1 , مجيد معظمي 2
1 - دانشگاه آزاد اسلامی واحد نجف آباد
2 - دانشگاه آزاد اسلامي واحد نجف آباد
الکلمات المفتاحية: پیش بینی احتمالاتی بلندمدت بار, تحلیل اجزای همسایگی, سیستم استنتاج عصبی- فازی, شبکه هوشمند, تولید بادی, تولید فوتوولتائیک,
ملخص المقالة :
امروزه با توجه به رشد گسترده و نفوذ استفاده از منابع توليد پراكنده در شبكه‌هاي هوشمند، پيش‌بيني بار خالص شبكه با در نظر گرفتن اثر توليدات پراكنده اهميت قابل توجهي پيدا كرده است. در اين مقاله يك روش بهينه‌سازي تركيبي به منظور پیش‌بینی احتمالاتي بلندمدت بار خالص شبكه با استفاده از روش تحلیل اجزای همسایگی و حل مسأله رگرسیون به روش mini-batch-LBFGS و ترکیب پیش‌بینی‌های به دست آمده با استفاده از سیستم استنتاج عصبی- فازی تطبیقی ارائه شده است. اين ساختار شامل تركيب چندين پيش‌بيني بلندمدت از جمله پیش‌بینی بار، توان يك ايستگاه خورشيدي و توان یک مزرعه بادی با توربینهای بادی مجهز به ژنراتور القایی دوسوتغذیه است. پیش‌بینی بار خالص و بررسی وابستگی موجود بین خطاهای پیش‌بینی بار و توانهای خورشیدی و بادی نیز در این مقاله مورد مطالعه قرار گرفته است. نتايج شبيهسازي روش پيشنهادي و مقایسه آن با مدلهای تائو و رگرسیون چندکی نشان می‌دهد که درصد میانگین مطلق خطا برای پیش‌بینی‌های بار و توانهای خروجی ایستگاه خورشیدی و مزرعه بادی به ترتیب به میزان 947/0%، 3079/0% و 0042/0% بهبود یافته است که کاهش خطای کلی پیش‌بینی را سبب می‌شود.
[1] J. Armstrong, Principles of Forecasting: A Handbook for Researchers and Practitioners, Springer; 2001.
[2] V. Genre, G. Kenny, A. Meyler, and A. Timmermann, "Combining expert forecasts: can anything beat the simple average?" Int J. Forecast, vol. 29, no. 1, pp. 108-121, Jan./Mar. 2013.
[3] K. Wallis, "Combining forecasts forty years later," Applied Financial Economics, vol. 21, no. 1-2, pp. 33-41, 2011.
[4] L. Rokach, "Ensemble-based classifiers," Artificial Intelligence Review, vol. 33, pp. 1-39, 2010.
[5] O. Abedinia, N. Amjady, and H. Zareipour, "A new feature selection technique for load and price forecast of electrical power systems," IEEE Trans. Power Syst, vol. 32, no. 1, pp. 62-74, Jan. 2017.
[6] G. Li, Y. Li, and F. Roozitalab, "Midterm load forcasting: a multistep approach based on phase space reconstruction and support vector machine," IEEE Systems Journal, vol. 14, no. 4, pp. 4967-4977, Dec. 2020.
[7] D. K. Ranaweera, G. G. Karady, and R. G. Farmer, "Effect of probabilistic inputs on neural network-based electric load forecasting," IEEE Trans. Neural Network, vol. 7, no. 6, pp. 1528-1532, Nov. 1996.
[8] W. Zhang, H. Quan, and D. Srinivasan, "An improved quantile regression neural network for probabilistic load forecasting," IEEE Trans. Smart Grid, vol. 10, no. 4, pp. 4425-4434, Jul. 2019.
[9] Z. Deng, B. Wang, Y. Xu, T. Xu, C. Liu, and Z. Zhu, "Multi-scale convolutional neural network with time-cognition for multi-step short-term load forecasting," IEEE Access, vol. 7, pp. 88058-88071, Jul. 2019.
[10] Z. Yu, Z. Niu, W. Tang, and Q. Wu, "Deep learning for daily peak load forecasting-a novel gated recurrent neural network combining dynamic time warping," IEEE Access, vol. 7, pp. 17184-17194, Jan. 2019.
[11] D. Niu, Y. Wang, and D. D. Wu, "Power load forecasting using support vector machine and ant colony optimization," Elsevier Expert Systems with Applications, vol. 37, no. 3, pp. 2531-2539, Mar. 2010.
[12] N. Amjady, F. Keynia, and H. Zareipour, "Short-term load forecast of microgrids by a new bilevel prediction strategy," IEEE Trans. Smart Grid, vol. 1, no. 3, pp. 286-294, Dec. 2010.
[13] N. Charlton and C. Singleton, "A refined parametric model for short term load forecasting," Int J. Forecast, vol. 30, no. 2, pp. 364-368, Apr./Jun. 2014.
[14] D. Fay and J. Ringwood, "On the influence of weather forecast errors in short-term load forecasting models," IEEE Trans. Power Syst, vol. 25, no. 3, pp. 1751-1758, Aug. 2010.
[15] J. W. Taylor and R. Buizza, "Neural network load forecasting with weather ensemble predictions," IEEE Trans. Power Syst, vol. 17, no. 3, pp. 626-632, Aug. 2002.
[16] T. Hong and P. Wang, "Fuzzy interaction regression for short term load forecasting," Fuzzy Optim. Decis. Making, vol. 13, no. 1, pp. 91-103, Mar. 2014.
[17] D. Saez, F. Avila, D. Olivares, C. Canizares, and L. Marin, "Fuzzy prediction interval models for forecasting renewable resources and loads in microgrids," IEEE Trans. Smart Grid, vol. 6, no. 2, pp. 548-556, Mar. 2015.
[18] Y. Semero, J. Zhang, and D. Zheng, "EMD-PSO-ANFIS-based hybrid approach for short-term load forcasting in microgrids," IET Generation, Transmission, and Distribution, vol. 14, no. 3, pp. 470-475, Feb. 2020.
[19] P. E. McSharry, S. Bouwman, and G. Bloemhof, "Probabilistic forecasts of the magnitude and timing of peak electricity demand," IEEE Trans. Power Syst, vol. 20, no. 2, pp. 1166-1172, May 2005.
[20] O. E. Dragomir, F. Dragomir, R. Gouriveau, and E. Minca, "Medium term load forecasting using ANFIS predictor," in Proc. 18th Mediterranean Conf. on Control and Automation, MED'10, pp. 23-25, Marrakech, Morocco, 23-25 Jun. 2010.
[21] B. Akdemir and N. Cetinkaya, "Long-term load forecasting based on adaptive neural fuzzy," Energy Procedia, vol. 14, pp. 794-799, 2012.
[22] B. Liu, J. Nowotarski, T. Hong, and R. Weron, "Probabilistic load forecasting via quantile regression averaging on sister forecasts," IEEE Trans. on Smart Grid, vol. 8, no. 2, pp. 730-737, Mar. 2017.
[23] J. Peng, S. Gao, and A. Ding, "Study of the short-term electric load forecast based on ANFIS," in Proc. 32nd Youth Academic Annual Conf. of Chinese Association of Automation, YAC’17, pp. 832-836, Hefei, China, 19-21 May 2017.
[24] I. Guler and E. D. Ubeyli, "Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients," J. of Neuroscience Methods, vol. 148, no. 2, pp. 113-121, 30 Oct. 2005.
[25] W. Yang, K. Wang, and W. Zuo, "Neighborhood component feature selection for high-dimensional data," J. of Computers, vol. 7, no. 1, pp. 161-168, Jan. 2012.
[26] R. Ross Jr, "Flat-plate photovoltaic array design optimization," in Proc. 14th Photovoltaic Specialists Conf., vol. 1, pp. 1126-1132, San Diego, CA, USA, 7-20 Jan. 1980.
[27] A. S. B. M. Shah, H. Yokoyama, and N. Kakimoto, "High-precision forecasting model of solar irradiance based on grid point value data analysis for an efficient photovoltaic system," IEEE Trans. Sustainable Energy, vol. 6, no. 2, pp. 474-481, Apr. 2015.
[28] N. Blair, et al., System Advisor Model, SAM 2014.1. 14: General Description, NREL Rep. No. TP-6A20-61019, Natl. Renew. Energy Lab. Golden, vol. 13, 2014.
[29] Y. T. Weng and Y. Y. Hsu, "Reactive power control strategy for a wind farm with DFIG," J. of Renewable Energy, vol. 94, pp. 383-390, Aug. 2016.
[30] J. Tian, C. Su, and Z. Chen, "Reactive power capability of the wind turbine with doubly fed induction generator," in Proc. IECON 39th Annual Conf. of the IEEE Industrial Electronics Society, pp. 5312-5317, Vienna, Austria, 10-13 Nov. 2013.
[31] J. G. Slootweg, S. W. H. de Haan, H. Polinder, and W. L. Kling, "General model for representing variable speed wind turbines in power system dynamics simulations," IEEE Trans. on Power System, vol. 18, no. 1, pp. 144-151, Feb. 2003.
