Regional wind power forecasting model with NWP grid data optimized

Abstract

Unlike the traditional fossil energy, wind, as the clean renewable energy, can reduce the emission of the greenhouse gas. To take full advantage of the environmental benefits of wind energy, wind power forecasting has to be studied to overcome the troubles brought by the variable nature of wind. Power forecasting for regional wind farm groups is the problem that many power system operators care about. The high-dimensional feature sets with redundant information are frequently encountered when dealing with this problem. In this paper, two kinds of feature set construction methods are proposed which can achieve the proper feature set either by selecting the subsets or by transforming the original variables with specific combinations. The former method selects the subset according to the criterion of minimal-redundancy-maximal-relevance (mRMR), while the latter does so based on the method of principal component analysis (PCA). A locally weighted learning method is also proposed to utilize the processed feature set to produce the power forecast results. The proposed model is simple and easy to use with parameters optimized automatically. Finally, a case study of 28 wind farms in East China is provided to verify the effectiveness of the proposed method.

Graphical abstract

Keywords

regional wind power forecasting / feature set / minimal-redundancy-maximal-relevance (mRMR) / principal component analysis (PCA) / locally weighted learning model

References

[1] Xinhua Net. China-U.S. joint presidential statement on climate change. 2017-1-12

[2] Pinson P, Chevallier C, Kariniotakis G N. Trading wind generation from short-term probabilistic forecasts of wind power. IEEE Transactions on Power Systems, 2007, 22(3): 1148–1156

[3] Botterud A, Zhou Z, Wang J, Bessa R J, Keko H, Sumaili J, Miranda V. Wind power trading under uncertainty in LMP markets. IEEE Transactions on Power Systems, 2012, 27(2): 894–903

[4] González-Aparicio I, Zucker A. Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain. Applied Energy, 2015, 159: 334–349

[5] Makarov Y V, Etingov P V, Ma J, Huang Z, Subbarao K. Incorporating uncertainty of wind power generation forecast into power system operation, dispatch, and unit commitment procedures. IEEE Transactions on Sustainable Energy, 2011, 2(4): 433–442

[6] Botterud A, Zhou Z, Wang J, Sumaili J, Keko H, Mendes J, Bessa R J, Miranda V. Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: A case study of Illinois. IEEE Transactions on Sustainable Energy, 2013, 4(1): 250–261

[7] Bessa R J, Matos M A, Costa I C, Bremermann L, Franchin I G, Pestana R, Machado N, Waldl H, Wichmann C. Reserve setting and steady-state security assessment using wind power uncertainty forecast: a case study. IEEE Transactions on Sustainable Energy, 2012, 3(4): 827–836

[8] Menemenlis N, Huneault M, Robitaille A. Computation of dynamic operating balancing reserve for wind power integration for the time-horizon 1–48 hours. IEEE Transactions on Sustainable Energy, 2012, 3(4): 692–702

[9] Pinson P, Papaefthymiou G, Klöckl B, Verboomen J. Dynamic sizing of energy storage for hedging wind power forecast uncertainty. In: Proceedings of PESGM 2009. Alberta: IEEE, 2009, 1760–1768

[10] Bludszuweit H, Domínguez-Navarro J A. A probabilistic method for energy storage sizing based on wind power forecast uncertainty. IEEE Transactions on Power Systems, 2011, 26(3): 1651–1658 doi:10.1109/TPWRS.2010.2089541

[11] Global Wind Energy Council. Global wind report 2015. Brussels, Belgium, 2016, 9–10

[12] Focken U, Lange M, Mönnich K, Waldl H P, Beyer H G, Luig A. Short-term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects. Journal of Wind Engineering and Industrial Aerodynamics, 2002, 90(3): 231–246

[13] Monteiro C, Bessa R, Miranda V, Botterud A, Wang J, Conzelmann G, Porto I. Wind power forecasting: state-of-the-art 2009. Office of Scientific & Technical Information Technical Reports, 2009

[14] Lobo M G, Sanchez I. Regional wind power forecasting based on smoothing techniques, with application to the spanish peninsular system. IEEE Transactions on Power Systems, 2012, 27(4): 1990–1997

[15] Siebert N. Development of methods for regional wind power forecasting. Dissertation for the Doctoral Degree. Paris: ENSMP (École Nationale Supérieure des Mines de Paris), 2008, 127–189

[16] Davò F, Alessandrini S, Sperati S, Delle Monache L, Airoldi D, Vespucci M T. Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting. Solar Energy, 2016, 134: 327–338

[17] Li P, Guan X, Wu J. Aggregated wind power generation probabilistic forecasting based on particle filter. Energy Conversion and Management, 2015, 96: 579–587

[18] Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226–1238

[19] Genest C, Favre A. Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering, 2007, 12(4): 347–368

[20] Jolliffe I T. Principal Component Analysis. Berlin: Springer, 1986, 111–147

[21] Atkeson C G, Moore A W, Schaal S. Locally weighted learning. In: Aha D W. ed. Lazy Learning. Dordrecht: Springer Netherlands, 1997, 11–73

[22] Freidman J H, Bentley J L, Finkel R A. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software, 1977, 3(3): 209–226 doi:10.1145/355744.355745

[23] Lagarias J C, Wright M H, Wright P E, Reeds J A. Convergence properties of the Nelder-Mead simplex method in low dimensions. Siam Journal on Optimization A Publication of the Society for Industrial & Applied Mathematics, 1998, 9(1): 112–147

Acknowledgements

This project was supported by the National Natural Science Foundation of China (Grant No. 51477156) and Science & Technology Foundation of SGCC (No. NY7116021).

RIGHTS & PERMISSIONS

2017 Higher Education Press and Springer-Verlag Berlin Heidelberg