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Wind Power Forecast Error Simulation Model

Authors: Josip Vasilj, Petar Sarajcev, Damir Jakus


One of the major difficulties introduced with wind power penetration is the inherent uncertainty in production originating from uncertain wind conditions. This uncertainty impacts many different aspects of power system operation, especially the balancing power requirements. For this reason, in power system development planing, it is necessary to evaluate the potential uncertainty in future wind power generation. For this purpose, simulation models are required, reproducing the performance of wind power forecasts. This paper presents a wind power forecast error simulation models which are based on the stochastic process simulation. Proposed models capture the most important statistical parameters recognized in wind power forecast error time series. Furthermore, two distinct models are presented based on data availability. First model uses wind speed measurements on potential or existing wind power plant locations, while the seconds model uses statistical distribution of wind speeds.

Keywords: wind power, Uncertainty, Stochastic Process, Monte Carlo simulation

Digital Object Identifier (DOI):

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[1] Y. Zhang and K. W. Chan, “The impact of wind forecasting in power system reliability,” in Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, April 2008, pp. 2781–2785.
[2] L. Soder, “Simulation of wind speed forecast errors for operation planning of multiarea power systems,” in International Conference on Probabilistic Methods Applied to Power Systems, Sept 2004, pp. 723–728.
[3] P. Haessig et al., “Energy storage sizing for wind power: impact of the autocorrelation of day-ahead forecast errors,” Wind Energy, pp. 1 – 18, 2013.
[4] X. Wang, P. Guo, and X. Huang, “A review of wind power forecasting models,” Energy Procedia, vol. 12, no. 0, pp. 770 – 778, 2011, the Proceedings of International Conference on Smart Grid and Clean Energy Technologies (ICSGCE 2011.
[5] F. Marzbani, A. Osman, M. Hassan, and T. Landolsi, “Short-term wind power forecast for economic dispatch,” in 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), April 2013, pp. 1–6.
[6] L. Landberg, “Short-term prediction of the power production from wind farms,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 80, no. 1, pp. 207 – 220, 1999.
[7] U. Focken et al., “Short-term prediction of the aggregated power output of wind farmsa statistical analysis of the reduction of the prediction error by spatial smoothing effects,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 90, no. 3, pp. 231 – 246, 2002.
[8] Y. Han and L. Chang, “A study of the reduction of the regional aggregated wind power forecast error by spatial smoothing effects in the maritime canada,” in 2nd IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), June 2010, pp. 942–947.
[9] M. Lange, “On the uncertainty of wind power predictions - analysis of the forecast accuracy and statistical distribution of errors,” Journal of Solar Energy Engineering, vol. 127, no. 2, pp. 177 – 184, 2005.
[10] X. Y. Ma, Y. Z. Sun, and H. L. Fang, “Scenario generation of wind power based on statistical uncertainty and variability,” IEEE Transactions on Sustainable Energy, vol. 4, no. 4, pp. 894–904, Oct 2013.
[11] H. Bludszuweit, J. A. Dominguez-Navarro, and A. Llombart, “Statistical analysis of wind power forecast error,” IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 983–991, Aug 2008.
[12] C. Cullen, Matrices and Linear Transformations: Second Edition, ser. Dover Books on Mathematics. Dover Publications, 2012.
[13] S. Kachigan, Multivariate Statistical Analysis: A Conceptual Introduction. Radius Press, 1991.