Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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A Comprehensive Model for Wind Power Forecast Error and its Application in Economic Analysis of Energy Storage Systems

  • Huang, Yu (School of Electrical Engineering, Southeast University) ;
  • Xu, Qingshan (School of Electrical Engineering, Southeast University) ;
  • Jiang, Xianqiang (School of Electrical Engineering, Southeast University) ;
  • Zhang, Tong (Electric Power Research Institute of State Grid Jiangsu Electric Power Company) ;
  • Liu, Jiankun (Electric Power Research Institute of State Grid Jiangsu Electric Power Company)
  • Received : 2017.08.04
  • Accepted : 2018.06.19
  • Published : 2018.11.01
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Abstract

The unavoidable forecast error of wind power is one of the biggest obstacles for wind farms to participate in day-ahead electricity market. To mitigate the deviation from forecast, installation of energy storage system (ESS) is considered. An accurate model of wind power forecast error is fundamental for ESS sizing. However, previous study shows that the error distribution has variable kurtosis and fat tails, and insufficient measurement data of wind farms would add to the difficulty of modeling. This paper presents a comprehensive way that makes the use of mixed skewness model (MSM) and copula theory to give a better approximation for the distribution of forecast error, and it remains valid even if the dataset is not so well documented. The model is then used to optimize the ESS power and capacity aiming to pay the minimal extra cost. Results show the effectiveness of the new model for finding the optimal size of ESS and increasing the economic benefit.

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References

  1. Hossain M J, Pota H R, Mahmud M A, et al. "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. https://doi.org/10.1109/JSYST.2011.2162991
  2. Nielsen T S, Joensen A, Madsen H, et al. "A new reference for wind power forecasting," Wind energy, vol. 1, no. 1, pp. 29-34, 1998. https://doi.org/10.1002/(SICI)1099-1824(199809)1:1<29::AID-WE10>3.0.CO;2-B
  3. Xu Q, He D, Zhang N, et al. "A Short-Term Wind Power Forecasting Approach With Adjustment of Numerical Weather Prediction Input by Data Mining," IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp. 1283-1291, 2015. https://doi.org/10.1109/TSTE.2015.2429586
  4. T. Ackermann. "Wind power in power systems", UK: John Wiley, 2005.
  5. Bludszuweit H, Dominguez-Navarro J A, Llombart A. "Statistical analysis of wind power forecast error," IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 983-991, 2008. https://doi.org/10.1109/TPWRS.2008.922526
  6. Atwa Y M, El-Saadany E F. "Optimal allocation of ESS in distribution systems with a high penetration of wind energy," IEEE Transactions on Power Systems, vol. 25, no. 4, pp. 1815-1822, 2010. https://doi.org/10.1109/TPWRS.2010.2045663
  7. Aalami H A, Nojavan S. "Energy storage system and demand response program effects on stochastic energy procurement of large consumers considering renewable generation," IET Generation, Transmission & Distribution, vol. 10, no. 1, pp. 107-114, 2016. https://doi.org/10.1049/iet-gtd.2015.0473
  8. Baziar A, Kavousi-Fard A. "Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices," Renewable Energy, vol. 59, pp. 158-166, 2013. https://doi.org/10.1016/j.renene.2013.03.026
  9. Lee, T. Y. "Optimal spinning reserve for a windthermal power system using EIPSO," IEEE Transactions on Power Systems, vol. 22, no. 4, pp. 1612-1621, 2007. https://doi.org/10.1109/TPWRS.2007.907519
  10. Pascual J, Barricarte J, Sanchis P, et al. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, vol. 158, pp. 12-25, 2015. https://doi.org/10.1016/j.apenergy.2015.08.040
  11. Bludszuweit H, Dominguez-Navarro J A. "A probabilistic method for energy storage sizing based on wind power forecast uncertainty," IEEE Transactions on Power Systems, vol. 26, no. 3, pp. 1651-1658, 2011. https://doi.org/10.1109/TPWRS.2010.2089541
  12. Elliott G, Timmermann A. "Optimal forecast combinations under general loss functions and forecast error distributions," Journal of Econometrics, 2004, 122(1): 47-79. https://doi.org/10.1016/j.jeconom.2003.10.019
  13. Chen N, Qian Z, Nabney I T, et al. "Wind power forecasts using Gaussian processes and numerical weather prediction," IEEE Transactions on Power Systems, vol. 29, no. 2, pp. 656-665, 2014. https://doi.org/10.1109/TPWRS.2013.2282366
  14. Taylor J W, McSharry P E, Buizza R. "Wind power density forecasting using ensemble predictions and time series models," IEEE Transactions on Energy Conversion, vol. 24, no. 3, pp. 775-782. https://doi.org/10.1109/TEC.2009.2025431
  15. Tewari S, Geyer C J, Mohan N. "A statistical model for wind power forecast error and its application to the estimation of penalties in liberalized markets," IEEE Transactions on Power Systems, vol. 26, no. 4, pp. 2031-2039, 2011. https://doi.org/10.1109/TPWRS.2011.2141159
  16. Hodge B M, Milligan M. "Wind power forecasting error distributions over multiple timescales," Power and Energy Society General Meeting, 2011 IEEE, pp. 1-8, 2011.
  17. Soman S S, Zareipour H, Malik O, et al. "A review of wind power and wind speed forecasting methods with different time horizons", North American Power Symposium (NAPS), 2010 IEEE, pp. 1-8, 2010.
  18. Gomez-Lazaro E, Bueso M C, Kessler M, et al. "Probability density function characterization for aggregated large-scale wind power based on Weibull mixtures," Energies, vol. 9, no. 2, pp. 2016, 9(2): 91. https://doi.org/10.3390/en9020091
  19. Fabbri A, Roman T G S, Abbad J R, et al. "Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market," IEEE Transactions on Power Systems, vol. 20, no. 3, pp. 1440-1446, 2005. https://doi.org/10.1109/TPWRS.2005.852148
  20. Lange M. "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. https://doi.org/10.1115/1.1862266
  21. Wu J, Zhang B, Li H, et al. "Statistical distribution for wind power forecast error and its application to determine optimal size of energy storage system," International Journal of Electrical Power & Energy Systems, vol. 55, pp. 100-107, 2014. https://doi.org/10.1016/j.ijepes.2013.09.003
  22. Wu J, Zhang B, Deng W, et al. "Application of costcvar model in determining optimal spinning reserve for wind power penetrated system," International Journal of Electrical Power & Energy Systems, vol. 66, pp. 110-115, 2015. https://doi.org/10.1016/j.ijepes.2014.10.051
  23. Liu G, Tomsovic K. "Quantifying spinning reserve in systems with significant wind power penetration," IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 2385-2393, 2012. https://doi.org/10.1109/TPWRS.2012.2207465
  24. Awad A S A, El-Fouly T H M, Salama M. "Optimal ESS allocation and load shedding for improving distribution system reliability," IEEE Transactions on Smart Grid, vol. 5, no. 5, pp. 2339-2349, 2014. https://doi.org/10.1109/TSG.2014.2316197
  25. Vilim M, Botterud A. "Wind power bidding in electricity markets with high wind penetration," Applied Energy, vol. 118, pp. 141-155, 2014. https://doi.org/10.1016/j.apenergy.2013.11.055
  26. Zhang Z S, Sun Y Z, Gao D W, et al. "A versatile probability distribution model for wind power forecast errors and its Gauapplication in economic dispatch," IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3114-3125, 2013. https://doi.org/10.1109/TPWRS.2013.2249596
  27. Vila J P, Schniter P. "Expectation-maximization Gaussian-mixture approximate message passing," IEEE Transactions on Signal Processing, vol. 61, no. 19, pp. 4658-4672, 2013. https://doi.org/10.1109/TSP.2013.2272287
  28. Yang X. "A higher-order Levenberg-Marquardt method for nonlinear equations," Applied Mathematics and Computation, vol. 219, no. 22, pp. 10682-10694, 2013. https://doi.org/10.1016/j.amc.2013.04.033
  29. Kurowicka D. "Dependence modeling: vine copula handbook," World Scientific, 2011.
  30. Sklar M. "Fonctions de repartition a n dimensions et leurs marges," Publ. Inst. Statist. vol. 8, pp. 229-231, 1959.
  31. Zakeri B, Syri S. "Electrical energy storage systems: A comparative life cycle cost analysis," Renewable and Sustainable Energy Reviews, vol. 42, pp. 569-596, 2015. https://doi.org/10.1016/j.rser.2014.10.011