Time-periodic model of wind speed and its application in risk evaluation of wind-power-integrated power systems

Time-periodic model of wind speed and its application in risk evaluation of wind-power-integrated power systems

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Risk evaluation of wind-power-integrated power systems requires a distinctive wind speed modelling technique. Existing techniques of mid- and long-term risk assessment mainly focus on annual wind speed pattern rather than its monthly and daily patterns, which cannot reflect the time-varying characteristics of power system risk. This article proposes a time-periodic model of wind speed which incorporates two parts. The first part is the monthly wind speed patterns which can be represented as time-periodic functions, while the other one is the fluctuation in daily wind speeds that can be denoted as a random variable following a certain probability distribution. With this model, a risk evaluation procedure for wind-power-integrated power system is developed. Collected wind speed data from four representative sites in China are used to verify the proposed model. The application of the proposed wind model and risk assessment method is tested by calculating annual and monthly risk indices of a modified IEEE-RTS79 system. Results can provide references for power system planning, mid- and long-term dispatching, and monthly generation scheduling.


    1. 1)
      • 1. The Global Wind Energy Council (GWEC).: ‘Global wind report 2016 – Annual market update’, 2017. Available at
    2. 2)
      • 2. She, S., Li, Z., Cai, X.: ‘A multi-time scale wind speed modeling method for simulation of wind power generation’, Power Syst. Technol., 2013, 37, (9), pp. 25592565.
    3. 3)
      • 3. Shao, H., Deng, X., Cui, F.: ‘Short-term wind speed forecasting using the wavelet decomposition and AdaBoost technique in wind farm of east China’, IET Gener., Trans. Distrib., 2016, 10, (11), pp. 25852592.
    4. 4)
      • 4. Chang, G., Lu, H., Chang, Y., et al: ‘An improved neural network-based approach for short-term wind speed and power forecast’, Renew. Energy, 2017, 105, pp. 301311.
    5. 5)
      • 5. Ren, Y., Suganthan, P.N., Srikanth, N.A.: ‘Novel empirical mode decomposition with support vector regression for wind speed forecasting’, IEEE Trans. Neural Netw. Learn. Syst., 2016, 27, (8), pp. 17931798.
    6. 6)
      • 6. Zheng, D., Abinet, T.E., Zhang, J., et al: ‘Short-term wind power forecasting using a double-stage hierarchical ANFIS approach for energy management in microgrids’, Prot. Control Mod. Power Syst., 2017, 2, p. 13o–1–10.
    7. 7)
      • 7. Ma, X., Yu, J., Dong, Q.: ‘A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting’, Appl. Soft Comput., 2017, 54, pp. 296312.
    8. 8)
      • 8. Yunus, K., Thiringer, T., Chen, P.: ‘ARIMA-based frequency-decomposed modeling of wind speed time series’, IEEE Trans. Power Syst., 2016, 31, (4), pp. 25462556.
    9. 9)
      • 9. Xiu, C., Ren, X., Li, Y., et al: ‘Short-term prediction method of wind speed series based on kalman filtering fusion’, Trans. China Electrotech. Soc., 2014, 29, (2), pp. 253259.
    10. 10)
      • 10. Xie, K., Liao, Q., Tai, H.-M., et al: ‘Non-homogeneous markov wind speed time series model considering daily and seasonal variation characteristics’, IEEE Trans. Sustain. Energy, 2017, 8, (3), pp. 12811290.
    11. 11)
      • 11. Ozay, C., Celiktas, M.S.: ‘Statistical analysis of wind speed using two-parameter weibull distribution in alacati region’, Energy Convers. Manage., 2016, 121, pp. 4954.
    12. 12)
      • 12. Ouarda, T.J., Charron, C., Chebana, F.: ‘Review of criteria for the selection of probability distributions for wind speed data and introduction of the moment and L-moment ratio diagram methods, with a case study’, Energy Convers. Manage., 2016, 124, pp. 247265.
    13. 13)
      • 13. Wais, P.: ‘A review of weibull functions in wind sector’, Renew. Sustain. Energy Rev., 2017, 70, pp. 10991107.
    14. 14)
      • 14. Sun, C., Bie, Z., Xie, M., et al: ‘Effects of wind speed probabilistic and possibilistic uncertainties on generation system adequacy’, IET Gener., Trans. Distrib., 2015, 9, (4), pp. 339347.
    15. 15)
      • 15. George, S.O., George, H.B., Nguyen, S.V.: ‘Risk quantification associated with wind energy intermittency in california’, IEEE Trans. Power Syst., 2011, 26, (4), pp. 19371944.
    16. 16)
      • 16. Wang, X., Xie, S., Wang, X., et al: ‘Decision-making model based on conditional risks and conditional costs in power system probabilistic planning’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 40804088.
    17. 17)
      • 17. Lannoye, E., Flynn, D., O'Malley, M.: ‘Evaluation of power system flexibility’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 922931.
    18. 18)
      • 18. Carvalho, L.M., Rosa, M.D., Silva, A.D., et al: ‘Probabilistic analysis for maximizing the grid integration of wind power generation’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 23232331.
    19. 19)
      • 19. Thapa, S., Karki, R., Billinton, R.: ‘Utilization of the area risk concept for operational reliability evaluation of a wind-integrated power system’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 47714779.
    20. 20)
      • 20. Zhang, N., Kang, C., Xia, Q., et al: ‘A convex model of risk-based unit commitment for day-ahead market clearing considering wind power uncertainty’, IEEE Trans. Power Syst., 2015, 30, (3), pp. 15821592.
    21. 21)
      • 21. Zhao, C., Guan, Y.: ‘Data-driven stochastic unit commitment for integrating wind generation’, IEEE Trans. Power Syst., 2016, 31, (4), pp. 25872596.
    22. 22)
      • 22. Negnevitsky, M., Nguyen, D.H., Piekutowski, M.: ‘Risk assessment for power system operation planning with high wind power penetration’, IEEE Trans. Power Syst., 2015, 30, (3), pp. 13591368.
    23. 23)
      • 23. Liang, C., Wang, P., Han, X., et al: ‘Operational reliability and economics of power systems with considering frequency control processes’, IEEE Trans. Power Syst., 2017, 32, (4), pp. 25702580.
    24. 24)
      • 24. Deng, W., Ding, H., Zhang, B., et al: ‘Multi-period probabilistic-scenario risk assessment of power system in wind power uncertain environment’, IET Gener., Trans. Distrib., 2016, 10, (2), pp. 359365.
    25. 25)
      • 25. Fazio, A.D., Russo, M.: ‘Wind farm modelling for reliability assessment’, Renew. Power Gener., 2008, 2, (4), pp. 239248.
    26. 26)
      • 26. Wen, J., Zheng, Y., Donghan, F.: ‘A review on reliability assessment for wind power’, Renew. Sustain. Energy Rev., 2009, 13, (9), pp. 22492485.
    27. 27)
      • 27. Qin, Z., Li, W., Xiong, X.: ‘Incorporating multiple correlations among wind speeds, photovoltaic powers and bus loads in composite system reliability evaluation’, Appl. Energy, 2013, 110, pp. 285294.
    28. 28)
      • 28. Xu, M., Zhuan, X.: ‘Optimal planning for wind power capacity in an electric power system’, Renew. Energy, 2013, 53, pp. 280286.
    29. 29)
      • 29. Qin, Z., Li, W., Xiong, X.: ‘Generation system reliability evaluation incorporating correlations of wind speeds with different distributions’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 551558.
    30. 30)
      • 30. Feng, G., Hou, W., Zhi, R., et al: ‘Detection, diagnosis and predictability research of extreme climate events’ (Science Press, Beijing, 2012).
    31. 31)
      • 31. Wang, J., Xiong, X., Zhou, N., et al: ‘Time-varying failure rate simulation model of transmission lines and its application in power system risk assessment considering seasonal alternating meteorological disasters’, IET Gener., Trans. Distrib., 2016, 10, (7), pp. 15821588.
    32. 32)
      • 32. Wang, J., Xiong, X., Liang, Y., et al: ‘Geographical and meteorological factor related transmission line risk difference assessment: method and indexes’, Proc. CSEE, 2016, 36, (5), pp. 12521259.
    33. 33)
      • 33. Wangdee, W., Billinton, R.: ‘Considering load-carrying capability and wind speed correlation of WECS in generation adequacy assessment’, IEEE Trans. Energy Convers., 2006, 21, (3), pp. 734741.
    34. 34)
      • 34. Billinton, R., Li, W.: ‘Reliability assessment of electrical power systems using monte carlo methods’ (Plenum Press, New York, 1994).

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