Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

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

  • Author(s): Jian Wang 1, 2 ; Xiaofu Xiong 1 ; Haoran Li 1 ; Xiaofeng Lu 2
    • View affiliations
  • Source: Volume 13, Issue 1, 08 January 2019, p. 46 – 54
    DOI:  10.1049/iet-gtd.2018.5619 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Received 01/02/2018, Accepted 28/09/2018, Revised 21/05/2018, Published 11/10/2018

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.

Inspec keywords: statistical distributions; wind power plants; power generation planning; power generation dispatch; risk management

Other keywords: power system planning; modified IEEE-RTS79 system; long-term dispatching; annual wind speed pattern; monthly generation scheduling; long-term risk assessment; time-periodic functions; mid-term risk assessment; China; mid-term dispatching; risk evaluation procedure; time-periodic model; monthly wind speed patterns; daily wind speeds; wind-power-integrated power systems; probability distribution; wind speed modelling technique

Subjects: Power system management, operation and economics; Wind power plants; Other topics in statistics; Power system planning and layout

References

    1. 1)
      • 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. 551–558.
    2. 2)
      • 25. Fazio, A.D., Russo, M.: ‘Wind farm modelling for reliability assessment’, Renew. Power Gener., 2008, 2, (4), pp. 239–248.
    3. 3)
      • 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. 296–312.
    4. 4)
      • 30. Feng, G., Hou, W., Zhi, R., et al: ‘Detection, diagnosis and predictability research of extreme climate events’ (Science Press, Beijing, 2012).
    5. 5)
      • 13. Wais, P.: ‘A review of weibull functions in wind sector’, Renew. Sustain. Energy Rev., 2017, 70, pp. 1099–1107.
    6. 6)
      • 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. 301–311.
    7. 7)
      • 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. 2546–2556.
    8. 8)
      • 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. 247–265.
    9. 9)
      • 26. Wen, J., Zheng, Y., Donghan, F.: ‘A review on reliability assessment for wind power’, Renew. Sustain. Energy Rev., 2009, 13, (9), pp. 2249–2485.
    10. 10)
      • 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. 1937–1944.
    11. 11)
      • 17. Lannoye, E., Flynn, D., O'Malley, M.: ‘Evaluation of power system flexibility’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 922–931.
    12. 12)
      • 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. 1359–1368.
    13. 13)
      • 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. 4080–4088.
    14. 14)
      • 21. Zhao, C., Guan, Y.: ‘Data-driven stochastic unit commitment for integrating wind generation’, IEEE Trans. Power Syst., 2016, 31, (4), pp. 2587–2596.
    15. 15)
      • 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. 339–347.
    16. 16)
      • 1. The Global Wind Energy Council (GWEC).: ‘Global wind report 2016 – Annual market update’, 2017. Available at http://gwec.net/publications/global-wind-report-2/global-wind-report-2016.
    17. 17)
      • 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. 1793–1798.
    18. 18)
      • 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. 734–741.
    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. 4771–4779.
    20. 20)
      • 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. 359–365.
    21. 21)
      • 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. 1582–1592.
    22. 22)
      • 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. 1281–1290.
    23. 23)
      • 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. 1582–1588.
    24. 24)
      • 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. 2585–2592.
    25. 25)
      • 28. Xu, M., Zhuan, X.: ‘Optimal planning for wind power capacity in an electric power system’, Renew. Energy, 2013, 53, pp. 280–286.
    26. 26)
      • 34. Billinton, R., Li, W.: ‘Reliability assessment of electrical power systems using monte carlo methods’ (Plenum Press, New York, 1994).
    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. 285–294.
    28. 28)
      • 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. 1252–1259.
    29. 29)
      • 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.
    30. 30)
      • 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. 2323–2331.
    31. 31)
      • 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. 49–54.
    32. 32)
      • 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. 253–259.
    33. 33)
      • 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. 2570–2580.
    34. 34)
      • 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. 2559–2565.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2018.5619
Loading

Related content

content/journals/10.1049/iet-gtd.2018.5619
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address