http://iet.metastore.ingenta.com
1887

access icon free Photovoltaic–wind joint power probability model based on multiple temporal and spatial scale

  • PDF
    2.115818977355957MB
  • XML
    76.947265625Kb
  • HTML
    83.4404296875Kb
Loading full text...

Full text loading...

/deliver/fulltext/iet-gtd/12/20/IET-GTD.2018.5491.html;jsessionid=dq0mqvml0djq.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fiet-gtd.2018.5491&mimeType=html&fmt=ahah

References

    1. 1)
      • 1. Nduka, O.S., Pal, B.C.: ‘Quantitative evaluation of actual loss reduction benefits of a renewable heavy DG distribution network’, IEEE Trans. Sustain. Energy, 2107, PP, (9), p. 1.
    2. 2)
      • 2. Ren, Z.Y., Yan, W., Zhao, X., et al: ‘Chronological probability model of photovoltaic generation’, IEEE Trans. Power Syst., 2014, 29, (3), pp. 10771088.
    3. 3)
      • 3. Wang, C.X., Lu, Z.X., Qiao, Y., et al: ‘Short-term wind power forecast based on non-parametric regression model’, Autom. Electr. Power Syst., 2010, 34, (16), pp. 7882.
    4. 4)
      • 4. Sun, C.S., Wang, Y.W., Li, X.R.: ‘A vector autoregression model of hourly wind speed and its applications in hourly wind speed forecasting’, Proc. CSEE, 2008, 28, (14), pp. 112117.
    5. 5)
      • 5. Zhou, S.L., Mao, M.Q., Su, J.H.: ‘Short-term forecasting of wind power and non-parametric confidence interval estimation’, Proc. CSEE, 2011, 31, (25), pp. 1016.
    6. 6)
      • 6. Zhou, F., Jin, L.S., Liu, J., et al: ‘Probabilistic wind power forecasting based on multi-state space and hybrid Markov chain models’, Autom. Electr. Power Syst., 2012, 36, (6), pp. 2933.
    7. 7)
      • 7. Feijoo, A., Villanueva, D.: ‘Four parameter models for wind farm power curves and power probability density functions’, IEEE Trans. Sustain. Energy, 2017, 8, (4), pp. 17831784.
    8. 8)
      • 8. Fan, G.F., Wang, W.S., Liu, C., et al: ‘Wind power prediction based on artificial neural network’, Proc. CSEE, 2008, 28, (34), pp. 118123.
    9. 9)
      • 9. Zhao, W.J., Zhang, N., Kang, C.Q., et al: ‘A method of probabilistic distribution estimation of conditional forecast error for photovoltaic power generation’, Autom. Electr. Power Syst., 2015, 39, (16), pp. 815.
    10. 10)
      • 10. Wu, W., Wang, K.Y., Han, B., et al: ‘A versatile probability model of photovoltaic generation using pair copula construction’, IEEE Trans. Sustain. Energy, 2015, 6, (4), pp. 13371345.
    11. 11)
      • 11. Soroudi, A., Aien, M., Ehsan, M.: ‘A probabilistic modeling of photovoltaic modules and wind power generation impact on distribution networks’, IEEE Syst. J., 2012, 6, (2), pp. 254259.
    12. 12)
      • 12. Yan, G.G., Wang, Z.H., Li, J.H., et al: ‘Research on output power fluctuation characteristics of the clustering photovoltaic–wind joint power generation system based on continuous output analysis’. IEEE Power System Technology Conf., Chengdu, China, October 2014, pp. 28522857.
    13. 13)
      • 13. Li, W., Bai, X.M., Dong, W.J.: ‘Probabilistic analysis of distribution network considering nonlinear correlation random variables’. IET Renewable Power Generation Conf., London, UK, p. 6.
    14. 14)
      • 14. Li, J.N., Qiao, Y., Lu, Z.X., et al: ‘Research on statistical modeling of large-scale wind farms output fluctuations in different special and temporal scales’, Power Syst. Prot. Control, 2012, 40, (19), pp. 713.
    15. 15)
      • 15. Ran, X.H., Miao, S.H., Liu, Y.S., et al: ‘Modeling of economic dispatch of power system considering joint effect of wind power, solar energy and load’, Proc. CSEE, 2014, 34, (16), pp. 25522560.
    16. 16)
      • 16. Tina, G.M., Gagliano, S.: ‘Probabilistic modelling of hybrid solar/wind power system with solar tracking system’, Renew. Energy, 2011, 36, (6), pp. 17191727.
    17. 17)
      • 17. Ran, X.H., Miao, S.H.: ‘Three-phase probabilistic load flow for power system with correlated wind, photovoltaic and load’, IET Gener. Transm. Distrib., 2016, 10, (12), pp. 30933101.
    18. 18)
      • 18. Aien, M., Biglari, A., Rashidinejad, M.: ‘Probabilistic reliability evaluation of hybrid wind-photovoltaic power systems’. 21st Iranian Conference on Electrical Engineering (ICEE), Mashhad, Iran, May 2013, pp. 16.
    19. 19)
      • 19. Ettoumi, F.Y., Sauvageot, H., Adane, A.E.H.: ‘Statistical bivariate modelling of wind using first-order Markov chain and Weibull distribution’, Renew. Energy, 2003, 28, (11), pp. 17871802.
    20. 20)
      • 20. Wu, L., Infield, D.G.: ‘Towards an assessment of power system frequency support from wind plant – modeling aggregate inertial response’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 22832291.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2018.5491
Loading

Related content

content/journals/10.1049/iet-gtd.2018.5491
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address