access icon free Emission mitigation with short-term wind statistics

© The Institution of Engineering and Technology
Received 21/10/2012, Accepted 07/10/2013, Revised 24/07/2013, Published 19/12/2013

In electricity generation, one of the direct benefits of exploiting wind energy is reducing the atmospheric emissions due to using conventional fuels. An emission mitigation model for oxides of nitrogen has been developed with short-term wind statistics, based on the Beta distribution. The probability infeasibility of random wind behaviour is taken into account. An optimisation model, based on economic dispatch models, is developed to minimise the impact caused by the oxides of nitrogen emissions.

Inspec keywords: optimisation; power generation economics; air pollution control; wind power; random processes; power generation dispatch; statistical distributions; nitrogen compounds

Other keywords: electricity generation; Beta distribution; wind statistics; NOx; probability; emission mitigation model; random wind behaviour; economic dispatch model; wind energy; optimisation model; atmospheric emission reduction

Subjects: Other topics in statistics; Wind power plants; Energy resources; Power system management, operation and economics; Optimisation techniques; Pollution detection and control

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
      • 1. National Energy Technology Laboratory: ‘A vision for the modern grid’, 2007, http://www.netl.doe.gov/moderngrid/opportunity/vision.html.
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • 23. Kantar, Y.M., Usta, I.: ‘Analysis of wind speed distribution’, Energy Convers. Manage., 2008, 49, pp. 962–973 (doi: 10.1016/j.enconman.2007.10.008).
    23. 23)
      • 9. Masters, G.M.: ‘Renewable and efficient electric power systems’ (Wiley, 2004).
    24. 24)
      • 26. Fabbri, A., Roman, T.G.S., Abbad, J.R., Quezada, V.H.M.: ‘Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market’, IEEE Trans. Power Syst., 2005, 20, (3), pp. 1440–1446 (doi: 10.1109/TPWRS.2005.852148).
    25. 25)
      • 1. National Energy Technology Laboratory: ‘A vision for the modern grid’, 2007, http://www.netl.doe.gov/moderngrid/opportunity/vision.html.
    26. 26)
      • 17. Carta, J.A., Ramírez, P., Velázquez, S.: ‘A review of wind speed probability distributions used in wind energy analysis: case studies in the Canary Islands’, Renew. Sustain. Energy Rev., 2009, 13, (5), pp. 933–955 (doi: 10.1016/j.rser.2008.05.005).
    27. 27)
      • 13. Liu, X., Xu, W.: ‘Economic load dispatch constrained by wind power availability: a here-and-now approach’, IEEE Trans. Sustain. Energy, 2010, 1, (1), pp. 2–9 (doi: 10.1109/TSTE.2010.2044817).
    28. 28)
      • 21. Chiodo, E., Lauria, D.: ‘Analytical study of different probability distributions for wind speed related to power statistics’. Proc. Int. Conf. Clean Electrical Power Renewable Energy Resources Impact, Capri, Italy, June 2009, pp. 733–738.
    29. 29)
      • 12. Hetzer, J., Yu, D.C., Bhattarai, K.: ‘An economic dispatch model incorporating wind power’, IEEE Trans. Energy Convers., 2008, 23, (2), pp. 603–611 (doi: 10.1109/TEC.2007.914171).
    30. 30)
      • 30. Bludszuweit, H.: Personal Communications.
    31. 31)
      • 27. Johnson, N.L., Kotz, S., Balakrishnan, N.: ‘Continuous univariate distributions’ (Wiley, 1995), vols. 1 and 2.
    32. 32)
      • 5. Das, D.B., Patvardhan, C.: ‘New multi-objective stochastic search technique for economic load dispatch’, Proc. IEE Gener. Transm. Distrib., 1998, 145, (6), pp. 747–752 (doi: 10.1049/ip-gtd:19982367).
    33. 33)
      • 29. Allan, R., Billinton, R.: ‘Probabilistic assessment of power systems’, Proc. IEEE, 2000, 88, (2), pp. 140–162 (doi: 10.1109/5.823995).
    34. 34)
      • 10. Chowdhury, B.H., Rahman, S.: ‘A review of recent advances in economic dispatch’, IEEE Trans. Power Syst., 1990, 5, (4), pp. 1248–1259 (doi: 10.1109/59.99376).
    35. 35)
      • 16. Saadat, H.: ‘Power system analysis’ (McGraw-Hill, 1999).
    36. 36)
      • 6. Gent, M.R., Lamont, J.W.: ‘Minimum-emission dispatch’, IEEE Trans. Power Appar. Syst., 1971, 90, (6), pp. 2650–2660 (doi: 10.1109/TPAS.1971.292918).
    37. 37)
      • 15. Bludszuweit, H., Dominguez-Navarro, J.A., Llombart, A.: ‘Statistical analysis of wind power forecast error’, IEEE Trans. Power Syst., 2008, 23, (3), pp. 983–991 (doi: 10.1109/TPWRS.2008.922526).
    38. 38)
      • 20. Atwa, Y.M., El-Saadany, E.F.: ‘Annual wind speed estimation utilizing constrained Grey predictor’, IEEE Trans. Energy Convers., 2009, 24, (2), pp. 548–550 (doi: 10.1109/TEC.2009.2015973).
    39. 39)
      • 3. Denny, E., O'Malley, M.: ‘Wind generation, power system operation, and emissions reduction’, IEEE Trans. Power Syst., 2006, 21, (1), pp. 341–347 (doi: 10.1109/TPWRS.2005.857845).
    40. 40)
      • 7. Talaq, J.H., El-Hawary, F., El-Hawary, M.E.: ‘Minimum emissions power flow’, IEEE Trans. Power Syst., 1994, 9, (1), pp. 429–435 (doi: 10.1109/59.317581).
    41. 41)
      • 19. Lange, P.M.: ‘On the uncertainty of wind power predictions – analysis of the forecast accuracy and statistical distributions of errors’, J. Solar Energy Eng., 2005, 127, pp. 177–184 (doi: 10.1115/1.1862266).
    42. 42)
      • 2. Sirikum, J., Techanitisawad, A., Kachitvichyanukul, V.: ‘A new efficient GA-benders’ decomposition method: for power generation expansion planning with emission controls’, IEEE Trans. Power Syst., 2007, 22, (3), pp. 1092–1100 (doi: 10.1109/TPWRS.2007.901092).
    43. 43)
      • 22. Cheng, H., Hou, Y., Wu, F.: ‘Probabilistic wind power generation model: derivation and applications’, Int. J. Energy, 2011, 5, (2), pp. 17–26.
    44. 44)
      • 8. Yokoyama, R., Bae, S.H., Morita, T., Sasaki, H.: ‘Multiobjective generation dispatch based on probability security criteria’, IEEE Trans. Power Syst., 1988, 3, pp. 317–324 (doi: 10.1109/59.43217).
    45. 45)
      • 28. Birge, J.R., Louveaux, F.: ‘Introduction to stochastic programming’ (Springer, New York, 1997).
    46. 46)
      • 25. Bofinger, S., Luig, A., Beyer, H.G.: ‘Qualification of wind power forecasts’. Proc. 2002 Global Wind Power Conf., 2002.
    47. 47)
      • 24. Villanueva, D., Feijoo, A.: ‘Wind power distributions: a review of their applications’, Renew. Sustain. Energy Rev., 2010, 14, pp. 1490–1495 (doi: 10.1016/j.rser.2010.01.005).
    48. 48)
      • 11. Padhy, N.P.: ‘Unit commitment – a bibliographical survey’, IEEE Trans. Power Syst., 2004, 19, (2), pp. 1196–1205 (doi: 10.1109/TPWRS.2003.821611).
    49. 49)
      • 14. Liu, X., Xu, W.: ‘Minimum emission dispatch constrained by stochastic wind power availability and cost’, IEEE Trans. Power Syst., 2010, 25, (3), pp. 1705–1713 (doi: 10.1109/TPWRS.2010.2042085).
    50. 50)
      • 18. Yu, Z., Tuzuner, A.: ‘Fractional Weibull wind speed modeling for wind power production estimation’. Proc. IEEE Power and Energy Society General Meeting, July 2009.
    51. 51)
      • 4. Abido, M.A.: ‘Environmental/economic power dispatch using multiobjective evolutionary algorithms’, IEEE Trans. Power Syst., 2003, 18, (4), pp. 1529–1537 (doi: 10.1109/TPWRS.2003.818693).
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