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

Active power dispatch strategy of the wind farm based on improved multi-agent consistency algorithm

Active power dispatch strategy of the wind farm based on improved multi-agent consistency algorithm

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Renewable Power Generation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

With the increase of wind power penetration in the power system, wind farm (WF) needs to limit active power and accurately track the instructions from the dispatch centre. Since a WF has many distributed wind turbines (WTs), it is a crucial issue to reasonably distribute power reference values to WTs. In this study, a novel active power dispatch (APD) strategy based on dynamic grouping of WTs is proposed. This strategy considers the characteristics and operating conditions of WTs, which can smoothen the power reference values to WTs and reduce fluctuations of key parameters of WTs. Then, a distributed dispatch strategy based on multi-agent system consistency algorithm (MASCA) is applied for APD, which provides a dispatch strategy for WTs that does not require a centralised control centre. And the segmental virtual consistency algorithm is presented as an improvement of MASCA, which innovatively allows MASCA to support the grouping strategy for APD. Finally, the simulations show that the proposed strategy can enable WTs to obtain smoother reference power to track the dispatching instruction while reducing fluctuations of rotor speed and pitch angle, which is helpful to alleviate the fatigue of WTs. The dispatch strategy also shows good robustness when some communication is interrupted.

References

    1. 1)
      • 18. Zhang, Z., Chow, M.Y.: ‘Convergence analysis of the incremental cost consensus algorithm under different communication network topologies in a smart grid’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 17611768.
    2. 2)
      • 21. Zhang, W., Xu, Y., Liu, W., et al: ‘Fully distributed coordination of multiple DFIGs in a microgrid for load sharing’, IEEE Trans. Smart Grid, 2013, 4, (2), pp. 806815.
    3. 3)
      • 2. Xiao, C.: ‘European and American wind power development experience and enlightenment’ (China Electric Power Press, Beijing, 2010).
    4. 4)
      • 8. Merahi, F., Berkouk, E.M., Mekhilef, S.: ‘New management structure of active and reactive power of a large wind farm based on multilevel converter’, Renew. Energy, 2014, 68, pp. 814828.
    5. 5)
      • 7. Tarca, S., Roughan, M., Ertugrul, N., et al: ‘Dispatchability of wind power with battery energy storage in south Australia’, Energy Proc., 2017, 110, pp. 223228.
    6. 6)
      • 6. Jin, J., Zhou, D., Zhou, P., et al: ‘Dispatching strategies for coordinating environmental awareness and risk perception in wind power integrated system’, Energy, 2016, 106, pp. 453463.
    7. 7)
      • 29. Olfati-Saber, R., Fax, J.A., Murray, R.M.: ‘Consensus and cooperation in networked multi-agent systems’, Proc. IEEE, 2007, 95, (1), pp. 215233.
    8. 8)
      • 9. Ebrahimi, F.M., Khayatiyan, A., Farjah, E.: ‘A novel optimizing power control strategy for centralized wind farm control system’, Renew. Energy, 2015, 86, pp. 399408.
    9. 9)
      • 11. Zhao, H., Wu, Q., Huang, S., et al: ‘Fatigue load sensitivity-based optimal active power dispatch for wind farms’, IEEE Trans. Sustain. Energy, 2017, 8, (3), pp. 12471259.
    10. 10)
      • 24. Fernando, D.B., Hernán, B., Ricardo, J.M.: ‘Wind turbine control systems: principles, modelling and gain dispatch design (advances in industrial control)’, 2006.
    11. 11)
      • 14. Zhang, J., Liu, Y., Infield, D., et al: ‘Optimal power dispatch within wind farm based on two approaches to wind turbine classification’, Renew. Energy, 2017, 102, pp. 487501.
    12. 12)
      • 30. Valldecabres, L., Nygaard, N.G., von Bremen, L., et al: ‘Very short-term probabilistic forecasting of wind power based on dual-Doppler radar measurements in the north sea’, J. Phys., Conf. Ser., 2018, 1037, (5), p. 052010.
    13. 13)
      • 3. Liu, J.Z., Liu, Y., Zeng, D.L., et al: ‘Optimal short-term load dispatch strategy in wind farm’, Sci China Tech. Sci., 2012, 55, (4), pp. 11401145.
    14. 14)
      • 15. Knudsen, T., Bak, T., Svenstrup, M.: ‘Survey of wind farm control – power and fatigue optimization’, Wind Energy, 2015, 18, (8), pp. 13331351.
    15. 15)
      • 19. Pengpeng, L., Zhao, J., Duanchao, L.I., et al: ‘A consensus-based collaborative algorithm for real time dispatch of island microgrid in cyber physical system’, Proc. CSEE, 2016, 36, (6), pp. 14711480.
    16. 16)
      • 25. Chichester, S.H.: ‘Grid integration of wind energy conversion systems’ (Wiley, New York, NY, USA, 1999), 385p.
    17. 17)
      • 22. Wang, L., Wen, J., Cai, M., et al: ‘Distributed optimization control schemes applied on offshore wind farm active power regulation’, Energy Proc., 2017, 105, pp. 11901198.
    18. 18)
      • 12. Zhang, J.H., Liu, Y.Q., Tian, D., et al: ‘Optimal power dispatch in wind farm based on reduced blade damage and generator losses’, Renew. Sustain. Energy Rev., 2015, 44, (44), pp. 6477.
    19. 19)
      • 16. Madjidian, D., Rantzer, A.: ‘A distributed coordination scheme for fatigue load minimization in wind farms’. American Control Conf. 2011, San Francisco, California, USA, 2011, pp. 52195224.
    20. 20)
      • 4. Zhao, X., Liu, S., Yan, F., et al: ‘Energy conservation, environmental and economic value of the wind power priority dispatch in China’, Renew. Energy, 2017, 111, pp. 666675.
    21. 21)
      • 20. Xu, Y., Li, Z.: ‘Distributed optimal resource management based on the consensus algorithm in a microgrid’, IEEE Trans. Ind. Electron., 2015, 62, (4), pp. 25842592.
    22. 22)
      • 17. Knudsen, T., Bak, T., Soltani, M.: ‘Distributed control of large-scale offshore wind farms’. European Wind Energy Conf. and Exhibition (EWEC) 2009. European Wind Energy Association (EWEA), Parc Chanot, Marseille, France, 16–19 March 2009.
    23. 23)
      • 28. Horn, R.A., Johnson, C.R.: ‘Matrix analysis’ (Cambridge University Press, Cambridge, UK, 1985).
    24. 24)
      • 5. Chen, J., Wu, W., Zhang, B., et al: ‘An active power real-time control method for large power grid under wind power curtailment’, Proc. CSEE, 2012, 32, (28), pp. 16.
    25. 25)
      • 31. Hong, D.Y., Ji, T.Y., Li, M.S., et al: ‘Ultra-short-term forecast of wind speed and wind power based on morphological high frequency filter and double similarity search algorithm’, Int. J. Electr. Power Energy Syst., 2019, 104, pp. 868879.
    26. 26)
      • 10. Xu, J., Yi, X., Sun, Y., et al: ‘Stochastic optimal scheduling based on scenario analysis for wind farms’, IEEE Trans. Sustain. Energy, 2017, 8, (4), pp. 15481559.
    27. 27)
      • 27. Olfati-Saber, R., Murray, R.M.: ‘Consensus problems in networks of agents with switching topology and time-delays’, IEEE Trans. Autom. Control, 2004, 49, (9), pp. 15201533.
    28. 28)
      • 13. Zhang, B., Soltani, M., Hu, W., et al: ‘Optimized power dispatch in wind farms for power maximizing considering fatigue loads’, IEEE Trans. Sustain. Energy, 2018, 9, (2), pp. 862871.
    29. 29)
      • 26. Chen, N., Wang, Q., Tang, Y., et al: ‘An optimal active power control method of wind farm using power prediction information’. 2012 IEEE Int. Conf. on Power System Technology (POWERCON), New Zealand, 2012, pp. 15.
    30. 30)
      • 1. World Wind Energy Association (WWEA): ‘2014 half-year report’. Available at http://www.wwindea.org/webimages/WWEA_half_year_report_2014.pdf, accessed 28 February 2015.
    31. 31)
      • 23. Baros, S., Ilić, M.D.: ‘Distributed torque control of deloaded wind DFIGs for wind farm power output regulation’, IEEE Trans. Power Syst., 2017, 32, (6), pp. 45904599.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2019.0900
Loading

Related content

content/journals/10.1049/iet-rpg.2019.0900
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address