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Modelling and solutions of coordinated economic dispatch with wind–hydro–thermal complex power source structure

Modelling and solutions of coordinated economic dispatch with wind–hydro–thermal complex power source structure

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This study presents an efficient optimisation strategy for solving coordinated economic dispatch problem with wind–hydro–thermal complex power source structure. The wind–hydro–thermal coordinated dispatch aims to minimise the total fuel costs of coal-fired thermal power units while satisfying all kinds of operating constraints. To better handle the random variables in the constraints introduced by wind power and load demand during analysis, a probabilistic analytical model is employed to describe the uncertainty of wind farm power output first; moreover, then an improved convolution method is applied to calculate the total stochastic power consisting of load demand and power output of wind farm. The two-stage stochastic linear programming method and stochastic chance constraints are employed to further form a new deterministic objective function with penalty items taken into account. An enhanced particle swarm optimisation method is applied in the solution of the proposed model. Finally, the simulations are performed on a 6-bus test system and a real-sized China power grid test system to investigate the effectiveness and validity of the proposed optimisation strategy.

References

    1. 1)
      • 1. El-Fouly, T.H.M., Zeineldin, H.H., El-Saadany, E.F., et al: ‘Impact of wind generation control strategies, penetration level and installation location on electricity market prices’, IET Renew. Power Gener., 2008, 2, (3), pp. 162169.
    2. 2)
      • 2. Martin-Martinez, S., Gomez-Lazaro, E., Molina-Garcia, A., et al: ‘Impact of wind power curtailments on the Spanish power system operation’. IEEE PES General Meeting, Washington DC, USA, July 2014, pp. 15.
    3. 3)
      • 3. China Electricity Council: ‘National electric power industry statistics 2015 express’ (National Energy Administration and Beijing, 2015), p. 1.
    4. 4)
      • 4. Lorca, A., Sun, X.A.: ‘Adaptive robust optimization with dynamic uncertainty sets for multi-period economic dispatch under significant wind’, IEEE Trans. Power Syst., 2015, 30, (4), pp. 17021713.
    5. 5)
      • 5. Botterud, A., Zhou, Z., Wang, J.H., et al: ‘Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: a case study of Illinois’, IEEE Trans. Sustain. Energy, 2013, 4, (1), pp. 250261.
    6. 6)
      • 6. Hetzer, J., Yu, D.C., Bhattarai, K.: ‘An economic dispatch model incorporating wind power’, IEEE Trans. Energy Convers., 2008, 23, (2), pp. 603611.
    7. 7)
      • 7. Shi, L.B., Wang, C., Yao, L.Z., et al: ‘Optimal power flow solution incorporating wind power’, IEEE Syst. J., 2012, 6, (2), pp. 233241.
    8. 8)
      • 8. Pappala, V.S., Erlich, I., Rohrig, K., et al: ‘A stochastic model for the optimal operation of a wind–thermal power system’, IEEE Trans. Power Syst., 2009, 24, (2), pp. 940950.
    9. 9)
      • 9. Miranda, V., Hang, P.S.: ‘Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers’, IEEE Trans. Power Syst., 2005, 20, (4), pp. 21432145.
    10. 10)
      • 10. Albadi, M.H., El-Saadany, E.F.: ‘Comparative study on impacts of wind profiles on thermal units scheduling costs’, IET Renew. Power Gener., 2011, 5, (1), pp. 2635.
    11. 11)
      • 11. Bai, Y., Wang, Y., Xia, Q., et al: ‘A full-scenario SCED with coordinative optimization of hydro–thermal–wind power’. Proc. of the CSEE, May 2013, vol. 33, no. 13.
    12. 12)
      • 12. Fu, Y., Liu, M., Li, L.: ‘Multiobjective stochastic economic dispatch with variable wind generation using scenario-based decomposition and asynchronous block iteration’, IEEE Trans. Sustain. Energy, 2016, 7, (1), pp. 139149.
    13. 13)
      • 13. Silva, S.R., de Queiroz, A.R., Lima, L.M.M., et al: ‘Effects of wind penetration in the scheduling of a hydro-dominant power system’. IEEE PES General Meeting, 2014, pp. 15.
    14. 14)
      • 14. Hu, B.Q., Marwali, L.W.M.: ‘On the robust solution to SCUC with load and wind uncertainty correlations’, IEEE Trans. Power Syst., 2014, 29, (6), pp. 29522964.
    15. 15)
      • 15. Wu, J.L., Zhang, B.H., Wang, K., et al: ‘Optimal economic dispatch model based on risk management for wind-integrated power system’, IET Gener. Transm. Distrib., 2015, 9, (15), pp. 21522158.
    16. 16)
      • 16. Park, J.B., Jeong, Y.W., Shin, J.R., et al: ‘An improved particle swarm optimization for nonconvex economic dispatch problems’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 156166.
    17. 17)
      • 17. Moeini-Aghtaie, M., Dehghanian, P., Fotuhi-Firuzabad, M., et al: ‘Multiagent genetic algorithm: an online probabilistic view on economic dispatch of energy hubs constrained by wind availability’, IEEE Trans. Sustain. Energy, 2014, 5, (2), pp. 699708.
    18. 18)
      • 18. Farhat, I.A., El-Hawary, M.E.: ‘Dynamic adaptive bacterial foraging algorithm for optimum economic dispatch with valve-point effects and wind power’, IET Gener. Transm. Distrib., 2010, 4, (9), pp. 989999.
    19. 19)
      • 19. Shi, L.B., Weng, Z.X., Yao, L.Z., et al: ‘An analytical solution for wind farm power output’, IEEE Trans. Power Syst., 2014, 29, (6), pp. 31223123.
    20. 20)
      • 20. Bo, R., Li, F.X.: ‘Probabilistic LMP forecasting considering load uncertainty’, IEEE Trans. Power Syst., 2009, 24, (3), pp. 12791289.
    21. 21)
      • 21. Oppenheim, A.V., Willsky, A.S., Hamid, S.: ‘Signals and systems’ (Prentice-Hall, London, UK, 1997, 2nd edn.).
    22. 22)
      • 22. Pierre, J.W.: ‘A novel method for calculating the convolution sum of two finite length sequences’, IEEE Trans. Educ., 1996, 39, (1), pp. 7780.
    23. 23)
      • 23. Birge, J.R., Louveaux, F.: ‘Introduction to stochastic programming’ (Springer, New York, 1997).
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