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Integration of renewable energy sources in dynamic economic load dispatch problem using an improved fireworks algorithm

Integration of renewable energy sources in dynamic economic load dispatch problem using an improved fireworks algorithm

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In electrical power system, economic load dispatch is a generic operation for optimal sharing of generation units to meet the system load. With the rapid development of the renewable infrastructure and wide encouragement for green energy have emerged hybrid generating systems in power systems. However, there continuous ever-increasing production is creating challenges as well as implicating economic factor also in operation. A collective cost function is considered with the conventional thermal generators along with the consideration of renewable energy sources to envisage the economic factor. For these renewable sources, like wind and solar, the proportional cost, their uncertainty and variability by overestimation and underestimation cost are considered. To achieve this economic day-ahead scheduling, dynamic operation in time scale of 1 h interval is performed. The stochastic nature of wind and solar is modelled by Weibull and Beta distributions, respectively. Moreover, economic optimisation is obtained by a newly developed algorithm called improved fireworks algorithm with non-uniform operator (IFWA-NMO). This introduces adaptive dimension strategy, limiting mapping operator and non-uniform operator. The effectiveness of proposed IFWA-NMO is investigated on standard dynamic economic load dispatch (DELD) system and also employed to solve conventional DELD with wind-solar system.

References

    1. 1)
      • 1. Arul, R., Ravi, G., Velusami, S.: ‘Chaotic self-adaptive differential harmony search algorithm based dynamic economic dispatch’, Int. J. Electr. Power Energy Syst., 2013, 50, pp. 8596.
    2. 2)
      • 2. Peng, C., Sun, H., Guo, J., et al: ‘Dynamic economic dispatch for wind-thermal power system using a novel bi-population chaotic differential evolution algorithm’, Electr. Power Energy Syst., 2012, 42, (1), pp. 119126.
    3. 3)
      • 3. Tang, Y., Zhong, J., Liu, J.: ‘A generation adjustment methodology considering fluctuations of loads and renewable energy sources’, IEEE Trans. Power Syst., 2015, 99, (1), pp. 18.
    4. 4)
      • 4. Kuo, C.-C.: ‘Wind energy dispatch considering environmental and economic factors’, Renew. Energy, 2010, 35, (10), pp. 22172227.
    5. 5)
      • 5. Khan, N.A., Awan, A.B., Mahmood, A., et al: ‘Combined emission economic dispatch of power system including solar photo voltaic generation’, Energy Convers. Manage., 2015, 92, pp. 8291.
    6. 6)
      • 6. Surender Reddy, S., Bijwe, P.R., Abhyankar, A.R.: ‘Real-time economic dispatch considering renewable power generation variability and uncertainty over scheduling period’, IEEE Syst. J., 2015, 9, (4), pp. 14401451.
    7. 7)
      • 7. Liu, Y., Nair, N.K.C.: ‘A two-stage stochastic dynamic economic dispatch model considering wind uncertainty’, IEEE Trans. Sustain. Energy, 2016, 7, (2), pp. 819829.
    8. 8)
      • 8. Peng, C., Xie, P., Pan, L., et al: ‘Flexible robust optimization dispatch for hybrid wind/photovoltaic/hydro/thermal power system’, IEEE Trans. Smart Grid, 2016, 7, (2), pp. 751762.
    9. 9)
      • 9. Krishnasamy, U., Nanjundappan, D.: ‘Hybrid weighted probabilistic neural network and biogeography based optimization for dynamic economic dispatch of integrated multiple-fuel and wind power plants’, Electr. Power Energy Syst., 2016, 77, pp. 385394.
    10. 10)
      • 10. Li, Y.Z., Wu, Q.H., Jiang, L., et al: ‘Optimal power system dispatch with wind power integrated using nonlinear interval optimization and evidential reasoning approach’, IEEE Trans. Power Syst., 2016, 31, (3), pp. 22462254.
    11. 11)
      • 11. Zhang, H., Yue, D., Xie, X.: ‘Robust optimization for dynamic economic dispatch under wind power uncertainty with different levels of uncertainty budget’, IEEE Access, 2016, 4, pp. 76337644.
    12. 12)
      • 12. Yang, L., He, M., Vittal, V., et al: ‘Stochastic optimization-based economic dispatch and interruptible load management with increased wind penetration’, IEEE Trans. Smart Grid, 2016, 7, (2), pp. 730739.
    13. 13)
      • 13. Wang, X., Jiang, C., Li, B.: ‘Active robust optimization for wind integrated power system economic dispatch considering hourly demand response’, Renew. Energy, 2016, 97, pp. 798808.
    14. 14)
      • 14. Zaman, F., Elsayed, S.M., Ray, T., et al: ‘Configuring two-algorithm-based evolutionary approach for solving dynamic economic dispatch problems’, Eng. Appl. Artif. Intell., 2016, 53, pp. 105125.
    15. 15)
      • 15. Liu, F., Bie, Z., Liu, S., et al: ‘Day-ahead optimal dispatch for wind integrated power system considering zonal reserve requirements’, Appl. Energy, 2017, 188, pp. 399408.
    16. 16)
      • 16. Tan, Y., Zhu, Y.: ‘Fireworks algorithm for optimization’, in ‘Advances in swarm intelligence’ (Springer, Berlin, 2010), 6145, pp. 355364.
    17. 17)
      • 17. Zheng, S., Janecek, A., Tan, Y.: ‘Enhanced fireworks algorithm’. IEEE Congress on Evolutionary Computation (CEC), 2013, pp. 20692077.
    18. 18)
      • 18. Zheng, S., Janecek, A., Li, J., et al: ‘Dynamic search in fireworks algorithm’. IEEE Congress on Evolutionary Computation (CEC), 2014, pp. 32223229.
    19. 19)
      • 19. Junzhi, L., Shaoqiu, Z., Ying, T.: ‘Adaptive fireworks algorithm’. IEEE Congress on Evolutionary Computation (CEC), 2014, pp. 32143221.
    20. 20)
      • 20. Yu, C., Li, J., Tan, Y.: ‘Improve enhanced fireworks algorithm with differential mutation’. Int. Conf. Systems, Man and Cybernetics, 2014, pp. 264269.
    21. 21)
      • 21. Deshmukh, M., Deshmukh, S.: ‘Modeling of hybrid renewable energy systems’, Renew. Sustain. Energy, 2008, 12, (1), pp. 235249.
    22. 22)
      • 22. Boyle, G.: ‘Renewable Energy’ (Oxford Univ. Press, Oxford, U.K., 2004).
    23. 23)
      • 23. Boukhezzar, B., Siguerdidjane, H., Maureen Hand, M.: ‘Nonlinear control of variable-speed wind turbines for generator torque limiting and power optimization’, ASME J. Energy Eng., 2006, 128, pp. 516546.
    24. 24)
      • 24. Papoulis, A.: ‘Probability, random variables and stochastic processes’ (McGraw Hill Science Engineering, Blacklick, OH, USA, 2001).
    25. 25)
      • 25. Atwa, Y.M., El-Saadany, E.F., Salama, M.M.A., et al: ‘Optimal renewable resources mix for distribution system energy loss minimizations’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 360370.
    26. 26)
      • 26. Bilil, H., Aniba, G., Maaroufi, M.: ‘Probabilistic economic emission dispatch optimization of multi-sources power system’, Energy Proc., 2014, 50, pp. 789796.
    27. 27)
      • 27. Ahmed, R., Wafa, A: ‘Optimization of economic/emission load dispatch for hybrid generating systems using controlled elitist NSGA-II’, Electr. Power Syst. Res., 2013, 105, pp. 142151.
    28. 28)
      • 28. Jadoun, V.K., Gupta, N., Niazi, K.R., et al: ‘Dynamically controlled particle swarm optimization for large scale non-convex economic dispatch problems’, Int. Trans. Electr. Energy Syst., 2015, 25, (11), pp. 30603074.
    29. 29)
      • 29. Jangamshetti, S.H., Rau, V.G.: ‘Site matching of wind turbine generators: a case study’, IEEE Trans. Energy Convers., 1999, 14, (4), pp. 15371543.
    30. 30)
      • 30. Chen, C.L., Lee, T.Y., Jan, R.M.: ‘Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity’, Energy Convers. Manage., 2006, 47, (18–19), pp. 34563472.
    31. 31)
      • 31. Selvakumar, A.I., Thanushkodi, K.: ‘Optimization using civilized swarm: solution to economic dispatch with multiple minima’, Electr. Power Syst. Res., 2009, 79, (1), pp. 816.
    32. 32)
      • 32. Michalewicz, Z.: ‘Genetic algorithms + data structures = evolution Programs’ (Springer, Berlin, 1996, 3rd Edn.).
    33. 33)
      • 33. Engelbrecht, A.P.: ‘Fundamentals of computational swarm intelligence’ (John Wiley & Sons, USA, 2006).
    34. 34)
      • 34. Zwe-Lee, G., Ting-Chia, O.: ‘Dynamic economic dispatch solution using fast evolutionary programming with swarm direction’. IEEE Conf. Industrial Electronics and Applied, 2009, pp. 15381544.
    35. 35)
      • 35. Gaing, Z.-L.: ‘Constrained dynamic economic dispatch solution using particle swarm optimization’. IEEE Conf. Power Engineering Society General Meeting, 2004, vol. 1, pp. 153158.
    36. 36)
      • 36. National Renewable Energy Laboratory (NREL). Available at www.nrel.gov.
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