Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

A New Constraint Handling Method for Economic Dispatch

  • Li, Xueping (Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, Yanshan University) ;
  • Xiao, Canwei (Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, Yanshan University) ;
  • Lu, Zhigang (Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, Yanshan University)
  • Received : 2017.10.04
  • Accepted : 2018.01.26
  • Published : 2018.05.01
Download PDF
⟨ Previous Next ⟩

Abstract

For practical consideration, economic dispatch (ED) problems in power system have non-smooth cost functions with equality and inequality constraints that makes the problems complex constrained nonlinear optimization problems. This paper proposes a new constraint handling method for equality and inequality constraints which is employed to solve ED problems, where the incremental rate is employed to enhance the modification process. In order to prove the applicability of the proposed method, the study cases are tested based on the classical particle swarm optimization (PSO) and differential evolution (DE) algorithm. The proposed method is evaluated for ED problems using six different test systems: 6-, 15-, 20-, 38-, 110- and 140-generators system. Simulation results show that it can always find the satisfactory solutions while satisfying the constraints.

Keywords

References

  1. G. S. S. Babu, D. B. Das and C. Patvardhan, "Simulated annealing variants for solution of economic load dispatch", J. Inst. Eng. India: Electr. Eng. Div., vol. 83, no. EDC., pp. 222-229, Dec. 2002.
  2. K. Bhattacharjee, A. Bhattacharya, S. H. N. Dey, "Chemical reaction optimisation for different economic dispatch problems," IET Gener. Transm. Distrib., vol. 8, no. 3, pp. 530-541, 2014. https://doi.org/10.1049/iet-gtd.2013.0122
  3. R. E. Perez-Guerrero, J. R. Cedeno-Maldonado, "Economic power dispatch with non-smooth cost functions using differential evolution," Proc. of the 37th Annual North American Power Symposium, pp. 183-190, Oct. 2005.
  4. I. Erlich, G. K Venayagamoorthy, N Worawat, "A mean-variance optimization algorithm," IEEE World Congr. Comput. Intell., pp. 344-349, July. 2010.
  5. Z. L. Gaing, "Particle swarm optimization to solving the economic dispatch considering generator constraints," IEEE Trans. Power Syst., vol. 18, no. 3, pp. 1187-1195, Aug. 2003.
  6. A. Bhattacharya, P. K. Chattopadhyay, "Biogeography-based optimization for different economic load dispatch problems," IEEE Trans. Power Syst., vol. 25, no. 2, pp. 1064-1077, 2010. https://doi.org/10.1109/TPWRS.2009.2034525
  7. B. Das, T. K. Sengupta, "Economic load dispatch using PSO and TLBO," Proc. Michael Faraday IET Int. Summit, Kolkata, India, pp. 212-219, Sept. 2015.
  8. S. Hemamalini, S. Simon, "Artificial bee colony algorithm for economic load dispatch problem with non-smooth cost functions," Electric Machines & Power Systems, vol. 38, no. 7, pp. 786-803, 2010. https://doi.org/10.1080/15325000903489710
  9. B. Saravanan, E. R. Vasudevan, D. P. Kothari, "Unit commitment problem solution using invasive weed optimization algorithm," Int. J. Electr. Power Energy Syst., vol. 55, pp. 344-349, 2015.
  10. K. T. Chaturvedi, M. Pandit, L. Srivastava, "Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch," Electrical Power and Energy Systems, vol. 31, pp. 249-257, 2009. https://doi.org/10.1016/j.ijepes.2009.01.010
  11. A. I. Selvakumar, K. Thanushkodi, "Anti-predatory particle swarm optimization: Solution to nonconvex economic dispatch problems," Electric Power Systems, vol. 78, pp. 2-10, 2008. https://doi.org/10.1016/j.epsr.2006.12.001
  12. L. Tawfak A. Bahrani, J. C. Patra, "Orthogonal PSO algorithm for economic dispatch of thermal generating units under various power constraints in smart power grid," Applied Soft Computing, vol. 58, pp. 401-426, 2017. https://doi.org/10.1016/j.asoc.2017.04.059
  13. J. Sun, V. Palade, X. Wu, "Solving the Power Economic Dispatch Problem With Generator Constraints by Random Drift Particle Swarm Optimization," IEEE Transactions on Industrial Informatics, vol. 10, no. 1, pp. 222-232, Feb. 2014. https://doi.org/10.1109/TII.2013.2267392
  14. T. Niknam, H. D. Mojarrad, H. Z. Meymand, "Non-smooth economic dispatch computation by fuzzy and self adaptive particle swarm optimization," Applied Soft Computing, vol. 11, pp. 2805-2817, 2011. https://doi.org/10.1016/j.asoc.2010.11.010
  15. S. Khamsawang, S. Jiriwibhakorn, "DSPSO-TSA for economic dispatch problem with nonsmooth and noncontinuous cost functions," Energy Conversion and Management, vol. 51, pp. 365-375, 2010. https://doi.org/10.1016/j.enconman.2009.09.034
  16. R. P. Parouha, K. N. Das,"A memory based differential evolution algorithm for unconstrained optimization," Appl. Soft Comput., vol. 38, pp. 501-517, Jan. 2016. https://doi.org/10.1016/j.asoc.2015.10.022
  17. J. Brest, S. Greiner, B. Boskovic, M Mernik, V Zumer. "Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems," IEEE Trans Evol Comput., vol. 10, no. 6, pp. 646-657, Dec. 2006. https://doi.org/10.1109/TEVC.2006.872133
  18. G. Wang, G. Q. Chen, F. Z. Bai, "Modeling and identification of asymmetric Bouc-Wen hysteresis for piezoelectric actuator via a novel differential evolution algorithm," Sensor Actuator A: Phys., vol. 235, pp. 105-118, 2015. https://doi.org/10.1016/j.sna.2015.09.043
  19. D. Zou, S. Li, G. G. Wang, Z. Li, H. Ouyang, "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Appl. Energy, vol. 181, pp. 375-390, Nov. 2016. https://doi.org/10.1016/j.apenergy.2016.08.067
  20. A. Bhattacharya, P. K. Chattopadhyay, "Hybrid Differential Evolution with Biogeography-Based Optimization for Solution of Economic Load Dispatch," IEEE Trans. Power Syst., vol. 25, no. 4, pp. 1955-1964, Mar. 2010. https://doi.org/10.1109/TPWRS.2010.2043270
  21. G. J. Xiong, D. Y. Shi, X. Z. Duan, "Multi-strategy ensemble biogeography-based optimization for economic dispatch problems," Appl. Energy, vol. 111, pp. 801-811, Nov. 2013. https://doi.org/10.1016/j.apenergy.2013.04.095
  22. A. Bhattacharya, P. Chattopadhyay, "Solution of economic power dispatch problems using opposetional biogeography-based optimization," Electric Machines & Power Systems, vol. 38, pp. 1139-1160, 2010. https://doi.org/10.1080/15325001003652934
  23. K. K. Vshwakarma, H. M. Dubey, "Simulated annealing based optimization for solving large scale economic load dispatch problems," Int. J. Eng. Res. Technol., vol. 1, no. 3, pp. 1-8, May. 2012. https://doi.org/10.15623/ijret.2012.0101001
  24. K. Bhattacharjee, A. Bhattacharya, S. H. Dey, "Oppositional real coded chemical reaction optimization for different economic dispatch problems," Int. J. Electr. Power Energy Syst., vol. 55, pp. 378-391, Feb. 2014. https://doi.org/10.1016/j.ijepes.2013.09.033
  25. A. K. Barisal, R. C. Prusty, "Large scale economic dispatch of power systems using oppositional invasive weed optimization," Applied Soft Computing, vol. 29, pp. 122-137, Apr. 2015. https://doi.org/10.1016/j.asoc.2014.12.014
  26. J. K. Min, H. Y. Song, J. B. Park, etc., "An improved mean-variance optimization for nonconvex economic dispatch problems," J. Electr. Eng. Technol., vol. 8, no. 1, pp. 80-89, Jan. 2013. https://doi.org/10.5370/JEET.2013.8.1.080
  27. K. H. Truong, P. Vasant, S. Balbir and N. V. Dieu, "Solving economic dispatch by using swarm based mean-variance mapping optimization($MVMO^S$)," Global J. Technol. Optim., vol. 6, no. 3, pp. 1-8, Jul. 2015.
  28. S. Ozyon, D. Aydin, "Incremental artificial bee colony with local search to economic dispatch problem with ramp rate limits and prohibited operating zones," Energy Conversion and Management, vol. 65, pp. 397-407, 2013. https://doi.org/10.1016/j.enconman.2012.07.005
  29. A. Homaifar, S.H.Y. Lai, X. Qi, "Constrained optimization via genetic algorithms," Simulation, vol. 62, no. 4, pp. 242-254, 1994. https://doi.org/10.1177/003754979406200405
  30. K. E. Parsopoulos, M. N. Vrahatis, "Particle swarm optimization method for constrained optimization problems," Intelligent Technologies-Theory and Applications: New Trends in Intelligent Technologies, vol. 76, pp. 214-220, 2002.
  31. J. Paredis, "Co-evolutionary constraint satisfaction," Proceeding of the 3rd Conference on Parallel Problem Solving from Nature, pp. 46-55, Oct. 1994.
  32. X. Liu, "On compact formulation of constraints induced by disjoint Prohibited-Zones," IEEE Trans. Power Syst., vol. 25, no. 4, pp. 2004-2005, Nov. 2010. https://doi.org/10.1109/TPWRS.2010.2045928
  33. J. B. Park, Y. W. Jeong, J. R. Shin, and K. Y. Lee, "An improved particle swarm optimization for non-convex economic dispatch problems," IEEE Trans. on Power Syst., vol. 25, no. 1, pp. 156-166, Feb.2010. https://doi.org/10.1109/TPWRS.2009.2030293
  34. N. Sinha, R. Chakrabarti, PK. Chattopadhyay, "Evolutionary programming techniques for economic load dispatch," IEEE Transactions on Evolutionary Computation, vol. 7, no. 1, pp. 83-94, 2003. https://doi.org/10.1109/TEVC.2002.806788
  35. G. T. Pulido, C. C. Coello, "A constraint-handling mechanism for particle swarm optimization," Proc. of the 2004 IEEE Cong. on Evolutionary Computation, pp. 1396-1403, 2004.
  36. J. Kennedy, R. Eberhart, "Particle swarm optimization," Proc. of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948, Nov. 1995.
  37. R. Storn, K. Price, "Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces," TR-95-012, ICSI, Mar. 1995.
  38. Ubaidullah; Shoab Ahmed Khan, "Accelerating MATLAB Slow Loop Execution With CUDA," 7th International Conference on Emerging Technologies, pp. 1-4, 2011.