Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

1.60

CiteScore

Hamed Aliyari1 , Reza Effatnejad2 , Mehdi Izadi This email address is being protected from spambots. You need JavaScript enabled to view it.3 and Seyed Hossein Hosseinian4

1Faculty of Electrical and Biomedical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Alborz, Iran
3Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
4Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran


 

Received: June 27, 2016
Accepted: January 9, 2017
Publication Date: June 1, 2017

Download Citation: ||https://doi.org/ 10.6180/jase.2017.20.2.01  

ABSTRACT


This paper presents a novel stochastic optimization approach to determine a feasible optimal solution for the economic dispatch (ED) problems, considering various generator constraints. Many practical constraints of generators, such as ramp rate limits and prohibited operating zones are investigated. In order to improve the performance of the particle swarm optimization (PSO) algorithm. These constraints alter the ED problem to a non-smooth minimization problem with constraints, an innovative approach based on PSO is chosen to solve the load-flow problem by combining the genetic algorithm (GA), i.e. some of the answers were found by the PSO algorithm itself; after that two possible procedures are studied: one is utilizing the PSO algorithm to find the optimum answer among the primary guesses and the other is using a GA implementation by means of the arithmetic crossover operators. The last step is choosing the best answer among the obtained results (GA, PSO). To show its efficiency and effectiveness, the proposed algorithm (GPSO) is applied to some types of ED problems containing non-smooth cost functions of 13 and 40 generating units systems (large scale systems). The experimental results show that the GPSO approach is comparatively capable to obtain higher quality solution.


Keywords: Particle Swarm Optimization, Genetic Algorithm, Economic Load Dispatch, Stochastic Search Algorithm


REFERENCES


  1. [1] Jabr, R., Coonick, A. H. and Cory, B. J., “A Homogeneous Linear Programming Algorithm for the Security Constrained Economic Dispatch Problem,” Power Systems, IEEE Transactions on, Vol. 15, pp. 930936 (2000). doi: 10.1109/59.871715
  2. [2] Nanda, J., “Economic Emission Load Dispatch with Line Flow Constraints Using a Classical Technique,” IEE Proceedings-Generation, Transmission and Distribution, Vol. 141, pp. 110 (1994). doi: 10.1049/ ip-gtd:19949770
  3. [3] Fan, J.-Y. and Zhang, L., “Real-time Economic Dispatch with Line Flow and Emission Constraints Using Quadratic Programming,” Power Systems, IEEE Transactions on, Vol. 13, pp. 320325 (1998). doi: 10.1109/ 59.667345
  4. [4] Ross, D. W. and Kim, S., “Dynamic Economic Dispatch of Generation,” Power Apparatus and Systems, IEEE Transactions on, pp. 20602068 (1980). doi: 10.1109/TPAS.1980.319847
  5. [5] Chiang, C.-L., “Improved Genetic Algorithm for Power Economic Dispatch of Units with Valve-point Effects and Multiple Fuels,” Power Systems, IEEE Transactions on, Vol. 20, pp. 16901699 (2005). doi: 10.1109/ TPWRS.2005.857924
  6. [6] Laouer, M., Allali, A., Chaker, A. and Hachemi, K., “New Approach of Optimal Power Flow with Genetic Algorithms,” Acta Electrotechnica et Informatica Vol, Vol. 8, pp. 3542 (2008).
  7. [7] Wong, K., “Short-term Hydrothermal Scheduling Part I. Simulated Annealing Approach,” IEE ProceedingsGeneration, Transmission and Distribution, Vol. 141, pp. 497501 (1994). doi: 10.1049/ip-gtd:19941350
  8. [8] Huang, S.-J., “Enhancement of Hydroelectric Generation Scheduling Using Ant Colony System Based Optimization Approaches,” Energy Conversion, IEEE Transactions on, Vol. 16, pp. 296301 (2001). doi: 10.1109/60.937211
  9. [9] Lin, W.-M., Cheng, F.-S. and Tsay, M.-T., “An Improved Tabu Search for Economic Dispatch with Multiple Minima,” Power Systems, IEEE Transactions on, Vol. 17, pp. 108112 (2002). doi: 10.1109/59.982200
  10. [10] Yalcinoz, T. and Short, M., “Neural Networks Approach for Solving Economic Dispatch Problem with Transmission Capacity Constraints,” Power Systems, IEEE Transactions on, Vol. 13, pp. 307313 (1998). doi: 10.1109/59.667341
  11. [11] Wei, L. D. P. T., “Chaotic Optimization for Economic Dispatch of Power Systems,” Proceedings of the CSEE, Vol. 20, pp. 3640 (2000).
  12. [12] Ma, R., Wang, P., Yang, H. and Hu, G., “Environmental/economic Transaction Planning Using Multiobjective Particle Swarm Optimization and Non-stationary Multi-stage Assignment Penalty Function,” 2005 IEEE/ PES Transmission & Distribution Conference & Exposition: Asia and Pacific, pp. 16 (2005). doi: 10. 1109/TDC.2005.1546775
  13. [13] Bansal, R., “Optimization Methods for Electric Power Systems: An Overview,” International Journal of Emerging Electric Power Systems, Vol. 2 (2005). doi: 10.2202/1553-779X.1021
  14. [14] Lee, K. Y. and El-Sharkawi, M. A., Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems, Vol. 39, John Wiley & Sons (2008).
  15. [15] Kennedy, J., “Particle Swarm Optimization,” Encyclopedia of Machine Learning, ed: Springer, pp. 760766 (2011). doi: 10.1007/978-0-387-30164-8_630
  16. [16] Shi, Y. and Eberhart, R., “A Modified Particle Swarm Optimizer,” Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, pp. 6973 (1998). doi: 10.1109/ICEC.1998.699146
  17. [17] Rafiee, Z., Ganjefar, S. and Fattahi, A., “A New PSS Tuning Technique Using ICA and PSO Methods with the Fourier Transform,” 2010 18th Iranian Conference on Electrical Engineering, pp. 806811 (2010). doi: 10.1109/IRANIANCEE.2010.5506964
  18. [18] Sinha, N., Chakrabarti, R. and Chattopadhyay, P., “Evolutionary Programming Techniques for Economic Load Dispatch,” Evolutionary Computation, IEEE Transactions on, Vol. 7, pp. 8394 (2003). doi: 10. 1109/TEVC.2002.806788
  19. [19] Yang, H.-T., Yang, P.-C. and Huang, C.-L., “Evolutionary Programming Based Economic Dispatch for Units with Non-smooth Fuel Cost Functions,” Power Systems, IEEE Transactions on, Vol. 11, pp. 112118 (1996). doi: 10.1109/59.485992
  20. [20] Chen, C.-L., “Non-convex Economic Dispatch: a Direct Search Approach,” Energy Conversion and Management, Vol. 48, pp. 219225 (2007). doi: 10.1016/ j.enconman.2006.04.010
  21. [21] Coelho, L. D. S. and Mariani, V. C., “Combining of Chaotic Differential Evolution and Quadratic Programming for Economic Dispatch Optimization with Valve-point Effect,” Power Systems, IEEE Transactions on, Vol. 21, p. 1465 (2006). doi: 10.1109/ TPWRS.2006.881220
  22. [22] Wang, S.-K., “Non-smooth/Non-convex Economic Dispatch by a Novel Hybrid Differential Evolution Algorithm,” Generation, Transmission & Distribution, IET, Vol. 1, pp. 793803 (2007). doi: 10.1049/ietgtd:20070183