Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch with Generator Constraints

- Journal title : Journal of Electrical Engineering and Technology
- Volume 9, Issue 1, 2014, pp.15-26
- Publisher : The Korean Institute of Electrical Engineers
- DOI : 10.5370/JEET.2014.9.1.015

Title & Authors

Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch with Generator Constraints

Abdullah, M.N.; Bakar, A.H.A; Rahim, N.A.; Mokhlis, H.; Illias, H.A.; Jamian, J.J.;

Abdullah, M.N.; Bakar, A.H.A; Rahim, N.A.; Mokhlis, H.; Illias, H.A.; Jamian, J.J.;

Abstract

This paper proposes a Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients (MPSO-TVAC) for solving economic load dispatch (ELD) problem. Due to prohibited operating zones (POZ) and ramp rate limits of the practical generators, the ELD problems become nonlinear and nonconvex optimization problem. Furthermore, the ELD problem may be more complicated if transmission losses are considered. Particle swarm optimization (PSO) is one of the famous heuristic methods for solving nonconvex problems. However, this method may suffer to trap at local minima especially for multimodal problem. To improve the solution quality and robustness of PSO algorithm, a new best neighbour particle called `rbest` is proposed. The rbest provides extra information for each particle that is randomly selected from other best particles in order to diversify the movement of particle and avoid premature convergence. The effectiveness of MPSO-TVAC algorithm is tested on different power systems with POZ, ramp-rate limits and transmission loss constraints. To validate the performances of the proposed algorithm, comparative studies have been carried out in terms of convergence characteristic, solution quality, computation time and robustness. Simulation results found that the proposed MPSO-TVAC algorithm has good solution quality and more robust than other methods reported in previous work.

Keywords

Economic load dispatch;Particle Swarm Optimization (PSO);Prohibited operating zone (POZ);Ramp rate limits;Time varying acceleration coefficients (TVAC);

Language

English

Cited by

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References

1.

A.J. Wood and B.F. Wollenberg. Power Generation, Operation and Control. 2nd ed. John Wiley and Sons, New York, 1996.

2.

S.O. Orero and M.R. Irving. Economic dispatch of generators with prohibited operating zones: a genetic algorithm approach. IEE Proceedings Generation, Transmission and Distribution. 143 (1996) 529-34.

3.

C. Wang and S.M. Shahidehpour. Effects of ramprate limits on unit commitment and economic dispatch. IEEE Transactions on Power Systems. 8 (1993) 1341-50.

4.

R.R. Shoults, S.V. Venkatesh, S.D. Helmick, G.L. Ward and M.J. Lollar. A Dynamic Programming Based Method for Developing Dispatch Curves When Incremental Heat Rate Curves Are Non-Monotonically Increasing. IEEE Transactions on Power Systems. 1 (1986) 10-6.

5.

N. Amjady and H. Nasiri-Rad. Economic dispatch using an efficient real-coded genetic algorithm. IET Generation, Transmission & Distribution. 3 (2009) 266-78.

6.

I. Ciornei and E. Kyriakides. A GA-API Solution for the Economic Dispatch of Generation in Power System Operation. IEEE Transactions on Power Systems. 27 (2012) 233-42.

7.

N. Sinha, R. Chakrabarti and P.K. Chattopadhyay. Evolutionary programming techniques for economic load dispatch. IEEE Transactions on Evolutionary Computation. 7 (2003) 83-94.

8.

M. Sharma, M. Pandit and L. Srivastava. Reserve constrained multi-area economic dispatch employing differential evolution with time-varying mutation. International Journal of Electrical Power & Energy Systems. 33 (2011) 753-66.

9.

S. Pothiya, I. Ngamroo and W. Kongprawechnon. Ant colony optimisation for economic dispatch problem with non-smooth cost functions. International Journal of Electrical Power & Energy Systems. 32 (2010) 478-87.

10.

W. Sa-ngiamvibool, S. Pothiya and I. Ngamroo. Multiple tabu search algorithm for economic dispatch problem considering valve-point effects. International Journal of Electrical Power & Energy Systems. 33 (2011) 846-54.

11.

K.P. Wong and C.C. Fung. Simulated annealing based economic dispatch algorithm. IEE Proceedings C Generation, Transmission and Distribution. 140 (1993) 509-15.

12.

V.B.A. Kasangaki, H.M. Sendaula and S.K. Biswas. Stochastic Hopfield artificial neural network for unit commitment and economic power dispatch. Electric Power Systems Research. 42 (1997) 215-23.

13.

Y. Wang, J. Zhou, Y. Lu, H. Qin and Y. Wang. Chaotic self-adaptive particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects. Expert Systems with Applications. 38 (2011) 14231-7.

14.

G. Zwe-Lee. Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Transactions on Power Systems. 18 (2003) 1187-95.

15.

J.-B. Park, K.-S. Lee, J.-R. Shin and K.Y. Lee. A particle swarm optimization for economic dispatch with nonsmooth cost functions. IEEE Transactions on Power Systems. 20 (2005) 34-42.

16.

A.I. Selvakumar and K. Thanushkodi. A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems. IEEE Transactions on Power Systems. 22 (2007) 42-51.

17.

K.T. Chaturvedi, M. Pandit and L. Srivastava. Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch. IEEE Transactions on Power Systems. 23 (2008) 1079-87.

18.

J. Cai, X. Ma, L. Li and P. Haipeng. Chaotic particle swarm optimization for economic dispatch considering the generator constraints. Energy Conversion and Management. 48 (2007) 645-53.

19.

D. Vo Ngoc, P. Schegner and W. Ongsakul. A newly improved particle swarm optimization for economic dispatch with valve point loading effects. 2011 IEEE Power and Energy Society General Meeting. 2011. pp. 1-8.

20.

J.-B. Park, Y.-W. Jeong, J.-R. Shin and K.Y. Lee. An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems. IEEE Transactions on Power Systems. 25 (2010) 156-66.

21.

H. Lu, P. Sriyanyong, Y.H. Song and T. Dillon. Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function. International Journal of Electrical Power & Energy Systems. 32 (2010) 921-35.

22.

T.-Y. Lee and C.-L. Chen. Unit commitment with probabilistic reserve: An IPSO approach. Energy Conversion and Management. 48 (2007) 486-93.

23.

A. Safari and H. Shayeghi. Iteration particle swarm optimization procedure for economic load dispatch with generator constraints. Expert Systems with Applications. 38 (2011) 6043-8.

24.

A. Ratnaweera, S.K. Halgamuge and H.C. Watson. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation. 8 (2004) 240-55.

25.

K.T. Chaturvedi, M. Pandit and L. Srivastava. Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch. International Journal of Electrical Power & Energy Systems. 31 (2009) 249-57.

26.

H. Saadat. Power System Analysis. 2 ed. McGraw Hill 2002.

27.

J. Kennedy and R. Eberhart. Particle swarm optimization. IEEE International Conference on Neural Networks. 1995. pp. 1942-8, Vol. 4.

28.

T. Niknam, H.D. Mojarrad and M. Nayeripour. A new fuzzy adaptive particle swarm optimization for nonsmooth economic dispatch. Energy. 35 (2010) 1764-78.

29.

Y. Shi and R.C. Eberhart. Empirical study of particle swarm optimization. 1999 CEC 99 Proceedings of the 1999 Congress on Evolutionary Computation. 1999. p. 1950, Vol. 3.

30.

D.N. Jeyakumar, T. Jayabarathi and T. Raghunathan. Particle swarm optimization for various types of economic dispatch problems. International Journal of Electrical Power & Energy Systems. 28 (2006) 36-42.

31.

R.C. Eberhart and Y. Shi. Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the Congress on Evolutionary Computation. 2000. pp. 84-8, Vol. 1.

32.

X. Yuan, A. Su, Y. Yuan, H. Nie and L. Wang. An improved PSO for dynamic load dispatch of generators with valve-point effects. Energy. 34 (2009) 67-74.

33.

B. Mohammadi-ivatloo, A. Rabiee and M. Ehsan. Time-varying acceleration coefficients IPSO for solving dynamic economic dispatch with non-smooth cost function. Energy Conversion and Management. 56 (2012) 175-83.

34.

X.-S. Yang, S.S. Sadat Hosseini and A.H. Gandomi. Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect. Applied Soft Computing. 12 (2012) 1180-6.

35.

M. Sydulu. A very fast and effective non-iterative "lambda logic based" algorithm for economic dispatch of thermal units. TENCON 99 Proceedings of the IEEE Region 10 Conference1999. pp. 1434-7, Vol. 2.

36.

O. Abedinia, N. Amjady and K. Kiani. Optimal Complex Economic Load Dispatch Solution Using Particle Swarm Optimization with Time Varying Acceleration Coefficient. International Review of Electrical Engineering. 7 (2012) 8.