The week's pick
Reseach Article
Optimal ? based Economic Emission Dispatch using Simulated Annealing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 10 |
| Year of Publication: 2010 |
| Authors: Sasikala. J, Ramaswamy. M |
10.5120/221-371
|
Sasikala. J, Ramaswamy. M . Optimal ? based Economic Emission Dispatch using Simulated Annealing. International Journal of Computer Applications. 1, 10 ( February 2010), 55-63. DOI=10.5120/221-371
Abstract
The economic emission dispatch (EED) assumes a lot of significance to meet the clean energy requirements of the society, while at the same time minimising the cost of generation. The solution schemes in an attempt to arrive at the global best through the use of evolutionary algorithms are however inadequate to cater to problems of large size. The search based EED approaches are computationally inefficient particularly for problems with large number of decision variables. This paper attempts to develop a new SA based modified approach with a single decision variable to solve the EED problem. The philosophy involves the introduction of a new decision variable through a prudent mathematical transformation of the relation between the decision variable and the optimal generations. It thus yields a reduction in the number of problem variables and contributes to realistically enhance the performance of the existing heuristic strategies. The feasibility of the proposed approach is evaluated through two test systems and the results are compared with the available methods to highlight its suitability for online applications.
References
- 1. Wood. AJ & Woolenberg. BF. (1996). Power generation, operation and control, John Willey & Sons, New York.
- 2. Chowdhury.B.H and Rahman. S. (1990). A review of recent advances in economic dispatch, IEEE Trans. on Power Systems, 5(4), 1248-1259.
- 3. Al-Sumait. J.S, Sykulski. J.K, Al-Othman. A.K. (2008). Solution of different types of economic load dispatch problems using a pattern search method, Electric Power Components and Systems, 36, 250-265.
- 4. Abdelaziz. A.Y, Mekhamer. S.F, Badr. M.A.L and Kamh. M.Z. (2008). Economic Dispatch using an enhanced Hopfield neural network, Electric Power Components and Systems, 36, 719-732.
- 5. Mekhamer. S.F, Abdelaziz. A.Y, Kamh. M.Z and Badr. M.A.L. (2009). Dynamic economic dispatch using a hybrid neural network quadratic programming based technique, Electric Power Components and Systems, 37, 253-264.
- 6. Adhinarayanan. T and Sydulu. M. (2008). Diretional search genetic algorithm applications to economic dispatch of thermal units, International Journal for Computational Methods in Engineering Science and Mechanics, 9, 211-216.
- 7. Panigrahi. C.K, Chattopadhyah. P.K, Chakrabarti. R.N and Basu. N. (2006). Simulated annealing technique for dynamic economic dispatch, Electric Power Components and Systems, 34, 577-586.
- 8. Manoharan. P.S, Kannan. P.S, Baskar. S, Willjuice Iruthayarajan and Dhananjeyan. (2009). Covarience matrix adopted evolution strategy algorithm-based solution to dynamic economic dispatch problems, Engineering Optimization, 41(7), 635-657.
- 9. Immanuel Selvakumar. A and Thanuskodi. K. (2008). Anti-predatory particle swarm optimisation: solution to nonconvex economic dispatch problems, Electric Power Systems Research, 78, 2-10.
- 10. Shin-Der Chan and Jiann-Fuh Chen. (2003). A direct Newton-Raphson economic emission dispatch, Electrical Power and Energy Systems, 25, 411-17.
- 11. Muralidharan. S, Srikrishna. K and Subramanian. (2006). Emission constrained economic dispatch-a new recursive approach, Electric Power Components and Systems, 34, 343-53.
- 12. Tankut. Y and Onur. K. (2007). A multiobjective optimisation method to environmental economic dispatch, Electrical Power and Energy Systems, 29, 42-50.
- 13. Balamurugan. R and Subramanian. S. (2008). A simplified recursive approach to combined economic emission dispatch, Electric Power Components and Systems, 36(1), 17-27.
- 14. Palanichamy. C and Sundar Babu. (2008). Analytical solution for combined economic and emission dispatch, Electric Power Systems Research, 78, 1129-37.
- 15. Basu. M. (2004). An interactive fuzzy satisfying-based simulated annealing technique for economic emission load dispatch with nonsmooth fuel cost and emission level functions, Electric Power Components and Systems, 36, 163-73.
- 16. Basu. M. (2006). Particle swarm optimization based goal-attainment method for dynamic economic emission dispatch, Electric Power Components and Systems, 34, 1015-25.
- 17. Lingfeng Wang and hanan Singh. (2007). Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimisation algorithm, Electric Power Systems Research, 77, 1654-64.
- 18. Osman. M.S, Abo-Sinna. M.A and Mousa. A.A. (2009). An -dominance-based multiobjective genetic algorithm for economic emission load dispatch optimisation problem, Electric Power Systems Research, 79(11), pp. 1561-67.
- 19. Palanichamy. C and Srikrishna. K. (1991). Economic thermal power dispatch with emission constraint, J. Indian Institute of Eng. (India), 72, pp. 11-18.
- 20. Kirkpatrick. S, Gelatt Jr. C.D and Vecchi. M.P. (1983). Optimization by simulated annealing, Science, 220, 671-80.
- 21. Metropolis. N, Rosenbluth. A.W, Rosenbluth. M.N, Teller. A.H and Teller. E. (1953). Equations of state calculations by fast computing machines, J Chem Phys, 21, 1087-92.
Index Terms
Keywords