Perbandingan Metode Gaussian Particle Swarm Optimization (GPSO) dan Lagrange Multiplier pada Masalah Economic Dispatch


  • Siti Komsiyah Bina Nusantara University



power system, load demand, economic dispatch, Gaussian Particle Swarm Optimization (GPSO), Lagrange Multiplier


On electric power system operation, economic planning problem is one variable to take into account due to operational cost efficiency. Economic Dispatch problem of electric power generation is discussed in this study to manage the output division on several units based on the the required load demand, with minimum operating cost yet is able to satisfy equality and inequality constraint of all units and system. In this study the Economic Dispatch problem which has non linear cost function is solved using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving is inspired by swarm intelligent and probabilities theories. To analize its accuracy, the Economic Dispatch solution by GPSO method is compared with Lagrange Multiplier method. From the test result it is proved that GPSO method gives economic planning calculation better than Lagrange Multiplier does.


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Aziz, A. M. A., Musirin, J. I., dan Rahman, T. K. A. (2006). Solving Economic Dispatch Using Evolutionary Programming. First International Power and Energy Conference PECon, Putra Jaya, Malaysia, 144-149.

Coelho, L.S dan Lee, C.S. (2008). Solving Economic Load Dispatch Problem in Power System. Journal of Electrical Power and Energy Systems, 30, 297-307.

Dieu, V. N. & Ongsakul, W. (2007). Augmented Lagrange Hopfield Network for Large Scale Economic Dispatch. International Symposium on Electrical and Electronics Engineering, HCM City, Vietnam, 2, 19-26.

El-Ela, A.A.A. dan El-Sehiemy, R.A.A. (2007). Optimized Generation Costs Using Modified Particle Swarm Optimization Version. WSEAS Transactions on Power Systems, 2(10), 225-232.

Laoufi, A., Hazzab, A., & Rahli, M. (2006). Economic Power Dispatch Using Fuzzy-Genetic Algorithm. International Jourrnal of Applied Engineering Research, 1(3), 409-426.

Marsudi, D. (2006). Operasi Sistem Tenaga Listrik, (edisi pertama). Yogyakarta: Graha Ilmu.

Ongsakul, W., Dechanupaprittha, S., & Ngamroo, I. (2004). Parallel Tabu Search Algorithm for Constrained Economic Dispatch. IEE Proceeding of Generation, Transmission and Distribution, 151, 157-166.

Panigrahi, B. K., Pandi, V. R., & Das, S. (2008). Adaptive Particle Swarm Optimization Approach for Static and Dynamic Economic Load Dispatch. Journal of Energy Conversion and Management, 49, 1407-1415.

Park, J. B, Shin, J.R., & Jeong, Y.W. (2006). An Improved Particle Swarm Optimization for Economic Dispatch with Valve – Point Effect. International journal of Innovation in Energy System and

Power, 1(1), 1-6.

Slimani, L, dan Bouktir, T. (2007). Economic Power Dispatch of Power System with Pollutan Control Using Multiobjective Ant Colony Optimization. International Journal of Computational

Intelligence Research, 3(2), 145-153.

Wahyono, A.Y. (2000). Economic Dispatch Dengan Optimasi terhadap Daya Aktif dan Daya reaktif. Tugas Akhir. Institut Teknologi Sepuluh Nopember Surabaya, Surabaya.

Wong, K. P. & Chung, C.C. (1993). Simulated Annealing Based Economic Dispatch Algorithm. IEE Proceeding of Generation, Transmission and Distribution, 140, 509-515.

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

Zhao, B. dan Cao, Y. J. (2005). Multiple Objective Particle Swarm Optimization Technique for Economic Load Dispatch. Journal of Zhejiang University SCIENCE, 6(5), 420-427.






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