Implementation of improved grasshopper optimization algorithm to solve economic load dispatch problems

Year 2019, , 1570 - 1589, 08.10.2019

Muhammad Sulaiman , Masihullah Masihullah Zubair Hussain Sohail Ahmad Wali Khan Mashwani , Muhammad Asif Jan Rashida Adeeb Khanum

https://doi.org/10.15672/hujms.507579

Cited By: 6

Abstract

The costs of different fuels are increasing gradually, for operation of power production units. Thus new optimization techniques are needed to tackle the complex problems of Economic Load Dispatch (ELD). Metaheuristics are very helpful for policy and decision makers in achieving the best results by minimizing the cost function. In this paper, we have updated the Grasshopper Optimization Algorithm (GOA) with a better initialization strategy to balance the search capability of GOA. The new algorithm is named as Improved Grasshopper Algorithm (IGOA). GOA is inspired by the swarms of grasshopper and mimics their biological behavior. Furthermore, IGOA is used to solve the ELD problems by tacking four case studies from literature. The objective in these problems is to find best decision variables for dispatching the available power with lowest cost, better efficiency and more reliability. To validate the efficiency of our proposed algorithm, we have tested it by solving 4 case studies of ELD with 1263MW, 600MW, 800MW and 2500MW demands respectively. IGOA is better in terms of convergence rate and quality of solutions obtained for the problems considered in literature for other metaheuristics.

Keywords

constrained optimization, metaheuristics, improved grasshopper optimization algorithm (IGOA), economic load dispatch

References

• [1] I. Aljarah , A.Z. AlaM, H. Faris, M.A. Hassonah, S. Mirjalili and H. Saadeh, Simulta- neous feature selection and support vector machine optimization using the grasshopper optimization algorithm, Cognitive Computation, 21,1-18, 2018.
• [2] A. Bhattacharya and P.K. Chattopadhyay, Biogeography-based optimization for different economic load dispatch problems, IEEE transactions on power systems, 25, 1064-1077, 2010.
• [3] A.H. Bindu and M.D. Reddy, Economic load dispatch using cuckoo search algorithm, Int. Journal Of Engineering Research and Applications, 3, 498-502, 2013.
• [4] M. Basu, A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems, International Journal of Electrical Power & Energy Systems, 27, 147-153, 2005.
• [5] A. Bhattacharya and P.K. Chattopadhyay, Solving complex economic load dispatch problems using biogeography-based optimization, Expert Systems with Applications, 37, pp.3605-3615, 2010.
• [6] P.H. Chen and H.C. Chang, Large-scale economic dispatch by genetic algorithm, IEEE transactions on power systems, 10, 1919-1926, 1995.
• [7] H.M. Dubey, M. Pandit, B.K. Panigrahi and M. Udgir, Economic load dispatch by hybrid swarm intelligence based gravitational search algorithm, International Journal of Intelli- gent Systems and Applications, 5, 21, 2013.
• [8] M. Fesanghary, and M.M. Ardehali, A novel meta-heuristic optimization methodology for solving various types of economic dispatch problem, Energy, 34, 757-766, 2009.
• [9] Z.W. Geem, J.H. Kim and G.V. Loganathan, A new heuristic optimization algorithm: harmony search. simulation, 76, 60-68, 2001.
• [10] Z.L. Gaing, Particle swarm optimization to solving the economic dispatch considering the generator constraints, IEEE transactions on power systems. 18, 1187-95, 2003.
• [11] Y.C. Ho and Q.C. Zhao and D.L. Pepyne, The no free lunch theorems: Complexity and security, IEEE Transactions on Automatic Control, 48, 83-793, 2003.
• [12] S. Hemamalini and S.P. Simon, Artificial bee colony algorithm for economic load dispatch problem with non-smooth cost functions, Electric Power Components and Systems, 38, 786-803, 2010.
• [13] A. Lewis, LoCost: a spatial social network algorithm for multi-objective optimisation, In Evolutionary Computation, CEC’09, IEEE Congress, 2866-2870, 2009.
• [14] S.Z. Mirjalili, S. Mirjalili, S. Saremi, H. Faris and I. Aljarah, Grasshopper optimization algorithm for multi-objective optimization problems, Applied Intelligence, 48, 805-820, 2018.
• [15] M.M. Nischal and S. Mehta, Optimal load dispatch using ant lion optimization, Int. J Eng Res Appl, 5, 10-19, 2015.
• [16] N. Noman and H. Iba, Differential evolution for economic load dispatch problems. Electric Power Systems Research, 78, 1322-1331, 2008.
• [17] R.E. Perez-Guerrero and J.R. Cedeno-Maldonado, Economic power dispatch with non- smooth cost functions using differential evolution, In Power Symposium, Proceedings of the 37th Annual North American, 183-190, 2005.
• [18] V.R. Pandi, B.K. Panigrahi, R.C. Bansal, S. Das and A. Mohapatra, Economic load dispatch using hybrid swarm intelligence based harmony search algorithm, Electric power components and systems, 39, 751-767, 2011.
• [19] A. Pereira-Neto, C. Unsihuay, and O.R. Saavedra, Efficient evolutionary strategy op- timisation procedure to solve the nonconvex economic dispatch problem with generator constraints, IEE Proceedings-Generation, Transmission and Distribution, 152, 653-660, 2005.
• [20] S. Pothiya, I. Ngamroo and W. Kongprawechnon, Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints, Energy Conversion and Management. 49, 506-16, 2008.
• [21] S.R. Rayapudi, An intelligent water drop algorithm for solving economic load dispatch problem, International Journal of Electrical and Electronics Engineering, 5, 43-49, 2011.
• [22] K.S. Reddy, and M.D. Reddy, Economic load dispatch using firefly algorithm, Interna- tional journal of Engineering Research and Applications, 2, 2325-2330, 2012.
• [23] S.M. Rogers, T. Matheson, E. Despland, T. Dodgson, M. Burrows and S.J. Simpson, Mechanosensory-induced behavioural gregarization in the desert locust Schistocerca gre- garia, Journal of Experimental Biology, 206, 3991-4002, 2003.
• [24] S. Saremi, S. Mirjalili and A. Lewis, Grasshopper optimisation algorithm: theory and application, Advances in Engineering Software, 105, 30-47, 2017.
• [25] C.T. Su, and C.T. Lin, New approach with a Hopfield modeling framework to economic dispatch, IEEE Transactions on Power Systems, 15, 541-545, 2000.
• [26] N. Sinha, R. Chakrabarti and Chattopadhyay P.K. Evolutionary programming techniques for economic load dispatch, IEEE Transactions on Evolutionary Computation. 7, 83-94, 2003.
• [27] S. Sayah and A. Hamouda, A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems. Applied Soft Computing, 13, 1608-1619, 2013.
• [28] R.K. Swain, N.C. Sahu and P.K. Hota, Gravitational search algorithm for optimal eco- nomic dispatch, Procedia technology, 6, 411-419, 2012.
• [29] C.M. Topaz, A.J. Bernoff, S. Logan and W. Toolson, A model for rolling swarms of locusts, The European Physical Journal Special Topics, 157, 93-109, 2008.
• [30] J. Wu, H. Wang, N. Li, P. Yao, Y. Huang, Z. Su and Y. Yu, Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm, Aerospace Science and Technology, 70, 497-510, 2017.
• [31] L. Wang and L.P. Li , An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems, International Journal of Electrical Power & Energy Systems, 44, 832-843, 2013.
• [32] A.J. Wood, and F.W. Bruce, Power generation, operation, and control, John Wiley and Sons, 2012.
• [33] H.K. Youssef and K.M. El-Naggar, Genetic based algorithm for security constrained power system economic dispatch, Electric Power Systems Research, 53, 47-51, 2000.
• [34] X.S. Yang, S.S.S. Hosseini and A.H. Gandomi, Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect, Applied Soft Computing, 12, 1180- 1186, 2012.
• [35] T. Yalcinoz, H. Altun, and M. Uzam, Economic dispatch solution using a genetic algo- rithm based on arithmetic crossover, In Power Tech Proceedings, IEEE Porto, 2, 4-pp, 2001.