Abstract
Scheduling of wind-thermal electrical generators is a challenging constrained optimization problem, where the main goal is to find the optimal allocation of output power among various available generators to serve the system load. Over the last few decades, a large number of solution approaches, including evolutionary algorithms, have been developed to solve this problem. However, these approaches are usually ineffective and time consuming. In this paper, we apply two variants of genetic algorithm (GA) for solving the problem where the first variant is to optimize the allocation and the second one is to rank the generators for allocation. The proposed algorithm is applied to a recent wind-thermal benchmark that comprises five thermal and 160 wind farms. The model includes a stochastic nature of wind energy and gas emission effects of thermal plants. The simulation results show that the proposed method is superior to those results of different variants of GA and the state-of-the-art algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zaman, M.F., Elsayed, S.M., Ray, T., Sarker, R.A.: Evolutionary algorithms for dynamic economic dispatch problems. IEEE Trans. Power Syst. PP(99), 1–10 (2015)
Peng, C., Sun, H., Guo, J., Liu, G.: Dynamic economic dispatch for wind-thermal power system using a novel bi-population chaotic differential evolution algorithm. Int. J. Electr. Power Energy Syst. 42, 119–126 (2012)
Hindi, K.S., Ghani, M.R.A.: Dynamic economic dispatch for large scale power systems: a Lagrangian relaxation approach. Int. J. Electr. Power Energy Syst. 13, 51–56 (1991)
Irisarri, G., Kimball, L.M., Clements, K.A., Bagchi, A., Davis, P.W.: Economic dispatch with network and ramping constraints via interior point methods. IEEE Trans. Power Syst. 13, 236–242 (1998)
Chen, C.L., Lee, T.Y., Jan, R.M.: Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity. Energy Convers. Manag. 47, 3456–3472 (2006)
Chiong, R., Weise, T., Michalewicz, Z.: Variants of evolutionary algorithms for real-world applications. Springer, Berlin (2012)
Lee, J.C., Lin, W.M., Liao, G.C., Tsao, T.P.: Quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system. Int. J. Electr. Power Energy Syst. 33, 189–197 (2011)
Zaman, F., Sarker, R.A., Ray, T.: Solving an economic and environmental dispatch problem using evolutionary algorithm. In: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1367–1371 (2014)
Panigrahi, C.K., Chattopadhyay, P.K., Chakrabarti, R.N., Basu, M.: Simulated Annealing Technique for Dynamic Economic Dispatch. Electric Power Compon. Syst. 34, 577–586 (2006)
Aghaei, J., Niknam, T., Azizipanah-Abarghooee, R., Arroyo, J.M.: Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties. Int. J. Electr. Power Energy Syst. 47, 351–367 (2013)
Attaviriyanupap, P., Kita, H., Tanaka, E., Hasegawa, J.: A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Trans. Power Syst. 22, 77 (2002)
Victoire, T.A.A., Jeyakumar, A.E.: A modified hybrid EP-SQP approach for dynamic dispatch with valve-point effect. Int. J. Electr. Power Energy Syst. 27, 594–601 (2005)
Hetzer, J., Yu, D.C., Bhattarai, K.: An economic dispatch model incorporating wind power. IEEE Trans. Energy Convers. 23, 603–611 (2008)
Deep, K., Singh, K.P., Kansal, M.L., Mohan, C.: A real coded genetic algorithm for solving integer and mixed integer optimization problems. Appl. Math. Comput. 212, 505–518 (2009)
Elsayed, S.M., Sarker, R.A., Essam, D.L.: Adaptive configuration of evolutionary algorithms for constrained optimization. Appl. Math. Comput. 222, 680–711 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Zaman, M.F., Elsayed, S.M., Ray, T., Sarker, R.A. (2016). A Double Action Genetic Algorithm for Scheduling the Wind-Thermal Generators. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-28270-1_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28269-5
Online ISBN: 978-3-319-28270-1
eBook Packages: Computer ScienceComputer Science (R0)