Abstract
In this paper, a metaheuristic algorithm known as “Shuffled Differential Evolution (SDE)” had been applied to resolve the Combined heat and power emission dispatch (CHPEmD) issue. This SDE algorithm combines features of both the Differential evolution algorithm, shuffled frog-leaping algorithm by incorporating splitting into the partitions and shuffling. To verify the efficacy of this SDE algorithm and also to determine the exemplary solution for the CHPEmD problem, two caliber test systems are considered. The outcomes realized by this SDE algorithm are confronted with the optimization algorithms available in the previous literary works. The contrast of the results shows that SDE technique exhibits impressive performance in delivering the optimal results in terms of convergence and solutions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Karki, S., Kulkarni, M., Mann, M.D., Salehfar, H.: Efficiency improvements through combined heat and power for on-site distributed generation technologies. Cogener. Distrib. Gener. J. 22(3), 19–34 (2007)
Vasebi, A., Fesanghary, M., Bathaee, S.: Combined heat and power economic dispatch by harmony search algorithm. Electr. Power Energy Syst. 29, 713–719 (2007)
Spall, J.C.: Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, vol. 65. Wiley, Hoboken (2005)
Herrera, F., Lozano, M., Verdegay, J.L.: Tracking real-coded genetic algorithms: operators and tools for behavioral analysis. Artif. Intell. Rev. 12(4), 265–319 (1988)
Deb, K., Agarawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9(2), 115–148 (1995)
Jayakumar, N., Subramanian, S., Ganesan, S., Elanchezhian, E.B.: Grey wolf optimization for combined heat and power dispatch with cogeneration systems. Electr. Power Energy Syst. 74, 252–264 (2016)
Basu, M.: Combined heat and power economic emission dispatch using nondominated sorting genetic algorithm-II. Electr. Power Energy Syst. 53, 135–141 (2013)
Abido, M.A.: Environmental/economic power dispatch using multi objective evolutionary algorithms. IEEE Trans. Power Syst. 18(4), 1529–1537 (2003)
Eusuff, M., Lansey, K., Pasha, F.: Shuffled frog-leaping algorithm: a memetic metaheuristic for discrete optimization. Eng. Optim. 38(2), 129–154 (2006)
Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)
Srinivasa Reddy, A., Vaisakh, K.: Shuffled differential evolution for large scale economic dispatch. Electr. Power Syst. Res. 96, 237–245 (2013)
Srinivasa Reddy, A., Vaisakh, K.: Shuffled differential evolution for economic dispatch with valve point loading effects. Electr. Power Syst. 46, 342–352 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nagaraju, S., Srinivasreddy, A., Vaisakh, K. (2020). Shuffled Differential Evolution Algorithm Based Combined Heat and Power Emission Dispatch. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_36
Download citation
DOI: https://doi.org/10.1007/978-981-13-9042-5_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9041-8
Online ISBN: 978-981-13-9042-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)