Abstract
This paper focuses on unit sizing of stand-alone hybrid energy system using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and comparative performance study of these two meta-heuristic techniques on hybrid energy system. The hybrid system is designed focusing on the viability and combining different renewable energy sources like wind turbines, solar panels along with micro-hydro plant as well as fuel cells to compensate the deficit generation in different hours. Apart from the non-conventional sources, the system has been optimized with converters, electrolyzers and hydrogen tanks. Net present cost (NPC), cost of energy (COE) and generation cost (GC) for power generation have been considered while optimal unit sizing of the system are performed. Feasibility of the system is made based on net present cost (NPC). The performances of two algorithms have been checked for different values of variants of the respective algorithms and a comparative study has been carried out based on number of iterations taken to find optimal solution, CPU utilization time and also quality of solutions. The comparative analysis shows that the Particle Swarm Optimization technique performs better than Genetic Algorithm when applied for the sizing problem.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
BP Statistical Review of World Energy (June 2011), http://www.bp.com
Koutroulis, E., Kolokotsa, D., Potirakis, A., Kostas, K.: Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Solar Energy 80, 1072–1088 (2006)
Dufo-Lopez, R., Bernal-Agustin, J.L.: Design and control strategies of PV-Diesel systems using genetic algorithms. Solar Energy 79, 33–46 (2005)
Ould Bilal, B., Sambou, V., Ndiaye, P.A., Kebe, C.M.F., Ndongo, M.: Optimal design of a hybrid solar-wind-battery system using the minimization of the annualized cost system and minimization of the loss of power supply probability (LPSP). Technical note, Renewable Energy 35, 2388–2390 (2010)
Hakimi, S.M., Moghaddas-Tafreshi, S.M.: Optimal sizing of a stand-alone hybrid power system via particle swarm optimization for Kahnouj area in south-east of Iran. Renewable Energy 34, 1855–1862 (2009)
Hakimi, S.M., Tafreshi, S.M., Kashefi, A.: Unit sizing of a stand-alone hybrid power system using particle swarm optimization (PSO). In: Proceedings of the IEEE International Conference on Automation and Logistics, Jinan, China, August 18-21 (2007)
Hakimi, S.M., Tafreshi, S.M., Rajati, M.R.: Unit sizing of a stand-alone hybrid power system using model free optimization. In: 2007 IEEE International Conference on Granular Computing (2007)
Li, M., Wang, C.: Research on optimization of wind and PV hybrid power systems. In: Proceedings of the World Congress on Intelligent Control and Automation, Chongqing, China, June 25-27 (2008)
Qi, Y., Jianhua, Z., Zifa, L., Shu, X., Weiguo, L.: A new methodology for optimizing the size of hybrid PV/wind system. In: ICSET (2008)
Xu, D., Kang, L., Cao, B.: Graph-Based Ant System for Optimal Sizing of Standalone Hybrid Wind/PV Power Systems. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS (LNAI), vol. 4114, pp. 1136–1146. Springer, Heidelberg (2006)
Katsigiannis, Y.A., Georgilakis, P.S.: Optimal sizing of small isolated hybrid power systems using tabu search. Journal of Optoelectronics and Advanced Materials 10(5), 1241–1245 (2008)
Dufo-Lopez, R., Bernal-Agustin, J.L.: Design and control strategies of PV-Diesel systems using genetic algorithms. Solar Energy 79, 33–46 (2005)
Katsigiannis, Y.A., Georgilakis, P.S., Karapidakis, E.S.: Genetic Algorithm Solution to Optimal Sizing Problem of Small Autonomous Hybrid Power Systems. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds.) SETN 2010. LNCS, vol. 6040, pp. 327–332. Springer, Heidelberg (2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks IV, pp. 1942–1948 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tudu, B., Majumder, S., Mandal, K.K., Chakraborty, N. (2011). Comparative Performance Study of Genetic Algorithm and Particle Swarm Optimization Applied on Off-grid Renewable Hybrid Energy System. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-27172-4_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27171-7
Online ISBN: 978-3-642-27172-4
eBook Packages: Computer ScienceComputer Science (R0)