APPLICATION OF ANT COLONY OPTIMIZATION (ACO) FOR DESIGNING A HYBRID SYSTEM

Yıl 2014, Cilt: 6 Sayı: 2, 52 - 63, 01.06.2014

Alireza Askarzadeh

https://doi.org/10.24107/ijeas.251221

Öz

Inspired by the behavior of ants seeking the shortest path between their nest and a food source, ant colony optimization (ACO) is the most popular technique to effectively solve combinatorial optimization problems. Combinatorial optimization is a branch of optimization which is concerned with the optimization of functions with discrete decision variables. Finding optimum size of a PV/wind/battery hybrid system belongs to combinatorial optimization problems with the aim of determining three discrete decision variables, namely, number of PV panels, wind turbines and batteries. This paper proposes ACO to optimally size a PV/wind/battery hybrid system for having a reliable system. In order to evaluate the effectiveness of the proposed methodology, ACO performance is compared with that of two other well-known metaheuristic algorithms, namely, harmony search (HS) and particle swarm optimization (PSO). It is observed that ACO yields more promising results than the other studied methodologies

Anahtar Kelimeler

hybrid system, optimum sizing, combinatorial optimization, ant colony optimization

Kaynakça

• [1] Diaf, S., Diaf, D., Belhamel, M., Haddadi, M., Louche, A., 2007. A methodology for optimal sizing of autonomous hybrid PV/wind system. Energy Policy 35, 5708-5718.
• [2] Borowy, B.S., Salameh, Z.M., 1996. Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system. IEEE Transactions on Energy Conversion 11, 367–373.
• [3] Shrestha, G.B., Goel, L., 1998. A study on optimal sizing of stand-alone photovoltaic stations. IEEE Transactions on Energy Conversion 13 (4), 373–378.
• [4] Maghraby, H.A.M., Shwehdi, M.H., Al-Bassam, G.K., 2002. Probabilistic assessment of photovoltaic (PV) generation systems. IEEE Transactions on Power Systems 17 (1), 205–208.
• [5] Kellogg, W.D., Nehrir, M.H., Venkataramanan, G., Gerez, V., 1998. Generation unit sizing and cost analysis for stand-alone wind, photovoltaic and hybrid wind/PV systems. IEEE Transactions on Energy Conversion 13 (1), 70–75.
• [6] Prasad, A.R., Natarajan, E., 2006. Optimization of integrated photovoltaic–wind power generation systems with battery storage. Energy 31, 1943–1954.
• [7] Roy, S., 1997. Optimal planning of wind energy conversion systems over an energy scenario. IEEE Transactions on Energy Conversion 12, 248-253.
• [8] Rohani, A., Mazlumi, K., Kord, H., 2010. Modeling of a hybrid power system for economic analysis and environmental impact in HOMER. Iranian Conference on Electrical Engineering (ICEE), 819-823.
• [9] Luna-Rubio, R., Trejo-Perea, M., Vargas-Vázquez, D., Ríos-Moreno, G.J., 2012. Optimal sizing of renewable hybrids energy systems: A review of methodologies. Solar Energy 86, 1077- 1088.
• [10] Koutroulis, E., Kolokotsa, D., Potirakis, A., Kalaitzakis, K., 2006. Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Solar Energy 80, 1072-1088.
• [11] Yang, H., Zhou, W., Lu, L., Fang, Z., 2008. Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm. Solar Energy 82, 354-367.
• [12] Kaviani, A.K., Baghaee, H.R., Riahy, G.H., 2009. Optimal sizing of a stand-alone wind/photovoltaic generation unit using particle swarm optimization. Journal of Simulation 85, 89-99.
• [13] Ekren, O., Ekren, B.Y., 2010. Size optimization of PV/wind hybrid energy conversion system with battery storage using simulated annealing, Applied Energy 87, 592-598.
• [14] Javadi, M.R., Mazlumi, K., Jalilvand, A., 2011. Application of GA, PSO and ABC in optimal design of a stand-alone hybrid system for north-west of Iran. ELECO 2011 7th International Conference on Electrical and Electronics Engineering, Bursa, Turkey, 204-211.
• [15] Geem, Z.W., 2012. Size optimization for a hybrid photovoltaic–wind energy system. International Journal of Electrical Power & Energy Systems 42, 448-451.