In this paper, a (photovoltaic-wind-battery) hybrid system is designed to provide a network-independent pattern. The purpose of this paper is to provide the necessary energy and minimize production total costs over the life of the system. By using a new algorithm called "particle swarm optimization algorithm with constriction factor", which has advantages like high convergence speed, the optimal size and minimum cost of these resources are determined. The system’s components optimal size has been studied under various operating conditions using real-time information and meteorological data of one of Iran’s Southern, North West and North East regions. The results show that the "particle swarm optimization algorithm with constriction factor" is more efficient than the "particle swarm optimization algorithm" and ''tabu search''.
Ameri, M., Maleki, A., & Keynia, F. (2023). Optimal Size of Hybrid System, Photovoltaic/Wind/Batteries Using Particle Swarm with Constriction Factor Algorithm. Energy Engineering and Management, 4(4), 50-61.
MLA
Mehran Ameri; Akbar Maleki; Farshid Keynia. "Optimal Size of Hybrid System, Photovoltaic/Wind/Batteries Using Particle Swarm with Constriction Factor Algorithm", Energy Engineering and Management, 4, 4, 2023, 50-61.
HARVARD
Ameri, M., Maleki, A., Keynia, F. (2023). 'Optimal Size of Hybrid System, Photovoltaic/Wind/Batteries Using Particle Swarm with Constriction Factor Algorithm', Energy Engineering and Management, 4(4), pp. 50-61.
VANCOUVER
Ameri, M., Maleki, A., Keynia, F. Optimal Size of Hybrid System, Photovoltaic/Wind/Batteries Using Particle Swarm with Constriction Factor Algorithm. Energy Engineering and Management, 2023; 4(4): 50-61.
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