skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected systems

Abstract

Particle Swarm Optimization (PSO) is a highly efficient evolutionary optimization algorithm. In this paper a multiobjective optimization algorithm based on PSO applied to the optimal design of photovoltaic grid-connected systems (PVGCSs) is presented. The proposed methodology intends to suggest the optimal number of system devices and the optimal PV module installation details, such that the economic and environmental benefits achieved during the system's operational lifetime period are both maximized. The objective function describing the economic benefit of the proposed optimization process is the lifetime system's total net profit which is calculated according to the method of the Net Present Value (NPV). The second objective function, which corresponds to the environmental benefit, equals to the pollutant gas emissions avoided due to the use of the PVGCS. The optimization's decision variables are the optimal number of the PV modules, the PV modules optimal tilt angle, the optimal placement of the PV modules within the available installation area and the optimal distribution of the PV modules among the DC/AC converters. (author)

Authors:
 [1]
  1. Technical University of Crete, Department of Electronic and Computer Engineering, Chania (Greece)
Publication Date:
OSTI Identifier:
21396177
Resource Type:
Journal Article
Journal Name:
Solar Energy
Additional Journal Information:
Journal Volume: 84; Journal Issue: 12; Other Information: Elsevier Ltd. All rights reserved; Journal ID: ISSN 0038-092X
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; PHOTOVOLTAIC CELLS; OPTIMIZATION; ALGORITHMS; DESIGN; ORIENTATION; FUNCTIONS; ECONOMICS; POWER SYSTEMS; DISTRIBUTION; EMISSION; SOLAR EQUIPMENT; Multiobjective optimization; Particle Swarm Optimization

Citation Formats

Kornelakis, Aris. Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected systems. United States: N. p., 2010. Web. doi:10.1016/J.SOLENER.2010.10.001.
Kornelakis, Aris. Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected systems. United States. doi:10.1016/J.SOLENER.2010.10.001.
Kornelakis, Aris. Wed . "Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected systems". United States. doi:10.1016/J.SOLENER.2010.10.001.
@article{osti_21396177,
title = {Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected systems},
author = {Kornelakis, Aris},
abstractNote = {Particle Swarm Optimization (PSO) is a highly efficient evolutionary optimization algorithm. In this paper a multiobjective optimization algorithm based on PSO applied to the optimal design of photovoltaic grid-connected systems (PVGCSs) is presented. The proposed methodology intends to suggest the optimal number of system devices and the optimal PV module installation details, such that the economic and environmental benefits achieved during the system's operational lifetime period are both maximized. The objective function describing the economic benefit of the proposed optimization process is the lifetime system's total net profit which is calculated according to the method of the Net Present Value (NPV). The second objective function, which corresponds to the environmental benefit, equals to the pollutant gas emissions avoided due to the use of the PVGCS. The optimization's decision variables are the optimal number of the PV modules, the PV modules optimal tilt angle, the optimal placement of the PV modules within the available installation area and the optimal distribution of the PV modules among the DC/AC converters. (author)},
doi = {10.1016/J.SOLENER.2010.10.001},
journal = {Solar Energy},
issn = {0038-092X},
number = 12,
volume = 84,
place = {United States},
year = {2010},
month = {12}
}