DocumentCode :
1896828
Title :
Optimal placement of hybrid PV-wind systems using genetic algorithm
Author :
Masoum, Mohammad A S ; Badejani, Seyed M Mousavi ; Kalantar, Mohsen
Author_Institution :
Curtin Univ. of Technol., Perth, WA, Australia
fYear :
2010
fDate :
19-21 Jan. 2010
Firstpage :
1
Lastpage :
5
Abstract :
Genetic algorithms are proposed for optimal placement of hybrid PV-wind system (HPWS) and for determining the optimal ratio of wind/solar power contributions. The total capacity of HPWS is determined based on estimated annual power demand, average wind speed and sun radiation. Each PV and wind unit is defined based on real environmental conditions. To improve HPWS performance under different operating and environmental conditions, maximum power point tracking of PV units and blade angle pitch control of wind turbines are considered. For each candidate location, cost functions corresponding to PV, wind and battery units, as well as surplus produced power are defined and genetically minimized to determine the best location of HPWS. The proposed algorithm is used for optimal placement of a 1MVA hybrid PV-wind system in United States (considering 265 candidate locations) and to compute the optimal number of 40kW-PV and 68.46kW-wind units.
Keywords :
blades; genetic algorithms; maximum power point trackers; photovoltaic power systems; solar power; solar radiation; wind power; wind power plants; wind turbines; HPWS; United States; average wind speed; blade angle pitch control; cost functions; estimated annual power demand; genetic algorithm; hybrid PV-Wind systems optimal placement; maximum power point tracking; optimal ratio; power 40 kW; power 68.46 kW; solar power; sun radiation; wind power; wind turbines; Batteries; Blades; Control systems; Cost function; Genetic algorithms; Hybrid power systems; Induction generators; Power system reliability; Wind energy generation; Wind turbines; Hybrid PV-wind systems; cost function; genetic algorithms; optimal location;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Smart Grid Technologies (ISGT), 2010
Conference_Location :
Gaithersburg, MD
Print_ISBN :
978-1-4244-6264-3
Electronic_ISBN :
978-1-4244-6333-6
Type :
conf
DOI :
10.1109/ISGT.2010.5434746
Filename :
5434746
Link To Document :
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