Optimization of Wind-Marine Hybrid Power System Configuration Based on Genetic Algorithm

Multi-energy power systems can use energy generated from various sources to improve power generation reliability. This paper presents a cost-power generation model of a wind-tide-wave energy hybrid power system for use on a remote island, where the configuration is optimized using a genetic algorith...

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Vydáno v:Journal of Ocean University of China Ročník 16; číslo 4; s. 709 - 715
Hlavní autoři: Shi, Hongda, Li, Linna, Zhao, Chenyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Heidelberg Science Press 01.08.2017
Springer Nature B.V
College of Engineering, Ocean University of China, Qingdao 266100, P.R.China
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ISSN:1672-5182, 1993-5021, 1672-5174
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Shrnutí:Multi-energy power systems can use energy generated from various sources to improve power generation reliability. This paper presents a cost-power generation model of a wind-tide-wave energy hybrid power system for use on a remote island, where the configuration is optimized using a genetic algorithm. A mixed integer programming model is used and a novel object function, including cost and power generation, is proposed to solve the boundary problem caused by existence of two goals. Using this model, the final optimized result is found to have a good fit with local resources.
Bibliografie:37-1415/P
Multi-energy power systems can use energy generated from various sources to improve power generation reliability. This paper presents a cost-power generation model of a wind-tide-wave energy hybrid power system for use on a remote island, where the configuration is optimized using a genetic algorithm. A mixed integer programming model is used and a novel object function, including cost and power generation, is proposed to solve the boundary problem caused by existence of two goals. Using this model, the final optimized result is found to have a good fit with local resources.
genetic algorithm; cost-power generation function; multi-energy system; marine energy
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content type line 14
ISSN:1672-5182
1993-5021
1672-5174
DOI:10.1007/s11802-017-3343-3