Please use this identifier to cite or link to this item:
Title: Study on offshore wind power potential and wind farm optimization in Hong Kong
Authors: Gao, XX
Yang, HX 
Lu, L 
Keywords: Hong Kong offshore
Wind power potential
Wind farm optimization
Multi-Population Genetic Algorithm (MPGA)
Monthly power generation
Issue Date: 2014
Publisher: Pergamon Press
Source: Applied energy, 2014, v. 130, p. 519-531 How to cite?
Journal: Applied energy 
Abstract: This paper investigates the potential and feasibility of the offshore wind energy in Hong Kong. The potential offshore wind farm locations are selected after considering the man-made activities in Hong Kong waters that may come into conflict with the development of the offshore wind farms. The hourly wind data of ten years (2002-2011) in the corresponding potential wind farms are analyzed. The assessment for offshore power potential is obtained with the potential wind farm being optimized using the Multi-Population Genetic Algorithm (MPGA) which aims at getting a minimum cost of energy (COE) with a maximum power generation. The optimal wind turbine layout configurations are proposed with an economic analysis and monthly power generation. Results show that the potential offshore wind farm area in and beyond (2 km away from its boundary) Hong Kong is 357.78 km(2) (21.68% of the HK water area) and southeastern water area is the most suitable location for offshore wind farm development. In additions, the potential annual offshore wind power generation is 112.81 x 10(8) kW h which accounts for 25.06% of the total annual power consumption in 2011 if the wind turbine layout is optimized. The potential monthly offshore wind power contribution to the electricity demand is the highest in October (46.46%) and lowest in August (7.93%).
Description: 5th International Conference on Applied Energy (ICAE), Pretoria, South Africa, 1-4 July 2013
ISSN: 0306-2619
EISSN: 1872-9118
DOI: 10.1016/j.apenergy.2014.02.070
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Aug 12, 2018


Last Week
Last month
Citations as of Aug 17, 2018

Page view(s)

Last Week
Last month
Citations as of Aug 13, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.