Open Access
Issue
E3S Web Conf.
Volume 57, 2018
2018 3rd International Conference on Sustainable and Renewable Energy Engineering (ICSREE 2018)
Article Number 03005
Number of page(s) 5
Section Smart Grid and Power System
DOI https://doi.org/10.1051/e3sconf/20185703005
Published online 05 October 2018
  1. Turner SDO, Romero DA, Zhang PY, Amon CH, Chan TCY. A new mathematical programming approach to optimize wind farm layouts, 2014, Renewable Energy. 674–680. [CrossRef] [Google Scholar]
  2. Jensen NO,. A note on wind generator interaction RISO National Laboratory; 1983 November; Roskilde, DK-4000, Denmark. [Google Scholar]
  3. Mosetti G, Poloni C, Daviacco B. Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. 1994, J Wind Eng Ind Aerod. 51:105–116. [CrossRef] [Google Scholar]
  4. Grady S, Hussaini M, Abdullah M. Placement of wind turbines using genetic algorithms. 2005, Renewable Energy. 30: 259–270. [CrossRef] [Google Scholar]
  5. Marmidis G, Lazarou S, Pyrgioti E. Optimal placement of wind turbines in a wind park using monte carlo simulation, 2008, Renewable Energy. 33: 1455–1460. [CrossRef] [Google Scholar]
  6. Wolsey LA,. Integer Programming, Wiley, 1998. [Google Scholar]
  7. Mittal A. Optimization of the Layout of Large Wind Farms using a Genetic Algorithm. Department of Mechanical and Aerospace Engineering, 2010, Case Western Reserve University, Cleveland. [Google Scholar]
  8. Extended LINGO/Win 64. Release 17.0.65. Lindo System Inc. Chicago, September 2017. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.