Reference : A discrete-event simulation approach to evaluate the effect of stochastic parameters ...
Scientific journals : Article
Engineering, computing & technology : Mechanical engineering
http://hdl.handle.net/2268/218207
A discrete-event simulation approach to evaluate the effect of stochastic parameters on offshore wind farms assembly strategies
English
Tekle Muhabie, Y. [University of Liège, Quartier Polytech 1, Allée de la découverte 9, Liège, Belgium]
Rigo, Philippe mailto [Université de Liège - ULiège > Département ArGEnCo > Constructions hydrauliques et navales >]
Cepeda, M. [Federal University of Rio de Janeiro, Ilha Fundão, Rio de Janeiro, Brazil]
de Almeida D'Agosto, M. [Federal University of Rio de Janeiro, Ilha Fundão, Rio de Janeiro, Brazil]
Caprace, Jean-David mailto [Université de Liège - ULiège > Département ArGEnCo > Constructions hydrauliques et navales >]
2018
Ocean Engineering
Elsevier Ltd
149
279-290
Yes (verified by ORBi)
International
0029-8018
[en] Decision support systems ; Logistics ; Metocean ; Offshore ; Simulation ; Stochastic processes ; Artificial intelligence ; Discrete event simulation ; Electric utilities ; Meteorology ; Offshore wind turbines ; Random processes ; Stochastic models ; Stochastic systems ; Wind power ; Wind turbines ; Effective approaches ; Environmental conditions ; Installation procedures ; Stochastic nature ; Stochastic parameters ; Offshore wind farms
[en] The wind industry is facing new challenges due to the planned construction of thousands of offshore wind turbines all around the world. However, with their increasing distance from the shore, greater water depths, and increasing sizes of the plants, the industry has to face the challenge to develop sustainable installation procedures. Important limiting factors for offshore wind farm installation are the weather conditions and installation strategies. In this context, the focus of this research is the investigation of the most effective approach to installing offshore wind farms at sea, including the effects of weather conditions. This target is achieved through the implementation of a discrete-event simulation approach which includes the analysis of the environmental conditions, distance matrix, vessel characteristics, and assembly scenarios. The model maps the logistics chain in the offshore wind industry. A deterministic and a probabilistic metocean data method have been compared and cross validated. The results point to a good agreement between the two considered models, while highlighting the huge risks to the time and cost of the installation due to the stochastic nature of the weather. We suggest that simulations may improve and reduce these risks in the planning process of offshore wind farms. © 2017 Elsevier Ltd
Researchers ; Professionals
http://hdl.handle.net/2268/218207
10.1016/j.oceaneng.2017.12.018

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