DEVELOPMENT OF A DISCRETE EVENT SIMULATION BASED TOOL FOR OFFSHORE LOGISTICS IN WIND FARM INSTALLATION

The o shore wind energy development has shown a progress in the last ten years. Distance from
the coast and the depth of the water are increasing continuously. The transport and installation
of o -shore wind turbines is highly dependent on weather condition at sea. Delaying an activity in
the logistics chain could have a signi cant impact on overall project completion. Hence there is a
need to have a well-organized and thoroughly analyzed planning of the overall processes starting
from the producers
sites until the o shore site where the turbines will be installed.
The weather conditions and the installation strategies are the major limit factors in
uencing the
o shore wind farm installations. In this thesis a Discrete Event Simulation (DES) based tool has
been developed for o shore logistics in wind farm installation. It takes into account the weather
restriction limits (wind speed and wave height), distance matrix, vessel characteristics and installation
strategies. The logistics model maps out the activities of the o shore wind farm installations.
Both historical and probabilistic weather data approaches have been implemented, analyzed and
compared. The results indicated that the probabilistic approach is highly a ected by the random
seed numbers used to set up the stochastic behavior of the input variable.
Application of the developed tools to real O shore Wind Farm (OWF) con guration illustrated
that there is a good agreement between the results obtained from the two approaches. However,
it has been identi ed that the probabilistic approach may slightly underestimate the project lead
time compared to the results obtained using historical data. Furthermore, weather downtime and
sensitivity analysis have been carried out for di erent weather scenarios in order to identify the
parameters that a ect the logistic chain signi cantly during the o shore wind farm installation. In
this context, it is indicated that the lifting operation (limited by wind speed) causes the highest
down time compared to other activities and improving the lifting operations could result in a signi
cant reduction (10-15 %) in the overall project completion time.
In conclusion, overall analysis indicated that the application of a DES tool improves the decision
support system including the planning and control of the logistics processes of the o shore wind
farm installation, reducing the associated risks.