Impacts of water depth increase on offshore floating wind turbine dynamics

Offshore Wind and Wave Energy Complementarity in the Greek Seas Based on ERA5 Data

“…From these locations, the ones with water depths greater than 300 m were excluded from further analysis. This is in agreement with [53], where it is stated that "The considered water depth is between 200 and 300 m, which is the deep-water range used in the current floating offshore wind turbine (FOWT) industry". Let it be noted however that, regarding the offshore oil and gas sector, the relevant water depths refer to the level of thousands of meters [53].…”

“…This is in agreement with [53], where it is stated that "The considered water depth is between 200 and 300 m, which is the deep-water range used in the current floating offshore wind turbine (FOWT) industry". Let it be noted however that, regarding the offshore oil and gas sector, the relevant water depths refer to the level of thousands of meters [53]. Based on the theory presented in Section 3 and the results depicted in Figure 6, 50 locations characterized by the highest values of wind to wave complementarity (Figure 6 a) and synergy (Figure 6c) indices were identified.…”

Assessment of the Offshore Wind Energy Potential in the Romanian Exclusive Economic Zone

“…Figure 1 presents the target area that is delimited by the EEZ borders, including the water depth details processed from GEBCO (general bathymetric chart of the oceans) [ A total of 84 points were defined in a grid (lines from A to J), the distance between each point being constant, approximately 20 km along the x and y directions. In addition, a water depth of 50 m was considered as a reference, since this represents the threshold that separates the fixed offshore wind farms from the floating ones [22,23]. By considering these points it is possible to obtain a general picture of the energy potential and the performances of a particular turbine, which can be further extended to a more detailed investigation.…”

Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)

“…Among them, wind energy has become one of the most attractive supplies, which is expected to provide 20% of electricity for the global demand by 2030 [1]. It can be seen that wind turbine installations are growing sharply [2], especially offshore wind turbines [3], which are expected to own over 234 GW capacity worldwide in recent decades [4]. As one of the most suitable locations for wind energy developments, the United Kingdom has committed to greatly extending offshore wind capacity [5].…”