Floating modular energy islands (FMEIs) are modular, interconnected floating structures designed to collectively produce, store, convert, and transport renewable energy. This review aims to establish a foundation for developing innovative approaches to sustainably harness multi-energy sources in offshore environments. It leverages existing technological expertise while exploring new solutions to address specific challenges associated with FMEIs. The review initially presents existing technologies for floating energy structures and assesses their applicability to FMEI. The structural materials that could be utilised for the construction of a floating energy island are subsequently reviewed. Next, the offshore construction technologies suitable for FMEI are reviewed. Finally, studies on the life cycle assessment of hybrid energy systems are examined, highlighting the environmental advantages of integrating multiple renewable energy sources, thereby underscoring the potential of FMEIs. ... Read more

Accurate modeling of wind conditions is vital for the efficient operation and management of wind farms. This study investigates the enhancement of weather simulations by assimilating local offshore light detection and ranging (lidar) and/or supervisory control and data acquisition (SCADA) data into a numerical weather prediction model while considering the presence of neighboring wind farms through wind farm parameterization. We focus on improving model output during storms impacting the Belgian–Dutch wind farm cluster located in the Southern Bight of the North Sea via the four-dimensional data assimilation (nudging) technique in the Weather Research and Forecasting (WRF) model. Our findings indicate that assimilating wind observations significantly reduces the relative root-mean-square error for wind speed at a wind farm located 47 km downwind from the offshore lidar platform. This leads to more accurate power production outputs. Specifically, at wind turbines experiencing wake effects, the wind speed error decreased from 10.5 % to 5.2 %, and the wind direction error was reduced by a factor of 2.4. A proposed artificial configuration, leveraging the upwind lidar measurements, showcases the potential for improving hour-ahead wind and power predictions. Moreover, we perform a thorough study to investigate the sensitivity to nudging parameters during versatile atmospheric conditions, which helps to identify the best assimilation practices for this offshore setting. These insights are expected to refine wind resource mapping and reanalysis of weather events, as well as motivate more measurement campaigns offshore. ... Read more

Floating Modular Energy Islands (FMEIs) are modularized, interconnected floating structures that function together to produce, store, possibly convert and transport renewable energy. Recent technological advancements in the offshore energy sector indicate that the concept of floating offshore energy islands has the potential to become more cost-effective and more widespread than previously anticipated. This review is specifically meant as a basis for the development of new approaches to the sustainable exploitation of multi-energy sources in the offshore environment leveraging the know-how of existing technologies and, at the same time, exploring new solutions for the specific challenges of FMEIs. The paper critically analyzes the current state of data-driven approaches and structural health monitoring techniques in the offshore energy sector. It also covers topics such as met-ocean data, load estimation, platform dynamics, coupling actions, nonlinear dynamics of mooring lines, modelling considerations, and control of electrical subsystems. It is believed that this systematic and multidisciplinary review will facilitate synergies and further enhance research and development of offshore renewable energies. ... Read more

The importance of wind energy as an alternative energy source has increased over the latest years with more focus on offshore winds. A good estimation of the offshore winds is thus of major importance for this industry. Up to now the effect of the wind–wave (mis)alignment has not yet been taken into account in coupled atmosphere–wave models to study the vertical wind profile and power production estimations of offshore wind farms. In this study the roughness length parametrization of Drennan et al. in 2003, and its extension addressing the wind–wave (mis)alignment proposed by Porchetta et al. in 2019, are investigated in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This study shows that the yearly mean wind estimation at hub height (100 m) is improved by the roughness length parametrization of Porchetta et al. compared to Drennan. This is mainly due to the increased roughness of the former parametrization compare to the latter, even in aligned wind–wave conditions. This difference in roughness is caused by the dataset used to obtain the constants, deep-water conditions versus mixed offshore conditions. Moreover, the roughness length parametrization of Porchetta et al. performs better in two of three alignment categories. Furthermore, similar model performances are obtained if we exclude the wind directions from the wind shadow zone of the measurement mast or the wind directions from the recently built Alpha Ventus wind farm, which is in close vicinity of the measurement mast. Investigating different wind conditions shows that the new roughness length parametrization of Porchetta et al. performs best for both offshore and onshore winds. Additionally, we show that the coupled model estimations of the vertical wind are only slightly affected by significant wave height estimations. Similar model performances for different accuracies of significant wave height estimations are presented. One exception is the perpendicular alignment category where the new roughness length of Porchetta et al. outperforms the roughness length of Drennan when investigating the wind estimations related to significant wave heights with a higher accuracy. The roughness length parametrization of Porchetta et al. reduced the power production overestimation of the coupled model from 5.7 to 2.8%. We also show that the standalone atmospheric model including the roughness length of Charnock in 1955 has a degraded performance compared to the coupled model including the roughness length parametrization of Porchetta et al. for yearly average wind profiles. ... Read more

Two-way feedback occurs between offshore wind and waves. However, the influence of the waves on the wind profile remains understudied, in particular the momentum transfer between the sea surface and the atmosphere. Previous studies showed that for swell waves it is possible to have increasing wind speeds in case of aligned wind–wave directions. However, the opposite is valid for opposed wind–wave directions, where a decrease in wind velocity is observed. Up to now, this behavior has not been included in most numerical models due to the lack of an appropriate parameterization of the resulting effective roughness length. Using an extensive data set of offshore measurements in the North Sea and the Atlantic Ocean, we show that the wave roughness length affecting the wind is indeed dependent on the alignment between the wind and wave directions. Moreover, we propose a new roughness length parameterization, taking into account the dependence on alignment, consisting of an enhanced roughness length for increasing misalignment. Using this new roughness length parameterization in numerical models might facilitate a better representation of offshore wind, which is relevant to many applications including offshore wind energy and climate modeling. ... Read more