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This paper presents a literature review of the dynamics of offshore floating wind turbine platforms. When moving further offshore, there is an increase in the capacity of wind power. Generating power from renewable resources is enhanced through the extraction of wind energy from an offshore deep-water wind resource. Mounting the turbine on a platform that is not stable brings another difficulty to wind turbine modeling. There is a need to introduce platforms that are more effective to capture this energy, because of the complex dynamics and control of these platforms. This paper highlights the historical developments and progresses in the design of different types of offshore floating wind turbine platforms needed for harvesting the energy from offshore winds. The relative advantages and disadvantages of the platform types with the design challenges are discussed. The major types of floating platforms included in this study are tension leg platform (TLP) type, spar type, and semisubmersible type. This study reviews the previous work on the dynamics of the floating platforms for a single turbine and multiple turbines under various operating environmental conditions. The numerical methods to analyze the aerodynamics of the wind turbine and hydrodynamics of floating platforms are discussed in this paper. This paper also investigates the performance of analytical wake loss models of Jensen, Larsen, and Frandsen that can provide guidelines for using these wake models in future applications. There are still a lot of challenges that need to be addressed to study the accurate behavior of floating platforms operating under combined wind–wave environmental conditions. With the current technological advancements, the offshore floating multi-turbine platform can be a potential solution to harness the abundant offshore wind resource. Based on this literature review, recommendations for future work are suggested.

Wind turbine wakes can be predicted somewhat accurately with mesoscale numerical models, such as the Weather Research and Forecast (WRF) model, via a wind farm parameterization (WFP) that treats the effects of the wakes, which are sub‐grid features, on power production and the environment. A few WFPs have been proposed in the literature, but none has been able to properly account for the individual wakes within a grid cell or the effects of overlapping wakes from multiple turbines. A solution to these two issues is a WFP that includes both a wake model, which is a simplified analytical model of the wind speed (or wind power) deficit caused by a wake, and a wake superposition model, which accounts for overlapping wakes. Several such WFPs are developed here for the WRF model—based on the Jensen, the Geometric, and the Gaussian wake models coupled with two wake superposition methods (based on a squared deficit and a squared velocity superposition)—and tested individually, as well as combined together in an ensemble (EWFP), at two modern offshore wind farms. Most WFPs perform satisfactorily alone, but the EWFP generally outperforms them at both farms. The issue of resolved versus sub‐grid wakes is explored for single‐ and multi‐cell cases and for directions of alignment and non‐alignment between the wind direction and the turbine columns. Although different combinations of wake loss and wake superposition models might be preferred at other wind farms, the general findings and detailed performance statistics given here might provide useful guidance in their selection.

The deployment of new wind farms in the North Sea is an essential part of Europe’s transition to low-emission energy sources. Here, we use the mesoscale climate model COSMO-CLM to simulate the energy production of a realistic 92 GW wind farm expansion scenario and compare it with that of the wind farm distribution in 2024. The simulations indicate that added inter-farm wakes would cause the annual energy production of 25 out of 69 operational wind farms to decline by over 5%, with 13 of them experiencing reductions exceeding 10%. These annual losses are composed of intermittent wake periods characterized by large production deficits. For instance, the planned Princess Elisabeth zone in Belgian waters is expected to reduce the output of the existing Belgian wind farm cluster by more than 15% during one out of every five 24 h periods with high production potential ( V¯>9 ms−1). Despite these enhanced inter-farm interactions, we find that the overall capacity factor over the basin increases by 1.6%pt after expansion due to the deployment of next-generation turbines and installation at low capacity densities in high-resource zones. However, negative impacts are highly region-dependent and many wind farms may face substantial losses. Although more validation studies are recommended to further constrain the uncertainty on the presented results, this work demonstrates the need for a coordinated wind farm expansion strategy, especially because spatial constraints are expected to intensify on the path to surpassing 200 GW of capacity in the North Sea by 2050.

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences in CF (and AEP) and wake losses that arise due to the selection of the wake parameterization have the same magnitude as varying the ICD within the likely range of 2–9 MW km−2. CF simulated with most wake parameterizations have a near-linear relationship with ICD in this range, and the slope of the dependency on ICD is similar to that in mesoscale simulations with the Weather Research and Forecasting (WRF) model. Microscale simulations show that remotely generated wakes can double AEP losses in individual lease areas (LA) within a large LA cluster. Finally, simulations with the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model are shown to differ in terms of wake-induced AEP reduction from those with the WRF model by up to 5%, but this difference is smaller than differences in CF caused by the wind farm parameterization used in the mesoscale modeling. Enhanced evaluation of mesoscale and microscale wake parameterizations against observations of climatological representative AEP and time-varying power production from wind farm Supervisory Control and Data Acquisition (SCADA) data remains critical to improving the accuracy of predictive AEP modeling for large offshore wind farms.

Wake losses inside a wind farm occur due to the aerodynamic interactions when a downwind turbine is in the wake of upwind turbines. The ability of floating offshore wind turbines (FOWTs) to relocate their positions in the horizontal plane introduces an opportunity to decrease the wake losses in a floating wind farm (FWF). Our goal is to use this ability to passively move the downwind FOWT out of the wake of upwind ones. Since the mooring system (MS) attached to a FOWT is responsible for its station keeping, the horizontal motions of the FOWT depend on the MS design. Hence, if we can design the MS to passively move the FOWT out of the wake, we can increase the FWF annual energy production (AEP). In this paper, we investigate if we can benefit from relocating FOWTs in a FWF and increase its AEP. In addition, we present a novel approach that considers the ability of a FOWT to relocate its position as a new degree of freedom (DoF) in the FWF layout design. This means we will have a self‐adjusting wind farm layout where the FOWTs passively re‐arrange themselves depending on the wind direction and the wind speed. Consequently, we will have a slightly different wind farm layout for every wind direction and every wind speed. To achieve this layout, we include the MS design as part of the FWF's layout design. In a self‐adjusting FWF layout, each FOWT is attached to a customized MS design allowing it to relocate its position in the best way possible according to the wind direction, to increase the overall AEP of the wind farm. The results of one case study show that the novel approach can increase the FWF's AEP by 1.6% when compared with a current state of the art optimized floating wind farm layout. Finally, we implemented our method as an open‐source python tool to be used and enhanced further within the wind energy community.

This study compares the performance of two wind farm sites located in Northern Europe: an onshore site and an offshore area in the eastern Baltic Sea region. This study investigates the optimisation of wind farm performance within a fixed project area by maximising annual energy production (AEP) and increasing energy density. Three wake-loss scenarios (≤10%, ≤15%, and ≤20%) were examined to assess the sensitivity of layout optimisation to aerodynamic interaction constraints. Several layout configurations were analysed to reduce wake losses and enhance overall energy output. Wind conditions were assessed using NORA3 reanalysis data, and wake interactions were modelled using the Jensen wake model to estimate AEP. Both wind farms were further compared across key criteria, including cost, power generation efficiency, installation and maintenance requirements, and site availability. Offshore wind farms achieve 1.5–1.7 times higher energy density under similar spatial conditions. However, offshore levelised cost of energy (LCOE) remains roughly 25% higher due to higher capital and infrastructure costs, while onshore LCOE demonstrates better economic performance, driven by lower CAPEX and O&M expenses. The findings highlight the trade-offs between cost efficiency and wake-driven energy performance for onshore and offshore wind development in the eastern Baltic Sea region.

Offshore wind-farm performance remains constrained by persistent wake deficits and turbulence that compound across intra-farm, intra-cluster, and inter-cluster scales, particularly under atmospheric neutral–stable stratification. A concept is advanced whereby offshore wind-farm yield may be augmented by pairing conventional horizontal-axis wind turbines (HAWTs) with lighter-than-air parafoil systems that entrain higher-momentum air and re-energise wakes, complementing yaw/induction-based wake control and enabling higher array energy density. A concise synthesis of wake physics and associated challenges motivates opportunities for active momentum re-injection, while a review of kite technologies frames design choices for lift generation and spatial keeping. Stability and control, spanning static and dynamic behaviours, tether dynamics, and response to extreme meteorological conditions, are identified as key challenges. System-integration pathways are outlined, including alignment and mounting options relative to turbine rows and prevailing shear. A staged validation programme is proposed, combining high-fidelity numerical simulation with wave-tank testing of coupled mooring–tether dynamics and wind-tunnel experiments on scaled arrays. Evaluation metrics emphasise net energy gain, fatigue loading, availability, and Levelized Cost of Energy (LCOE). The paper concludes with research directions and recommendations to guide standards and investment, and with a quantitative assessment of the techno-economic significance of kite–HAWT integration at scale.

A layout of the offshore wind farm (OSWF) plays a vital role in its capital cost of installation. One of the major contributions in the installation cost is electrical collector system (ECS). ECS includes: submarine cables, number of wind turbines (WTs), offshore platforms etc. By considering the above mentioned problem having an optimized design of OSWF provides the better feasibility in terms of economic considerations. This paper explains the methodology for optimized designing of ECS. The proposed methodology is based on combined elitist ant colony optimization and multiple travelling salesman problem. The objective is to minimize the length of submarine cable connected between WTs and to minimize the wake loss in the wind farm in order to reduce the cost of cable and cable power loss. The methodology is applied on North Hoyle and Horns Rev OSWFs connected with 30 and 80 WTs respectively and the results are presented.

This work demonstrates a techno-economical assessment of wind energy potential for four passes of Tamil Nadu (Aralvaimozhi, Shencottah, Palghat, and Cumbum) with uncertainty factors. First, a potential assessment was carried out with time-series data, and the Weibull parameters, such as c (scale) and k (shape), were determined using the modern-era retrospective analysis for research and applications (MEERA) data set. Using these parameters, the mean speed, most probable speed, power density, maximum energy-carrying speed of wind power were determined. From the analysis, it was observed that all four passes had better wind parameters; notably, the Aralvaimozhi pass attained a better range of about 6.563 m/s (mean wind speed), 226 W/m2 (wind power density), 6.403 m/s (most probable wind speed), and 8.699 m/s (max wind speed). Further, uncertainty factors, such as the probability of exceedance (PoE), wind shear co-efficient (WSC), surface roughness, and wake loss effect (WLE), were evaluated. The value of PoE was found to be within the bound for all the locations, i.e., below 15%. In addition, the ranged of WSC showed a good trend between 0.05 and 0.5. Moreover, the surface length of the passes was evaluated and recorded to be 0.0024 m with a 73% energy index. Further, output power, annual energy production (AEP), capacity factor (CF), and cost of wind energy of all four passes were computed using different wind turbine ratings in two cases, i.e., with and without WLE. It was observed that there was a huge profit in loss from all the four locations due to WLE that was estimated to be Rupees (Rs.) 10.07 crores without considering interest components and Rs. 13.66 crores with interest component at a 10% annual rate of interest.

The multi-wind turbine platform technology has the potential to harness the significant source of offshore wind energy in deep waters. However, the wake interference between the turbines on the multi-wind turbine platform can cause a reduction in power production; hence, it is important to study the wake effects in the initial phase of the design. This paper studies the effects of wake interference between the wind turbines on three different platform configurations to find a suitable configuration for the wind turbines on a multi-turbine platform. The analytical Larsen wake model and computational fluid dynamics (CFD) simulations are used for evaluating the wake effects. The platform configuration required for the wind turbines is determined based on the results of wake effects, and then a novel platform is designed. The free-floating stability behavior of the multi-wind turbine platform is analyzed using the hydrostatic analysis of the modeled platform. The wave-body interaction between the platform and the waves is predicted using the hydrodynamic analysis. A preliminary cost analysis of the multi-turbine platform concept is evaluated and compared with a single wind turbine floating concept. The results showed that the presented design is a promising concept that can enhance the offshore wind industry.