Electrical grid layout optimization should consider the placements of turbines and substations and include effects such as wake losses, power losses in cables, availability of different cable types, reliability-based power losses and operational/decommissioning cost besides the initial investment cost. Hence, optimizing the levelized cost of energy is beneficial capturing long-term effects. The main contribution of this review paper is to identify the current works and trends on electrical layout optimization for offshore wind farms as well as to analyze the applicability of the found optimization approaches to commercial-scale floating wind farms which have hardly been investigated so far. Considering multiple subproblems (i.e., micrositing and cabling), simultaneous or nested approaches are advantageous as they avoid sequential optimization of the individual problems. To cope with this combinatorial problem, metaheuristics seems to offer optimal or at least close-to-optimal results while being computationally much less expensive than deterministic methods. It is found that floating wind brings new challenges which have not (or only insufficiently) been considered in present optimization works. This will also be reflected in a higher complexity and thus influence the suitability of applicable optimization techniques. New aspects include the mobility of structures, the configurations and interactions of dynamic cables and station-keeping systems, the increased likelihood of prevailing heterogeneous seabeds introducing priority zones regarding anchor and riser installation, the increased importance of reliability and maintainability due to stricter weather limits, and new floating specific wind farm control methods to reduce power losses. All these facets are crucial to consider when thoroughly optimizing the levelized cost of energy of commercial-scale floating offshore wind farms.

The cable layout optimisation for floating offshore wind farms is a complex task due to the number of elements involved and their nature, such as dynamic cables with buoyant sections. The first step is defining a model of the suspended cables that connect wind turbines between them or to cables on the seabed (risers). A lumped mass model and the finite element method are used to find the static position and mechanical stresses of the cables, keeping a balance between computational time and the quality of the results. The model allows the inclusion of standard ancillary equipment such as bend stiffeners, buoyancy modules, clamps and tethers in order to simulate multiple configurations: double hanging, single hanging (catenary), lazy wave, steep wave and tethered wave. The external actions considered are marine growth and currents and the main cable properties are taken into account to calculate its weight, buoyancy, tensions, moments and drag forces. The information obtained after the simulation includes cable tensions, curvature, laid and suspended lengths and tether tension, when applicable. Next steps include the optimisation of the suspended cables and the full network in the farm.

Floating offshore wind (FOW) is rapidly gaining interest due to its large potential. In this regard, it is of special interest to determine the best locations for its installation. One of the main aspects when evaluating the feasibility of a project is the levelised cost of energy (LCOE), but there are many variables to consider when calculating it for FOW, and plenty of them are hard to find when the scope is all the suitable areas worldwide. This paper presents the calculation and analysis of the global LCOE with particular focus on the best countries and territories from an economic point of view, considering four types of platforms: semi-submersible, barge, spar, and tension leg platform (TLP). The model takes into account, on the one hand, wind data, average significant wave height, and distance to shore for an accurate calculation of delivered energy to the onshore substation and, on the other hand, bathymetry, distances, and existing data from projects to find appropriate functions for each cost with regression models (e.g., manufacturing, installation, operation and maintenance (O&M), and decommissioning costs). Its results can be used to assess the potential areas around the world and identify the countries and territories with the greatest opportunities regarding FOW. The lowest LCOE values, i.e., the optimal results, correspond to areas where wind resources are more abundant and the main variables of the site affecting the costs (water depth, average significant wave height, distance to shore, and distance to port) are as low as possible. These areas include the border between Venezuela and Colombia, the Canary Islands, Peru, the border between Western Sahara and Mauritania, Egypt, and the southernmost part of Argentina, with LCOEs around 90 €/MWh. Moreover, there are many areas in the range of 100–130 €/MWh.

This study explores virtual sensing techniques for the Eolink floating offshore wind turbine (FOWT), which features a pyramidal platform and a single-point mooring system that enables weathervaning to maximize power production and reduce structural loads. To address the challenges and costs associated with monitoring submerged components, virtual sensors are investigated as an alternative to physical instrumentation. The main objective is to design a virtual sensor of mooring hawser loads using a reduced set of input features from GPS, anemometer, and inertial measurement unit (IMU) data. A virtual sensor is also proposed to estimate the bending moment at the joint of the pyramid masts. The FOWT is modeled in OrcaFlex, and a range of load cases is simulated for training and testing. Under defined sensor sampling conditions, both supervised and physics-informed machine learning algorithms are evaluated. The models are tested under aligned and misaligned environmental conditions, as well as across operating regimes below- and above-rated conditions. Results show that mooring tensions can be estimated with high accuracy, while bending moment predictions also perform well, though with lower precision. These findings support the use of virtual sensing to reduce instrumentation requirements in critical areas of the floating wind platform.