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.

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.

Floating offshore wind is now receiving much attention as an expansion to bottom-fixed, especially in deep waters with large wind resources. In this regard, improving the performance and efficiency of floating offshore wind farms (FOWFs) is currently a highly addressed topic. The inter-array (IA) cable connection is a key aspect to be optimised. Due to floating offshore wind (FOW) particularities such as dynamic cable designs, higher power capacities, and challenging installation, IA cabling is expected to be a primary cost driver for commercial-scale FOWFs. Therefore, IA cabling optimisation can lead to large cost reductions. In this work, an optimisation with an adaptive particle swarm optimisation (PSO) algorithm for such wind farms is proposed, considering the floating substructures’ horizontal translations and its impact on the dynamic cable length. The method provides an optimised IA connection, reducing acquisition costs and power losses by using a clustered minimum spanning tree (MST) as an initial solution and improving it with the PSO algorithm. The PSO achieves a reduction in the levelised cost of energy (LCOE) between 0.018% (0.022 EUR/MWh) and 0.10% (0.12 EUR/MWh) and a reduction in cable acquisition costs between 0.18% (0.3 M EUR) and 1.34% (3.8 M EUR) compared to the initial solution, showing great potential for future commercial-sized FOWFs.

This article presents the design and optimization of the mooring system for a floating wind platform inspired by W2Power, which incorporates two wind turbines on a semi-submersible structure that weathervanes using a single-point mooring (SPM) system. Although several industrial concepts have adopted SPM configurations, research on their performance remains limited. This work addresses that gap by developing and applying a set of optimization strategies for the mooring system of such a platform using OrcaFlex, with the objective of minimizing the capital expenditure while satisfying Ultimate Limit State (ULS) and Fatigue Limit State (FLS) cases. The methodology was tested across two distinct marine environments: the Atlantic (Gran Canaria, GC-1) and the Mediterranean (Catalonia, LEBA-1), both characterized by their irregular bathymetry. In Catalonia, the environmental conditions are almost omnidirectional, while the platform in Gran Canaria is exposed to highly unidirectional loads. The article presents the most cost-effective solution for single-point moorings with three, four, and five lines in each case. Results demonstrate the viability of SPM-based floating wind systems with twin-turbines under diverse site conditions.