Floating offshore wind farms enable wind energy deployment in deeper waters, where bottom-fixed turbines are not viable. However, integrating mooring systems and dynamic cables introduces additional challenges in cable routing, increasing design constraints and costs. While research on cable optimization for bottom-fixed wind farms is well-developed, studies focused on floating wind farms remain limited.
This thesis presents an optimization framework that minimizes inter-array cable length while ensuring compliance with mooring system constraints and loop topology requirements. A Mixed-Integer Linear Programming model is used to enforce spatial and technical constraints, structured into three phases: preprocessing, optimization, and postprocessing. Preprocessing defines feasible cable connections while preventing crossings with mooring lines. To manage complexity, turbines are grouped into clusters with defined boundaries to separate routing areas. The optimization phase determines the best cable layout while ensuring loop connectivity and balanced electrical loads. Postprocessing checks compliance with industry standards, identifying clearance violations and refining layouts for feasibility.
A case study of the London Array wind farm demonstrates the model’s effectiveness, achieving a 1.1% reduction in total cable length while maintaining the original layout structure. When adapted for floating wind, spatial constraints from mooring systems cause clearance conflicts, which are mostly resolved by scaling the layout. Minor turbine adjustments eliminate remaining issues. The study highlights that increasing the number of loops and reducing loop size creates more routing conflicts, particularly near the offshore substation and at cluster boundaries. It also finds that centrally positioned substations significantly reduce clearance violations compared to those at the field boundary.
This research provides practical insights into the spatial constraints of floating wind farms and offers a structured, computationally feasible approach to optimizing inter-array cable routing for large-scale farms.

In this thesis, the integration of mooring design with the layout optimisation of floating offshore wind farms is investigated. The objective of the research is to devise a method to unite the two design processes and then evaluate the benefits of this approach. Further, it will be investigated how to model mooring systems in an optimisation problem, the stage of the wind farm design process at which mooring design can be integrated, the couplings between mooring design, turbine placement and cable routing that exist and must be used to arrive at an improved design, and to what extent the approach improves the design of the floating wind farm.
Floating wind farms are still in a developmental phase, but will be key to meet the energy goals of the future. However, floating wind farm design brings with it challenges that do not exist for bottom-fixed offshore wind farms. Chief among these challenges is the mooring system, which not only has a significant impact on the performance of the turbine, but also due to its potentially large footprint can force alterations to the layout of the inter-array electrical cables with which turbines are connected to each other and the substations. Particular focus in this regard is on catenary mooring systems, given they are common in existing demonstrator projects and have a large footprint.
In this thesis, mooring design will be performed through a multi-objective optimisation using the NSGA-II algorithm, where the objectives will be to minimise the anchor radius as well as the system cost, with constraints to ensure adequate performance in terms of handling motions and loads. The mooring system will be described by 5 design variables, the line length ratio, synthetic fraction, anchor radius, synthetic line diameter, and chain line diameter. The Pareto-optimal solutions from this optimization will then be used to optimize the layout and cable routing using an algorithm based on the work of Cazzaro and property of Vattenfall AB.
It is found that the Pareto-optimal designs have anchor radii ranging from 394 to 494 m, with system costs ranging from \$3.352 million to \$6.441 million. However, with current layout design methods, the turbine placement is done independently of mooring system parameters, and the cable routing is not affected by a change in mooring system. Thus, using the cheapest mooring system regardless of footprint is optimal on a farm level, using current layout design methods.

This study presents an innovative algorithm designed to optimise the layout of floating offshore wind farms (FOWFs), addressing inter-array cable length and the number of anchor structures required. This research is particularly noteworthy as no prior work has been conducted on wind farm layout optimisation for FOWFs. The Local-Search-Inspired Layout Optimisation (LSILO) algorithm was developed, incorporating a variety of novel heuristics such as Edge Impact Destroy (EID), diversifying repair, and neighbourhood tabu search, most of which were innovatively designed for this research. The LSILO algorithm is capable of optimising cable routing while facilitating anchor-sharing and minimising cable length. In comparison to the industry standard, LSILO displayed promising results, outperforming the random benchmark by a 24% improvement in cable length. However, the algorithm does plateau at a certain turbine density. Turbine density has a substantial influence on the algorithm’s speed and rationality. Therefore, we recommend utilising LSILO with an EID of three turbines, diverse repair without alternatives, and traditional tabu search, on current floating wind turbine sites.