Session: 08-06-01 Integrated Design & Environmental Assessment of Floating Offshore Wind Systems

Submission Number: 180605

Coupled Blockage and Cable Layout Optimisation for Multi-Objective Offshore Wind Farm Design 

Wind Farm Layout Optimisation (WFLO) is a crucial step in the design of offshore wind farms, directly influencing both energy yield and capital expenditure. Conventional WFLO approaches often rely on high-fidelity wake models and complex optimisation algorithms, which can be computationally demanding. As offshore wind farms scale to gigawatt capacities, the number of layout variables and design constraints explodes, making traditional high-fidelity optimisation computationally prohibitive. This cost barrier often forces designers to decouple aerodynamic and electrical layout design, leading to sub-optimal Levelized Cost of Energy (LCOE). As a result, this study introduces a computationally efficient, multi-objective WFLO framework that couples a lightweight blockage-based model with a cable layout optimisation module.

Simple, physics-informed geometric blockage metrics are utilised as surrogate indicators of wake effects and flow interference between turbines, namely Blockage Ratio (BR) and Blockage Distance (BD). BR is defined as the coverage of the swept area of a turbine that is influenced by an upstream turbine's wake, while BD measures the geometric spacing between turbines in the dominant flow direction.

These metrics are integrated directly into metaheuristic and machine learning optimisation algorithms as objective functions, avoiding the need for repeated computational fluid dynamics (CFD) simulations or detailed wake models. Three optimisation strategies prominent in WFLO research are investigated and compared: a Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and a Reinforcement Learning (RL) model. This selection enables the assessment of both heuristic and adaptive learning paradigms within the same modelling framework.

In parallel, the cable layout optimisation module minimises total inter-array cable length and electrical losses while ensuring a balanced radial or branched network topology compatible with practical installation constraints, such as cable crossing avoidance, offshore substation (OSS) connection limits, and seabed exclusion zones. This integrated framework enables simultaneous evaluation of turbine interference (via blockage metrics) and electrical cost, supporting the identification of layouts that balance energy yield with infrastructure cost.

Results from theoretical and case studies, including the IEA Task 55 wind farm benchmark, demonstrate that the combined blockage and cable optimisation achieves layouts comparable in power output to those derived from traditional wake-based WFLO approaches. Achieving a 10% power increase against the IEA Task 55 array and reducing computational cost by a factor of ≈140 (2.15 orders of magnitude) at single wind speeds, when compared to traditional power-based optimisation methods.

Overall, this research presents a fast, flexible, and interpretable framework for multi-objective WFLO that bridges the turbine layout design and electrical design constraints. By coupling geometric blockage modelling with cable layout optimisation, the method provides a practical route toward integrated, cost-efficient offshore wind farm design, supporting future large-scale developments where computational scalability and cost efficiency are essential. This tool is particularly suited for the early-stage design and conceptual engineering phases, where rapid iteration and exploration of dozens of layout scenarios are necessary.

Presenting Author: Jack Lewis University of Strathclyde

Presenting Author Biography: Jack is a PhD researcher at the University of Strathclyde, specialising in the optimisation of offshore wind farms using machine learning and ocean forecasting. Their research focuses on novel methods to enhance energy yield and reduce the Levelized Cost of Energy (LCOE) for multi-objective wind farm design. Previously, Jack was a Research Officer at Bangor University's Marine Energy Engineering Centre of Excellence (MEECE), working to accelerate the commercialisation of the wave, tidal, and offshore wind sectors.

Authors:

Jack Lewis University of Strathclyde
Laibing Jia University of Strathclyde