Offshore wind farms, critical for sustainable energy production, face the challenge of optimization among many parameters influencing the objectives in conflicting ways. In response, this research introduces the novel Integrative Maximized Aggregated Preference Wind Farm Optimization (IMAP-WFO) framework—a comprehensive tool designed to enhance the flexibility, accuracy, and uncertainty quantification of offshore wind farm design and operation.

Existing methods often fall short due to limitations in adaptability and precision, especially when modeling complex multi-physical behaviors under uncertain conditions. IMAP-WFO represents a fundamental change by combining advanced statistical techniques and simulation methods.

At its core are parametric design performance functions, capturing critical aspects of wind farm behavior, including energy production, material usage, and structural fatigue. These functions rely on Kriging meta-models, known for their accurate predictions of unknown values. To address inherent uncertainty, Monte Carlo simulations provide a probabilistic assessment of outcomes. IMAP-WFO’s true innovation lies in translating technical functions into socio-economic objectives. These include sustainability metrics, Annual Energy Production (AEP), Capital Expenditure (CAPEX), Operational Expenditure (OPEX), model uncertainty, and lifetime fatigue. Stakeholders can dynamically weigh these objectives based on their preferences, showcasing IMAP-WFO’s versatility.

A validation process described in this research ensures the accuracy of design performance functions, comparing simulated results with real-world data from operational wind farms. IMAP-WFO’s application is demonstrated through case studies: optimizing the Levelized Cost of Energy (LCOE) and exploring wind farm control strategies. Overall, IMAP-WFO bridges the gap between technical challenges and socio-economic goals, helping offshore wind farm design to increase in efficiency

This paper investigates the technical, life cycle, and economic feasibility of a 30 MW upscaled downwind turbine, comparing it to a 15 MW X1 Wind PivotBuoy downwind turbine and a benchmark 15 MW IEA Umaine VolturnUS-S upwind turbine in the 450 MW Sud de la Bretagne I wind farm site. The study is significant due to the rising energy demand, the potential for decreasing the levelized cost of energy with increased turbine size, and the optimized use of space. The size limit of current upwind turbine designs could be addressed using a downwind turbine solution.

The research is conducted by modelling the global dynamic response of the structure using OpenFAST and computing the natural frequencies and stresses using a finite element model. A lifecycle analysis is performed to identify potential pitfalls and bottlenecks by analysing the individual lifecycle phases. The economic feasibility is assessed by simulating the annual energy production using TOPFARM and utilizing structural analysis and lifecycle assessment to quantify capital, operational, and abandonment expenditures. Based on the annual energy production and the performance indicators the levelized cost of energy is calculated.

The findings indicate that while the global stability is within boundaries, the stress in members is too high with a simple scale-up of the proposed design. Bottlenecks are found in lifting operations and supply chain readiness. The levelized cost of energy and capital expenditure increased due to substructure self-weight, rendering the proposed 30 MW scale-up currently unfeasible when compared to the other two wind farms.
These findings are important as they demonstrate that the 15 MW X1 Wind PivotBuoy is not scalable without design changes. The levelized cost of energy does not decrease with an increased floater solution. The 15 MW X1 Wind PivotBuoy downwind turbine seems more economically viable, making it a more interesting option for future development.