Decision Support for Offshore Wind Farm End-of-Life Planning

Abstract

Offshore wind farms are beginning to approach the later stages of their design lives, creating two connected planning challenges: selecting an appropriate end-of-life strategy and organising the resources needed for decommissioning. This thesis develops an applied operational research decision-support approach for both challenges, using the Rampion offshore wind farm in the UK as the case study.
The first objective addresses end-of-life strategy selection. Five strategies are evaluated: lifetime extension, refurbishment, repowering, partial decommissioning, and full decommissioning. The assessment uses technical, environmental, economic, and social criteria developed from the literature and refined through expert input. Criteria weights are derived using the Analytic Hierarchy Process, and the strategies are evaluated using PROMETHEE II and ELECTRE III. The results identify refurbishment as the preferred strategy under both methods. Lifetime extension and repowering form the main method-sensitive comparison, while partial and full decommissioning remain lower-ranked options.
The second objective addresses bi-objective decommissioning resource optimisation, focused on balancing project cost and duration. A hybrid framework links discrete event simulation, cost modelling, Pareto frontier generation, and compromise programming to compare vessel configurations for turbine and foundation removal. The results show that decommissioning performance depends less on the total number of vessels deployed and more on the balance between turbine-removal and foundation-removal capacity. The compromise analysis identifies two strong cost–time configurations under the tested assumptions: an L₁-efficient option that provides the strongest overall weighted compromise, and an L∞-balanced option that limits the largest deviation from the ideal solution.
Together, the two objectives show that offshore wind end-of-life planning is not only a technical or financial task. It is a strategic and operational decision problem shaped by asset condition, environmental effects, economic value, social outcomes, safety, regulation, vessel availability, and cost–time trade-offs. The thesis contributes by developing transparent decision-support structures for offshore wind end-of-life planning under limited evidence. While the results are case-dependent, the approach can be recalibrated as better data, wider stakeholder input, and further end-of-life experience become available.

Date of Award	1 Jul 2026
Original language	English
Awarding Institution	University of Portsmouth
Supervisor	Graham Wall (Supervisor), Maria Barbati (Supervisor) & Dylan Jones (Supervisor)