Presentations

Abstract

Repowering offshore wind farms requires accurate resource‑assessment techniques capable of resolving complex meso‑ to microscale interactions in an increasingly congested marine environment. Belgium, a pioneer in offshore wind deployment, faces the upcoming decommissioning of its first‑generation wind farms between 2038 and 2049. Consequently, the repowering planning must start without delays and rely on estimates of long-term production, wake losses and energy availability during the transition phase. In addition, assessments must incorporate the development of the Princess Elisabeth Zone (PEZ), whose commissioning timeline, initially expected to start around 2033, still involves uncertainties that directly influence repowering strategies. To address these requirements and associated uncertainties, we develop a multi‑fidelity resource‑assessment methodology integrating mesoscale atmospheric modelling, engineering wake tools, statistical post‑processing, and scenario‑based system analysis. These methods are then applied to investigate the Belgian Eastern offshore wind zone. At the core of the methodology lies a multi‑fidelity modelling chain. Mesoscale atmospheric simulations are produced using the Weather Research and Forecasting (WRF) model configured with wind‑farm parameterization schemes and nested domains. This setup captures large‑scale farm-to-farm interactions, including the impact of the expansion of concessions surrounding the Belgian cluster. To ensure representativeness of long‑term offshore wind variability, we derive a Typical Meteorological Year (TMY) from multi‑decadal reanalysis and in‑situ measurement datasets. This TMY allows us to resolve sub‑annual variability and to analyze adequacy‑relevant impacts during phased dismantling and reconstruction. A base scenario covering three decades of future offshore development is defined to validate and apply the methodology, focused on the Eastern Zone and the PEZ case study. Scenario is defined through consultation with relevant stakeholders and policymakers, and integrates the latest information on concession boundaries, repowering timelines, and technical constraints. This base case is complemented by additional scenarios designed to highlight the sensitivities of repowering outcomes to spatial clustering, concession geometry, turbine density and repowering sequencing, including those originating from neighboring Dutch, French, and UK wind farms. The assessment framework is implemented within a large‑scale scenario matrix covering alternative repowering timings, turbine scale‑ups, concession regrouping and strategies. Pre‑computed WRF outputs are coupled with pre-calibrated engineering models, enabling the exploration of alternative repowering scenarios. The outcomes of this multi‑fidelity modelling are then used to support an evidence‑based policy evaluation.  through the definition of decision‑ready indicators such as annual energy production, seasonal adequacy, energy losses, spatial production fields, and uncertainty bands.  By coupling mesoscale atmospheric modelling with scalable engineering surrogates and applying this approach to real offshore assets, we demonstrate a comprehensive and future‑proof resource‑assessment methodology tailored to repowering studies in densely populated offshore wind regions. These methods and tools empower policymakers, grid operators, and industry stakeholders to quantitatively evaluate repowering strategies, ensuring optimal use of scarce maritime space, maximized renewable‑energy yields, and improved long‑term planning under evolving climatic and grid‑integration constraints.  Acknowledgement: this work is funded by the Belgian FPS Economy, through the BePowering project.