Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
PO456: Turning Uncertainty into Insight: Probabilistic LCoE for Offshore Wind
Elodie Martin, Senior Engineer and Business Lead , DNV
Abstract
The offshore wind sector has entered a period of heightened volatility and cost pressure. Rising input prices, supply‑chain constraints, and elevated interest rates have increased project CAPEX and OPEX, while auction outcomes have exposed the fragility of current business models. Several recent tenders have failed or been redesigned, reflecting the squeeze between escalating costs and fixed revenue frameworks. To address this challenge, DNV’s Renewables.Architect techno‑economic wind farm modelling tool is applied to quantify the Levelised Cost of Energy (LCoE) and its associated uncertainty for a representative offshore wind project. The study is conducted in two stages. First, a global sensitivity analysis identifies the most influential cost drivers, including material and fabrication costs, labour indices, installation vessel day rates and availability, wind‑farm availability, and financial parameters such as the weighted average cost of capital (WACC). Second, uncertainty ranges and probability distributions are assigned to the dominant drivers, and a probabilistic analysis is performed to generate an LCoE distribution and associated risk metrics. The outputs include tornado charts, sensitivity indices, and cumulative distribution functions, and provide a transparent view of cost drivers and risk exposure. The findings demonstrate how probabilistic LCoE modelling offers a more robust basis for decision‑making in an environment characterised by inflationary pressures, supply‑chain bottlenecks, and financing uncertainty.
No recording available for this poster.
