Posters - WindEurope Annual Event 2024

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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 the academic community. We look forward to seeing you there!

PO084: Learning rates for the development and scale-up of hybrid offshore wind systems

Craig White, PhD Student, WavEC Offshore Renewables


One of the main hurdles for marine renewable energy (MRE) technologies is capturing a constantly fluctuating energy resource. For a relatively nascent technology like floating offshore wind (FOW), combining generation with other energy generators may help scale-up and commercialisation by widening the generation profile and increasing the consistency of production that could aid development, scale-up, and could reduce Levelized Cost of Energy. There is a clear gap in knowledge for the development of a new range of learning rates that can accurately predict the development of hybrid MRE systems, particularly relevant for the European market where approximately three quarters of current floating offshore wind projects are located. As shown below in Figure 1, FOW is outpacing wave energy deployment but this acceleration is still overshadowed by the dominant scale of the global offshore wind market. As such, both technologies and even breakthrough technology such as floating solar could aid in future build-out of floating offshore wind towards 2030 and beyond. This work presents a range of learning rates that react to different combinations of technology with varying deployment scenarios to aim to accurately predict future hybrid system deployment.

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