The University of Manchester has been awarded funding to lead a twelve-month research project to improve the understanding of the energy yield from offshore wind in the UK.
The Prediction Of UnqualifieD losseS from offshore wind farm wakes (POUNDS) project aims to provide a national-scale assessment of interactions between offshore wind farms, supporting policymakers and industry leaders to optimise offshore wind energy production in the drive to net zero.
The project will be carried out in partnership with the UK’s leading Offshore Renewable Energy (ORE) institutes, industry experts, and policymakers, including ORE Catapult, Arup, RWE, EDF, and The Crown Estate.
The UK government has set a target to reach 43 GW to 50 GW of offshore wind by 2030. Rapid progress has already been made with 16 GW now in operation, and further projects are in ongoing development under the recent Contract for Difference Allocations , said the University of Manchester. Nevertheless, achieving the 2030 target requires an up to three-fold increase in capacity, potentially reaching over 100 GW installed capacity by 2050.
Such expansion of offshore wind farms means they must be built closer together, making it crucial to understand how this affects predictions of annual energy production, according to the university.
Wakes have been observed extending 65 kilometres and are said to increasingly impact the performance of neighbouring farms, reducing the efficiency of the turbines in producing energy and causing conflicts between wind farm operators.
The POUNDS project aims to explore the impact of offshore wind farms on each other’s energy production and revenue, as well as to identify the best locations for future offshore wind farms to minimise these losses and support the UK’s renewable energy targets.
It also focuses on validating modelled performance data against operational data and improving the accuracy of wind farm energy production forecasts.
As for its methodology, POUNDS will use mesoscale models, which are a type of advanced numerical weather forecasting model, to model the performance of wind farms spanning the UK waters at a resolution of one kilometre.
It will assess both the offshore wind farms operational in 2023 and the thousands more turbines that are planned by 2030.
The analysis will evaluate the accuracy of the model relative to real-world data and quantify the effects of inter-farm wakes on predicted energy yield. It will also capture wind farm wakes and wind farm performance in comparison to energy export grid data.
By modelling the interactions between wind farms more precisely, the team hopes to provide better guidance for developers and policymakers, reduce investment risks, and resolve conflicts between wind farm operators.