Offshore wind forecasts and modeling uncertainty in Europe
Offshore wind energy output can be significantly overestimated or underestimated, by as much as 15 GW, if key modeling assumptions change. Variations in turbine specifications, input meteorological data, and wake simulation methods can substantially alter projected electricity generation.
This is the conclusion of a new study published in Wind Energy Science, focusing on the North Sea. The research highlights how sensitive energy forecasts are to technical parameters, with direct implications for system planning.
Technical assumptions reshape offshore wind projections
For the European energy system, the issue is strategic. Errors at this scale affect investment decisions, grid infrastructure, auction design, and industrial planning. The study finds that the largest discrepancies stem from turbine type, while atmospheric conditions and wake interactions between nearby wind farms also play a significant role.
Researchers therefore recommend adopting more realistic and detailed models to estimate the future growth of offshore wind capacity.
North Sea expansion targets increase modeling complexity
The study, conducted by scientists from Helmholtz-Zentrum Hereon and the University of Hamburg, is grounded in a clear policy context. Countries within the North Seas Energy Cooperation aim to expand offshore capacity from 26 GW in 2022 to at least 60 GW by 2030, and up to 300 GW by 2050.
Such rapid expansion will create denser clusters of wind farms, intensifying interactions between installations and increasing pressure on forecasting systems.
To assess these dynamics, the authors used the regional climate model COSMO6.0-CLM, enhanced with a dedicated wind farm module capable of simulating different turbine types, rotor diameters, hub heights, and phased deployment over time.
Turbine design drives the largest share of forecast errors
The study finds that combined uncertainty linked to meteorological datasets and turbine configurations reaches around 15 GW, equivalent to 10% of the simulated total capacity of 150 GW. Of this, 11 GW is attributable to differences between turbine types, while 4 GW stems from meteorological inputs.
Researchers compared extreme scenarios featuring 3.6 MW turbines and next-generation 15 MW machines. Even with identical installed capacity, larger turbines deliver higher output. This is due to two main factors: fewer units are needed, reducing wake interference, and larger rotors with higher hub heights capture stronger and more consistent wind resources.
In the simulations, average annual generation rises from 62.6 GW with 3.6 MW turbines to 73.7 GW with 15 MW systems.
Capacity factor improves with next-generation offshore wind
The study also evaluates the load factor, defined as the ratio between actual energy production and theoretical installed capacity. Using ERA5 data, the average capacity factor increases from 0.42 with 3.6 MW turbines to 0.49 with 15 MW turbines.
Without wake effects, these values would reach 0.57 and 0.61 respectively.
In practical terms, a significant share of energy losses does not depend on available wind resources, but on interactions between wind farms. This has direct implications for developers, utilities, and grid operators, as it affects expected revenues and integration costs in electricity markets.
Wake effects extend over long distances
One of the most critical aspects concerns the wake effect. When a turbine extracts kinetic energy from the wind, it creates a downstream wake with lower wind speed and higher turbulence. If another wind farm is located downwind, it receives less available energy.
Simulations and aerial measurements show that these wakes can extend beyond 50 km in the North Sea. In the analyzed cases, wind speed deficits immediately downstream reach around 1 m/s, while vertical reductions can reach 2.8 to 2.9 m/s, about 45% lower compared to scenarios without wind farms.
This means that the geographic layout of new installations becomes both a technical and economic variable.
Summer conditions amplify offshore wind variability
The research also shows that atmospheric conditions strongly influence performance. During summer, average wind speeds are lower and atmospheric stability is higher. In these conditions, natural turbulence decreases and wakes dissipate more slowly, increasing their impact on downstream turbines.
Adjusting turbulence parameters in the model reduces average output by about 2 GW, slightly above 1% of installed capacity. The relative impact is more pronounced in summer than in winter, as wind speeds more frequently fall below turbine rated power thresholds.
Implications for grids, auctions, and investments
For Europe’s electricity market, the difference between 62 GW and 74 GW of simulated average output is far from marginal. It affects project valuation, revenue forecasts, storage sizing, cross-border transmission capacity, and congestion costs.
Overly optimistic offshore wind forecasts risk leading to flawed business plans and underestimation of flexibility needs. Conversely, overly conservative estimates may slow down investment and industrial development.
For this reason, the authors call for wind farms to be explicitly represented in both meteorological models and historical reanalysis datasets.
What this means for Europe’s energy transition
The growth of offshore wind will not depend solely on permitting, ports, and supply chains. It will also rely on the accuracy of forecasting models.
With expected capacity reaching up to 300 GW in the North Sea, even small percentage errors can translate into tens of gigawatts. In the next phase of Europe’s energy transition, selecting the right turbine technologies and optimizing spacing between wind farms will be as critical as installing new capacity.