From Climate DT to Hectoscale Forecasts For Renewable Energy Systems

 

Combining Climate Digital Twin (DT) and Extremes DT offers significant benefits for renewable energy planning. Climate DT provides long-term simulations, while Extremes DT focuses on detecting high-impact events. Although Climate DT includes wind energy aspects, it lacks emphasis on extreme events. Integrating both approaches can address uncertainties in renewable energy supply under current and future climate conditions, as PV and wind are highly sensitive to short-term changes. Within the presented study we aim to evaluate the added value of downscaling Climate DT data from ~5 km to hectometric resolution (400–800 m) to better represent local conditions. Further, we analyse the usability of Climate DT output for the renewable energy sector, either directly or as stated above, as input for setting up dynamical climate simulations at the hecto-scale by using regional climate simulation models WRF and ICON. We therefore have the following objectives: (i) to assess the skill of the Global Climate DT scenarios with respect to representativeness of extreme (meteorological) events, synoptic patterns, and their impact on renewables; (ii) estimate the added value of highly resolved climate scenarios dynamically downscaled to hectometric spatial resolution (and higher vertical resolution) with respect to selected renewables extreme events (negatively affecting either the supply or the infrastructure itself).

For assessing the added value of hecto-scale simulations, on the one hand, regional climate simulations using the WRF and ICON model were conducted – initiated by ERA5 data – for 5 km, 1.6 km, 800 m and 400 m. These simulations display that higher regional climate model resolution from 5 km down to 1.6 km, to 400 m increases the model skill to represent local wind patterns.

On the other hand, to evaluate the skill of Climate DT versus hecto-scale simulations initialized by Climate DT, a model year representative of a real year is selected and simulated using ICON and WRF. Consequently, the meteorological parameters (e.g. wind speed, radiation, temperature) as well as the post-processed energy production (e.g. mean annual and mean monthly values) data are validated.