Decadal predictions of wind and solar power indicators to support the renewable energy sector

Renewable energy production is directly influenced by weather conditions, making the energy sector highly sensitive to seasonal to decadal climate variations. Decadal climate predictions, which forecast climate variability over the next 1 to 10 years, are essential for optimising renewable energy deployment. For example, reliable long-term forecasts can help identify the most suitable locations for wind farms and solar plants, ensuring stable energy production and reducing risks associated with climate variability and change.

This study calculates climate impact indicators for the energy sector based on decadal climate predictions. Climate indicators are used to quantify the impact of climate variability on energy production, which is ultimately the most useful information for the energy industry. 

To calculate the indicators, different variables and temporal resolutions are required for each energy source. For solar energy, daily mean values of near-surface air temperature (TAS) and solar radiation (RSDS) are required. We generated the indicators using multi-model ensembles that combine predictions from climate forecast systems participating in the Decadal Climate Prediction Project (DCPP) component of Coupled Model Intercomparison Project Phase 6 (CMIP6). The ensemble includes 13 systems for solar and 4 for wind, depending on variable availability. To assess the forecast quality for the indicators, we use the ERA5 reanalysis as the reference dataset. The evaluation is carried out using both deterministic and probabilistic metrics.

For renewable energy, one important indicator is the capacity factor (CF), which measures the ratio of actual energy production to the maximum potential energy production if the system operates at full capacity. For solar energy, the CF is calculated based on RSDS and TAS. For wind energy, the CF depends on sfcWind and the turbine type, as turbine efficiency varies depending on their weight or height.

Additionally, we define an indicator of the number of effective days, which refers to the number of days when RSDS exceeds the threshold of 208 W/m², the minimum radiation necessary for effective solar energy production. Furthermore, we calculate the number of days when TAS surpasses 45°C, a threshold beyond which solar panels lose efficiency. Similarly, we define minimum and maximum wind speed thresholds for each turbine, within which energy production is possible. The number of days when wind speeds fall within these thresholds will indicate the number of days available for energy production.

The potential benefit of the decadal predictions of tailored indicators will be assessed through a co-evaluation process involving multiple stakeholders from the renewable energy sector. This co-evaluation process will be conducted within the framework of the BOREAS project and it will enable the quantification of the added value provided by this new source of climate information and its potential to ensure the resilience of the Spanish renewable sector.