Both renewable energy generation (wind power, solar power and hydropower) and energy demand are highly dependent on atmospheric conditions. In fact, one of the main barriers for a stronger penetration of renewable sources in the energy mix is associated with their variability, as it can be difficult to forecast energy production and demand. Additionally, climate change directly affects renewable sources and energy demand. Since weather predictions do not go beyond a few days, the renewable energy sector cannot reliably plan weeks or months in advance for the energy demand during an extreme weather event, such as a cold spell or a heatwave. The EU-funded S2S4E project set out to explore the usefulness of sub-seasonal and seasonal predictions for the energy sector to anticipate both the renewable energy production and the demand several weeks and months ahead. “When discussing the issue of seasonal forecasts with the energy industry, we saw a gap between the predictions they were already using and the potential use of the seasonal forecasts,” says Albert Soret, S2S4E project coordinator.
S2S4E Decision Support Tool
The project team consisting of industrial and academic partners developed an online forecasting service, the S2S4E Decision Support Tool (DST) that provides an innovative service for the renewable energy sector to be resilient to climate change and extreme events. This service is tailored for the energy sector and integrates sub-seasonal climate predictions up to 4 weeks with seasonal climate predictions of up to 3 months. S2S4E climate forecasts are based on sub-seasonal and seasonal climate data, which are post-processed to improve their reliability and to produce energy indicators useful for the renewable energy sector. The forecasts available in the DST include essential climate variables, such as temperature, rain, wind speed and solar radiation; they are also used to produce forecasts of energy indicators, such as energy demand due to an intensive use of air conditioning systems for cooling during a heatwave. The S2S4E team took a transdisciplinary approach. Scientists and the energy industry came together to match the most advanced climate science with the needs, risk management practices and decision-making procedures of the perspective users of the DST. “Moreover, this forecasting tool can also be useful for people working in other sectors such as agriculture, insurance and tourism,” Soret adds. The Head of Meteorological Models and Special Tasks at EDP Renewables, Daniel Cabezón Martinez observes: “In winter 2015 there was a strong Super El Niño and a wind drought in USA. We started to look for scientific answers to these situations in relation to renewables, and for solutions on how to predict and cope with extreme events and got involved with developing the S2S4E Decision Support Tool.”
To improve the interface, the usability and relevance of the DST, the project members organised workshops and interviews, conducted user testing and made use of technologies such as eye tracking. The DST became operational in June 2019 and the tool is accessible via free registration. The DST is well-suited for commercial exploitation, but this has been a challenge for the team of researchers unfamiliar with selling a product for profit. Therefore, S2S4E partnered with an expert in commercialisation to help consortium through the usual aspects of commercial exploitation, such as finding adequate business models. “The S2S4E project does not finish after launching the DST, as we have a year and a half to validate how the tool is working and to make sure that it is useful for the renewable energy industry,” reports Soret.
S2S4E, renewable energy, forecasts, climate change, energy demand, seasonal forecast, decision support tool, energy indicators