Renewable energy forecasting: results of the Smart4RES project and future research directions.

The European Horizon 2020 project Smart4RES (http://www.smart4res.eu), which started in 2019 and runs until April 2023, aims at improving modelling and forecasting of weather variables necessary to optimize the integration of weather-dependent renewable energy (RES) production (i.e. wind, solar) into power systems and electricity markets. It gathers experts from several disciplines ranging from meteorology, data science, power systems a.o. It aims to contribute to the pathway towards energy systems with very high RES penetrations by 2030 and beyond.

This presentation has a double objective:

(1) To present a comprehensive overview in terms of KPI improvements of the final results obtained by the project. These results cover thematic objectives including:

• Improvement of weather and RES forecasting;
• Streamlined extraction of optimal value from the data through data sharing, data market places, and novel business models for the data;
• New data-driven optimization and decision-aid tools for market and grid management applications;
• Validation of new models in living labs and assessment of forecasting value vs costly remedies to hedge uncertainties (i.e. storage). 

The results obtained are numerous. Without being exhaustive, they include: improved forecasting of weather variables with focus on extreme situations and also through innovative measuring settings (i.e. a network of sky cameras); A seamless approach to couple outputs from different ensemble numerical weather prediction (NWP) models with different temporal resolutions; Advances from ultra-high resolution NWPs based on Large Eddy Simulation; Approaches for RES production forecasting aiming at efficiently combining highly dimensionally input (various types of satellite images, NWPs, spatially distributed measurements etc.); Seamless probabilistic RES forecasting covering multiple time frames and data inputs; Resilient energy forecasting. In the front of applications methods are proposed to optimally use forecasts for the management of storage systems coupled with renewables, for the optimal trading of renewables in multiple markets and for grid management optimization and dynamic security assessment. Prescriptive analytics and explainable AI methods are proposed to optimize decision making.  A cost benefit analysis is performed to assess the contribution of different types of data in forecasting problems.

(2) To present hierarchized proposals for future research directions. An international workshop is organized by the project (14/04/2023), where experts are invited to assess where RES predictability stands today and propose research directions for the future. In this presentation we will present the conclusions of this workshop. This will be a useful insight for academics, industrials as well as policy makers in the field.