EGU22-12923, updated on 28 Mar 2022
https://doi.org/10.5194/egusphere-egu22-12923
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Highlight results of the Smart4RES project on weather modelling and forecasting dedicated to renewable energy applications

Georges Kariniotakis1, Simon Camal1, and the Smart4RES team*
Georges Kariniotakis and Simon Camal and the Smart4RES team
  • 1MINES ParisTech, PSL University, Centre PERSEE, Sophia-Antipolis, France (georges.kariniotakis@mines-paristech.fr)
  • *A full list of authors appears at the end of the abstract

In this presentation we detail highlight results obtained from the research work within the European Horizon 2020 project Smart4RES (http://www.smart4res.eu). The project, which started in 2019 and runs until 2023, aims at a better modelling and forecasting of weather variables necessary to optimise the integration of weather-dependent renewable energy (RES) production (i.e. wind, solar, run-of-the-river hydro) into power systems and electricity markets. Smart4RES gathers experts from several disciplines, from meteorology and renewable generation to market- and grid-integration. It aims to contribute to the pathway towards energy systems with very high RES penetrations by 2030 and beyond, through thematic objectives including:

  • Improvement of weather and RES forecasting,
  • Streamlined extraction of optimal value through new forecasting products, data market places, and novel business models;
  • 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). 

In this presentation we will focus on our results on models that permit to improve forecasting of weather variables with focus on extreme situations and also through innovative measuring settings (i.e. a network of sky cameras). Also results will be presented on the development of seamless approach able to couple outputs from different ensemble numerical weather prediction (NWP) models with different temporal resolutions. Advances on the contribution of ultra-high resolution NWPs based on Large Eddy Simulation will be presented with evaluation results on real case studies like the Rhodes island in Greece.

When it comes to forecasting the power output of RES plants, mainly wind and solar, the focus is on improving predictability using multiple sources of data. The proposed modelling approaches aim to efficiently combine highly dimensionally input (various types of satellite images, numerical weather predictions, spatially distributed measurements etc.). A priority has been to propose models that permit to generate probabilistic forecasts for multiple time frames in a seamless way. Thus, the objective is not only to improve accuracy and uncertainty estimations, but also to simplify complex forecasting modelling chains for applications that use forecasts at different time frames (i.e. a virtual power plant - VPP- with or without storage that participates in multiple markets). Our results show that the proposed seamless models permit to reach these performance objectives. Results will be presented also on how these approaches can be extended to aggregations of RES plants which is relevant for forecasting VPP production.

Smart4RES team:

• Georges Kariniotakis, Simon Camal, Dennis van der Meer, Akylas Stratigakos; MINES ParisTech, PSL University, Centre PERSEE, France. • Gregor Giebel, Tuhfe Göçmen, Pierre Pinson; DTU, Denmark. • Ricardo Bessa; Carla Goncalves, INESC TEC, Portugal. • Ivana Aleksovska, Bastien Alonzo, Marie Cassas, Quentin Libois, Laure Raynaud; Meteo France, France. • Gerrit Deen, Daan Houf, Remco Verzijlbergh; Whiffle, The Netherlands. • Matthias Lange, Björn Witha; Energy and Meteo Systems, Germany. • Jorge Lezaca, Bijan Nouri, Stefan Wilbert; DLR, Germany. • Maria Ines Marques, Manuel Silva; EDP, Portugal. • Wouter De Boer, Marcel Eijgelaar, Ganesh Sauba; DNV, The Netherlands. • John Karakitsios, Theodoros Konstantinou, Dimitrios Lagos, George Sideratos; NTUA/ICCS, Greece. • Theodora Anastopoulou, Efrosini Korka, Christos Vitellas; DEDDIE, Greece. • Stephanie Petit; Dowel Innovation, France.

How to cite: Kariniotakis, G. and Camal, S. and the Smart4RES team: Highlight results of the Smart4RES project on weather modelling and forecasting dedicated to renewable energy applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12923, https://doi.org/10.5194/egusphere-egu22-12923, 2022.

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