1. Smart4RES: Next generation solutions for renewable energy forecasting and applications with focus on distribution grids
- Author
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Camal, Simon, Kariniotakis, Georges, Sossan, Fabrizio, Libois, Quentin, Legrand, Raphael, Raynaud, Laure, Lange, Matthias, Mehrens, Annah, Pinson, Pierre, Pierrot, Amandine, Giebel, Gregor, Göcmen, Tufhe, Bessa, Ricardo, Gouveia, Jose, Teixeira, Luis, Neto, Alexandre, Santos, Ricardo, Mendes, Gisela, Nouri, Bijan, Lezaca, Jorge, Verziljbergh, Remco, Deen, Gerrit, Sideratos, George, Vitellas, Christos, Sauba, Ganesh, Eijgelaar, Marcel, Petit, Stéphanie, Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques (PERSEE), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, energy (EMSYS - Energy & Meteo Systems), Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Institute for Systems and Computer Engineering, Technology and Science [Porto] (INESC TEC), EDP New Energy World – Center for New Energy Technologies, EDP Distribuição, Deutsches Zentrum für Luft- und Raumfahrt (DLR), WHIFFLE, National Technical University of Athens [Athens] (NTUA), DEDDIE, DNV GL, Dowel Innovation, CIRED, and European Project: 864337,Smart4RES
- Subjects
RENEWABLE ENERGY ,power distribution economics ,SMART GRID ,[STAT.AP]Statistics [stat]/Applications [stat.AP] ,optimisation ,DATA SCIENCE ,[SPI.NRJ]Engineering Sciences [physics]/Electric power ,load forecasting ,ENERGY METEOROL-OGY ,data handling ,[MATH.MATH-PR]Mathematics [math]/Probability [math.PR] ,[STAT.ML]Statistics [stat]/Machine Learning [stat.ML] ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,FORECASTING ,power markets ,DISTRIBUTION GRIDS ,[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] ,weather forecasting ,renewable energy sources - Abstract
International audience; This paper presents the solutions on renewable energy forecasting proposed by the Horizon2020 Project Smart4RES. The ambition of the project is twofold: (1) increase substantially the performance of short-term forecasting models of Renewable Energy Sources (RES) production and associated weather forecasting and (2) optimize decisions subject to RES uncertainty in power systems and electricity markets. Developments are based on latest advances in meteorology and original use of data science (combination of multiple data sources, data-driven approaches for trading and grid management). Finally, solutions such as flexibility forecast of distributed resources and data markets are oriented towards value for power system stakeholders. Although the project covers a broad scope, in this paper we focus on a selection of use cases that concern the integration of renewables in distribution grids.
- Published
- 2021
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