5 results on '"Alonzo, Bastien"'
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2. Profitability and Revenue Uncertainty of Wind Farms in Western Europe in Present and Future Climate
- Author
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Alonzo, Bastien, primary, Concettini, Silvia, additional, Creti, Anna, additional, Drobinski, Philippe, additional, and Tankov, Peter, additional
- Published
- 2022
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3. Highlight results of the Smart4RES project on weather modelling and forecasting dedicated to renewable energy applications
- Author
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Kariniotakis, Georges, Camal, Simon, Meer, Dennis van Der, Stratigakos, Akylas, Giebel, Gregor, Göçmen, Tuhfe, Pinson, Pierre, Bessa, Ricardo, Goncalves, Carla, Aleksovska, Ivana, Alonzo, Bastien, Cassas, Marie, Libois, Quentin, Raynaud, Laure, Deen, Gerrit, Houf, Daan, Verzijlbergh, Remco, Lange, Matthias, Witha, Björn, Lezaca, Jorge, Nouri, Bijan, Wilbert, Stefan, Marques, Maria Ines, Silva, Manuel, Boer, Wouter De, Eijgelaar, Marcel, Sauba, Ganesh, Karakitsios, John, Konstantinou, Theodoros, Lagos, Dimitrios, Sideratos, George, Anastopoulou, Theodora, Korka, Efrosini, Vitellas, Christos, Petit, Stephanie, 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), Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Institute for Systems and Computer Engineering, Technology and Science [Porto] (INESC TEC), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, WHIFFLE, energy (EMSYS - Energy & Meteo Systems), Deutsches Zentrum für Luft- und Raumfahrt (DLR), EDP New Energy World – Center for New Energy Technologies, EDP Distribuição, DNV GL, National Technical University of Athens [Athens] (NTUA), DEDDIE, Dowel Innovation, and European Project: 864337,Smart4RES
- Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP] ,Data Science ,[SPI.NRJ]Engineering Sciences [physics]/Electric power ,Uncertainty ,Predictive analytics ,Renewable energy forecasting ,Weather forecasting ,[MATH.MATH-PR]Mathematics [math]/Probability [math.PR] ,Digitalisation ,Prescriptive anaytics ,[STAT.ML]Statistics [stat]/Machine Learning [stat.ML] ,Artificial Intelligence ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,European project ,Renewable Energy ,[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] ,SDG 7 - Affordable and Clean Energy ,Energy Meteorology - 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.
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- 2022
4. Measuring the Risk of Supply and Demand Imbalance at the Monthly to Seasonal Scale in France
- Author
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Alonzo, Bastien, primary, Drobinski, Philippe, additional, Plougonven, Riwal, additional, and Tankov, Peter, additional
- Published
- 2020
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5. Data Science for Next Generation Renewable Energy Forecasting - Highlight Results from the Smart4RES Project
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Kariniotakis, George, Camal, Simon, Sossan, Fabrizio, Nouri, Bijan, Lezaca, Jorge, Lange, Matthias, Alonzo, Bastien, Libois, Quentin, Pinson, Pierre, Bessa, Ricardo, Goncalves, Carla, 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), Deutsches Zentrum für Luft- und Raumfahrt (DLR), energy (EMSYS - Energy & Meteo Systems), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Institute for Systems and Computer Engineering, Technology and Science [Porto] (INESC TEC), EnergyNautics GmbH, and European Project: 864337,Smart4RES
- Subjects
Optimization ,Renewable energy ,[STAT.AP]Statistics [stat]/Applications [stat.AP] ,[SPI.NRJ]Engineering Sciences [physics]/Electric power ,Grid management ,Renewable energy, Forecasting, High resolution, Data Market, Optimization, Grid management ,Renewable energy sources ,Data science ,[MATH.MATH-PR]Mathematics [math]/Probability [math.PR] ,[STAT.ML]Statistics [stat]/Machine Learning [stat.ML] ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,High resolution ,[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] ,Smart Grid ,Data Market ,Forecasting - Abstract
Smart4RES is a European Horizon2020 project developing next generation solutions for renewable energy forecast- ing. This paper presents highlight results obtained during the first year of the project. Data science is used throughout the proposed solutions in order to process the large amount of heterogeneous data available to forecasters, and derive model-free approaches of forecasting and decision-aid tasks. This paper presents a series of solutions addressing relevant for Photovoltaics (PV) and storage applications. High-resolution Numerical Weather Predictions and regional solar irradiance forecasting provide detailed information on local weather conditions and their variability. PV power forecasting benefits from such new data sources, but also the proposed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management., This paper (presentation file) was presented at the 11th Solar & Storage Integration Workshop and published in the workshop's proceedings
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