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Communication Dans Un Congrès Année : 2021

A Holistic Approach to Improve the Model & Value Chain of Renewable Energy Forecasting - The Smart4RES Project

Résumé

In this presentation we will outline the research directions and innovative solutions developed in the European Horizon 2020 project Smart4RES (http://www.smart4res.eu) aiming at improving all main steps of the model and value chain of renewable energy forecasting. The aim is to optimise integration of renewable energy (RES) production (i.e. wind, solar, run-of-the-river hydro) into power systems and electricity markets. Smart4RES started in November 2019 and gathers experts from several disciplines, from meteorology and renewable generation to market- and grid-integration. It aims to contribute to reach very high RES penetrations in power grids of 2030 and beyond, through thematic objectives including: • Improvement of weather forecasting with focus on variables that are important for RES applications; • Improvement of RES power forecasting and better estimation of uncertainties; • 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). Smart4RES focuses both on improving forecasting models of weather (e.g. physical models, data assimilation, Large Eddy Simulation, enabling weather forecasts seamless) and RES production (e.g. seamless models, highly resolved predictions), and on addressing applications in power grids. Developments in the project have been formalized in Use Cases that cover a large range of time frames, technologies and geographical scales. For example, use-cases on power grids refer to the provision of ancillary services to the upper-level grid (e.g., balancing power) and the local grid (e.g., voltage control and congestion management), where the accurate forecasts of variable generation are key for accurate decision-making. A grid state forecasting will quantify dynamically the flexibility potential of RES in distribution grids. Collaborative forecasting investigates the improvement associated to local data sharing between distributed RES plants. This data sharing paves the way to a data market where agents exchange measurements, predictions or other types of valuable data. Lastly, data-driven approaches will streamline decision-making by simplifying the model chain of bidding RES production, storage dispatch or predictive management electricity grids. They will also provide interpretable hindsight to decision-makers by integrating the decisions of experts (human-in-the-loop) and will be tested in realistic laboratory conditions (software-in-the-loop). In this presentation we focus on the work done for the development of seamless numerical weather prediction (NWP) approaches to be able to couple outputs of NWP models with different resolutions and on the contribution of ultra-high resolution NWPs based on Large Eddy Simulation. A second group of results will focus on the optimal use of probabilisitic RES production and market price forecasts for trading in energy and ancillary service markets. Results from a new paradigm will be presented, where artificial intelligence methods are used to simplify the complex model chain (forecasting/trading) by a single decision making model that derives directly the bidding decisions using the available data. Results are based on data from real world test cases considered in the project.
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hal-03528716 , version 1 (17-01-2022)

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  • HAL Id : hal-03528716 , version 1

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Simon Camal, Georges Kariniotakis. A Holistic Approach to Improve the Model & Value Chain of Renewable Energy Forecasting - The Smart4RES Project. Wind Europe Technology Workshop 2021 - "Resource Assessment & Analysis of Operating Wind Farms", Wind Europe, Sep 2021, Online, Belgium. ⟨hal-03528716⟩
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