下一代可再生能源预测的数据科学- Smart4RES项目的重点结果

G. Kariniotakis, S. Camal, F. Sossan, B. Nouri, J. Lezaca, M. Lange, B. Alonzo, Q. Libois, P. Pinson, R. Bessa, C. Gonçalves
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摘要

smart4res是欧洲地平线2020的一个项目,旨在开发下一代可再生能源预测解决方案。本文介绍了该项目第一年取得的突出成果。数据科学在整个建议的解决方案中被使用,以处理预测者可用的大量异构数据,并派生出预测和决策辅助任务的无模型方法。本文提出了一系列解决光伏(PV)和存储应用相关的解决方案。高分辨率数值天气预报和区域太阳辐照度预报提供有关当地天气状况及其变化的详细资料。光伏发电预测受益于这些新的数据来源,同时也受益于所提出的协同数据交换。最后,数据驱动的方法简化了短期市场交易和网格管理的决策。
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Data Science for Next Generation Renewable Energy Forecasting - Highlight Results from the Smart4RES Project
—Smart4RES is a European Horizon2020 project de-veloping 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 pro- posed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management.
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Supervisory optimal control for photovoltaics connected to an electric power grid Synchronous energy storage system with inertia capabilities for angle, voltage and frequency stabilization in power grids Data Science for Next Generation Renewable Energy Forecasting - Highlight Results from the Smart4RES Project Determination of the voltage and frequency dependent behaviour of low voltage grids – test procedure for a modified mobile generator Evaluation of advanced reserve sizing methods based on RE variability and uncertainty
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