Strategies for continuous balancing in future power systems with high wind and solar shares

The use of wind power has grown strongly in recent years and is expected to continue to increase in the coming decades. Solar power is also expected to increase significantly. In a power system, a continuous balance is maintained between total production and demand. This balancing is currently mainly managed with conventional power plants, but with larger amounts of wind and solar power, other sources will also be needed. Interesting possibilities include continuous control of wind and solar power, battery storage, electric vehicles, hydrogen production, and other demand resources with flexibility potential. The aim of this article is to describe and compare the different challenges and future possibilities in six systems concerning how to keep a continuous balance in the future with significantly larger amounts of variable renewable power production. A realistic understanding of how these systems plan to handle continuous balancing is central to effectively develop a carbon-dioxide-free electricity system of the future. The systems included in the overview are the Nordic synchronous area, the island of Ireland, the Iberian Peninsula, Texas (ERCOT), the central European system, and Great Britain

Vorhersage der Prognosegüte verschieden großer Windpark-Portfolios

In der vorliegenden Arbeit wurde ein Windparkprognosemodell auf Basis Künstlicher Neuronaler Netze und Generalisierter Linearer Modelle entwickelt. Die beachtliche Güte des Modells konnte in einem internationalen Prognosewettbewerb bestätigt werden, in welchem die erzeugten Prognosen den geringsten Fehler im Vergleich zu elf weiteren Modellen aus Europa, Japan, Indien, USA und Australien aufwiesen. Das Modell wurde in dieser Arbeit eingesetzt, um mehr als 5000 Prognosen unterschiedlich großer Windparks und Windparkportfolios auf Basis 20 verschiedener Wetterprognosen für Zeithorizonte von 1 bis 48 Stunden zu berechnen. Die Güte der Prognosen wurde hinsichtlich räumlicher und zeitlicher Abhängigkeiten ausgewertet, um verschiedene offene Fragestellungen im Forschungsbereich der Windleistungsprognose verlässlich beantworten zu können. Dies betrifft beispielsweise die Integration von aktuellen Messwerten zur Verbesserung der Kürzestfristprognose, die Abhängigkeit der mittleren und maximalen Fehler vom Aggregationslevel, Zeithorizont und Wettermodell, die regionalen Unterschiede in der Vorhersagbarkeit verschiedener Wettermodelle sowie die Kombination von Wettermodellen

Benchmark of Spatio-temporal Shortest-Term Wind Power Forecast Models

Many European energy supply systems are increasingly penetrated by wind energy. In order to be able to act optimally on the market or in the operation of electricity grids, it is necessary to have high-quality intraday forecasts of the expected wind power production. For this purpose, numerical weather forecasts and recent power measurements transmitted in real time are used. This provides a lot of information to the forecaster. It is, on the one hand, necessary to be able to decide which information are beneficial, and on the other hand, to be able to handle proper forecasting models. Suitable models to calculate wind power forecasts are power curve-based models and models from the field of statistics as well as machine learning models. In this work we benchmark (first) different models ranging from power curve based to machine learning like random forests, artificial neural networks and extreme learning machines, and (second) the value of spatio-temporal information from surrounding wind parks