Digitalisierung und Energiesystemtransformation : Chancen und Herausforderungen

Welche Rolle spielt die Digitalisierung mit der Vielzahl ihrer Methoden und Anwendungen für die Energiewende - also für die Transformation unseres Energiesystems im Sinne der vereinbarten Klimaschutzziele? Ist sie notwendige Voraussetzung für den Systemumbau und ermöglicht beispielsweise erst den Übergang auf ein nahezu vollständig erneuerbares Energiesystem (Enabler) oder ist sie lediglich ein nützliches, den Umbau beschleunigendes Hilfsmittel (Facilitator)? Welche Veränderungen sind durch die Ziele der Energiewende getrieben und welche durch die Verbreitung von Techniken der Digitalisierung? All dies waren Fragen, die im Rahmen der Jahrestagung 2018 des Forschungsverbunds Erneuerbare Energien unter dem Titel "Die Energiewende - smart und digital" behandelt wurden. Dieser einführende Beitrag versucht einige Anhaltspunkte zur Beantwortung dieser Fragen zu liefern und in das Thema einzuführen

Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models

Wind energy is having an increasing influence on the energy supply in many countries, but in contrast to conventional power plants, it is a fluctuating energy source. For its integration into the electricity supply structure, it is necessary to predict the wind power hours or days ahead. There are models based on physical, statistical and artificial intelligence approaches for the prediction of wind power. This paper introduces a new short-term prediction method based on the application of evolutionary optimization algorithms for the automated specification of two well-known time series prediction models, i.e., neural networks and the nearest neighbour search. Two optimization algorithms are applied and compared, namely particle swarm optimization and differential evolution. To predict the power output of a certain wind farm, this method uses predicted weather data and historic power data of that wind farm, as well as historic power data of other wind farms far from the location of the wind farm considered. Using these optimization algorithms, we get a reduction of the prediction error compared to the model based on neural networks with standard manually selected variables. An additional reduction in error can be obtained by using the mean model output of the neural network model and of the nearest neighbour search based prediction approach.Variable selection Multivariate time series Neural networks Nearest neighbour search Evolutionary optimization Comparative studies Wind energy

Performance and Reliability of Wind Turbines: A Review

Performance (availability and yield) and reliability of wind turbines can make the difference between success and failure of wind farm projects and these factors are vital to decrease the cost of energy. During the last years, several initiatives started to gather data on the performance and reliability of wind turbines on- and offshore and published findings in different journals and conferences. Even though the scopes of the different initiatives are similar, every initiative follows a different approach and results are therefore difficult to compare. The present paper faces this issue, collects results of different initiatives and harmonizes the results. A short description and assessment of every considered data source is provided. To enable this comparison, the existing reliability characteristics are mapped to a system structure according to the Reference Designation System for Power Plants (RDS-PP®). The review shows a wide variation in the performance and reliability metrics of the individual initiatives. Especially the comparison on onshore wind turbines reveals significant differences between the results. Only a few publications are available on offshore wind turbines and the results show an increasing performance and reliability of offshore wind turbines since the first offshore wind farms were erected and monitored