Implementation of Risk Management Tools for Wind Power Industry


This transfer project aims at the prototype application of several risk management tools, which have been developed in the CRC 649 “Economic Risk”, in the context of wind energy production. First, the wind energy index will be refined and applied with regard to topical questions, such as local feed-in tariffs or wind park valuation. Second, we will work on managing grid stability with a volatility forecasting model for wind power production and hedging weather-dependent production losses with wind derivatives. Third, we will evaluate the efficiency of wind turbines to allow for specific technical improvements.


Principal Investigators
Ritter, Matthias Prof. Dr. (Details) (Quantitative Agricultural Economics especially Applied Econometrics)

Duration of Project
Start date: 01/2016
End date: 12/2018

Publications
Shen, Z., Ritter, M. (2016): Forecasting volatility of wind power production. Applied Energy 176: 295-308. http://dx.doi.org/10.1016/j.apenergy.2016.05.071.

Ritter, M., Pieralli, S., Odening, M. (2017): Neighborhood Effects in Wind Farm Performance: A Regression Approach. Energies 2017, 10(3), 365. http://dx.doi.org/10.3390/en10030365.

Ritter, M., Deckert, L. (2017): Site assessment, turbine selection, and local feed-in tariffs through the wind energy index. Applied Energy 185(2): 1087–1099. http://dx.doi.org/10.1016/j.apenergy.2015.11.081.

Helbing, G., Ritter, M. (2017): Power Curve Monitoring with Flexible EWMA Control Charts. Proceedings of the 2017 International Conference on Promising Electronic Technologies (ICPET), IEEE. https://doi.org/10.1109/ICPET.2017.29

Helbing, G., Ritter, M. (2018): Deep Learning for fault detection in wind turbines. Renewable and Sustainable Energy Reviews 98: 189-198. https://doi.org/10.1016/j.rser.2018.09.012.

Last updated on 2020-26-10 at 14:00