David Woelfle (M.Sc.)

Research Scientist

Werdegang

David Wölfle studied mechanical engineering in Karlsruhe. During his master studies he focused on wind energy technology and researched data sources for wind resource estimation. After graduating as Master of Science from Flensburg University of applied Applied Sciences in 2015, David worked as a R&D scientist at EWC Weather Consult GmbH (now UBIMET GmbH), where he designed and implemented software components for the estimation and predication of renewable energy power production. In 2016 David has been promoted to a team manager at EWC Weather Consult where he was responsible for the software engineering within the product development and as well as the design and execution of the project management. Besides these duties, he also developed innovative methods for estimating power production of airborne wind energy converters, using high-resolution meteorological data and machine learning methods.

Since early 2018 David works as a research scientist at FZI Research Center for Information Technology in the field of smart energy. His research focuses thereby on self-learning energy management systems using reinforcement learning techniques.

Publications

Conference Proceedings (7)

1. A Concept for Standardized Benchmarks for the Evaluation of Control Strategies for Building Energy ManagementInfoDetails

   David Wölfle, Kevin Förderer and Hartmut Schmeck, 2019

   Given the expected high penetration of renewable energy production in future electricity systems, it is common to consider buildings as a valuable source for the provisioning of flexibility to support the power grids. Motivated by this concept, a wide variety of control strategies for building energy management has been proposed throughout the last decades. % and especially for the previously mentioned components However, these algorithms are usually implemented and evaluated for very specific settings and considerations. Thus, a neutral comparison, especially of performance measures, is nearly impossible. Inspired by recent developments in reinforcement learning research, we suggest the use of common environments (i.e. benchmarks) for filling this gap and finally propose a general concept for standardized benchmarks for the evaluation of control strategies for building energy management.

2. Loss Function Theory 101Details

   David Wölfle, 2019

3. How To Teach Space Invaders To Your ComputerDetails

   David Wölfle, 2018

4. From 0 to Continuous Delivery in 30 minutesDetails

   David Wölfle, 2017

5. Long-term corrected wind resource estimation for AWE convertersInfoDetails

   David Wölfle, 2017

   At AWEC2015 an approach has been presented to estimate the potential power yield of airborne wind energy (AWE) systems over larger areas [1]. This approach has been demonstrated for an Enerkíte EK200 system. It has been shown thereby that the potential power yield may depend on certain AWE specific factors, like minimum wind speed for departure, the optimal operational height or de-icing measures of the airborne system. The presented results have only covered a two-year period, 2012 and 2013, due to the limited availability of high resolution meteorological input data. However, it is known from wind resource estimation for conventional wind power plants that coverage of a decade, better 30 years or more, is required for a power predication. This is as the average wind speed and wind speed distribution vary significantly from year to year. To achieve this long-term wind resource estimation for AWE, a technique commonly referred to as MeasureCorrelate-Predict (MCP) has been applied, which is state of the art for conventional wind turbines. Thereby the MERRA2 meteorological reanalysis dataset, which covers 1980 till today, has been employed as long-term reference. The year 2012 of the already presented wind resource estimation for AWE has been used as training data, while 2013 has been kept back as independent evaluation dataset. Transfer functions between the long-term reference values for 2012 and training data have been computed for potential power yield, as well as for the power not produced due to unmet starting conditions and deicing measures. By application of the transfer functions on the full time span covered by MERRA2 virtual longterm data has been computed. The quality of the longterm data has been evaluated by comparing the virtual long-term data of 2013 with the evaluation dataset. This presentation will cover a review of 2015’s results and methods, an introduction to the MCP method as well as the used transfer functions. The long-term results for potential power yield and power not produced due to unmet starting conditions and de-icing measures will be shown and compared to the equivalent measures generated from the two-year period. Finally, the quality of the long-term data will be discussed. References: [1] Brandt, D., et al.: Adapting wind resource estimation for airborne wind energy converters. In: Proceedings of the 6th Airborne Wind Energy Conference, Delft University of Technology, The Netherlands, 15-16 June 2015. http://resolver.tudelft.nl/uuid:cfc030a3- d6d1-4baf-99b3-d89e5fa8aefc

6. Die Kombination zweier Reanalysen als Grundlage des Site AssessmentDetails

   David Wölfle, Achim Strunk, 2016

7. Adapting Wind Resource Estimation for Airborne Wind Energy ConvertersInfoDetails

   David Wölfle, Martin Busch, Alexander Bormann, Max Ranneberg, 2015

   Wind resource estimation refers to any technique that assesses the wind potential in order to evaluate whether wind energy is economically viable at the investigated location. Thereby usually the annual energy production (AEP) is calculated based on the wind speed frequency distribution and the power curve. Interruption of operation, as necessary for maintenance and repairs, is usually considered as a capacity factor, e.g. 0.98. Regarding airborne wind energy converters (AWEC) however, one may need to consider additional influences towards the capacity factor, as AWEC may need to land in order to: • Prevent lightning impact • Allow de-icing • Avoid aircraft collision at low visibility In this work the influence of the before named situations on the AEP is examined for an EnerKíte AWEC, for an area covering Germany and parts of the surrounding countries, and for the years from 2012 till 2013. The system operates on altitudes from 50 to 300 m and may also be forced to land if the wind speed falls below the lower operational limit of 2 m/s. After conditions have improved a take-off is required to continue operation. For which it is also necessary that the lower operational wind speed at the lower operational altitude is reached, regardless stronger winds at higher altitudes. The power production of the Enerkíte AWEC depends on altitude and wind speed. Therefore, for every investigated location a wind profile must be used to consider optimal height of operation with respect to power. The wind speeds are taken from the COSMO-DE dataset produced by the German weather service DWD. The values are available as timeseries with hourly resolution covering the above mentioned area with a mesh size of 2.8 km. In vertical direction the wind speeds are interpolated to 12 height levels that are uniformly distributed between 25 and 300 m above ground level. Regarding the prevention of lightning impact, it is first necessary to evaluate the actual likelihood of a lightning striking into the AWEC during the investigated time span. Based on professional lightning records by Nowcast it is assumed that every lightning that has occurred within a certain radius around a potential site would have hit the airborne system. The actual radius depends thereby on the operational height. Furthermore it is presumed, that lightning threat is only given for situations where the line between kite and ground station is wet, as a dry line is supposed to be non-conducting. Finally an indicator for lightning risk is derived from correlating the virtual lightning impacts with radar reflectivity data provided by the DWD which is a sensitive diagnostic for thunderstorms. Based on this indicator it is possible to remove power that would be produced under lightning threat from the AEP. Potential icing of the AWEC can be identified by evaluation of air temperature and humidity which can both be takenfrom COSMO-DE. These two measures are also used for the spotting of low visibility situations. As a result, the impact of the individual threats on the capacity factor will be shown as well as a site specific summarized capacity factor. The findings are discussed especially in the context of optimization strategies as well as with respect to operational management of AWECs.

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Kontakt

Telefon: +49 721 9654-553
E-Mail: woelfle@dont-want-spam.fzi.de

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