PhD Program System Integration of Renewable Energy

SEE Ph.D. Students

Markus Bortolamedi, M.Sc.
Carl von Ossietzky Universität Oldenburg
Department of Economics
Chair of Economic Policy
Uhlhornsweg 84 - A5 1-106
26111 Oldenburg
Fon: +49 441 798-4670
E-Mail: markus.bortolamedi(at)uni-oldenburg.de

The discussion around the costs and benefits of climate protection not only refers to mitigation/ adaptation costs and the benefits of direct avoided damages of climate change (e.g. avoided flood, drought, extreme weather events). Very often benefits originating in increased energy security are also emphasized. The objective of this research project is to examine and to objectify the socialpolical discussion around energy security from an economic point of view. Concepts of energy security are backed up by insights from welfare economics and indicators of measurement of energy security are derived. 

Justin Heinermann, M.Sc.
Carl von Ossietzky Universität Oldenburg
Fakultät II - Department für Informatik
Arbeitsgruppe Computational Intelligence
Uhlhornsweg 84 - A5 2-234
26111 Oldenburg
Fon: +49 441 798-2863
E-Mail: justin.heinermann(at)informatik.uni-oldenburg.de

The successful integration of wind energy into the grid depends on precise predictions of the amount of energy produced. It has been shown that good forecast results can be achieved using machine learning algorithms. In this work, we propose the use of machine learning ensembles for wind power prediction. Multiple weak predictors are combined to one strong predictor, which leads to a reliably low prediction error. 

Christian Jepping, M.Sc.
Jade Hochschule Wilhelmshaven/Oldenburg/Elsfleth
Institut für Angewandte Photogrammetrie und Geoinformatik
Oldenburg
Fon: +49 441 7708-3349
E-Mail: christian.jepping(at)jade-hs.de

Ziel des Promotionsvorhabens ist die Weiterentwicklung berührungsloser 3D-Messverfahren zur Erfassung von Oberflächendaten im laufenden Betrieb einer Windenergieanlage und die darauf aufbauende Modellierung von Rotorblattgeometrien auf Basis von zeitabhängigen 3D-Massendaten. Die Erfassung von Deformationen eines Rotorblattes ist insbesondere für die Optimierung sowie zur Inspektion von Windkraftanlagen wichtig, da auf dieser Basis Verformungs- und Belastungsanalysen durchgeführt werden können. 

Daniel Kimmich, M.Sc.
Carl von Ossietzky Universität Oldenburg
Fakultät V - Institut für Reine und Angewandte Chemie
Arbeitsgruppe Physikalische Chemie - AG Wittstock
Carl von Ossietzky Str. 9-11 W3 1-133a
26129 Oldenburg
Fon: +49 441 798-3977
E-Mail: daniel.kimmich(at)uni-oldenburg.de

Balancing the temporal patterns of energy supply from renewable sources and energy demand represents one of the grand technological challenges in the Energiewende. Hydrogen can be an effective energy vector that facilitates transport, storage, back conversion of electricity or emission free thermal utilization. The objective of this PhD project is the development of a screening method for the effective search for new materials for the photochemical water splitting. A so called photoelectrolyzer combines the harnessing of solar energy and the electrolysis of water in one step and can therefore reach theoretically higher overall efficiencies in comparison to approaches with more than one operating units.

Daniel Lückehe, M.Sc.
Carl von Ossietzky Universität Oldenburg
Fakultät II - Department für Informatik
Arbeitsgruppe Computational Intelligence
Uhlhornsweg 84 - A5 2-228
26111 Oldenburg
Fon: +49 441 798-2863
E-Mail: daniel.lueckehe(at)uni-oldenburg.de

In order to reduce the load of power grids and to optimize output from regenerative energy sources, geo-planning has an important part to play. In particular, the increasing number of renewable energy sources leads to the requirement of their careful integration into the landscape Therefore, many different parameters, which affect power output, have to be considered. The objective is to introduce a new optimization for geo-planning, which can find optimal positions for different power plants in a defined area. In the case of a wind turbine placement optimization problem, a solution is benchmarked w.r.t. different objectives.

Anna Mehrens, M.Sc.
Carl von Ossietzky Universität Oldenburg
Fakultät V - Institut für Physik
ForWind - Zentrum für Windenergieforschung
26129 Oldenburg
Fon: +49 441 798-5079
E-Mail: anna.mehrens(at)forwind.de

Eine sichere Integration von Windenergie in unsere Übertragungsnetze erfordert eine gute Vorhersage der Fluktuationen. Die Promotion beschäftigt sich mit der Charakterisierung und Vorhersage von Windgeschwindigkeitsfluktuationen auf der Mesoskala. Die Mesoskala umfasst Prozesse mit einer Dauer von einer Minute bis zu mehreren Stunden. Messdaten eines Offshore Windparks und meteorologische Simulationsdaten über der Nordsee kommen zum Einsatz.

Dipl.-Phys. Peter Michalowski
Carl von Ossietzky Universität Oldenburg
Fakultät V - Institut für Physik
Abteilung EHF
W2 1-184
26129 Oldenburg
Fon: +49 441 798-3007
E-Mail: peter.michalowski(at)uni-oldenburg.de

With the shift of electricity production from fossil fuels to fluctuating sustainable energy sources and the rise of electric vehicles, the development of energy storage devices faces substantial challenges. An important type of such devices are batteries based on lithium ion technology. The aim of the project is the preparation, characterization and optimization of solid-state thin-film batteries. The deposition of the cathode and the electrolyte film is realized by spin coating or drop casting. 

M.A. Charlotte von Möllendorff
Carl von Ossietzky Universität
Fakultät II - Department of Economics
Postadresse: Uhlhornsweg 84, A5 0-021
26121 Oldenburg
Fon: +49 441 798-4886
E-Mail: charlotte.moellendorff(at)uni-oldenburg.de
URL: http://www.uni-oldenburg.de/vwl

The promotion of renewable energies gained wide support throughout Germany as renewable sources avoid disadvantages of conventional power generation. However, in some regions renewable energy development gave rise to local opposition which is often referred to as the not-in-my-backyard (NIMBY) problem. The aim of this research project is to empirically study externalities associated with the expansion of renewable energies and to gather knowledge on factors of influence for social acceptance. 

Judith Neugebauer, M.Sc.
Carl von Ossietzky Universität Oldenburg
Fakultät II - Department für Informatik
Arbeitsgruppe Umweltinformatik
Uhlhornsweg 84 - A5 2-235
26111 Oldenburg
Fon: +49 441 798-2754
E-Mail: judith.neugebauer(at)uni-oldenburg.de

The advancing integration of renewable energies into the electricity grids leads to structural changes of these grids. While some large power plants dominated the electricity grid in the past, a lot of small and delocalised renewable energy resources will dominate the electricity grid in the future. The task of this thesis is to develop an algorithm and a software system that reduce the complex models (white box models) to easier data models (black box models). The algorithm and the software system should satisfy the given qualitative requirements concerning the output parameters, the temporal resolution and the desired accuracy. 

Dr. Anja Ohsenbrügge
OFFIS-Institut für Informatik
FuE Bereich Energie, R&D Division Energy
Postadresse: Escherweg 2
Besucheradresse: Industriestr. 6
26121 Oldenburg - Germany
Fon/Fax: +49 441 9722-739/771
E-Mail: anja.ohsenbruegge(at)offis.de 
URL: http://www.em.offis.de

To ensure a constant power frequency and thus a stable quality of supply, the permanent balance of power demand and supply is the most crucial constraint in an electrical power system. Therefore there is a need for reserve and balancing power to cover prediction-deviations for example of wind generation and loads or unpredictable events like power plant outtakes. The objective of this work is to develop a dynamic strategy for reserve and reliability that factors in these altered circumstances, in particular the provision of ancillary services by decentralized self-organized coalitions of small active units. 

Marius Paschen, M.A.
Carl von Ossietzky Universität Oldenburg
Institut: Volkswirtschaftslehre
Uhlhornsweg 84 - A5 0-027
26111 Oldenburg
Fon: +49 441 798-4523
E-Mail: marius.paschen(at)uni-oldenburg.de

In the past, the construction and maintenance of electricity networks took place in a widely static environment. These networks are changing dynamically due to the transformation of the energy system. This shows a need to deal with a larger volatility and a new spatial structure of energy production. One focus of this work lies on the investment decisions and whether they lead to efficient development of production and transmission capacity. In order to analyse the regulatory interventions described above, mathematical models are considered. 

M.Sc. Wiebke Schulte
Carl von Ossietzky Universität Oldenburg
Fakultät V - Institut für Reine und Angewandte Chemie
Arbeitsgruppe Physikalische Chemie - AG Wittstock
Carl von Ossietzky Str. 9-11 W3 1-133
26129 Oldenburg
Fon: +49 441 798-3976
E-Mail: wiebke.schulte(at)uni-oldenburg.de

Ressourcenknappheit fossiler Brennstoffe, steigende CO2-Emissionen und der damit verbundene Klimawandel machen die Entwicklung und Verbesserung der Energieversorgung durch erneuerbare Energien notwendig. Brennstoffzellen unterschiedlicher Bauweisen stellen neben Photovoltaik und Windenergie eine vielversprechende Komponente der umweltfreundlichen Energieversorgung dar. Eine entscheidende Reaktion in einer Brennstoffzelle ist die katalysierte Reduktion von Sauerstoff, wobei unterschiedliche Produkte entstehen können. Ziel ist es, Katalysatoren möglichst günstig und schnell zu testen, um Platin als Katalysator ersetzen zu können. 

Cornelius Steinbrink, M.Sc.
Carl von Ossietzky Universität Oldenburg
Department of Computing
Arbeitsgruppe Energieinformatik
+49 441 9722-422 (S. Rohjans)
26111 Oldenburg
E-Mail: cornelius.steinbrink(at)uni-oldenburg.de

Smart grids are complex systems that link power distribution, information- and communication technology, and novel control strategies. New components for these systems can not immediately be tested in hardware setups due to reasons of flexibility, cost, and power quality. Therefore, simulation tools are needed, e.g. the platform “mosaik” that allows steady state simulation of large scale scenarios with pre-existing grid component models. In this PhD project the software mosaik is coupled with a high performance real-time simulator that provides Hardware-in-the-Loop capabilities and allows high temporal resolution. Uncertainty quantification allows assessment of the result accuracy.

Dipl. Phys. Nils André Treiber
Carl von Ossietzky Universität Oldenburg
Fakultät II - Department für Informatik
Arbeitsgruppe Computational Intelligence
Uhlhornsweg 84 - A5 2-235
26111 Oldenburg
Fon: +49 441 798-4374
E-Mail:nils.andre.treiber(at)uni-oldenburg.de

A large contribution to a shift to renewable energy is provided by wind turbines. In comparison with conventional power plants, their output is variable due to changing weather conditions. A significant improvement in forecasting of renewable energies is expected by the application of new statistical learning methods. Generally, the employed models derive functional dependencies directly from the observations and finally get the ability to make the desired predictions.

Mehdi Vali, M.Sc.
Carl von Ossietzky Universität Oldenburg
Institute of Physics - Forwind
Abteilung: Windenergiesysteme
Fon: +49 441 / 798 - 5090 (Frau Meints)
26129 Oldenburg
E-Mail: mehdi.vali(at)uni-oldenburg.de

So far, the WE-Sys wind farm controller is developed based on the model-free game theoretic approach by formulating the WFC as a cooperative control problem. It is shown that this distributed algorithm can maximize energy production in wind farms without explicitly modeling the aerodynamic interactions among the turbines. A challenge with model-free approach is that there is a significant delay between a change in the control settings and its effects on the downstream turbine due to propagation of the wake. A control-oriented dynamic model for predicting the wake effects is developed based on the axial induction factor and flow redirection of the wind turbines.

Dipl. Phys. Benjamin Wahl
Carl von Ossietzky Universität Oldenburg
ICBM - Institut für Chemie und Biologie des Meeres
Arbeitsgruppe Complex Systems Research Group
Carl-von-Ossietzky-Str. 9- 11
26129 Oldenburg
E-Mail:benjamin.wahl(at)uni-oldenburg.de

The focus of this work is the development, implementation and application of causality measures on time series and noisy dynamical systems. These measures are interesting firstonce in theoretical terms. On the other hand it can be applied to real data and stochastic models. A special area of application is the time series analysis of the power outputs of wind power plants (WPP) in a wind farm. The coupling of these power outputs shall be investigated depending on the wind conditions. In this context the power outputs correspond to the stochastic variables. 

Dipl.-Phys. Stefan Weitemeyer
Carl von Ossietzky Universität Oldenburg
EWE-Forschungszentrum NEXT-ENERGY
Arbeitsgruppe Energiesystem-Modellierung
26129 Oldenburg
Fon: +49 441-99906-105
E-Mail: stefan.weitemeyer(at)next-energy.de

Recent studies have shown that a transition of the current power supply system in Europe to a system almost entirely based on fluctuating Renewable Energy Sources (RES) by mid-century is possible. However, most of these scenarios require a significant amount of balancing and controlling back-up power capacities to ensure the security of electricity supply. In the PhD project, the simulation model MOSES is developed and applied to analyse different target states of the European electricity system in 2050. In this model long-term meteorological data series are used to optimise the capacity mix of RES in Europe. One of the main elements of the tool is a simplified European grid model which will be used to calculate the load flows in a transnational electricity network.

Björn Wolff, M.Sc.
Carl von Ossietzky Universität Oldenburg
Department of Physics
EHF Laboratory
26129 Oldenburg
Fon: +49 441 798-3577
E-Mail: bjoern.wolff(at)uni-oldenburg.de

In Germany, with an installed capacity of more than 35 GW at the end of 2013, solar power prediction services are already an essential part of the grid control. The goal of this thesis lies in the improvement of short-term (up to six hours) solar power prediction accuracy by efficiently combining different measurement and forecast datasets. Thus, current statistical learning methods, e.g. support vector regression, are applied and optimized for solar power prediction. These methods are able to identify structures in historical data and generate regression functions that predict possible future outputs.

Mehrnaz Anvari
Carl von Ossietzky Universität Oldenburg
Department of Physics
Turbulence, Wind Energy and Stochastics - TWIST
26111 Oldenburg
Fon: +49 441 798-5052
E-Mail: mehrnaz.anvari(at)uni-oldenburg.de

The power output of wind turbines is influenced by atmospheric turbulence and it possesses frequent extreme events. Such events endanger the stability of power grids and cause voltage instability in very short time scales. The understanding and characterization of its stochastic properties has great scientific and practical importance. In this thesis we will use the Markovian approach for study the wind power fluctuations. Our aim is to show that the power outputs for single wind turbine and wind farm, can be modeled via Langevin equation with multiplicative noise.