Energy Group


The increasing amount of renewable energy in the European Energy System raises many new technic and economic challenges. Many of those challenges, regarding for example reliant power supply and cost of energy are closely linked to automation and control topics.The research team Energy develops solutions for control-related issues within the field auf power generation and energy storage. This comprises automatic control of renewable and innovative fossil fuel power plants as well as the optimization and control of energy storage systems and their operation in energy systems.


Photo of Thomas Konrad


Thomas Konrad

Energy Group Manager


+49 241 80 28014



Focus: Wind Energy

One focus of our research group is Wind Energy. Thereby, automatic control of single wind turbines and wind parks is addressed as well as the development and implementation of multi-physical Hardware-in-the-Loop systems that allow full-scale system test benches for wind turbines.

Wind Energy is an interdisciplinary topic which can only be fully addressed in close cooperation with partners of various domains. Therefore the Institute of Automatic Control is one of the seven founding institutes of the Center for Wind Power Drives, CWD for short. In this center, we collaboratively work on research topics in the field of Wind Energy.

  wind turbine RWTH

Generally, wind turbine control has two main goals:

  • Maximization of produced power and
  • Minimization of mechanical and electrical stress.

From the control theory point of view, the system “wind turbine” can be characterized by the following properties:

  • A wind turbine is a non-linear and
  • A multiple input multiple output system. The inputs are generator torque and pitch angle of the rotor blades, while outputs depend on the control problem at hand. From an energetic perspective, the most important output is generator current and power. On a more detailed level, mechanical loads and stresses are outputs that gain increasing attention due to high demands on reliability.
  • The system is mainly influenced by disturbance that is not well-measureable, for example changing speed of wind. Therefore, the control task can be interpreted as disturbance rejection control. Hereby the impact that the low-power, high-frequency portion of the incoming wind has on power output and mechanical stress is to be rejected. At the same time, the low-frequency amount provides the energy that is to be harvested.

New control concepts for wind turbines are developed and prepared for testing in real-word applications in close cooperation with industrial and academic partners. To accomplish this aim, the following methods are of main interest:

  • Development of adaptive Model Predictive Controllers in order to reduce loads at rotor blades, tower and drive train.
  • Utilization of wind predictions in feedback and in feedforward control.
  • Observer design and validation for estimation of wind characteristics and turbine states.
  • Development of reduced-order, real-time capable white box turbine models.
  • Validation of innovative control concepts using Co-Simulations of well-respected, high fidelity simulation tools such as FAST, Bladed, Simpack or alaskaWind and implementation on industrial controller hardware.
  nacelle test benche CWD RWTH 4MW Hardware-in-the-Loop system test bench for multi-MW wind turbines

Another topic within the focus area Wind Energy are multi-physical Hardware-in-the-Loop systems and their application within full-scale nacelle test benches for testing multi-MW wind turbines. The test bench at CWD Aachen is shown in Figure.

For control engineering purposes, such Hardware-in-the-Loop systems provide the following tasks:

  • Compensation of dynamics inevitably induced by the test bench in order to increase the accuracy of the load application.
  • Guaranteeing stability of Hardware-in-the-Loop system whilst communication-related time-delay is present.
  • Emulation of missing components such as rotor inertia in order to reproduce system dynamics of the real wind turbine at the test bench.

Focus: Energy Storage Operation

Power supply fluctuation in the future will be more and more compensated by grid-integrated storage systems. Crucial factors for their technical and economic success are intelligent control strategies which consider both, technical and economic aspects, such as price variability. The research group Energy develops control strategies for an optimal operation of storage systems.

  StorageSystem IRT

Energy storages are operated within various environments: in industrial processes, within renewable power plants or at crucial grid nods. In any case, energy storage operation has to cope with the following two tasks:

  • Technical task: compensate energy deficits
  • Economic task: minimize operational costs

Again, those systems can be characterized from the control point of view as follows:

  • Stored energy often links different energy-consuming sectors related to electrical power, heat and mobility. Therefore, the system is a multiple-output system.
  • Depending on the storage technology, the systems are non-linear or distributed. Electrolysis storage systems for instance are highly non-linear, while thermal storage systems are best described as distributed systems based on partial differential equations.
  • The system is mainly influenced by stochastic quantities such as future price data, fed-in power, consumed power. The control strategies need to cope with these properties.
  • Consideration of the pure technology of the systems is not sufficient; it needs to be accompanied by economic aspects.
  SpecCosts IRT

In close cooperation with experts in the fields of storage technology and large energy systems, the research group Energy designs control algorithms that satisfy the above requirements. These investigations are based on the results of energy studies, such as „Energiekonzept 2050“ of the German federal government. Our research focuses on:

  • Design of control concepts that link economic and technical aspects. Due to largely differing time constants, multi-stage control concepts are commonly used.
  • Development of white-box models of storage and energy systems, systematic order reduction of such for use in real-time application.
  • Implementation of adaptive Model Predictive Control, utilizing mixed-integer and non-linear optimization schemes for optimal storage operation.
  • Utilizing price and power predictions that are limited in time and accuracy in order to investigate whether these control schemes can operate energy storages as proposed by large-scale energy system studies.

We are always looking for motivated students who want to work in the above mentioned areas within their project-, bachelor- or master-thesis or are looking for a student job. You are very welcome to send us your application.