Distributed Energy Systems and Networks: Research


1. Model-based power systems analysis considering grid restrictions

  • Regional power system development and nodal pricing
  • Analysis of grid congestions and load flow management
  • Development of the robustness of power systems

Main model(s) supporting the research:
PERSEUS-NET, an advanced version of PERSEUS (Program Package for Emission
Reduction Strategies in Energy Use and Supply), allowing for the consideration of grid restrictions.

2. Model-based analysis of distributed power systems

  • Analysis of load shifting potential and user acceptance of(price-based) demand side management (DSM)
  • Stochastic approaches for layout planning, energy management and forecasting in distributed energy systems
  • Market design of distributed energy systems

Main model(s) supporting the research:
PowerACE, an agent-based simulation model, developed within the research group
"Energy markets and energy systems analysis", which was adapted and extended for
the use in decentralised systems.

3. Socio-techno-economic analysis of linked power systems

  • Analysis of interaction between human and smart home under real living conditions in laboratory
    "Energy Smart Home Lab"
  • Identifying load shift potentials
  • Analysis of acceptance and willingness to adopt for innovative power systems, for example
    energy management systems or different incentive systems for the adaption of energy consumption behavior
  • Collection of user preferences for different designs of new power systems


  • Choice experiments:

Choice experiments are a selection or decision-based method for ascertaining economic preferences, in which data on hypothetical or real decision situations are collected. Econometric models are used to analyze the data.

  • Qualitative surveys such as expert interviews:

Qualitative methods such as interviews offer the possibility to query attitudes, preferences or opinions in a relatively free format and are therefore particularly suitable for explorative research, for example.

  • Quantitative surveys:

In quantitative surveys, constructs such as opinions, attitudes or behavioral intentions are recorded using questionnaires. The data collected can be statistically evaluated and modeled.

  • (Field) experiments / randomized controlled trials:

Experimental designs can be used to establish causal relationships by explicitly controlling or manipulating various variables. In addition to laboratory experiments, field experiments have the advantage that correlations can be analyzed in their natural context (“in the field”).

  • Large-scale nonlinear programming:

The detailed modeling of large-scale electrical energy systems, e.g. the German or European transmission network, as well as the question of its cost-optimal, low-loss or otherwise optimal operation results in complex, non-linear, large-scale optimization problems. In addition to deriving and implementing models that are as realistic as possible for the various components of the system, solving the optimization problems is also a major challenge; modern mathematical methods must be used for this and adapted to the respective problem.

  • Energy system optimization of residential buildings:

In order to be able to analyze developments in the residential building sector well, mixed-integer optimization models are used to determine techno-economically optimized energy systems. Various methods of statistics (Markov models, ...) and machine learning (clusters, ...) are used to simulate resident behavior, the thermal properties of residential buildings and to scale the results to the European residential building sector.

  • Determining renewable energy potential and optimizing energy systems in communities:

Many municipalities have neither the necessary expertise nor sufficient resources to optimally set up their energy system in the course of the energy transition. With the help of energy system analyzes, as well as the energy system optimization model RE³ASON (Renewable Energies and Energy Efficiency Analysis and System Optimization), the optimal community energy systems are determined based on public data (OpenStreetMap, census, satellite data). A particular focus is on determining optimal self-sufficient energy systems that have no access to the electricity and / or gas network.



RE³ASON is a deterministic investment and resource planning model that determines the optimal investment at community level in new energy conversion technologies. It can minimize overall system costs, emissions or energy imports and optimize technology investments and the use of all technologies and energy flows between district districts. RE³ASON can be classified as a mixed integer linear program (MILP) that determines a municipal energy system with the lowest cost, emissions or imports (or a combination thereof).