Distributed Artificial Intelligence The resilient and optimized operation of fully renewable and digitalized energy systems requires new paradigms in system operations. Intelligent multi-agent systems with self-x properties are therefore the focus of the work of the Distributed Artificial Intelligence (DAI) group.
The complete conversion of our energy supply to renewable energies is a central challenge of our time and an indispensable contribution to climate protection and energy autonomy. To achieve this, not only must the generation and consumption of energy be reconciled on the market, but energy systems must also be operated reliably and efficiently. With increasing digitalization, the historically evolved energy supply structures are becoming a complex and dynamic cyber-physical energy system in which thousands of components interact with each other. The digital systems must be able to adapt to different operating conditions and compensate for faults in operation as independently as possible, while meeting the requirements and needs of human users in a trustworthy manner.
Vision: self-organising, self-optimising and self-healing cyber-physical energy systems
The DAI group is therefore working on enhancing the distributed components of a cyber-physical energy system with intelligence and autonomy and networking them with each other based on concepts of the so-called Organic Computing. In research and development projects, we investigate the possibilities and limits of self-organising, self-optimising and self-healing cyber-physical energy systems.
Our main research topics
In the context of these characteristics of digitalized systems, collectively referred to as "self-x properties," we address the following key issues:
Resilient Self-X-Systems
Resilience is a system's ability to quickly restore stable system operation and become more robust to disruptions in the long term.
Learning in Self-X-Systems
The ability of agents to learn is a basic prerequisite for adapting system behaviour to new or changing conditions.
Transparency and Acceptance of Self-X-Systems
The acceptance of autonomously deciding, digitalized systems also depends on their ability to explain decisions in a transparent and comprehensible way. Find out more..
Modelling and Agent-based Management of Flexibility
Flexibility is the basis of almost all decision-making processes in digitized energy systems, especially when it comes to the use of decentralized energy plants in the distribution network. Find out more..
Open Science – freely available and open scientific results
As a research group, we are committed to transparent science - Open Science, which makes research results freely and openly available. The artefacts, data and publications we produce should be able to be used collectively and without hurdles in order to improve the quality of research and contribute to progress in society. An essential aspect of this is also the possibility of reproducing research results. Further information..
Persons 
F
G
Dr. rer. nat. Benjamin Giesers
E-Mail: benjamin.giesers(at)offis.de, Phone: +49 441 9722-747, Room: E128
H
O
R
S
T
Dr. rer. nat. Martin Tröschel
E-Mail: martin.troeschel(at)offis.de, Phone: +49 441 9722-150, Room: E128
W
Projects
D
DEER
Dezentraler Redispatch (DEER): Schnittstellen für die Flexibilitätsbereitstellung
Duration: 2022 - 2025I
N
NFDI4Energy
National Research Data Infrastructure for the Interdisciplinary Energy System Research
Duration: 2023 - 2028R
REMARK
Resilienz im digitalisierten Stromsystem: Toolbox zur Bewertung von Systemdienstleistungsmärkten
Duration: 2022 - 2024W
WWNW
WärmewendeNordwest – Digitalisierung zur Umsetzung von Wärmewende- und Mehrwertanwendungen für Gebäude, Campus, Quartiere und Kommunen im Nordwesten
Duration: 2021 - 2025 Publications
2023
A Multi-Criteria Metaheuristic Algorithm for Distributed Optimization of Electric Energy Storage
Schrage, Rico and Tiemann, Paul Hendrik and Niesse, Astrid; SIGENERGY Energy Inform. Rev.; feb / 2023
Coupling OMNeT++ and mosaik for integrated Co-Simulation of ICT-reliant Smart Grids
Frauke Oest, Emilie Frost, Malin Radtke, Sebastian Lehnhoff; ACM SIGENERGY Energy Informatics Review; 03 / 2023
2022
Choosing the right model for unified flexibility modeling
Brandt, Jonathan and Frost, Emilie and Ferenz, Stephan and Tiemann, Paul Hendrik and Bensmann, Astrid and Hanke-Rauschenbach, Richard and Nieße, Astrid; Energy Informatics; 2022
Constraint-based Modeling of Smart Grid Services in ICT-Reliant Power Systems
Oest, Frauke and Lehnhoff, Sebastian; ENERGY 2022, The Twelfth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies; Mai / 2022
Operational flexibility for multi-purpose usage of pooled battery storage systems
Tiemann, Paul Hendrik and Nebel-Wenner, Marvin and Holly, Stefanie and Frost, Emilie and Jimenez Martinez, Adrian and Nieße, Astrid; Energy Informatics; 2022
2021
A Sketch of Unwanted Gaming Strategies in Flexibility Provision for the Energy System
Buchholz, Sebastian and Tiemann, Paul Hendrik and Wolgast, Thomas and Scheunert, Alexandra and Gerlach, Jana and Majumdar, Neelotpal and Breitner, Michael and Hofmann, Lutz and Nieße, Astrid and Weyer, Harmut; Energy Informatics and Electro Mobility ICT; 2021
Assumptions on a Distributed and Hierarchical Market Concept for Balancing Reserve Aggregation
Tiemann, Paul Hendrik and Nieße, Astrid; Energy Informatics and Electro Mobility ICT; 2021
Balancing grid islands with distributed energy resources
Tiemann, Paul Hendrik; Abstracts of the 10th DACH+ Conference on Energy Informatics; 2021
Battery Storage Demonstration Projects: an Overview across Europe
Divshali, Poria Hasanpor and Mäki, Kari and Evens, Corentin and Papadimitriou, Christina and Efthymiou, Venizelos and Nieße, Astrid and Holly, Stefanie and Marinelli, Mattia and Gabderakhmanova, Tatiana and Meléndez, Joaquim and others; 2021 IEEE PES Innovative Smart Grid Technologies Conference Europe; 2021
Distributed fitness landscape analysis for cooperative search with domain decomposition
Holly, Stefanie and Nieße, Astrid; IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021); 2021
