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 
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Dr. rer. nat. Benjamin Giesers
E-Mail: benjamin.giesers(at)offis.de, Phone: +49 441 9722-747, Room: Flx-E
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Dr. rer. nat. Stefanie Holly
E-Mail: stefanie.holly(at)offis.de, Phone: +49 441 9722-732, Room: Flx-E
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Dr. rer. nat. Martin Tröschel
E-Mail: martin.troeschel(at)offis.de, Phone: +49 441 9722-150, Room: Flx-E
Projects
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DEER
Dezentraler Redispatch (DEER): Schnittstellen für die Flexibilitätsbereitstellung
Duration: 2022 - 2025E
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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 - 2024T
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WWNW
WärmewendeNordwest – Digitalisierung zur Umsetzung von Wärmewende- und Mehrwertanwendungen für Gebäude, Campus, Quartiere und Kommunen im Nordwesten
Duration: 2021 - 2025 Publications
2025
A multi-agent approach with verifiable and data-sovereign information flows for decentralizing redispatch in distributed energy systems
Heess, Paula and Holly, Stefanie and Körner, Marc-Fabian and Nieße, Astrid and Radtke, Malin and Schick, Leo and Stark, Sanja and Strüker, Jens and Zwede, Till; Energy Informatics; February / 2025
Amplify: Multi-purpose flexibility model to pool battery energy storage systems
Paul Hendrik Tiemann, Marvin Nebel-Wenner, Stefanie Holly, Emilie Frost and Astrid Nieße; Applied Energy; 2025
Communication Modeling Approaches in Energy System Applications: A Systematic Overview
Radtke, Malin and Frost, Emilie and Nieße, Astrid and Lehnhoff, Sebastian; IEEE Access; 2025
Simulative Analysis of Multi-Agent Systems in Energy Systems: Impact of Communication Networks
Radtke, Malin and Frost, Emilie; Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025) - Volume 1; 01 / 2025
Ten Recommendations for Engineering Research Software in Energy Research
Ferenz, Stephan and Frost, Emilie and Schrage, Rico and Wolgast, Thomas and Beyers, Inga and Karras, Oliver and Werth, Oliver and Nieße, Astrid; Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems; June / 2025
2024
A Digital Infrastructure for Integrating Decentralized Assets Into Redispatch : Decentralized Redispatch (DEER): Interfaces for providing flexibility
Körner, Marc-Fabian and Nolting, Lars and Heeß, Paula and Schick, Leo and Lautenschlager, Jonathan and Zwede, Till and Ehaus, Marvin and Wiedemann, Stefanie and Babel, Matthias and Radtke, Malin; 2024
cosima-mango: Investigating Multi-Agent System robustness through integrated communication simulation
Emilie Frost and Malin Radtke and Marvin Nebel-Wenner and Frauke Oest and Sanja Stark; SoftwareX; 2024
Enabling Moral Agency in Distributed Energy Management An Ethics Score for Negotiations in Multi-Agent Systems
Malte Stomberg; Martin Tröschel; ACM SIGENERGY Energy Informatics Review; October / 2024
Enhancing Cyber-Physical Energy Systems Simulations Through Communication Behavior Classification
Radtke, Malin and Lehnhoff, Sebastian; European Simulation and Modelling Multiconference (ESM '24); Oktober / 2024
Mango.jl: A Julia-Based Multi-Agent Simulation Framework
Sager, Jens and Schrage, Rico; Journal of Open Source Software; October / 2024
