University of Oldenburg
FK II – Department for Computer Science
Digitalized Energy Systems Group
D-26111 Oldenburg


Meike Burke

Regina Knippenberg

A05 2-227

+49 (0) 441 - 798 2878

+49 (0) 441 - 798 2756


Prof. Dr.-Ing. Astrid Niesse 

A05 2-226

+49 (0) 441 - 798 2750

+49 (0) 441 - 798 2756


Mitigation of Emerging Controller Conflicts in Multimodal Smart Energy Systems

With the research in this project, we extend our work from our first DFG-project dealing with the : analysis of controller conflicts in multimodal Smart Grid systems using the concept of emergence in technical systems. While the identification and quantification of emerging controller conflicts in multimodal energy systems (MES) including gas, heat, electricity and coupling points has been in the focus of the first project, we will develop an approach to mitigate controller conflicts i.e. by means of cooperation in MES.              

The general research idea is to transfer the task of mitigating controller conflicts to mitigating conflicts between agents. This formulation allows us to apply methods from the field of game theory (GT), thus closing a gap between power system modeling and agent-based control using agent-based simulation. The interaction of controller agents in MES can be described as repetitive games in a dynamic environments. To avoid unrealistic preliminary condition of GT such as assuming fully observable environment and totally rational agents, we plan to use evolutionary game theory (EGT).

In EGT, agents have a mutable strategy in every iteration (called phenotype), even if this strategy is not the best strategy. Thus, strategies consist of a temporary choice of best-response and suboptimal response choices. In the context of multi-agent systems (MAS), this approach is consistent with exploration phases, where agents may follow suboptimal routes to learn about their environment and adapt themselves according to the dynamic of the environment. On the other hand, the natural selection leads to distinction or growing of some phenotypes. The outcome is the set of Evolutionary Stable Strategy (ESS). This ESS illustrates if the selected phenotypes lead to the emergence of cooperation as a conflict-free state in MES.

Emergence of the favorite level of cooperation can be reached in three steps: 1) individual learning 2) collective learning and 3) modifying regulatory settings. The first two steps necessitate to extend the developed model in the first phase of project with the ability to learn and ability to communicate. If some conflicts could not be mitigated by increasing the agents’ capabilities or via cooperation within the set of agents, a modification of the regulatory settings (i.e. the rules) can be suggested as a possibility to solve the problem of conflict emergence.

(Changed: 19 Jan 2024)  | 
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