## 1 Introduction  
A cyber-physical energy system (CPES) is an example of so-called systems of systems. Its dynamical behavior emerges from the interactions of various complex components and systems that themselves may display nonlinear dynamics. Additionally, CPES unite formerly separate domains from the physical energy systems, e.g., electric power grids, distributed energy resources (DER), or heat and gas networks, with the digital world of information and communication infrastructure (ICT), data privacy and cyber-security. But also human behavior plays an important role, be it from an economic point of view with energy markets or through the social aspects of consumption behavior. Such human-driven systems are sometimes called transactive energy systems or H-CPES, but in the context of this paper, social and economic dynamics are included in the understanding of CPES. Furthermore, environmental factors play an important role in the forcing (weather) as well as the assessment (sustainability) of CPES. Therefore, CPES should also be considered in regard to their response to meteorological events or techno-environmental feedback. One of the major challenges of designing and implementing CPES lies in the development and testing of technologies and concepts to guarantee functionality, stability and safety during operation. This is especially true for CPES components with complex behavior and many interfaces with different domains. Due to this complexity and interdisciplinarity, analytical testing on a formal basis is infeasible for CPES solutions (beyond the first conceptualization phase of new solutions). The alternative approach of hardware tests in laboratories or in the field is expensive and inflexible. It is thus applied only in the last stages of development. Therefore, software simulation proves to be valuable as a preparatory test stage before hardware testing, on the one hand, as well as an easily scalable test environment that allows consideration of larger and more complex setups. The role of simulation in the development of e.g., smart grid algorithms has been discussed in detail in [1]. A type of simulation that has become popular in CPES testing is co-simulation (see [2; 3]). It allows users to couple independent simulation tools representing different parts of the overall system. This greatly reduces the modeling effort and error-proneness related to monolithic representations of interdisciplinary systems. The different components of a co-simulation setup (called \"simulators\" in the following) are interfaced with each other and exchange data during runtime so that a simulation process can work in a distributed manner and across heterogeneous runtime environments. Furthermore, generic co-simulation systems can be used to reduce the interfacing effort and increase the transferability of solutions onto different application cases. Despite its advantages, working with co-simulation involves a number of challenges. A commonly discussed hurdle is the technical coupling of heterogeneous software tools. Identifying the most suitable tools, however, is often overlooked. Such a process requires experts and modelers from different domains to find a common understanding of their tools' capabilities and simulation goals. This is often hindered by the lack of a domain-crossing language for CPES description. Similarly, joining knowledge from different domains will typically involve different degrees of simplification and aggregation. Therefore, the quality and reliability of co-simulation outputs may vary over time and may be difficult to assess even for simulation experts. While these issues are widely recognized in the CPES co-simulation community, most approaches are so far focused on the more technical aspects of simulator coupling. A popular standard for co-simulation is the functional mock-up interface (FMI) [4], which describes the data and functions a simulator needs to provide in order to participate in a co-simulation.