## 1 Introduction
Thus, a central research question nowadays is to determine whether generic computational workflow execution platforms can be created to fully automate the execution of complex computational workflows and free the scientists from manually implementing different kinds of auxiliary tools for e.g., implementing coordination or data transformation. Considering the fast growing complexity of modern technical systems and the need for efficiently performing many interconnecting software-based scientific tasks for their evaluation, an organization's development of auxiliary tools and setup of a software environment that facilitates easy-to-use workflows is a strategic issue with significant impact on research efficiency and long-term consequences. Against the background of the software used by the respective scientific community, the choices of programming languages and environments, operating systems, optimization solvers and other supporting tools and libraries, interface standards, file formats, etc. directly influence the potential to couple the organization's model set with those of fellow researchers. Since the investigation of complex and interdisciplinary research questions is usually not handled by a single organization, but is made possible through collaborations within the community, this potential is of particular importance. At the same time, changing an organization's software strategy is not an easy task. Models and other executables are already implemented, making redevelopments utilizing other programming languages and environments seldom worthwhile. Additionally, the retraining of employees and the rebuilding of programming experiences constitute cumbersome exercises. This makes coupling environments that allow the integration of existing tools and models with as little effort as possible very valuable. Figure 1: Development cycle of model workflows in the context of computational science. In addition, while ensuring sufficient flexibility in model coupling, the requirements for efficiently processing the increasingly complex workflows--which is indispensable for the achievement of meaningful and reliable results--present a growing performance challenge. In contrast to white box integrated applications, which are often internally designed for parallel computing by using a parallel computing framework for implementation, loosely coupled workflows are usually constructed by chaining existing model components together without efficient parallel execution of the components in mind. It follows that the logic to run subprocesses and entire workflows in parallel as well as the communication control and consistency assurance mechanisms have to be designed, implemented, and tested with each newly drafted workflow. The workflow engineering and runtime environment should allow the instrumentation of such features as essential parts of a scientific computing workflow as easy as possible. Within this overall context of efficiently performing computational science related tasks for providing such a scientific workflow environment as essential software infrastructure is an open research question, especially for energy research projects that bundle the smart energy systems related research of several research centers. One key element of such a research environment is a software platform that easily allows the research centers to not only share data but especially integrate their partly software solutions for performing digital workflows (e.g., models of new storage solutions, technical energy plants, optimization and control algorithms) into bigger, fully automated workflows that implement integrated energy system co-simulation models or other complex computational scientific workflows.