Future Perspectives of Co-Simulation in the Smart Grid Domain

The recent attention towards research and development in cyber-physical energy systems has introduced the necessity of emerging multi-domain co-simulation tools. Different educational, research and industrial efforts have been set to tackle the co-simulation topic from several perspectives. The majority of previous works has addressed the standardization of models and interfaces for data exchange, automation of simulation, as well as improving performance and accuracy of co-simulation setups. Furthermore, the domains of interest so far have involved communication, control, markets and the environment in addition to physical energy systems. However, the current characteristics and state of co-simulation testbeds need to be re-evaluated for future research demands. These demands vary from new domains of interest, such as human and social behavior models, to new applications of co-simulation, such as holistic prognosis and system planning. This paper aims to formulate these research demands that can then be used as a road map and guideline for future development of co-simulation in cyber-physical energy systems

Ein generisches und hoch skalierbares Framework zur Automatisierung und Ausführung wissenschaftlicher Datenverarbeitungs- und Simulationsworkflows

Scientists and engineers designing and implementing complex system solutions use computational workflows for simulations, analysis, and evaluations. Along with growing system complexity, the complexity of these workflows also increases. However, without integration tools, scientists and engineers are more concerned with implementing additional interfaces to integrate software tools and model sets, which hinders their original research or engineering aims. Therefore, efficient automation and parallel computation of complex workflows are increasingly important in order to perform computational science in many scientific fields like energy and environmental informatics. When coupling heterogeneous models and other executables, a wide variety of software infrastructure requirements must be considered to ensure the compatibility of workflow components. The consistent utilization of advanced computing capabilities and the implementation of sustainable software development concepts that guarantee maximum efficiency and reusability are further issues that scientists within research organizations must regularly meet. This thesis addresses these challenges by presenting a new generic, modular, and highly scalable process operation framework for efficient coupling and automated execution of computational scientific workflows. Based on a microservice architecture utilizing container virtualization and orchestration, the framework supports the flexible and efficient parallelization of computational tasks on distributed cluster nodes. Using distributed message-oriented middleware and different I/O adapters provides a scalable and high-performance communication infrastructure for data exchange between executables, allowing the computation of workflows without requiring the adjustment of executables or the implementation of interfaces or adapters. The convenient user interface based on Apache NiFi technology ensures the simplified specification, processing, controlling, and evaluation of computational scientific workflows. Due to the framework’s high scalability and extended flexibility, use cases benefitting from parallel execution are parallelized, thereby significantly saving runtime and improving operational efficiency, especially during complex tasks like iterative grid optimization