Biblio

Increasing complexity in the power system and the transformation towards a smart grid lead to the necessity of new tools and methods for the development and testing of new technologies. One testing method is co-simulation, which allows coupling simulation components from different domains to test their interaction. Because the manual configuration of complex large-scale co-simulation scenarios can be error-prone, we propose an approach for assisting the user in the development of co-simulation scenarios. Our approach uses an information model, a component catalog implemented in a Semantic Media Wiki, and Semantic Web technologies to assist the high-level modeling of co-simulation scenarios, recommend suitable simulation components, and validate co-simulation scenarios. This assistance aims to improve the usability of co-simulation in the development of interdisciplinary, large-scale scenarios.

To reshape energy systems towards renewable energy resources, decision makers need to decide today on how to make the transition. Energy scenarios are widely used to guide decision making in this context. While considerable effort has been put into developing energy scenarios, researchers have pointed out three requirements for energy scenarios that are not fulfilled satisfactorily yet: The development and evaluation of energy scenarios should (1) incorporate the concept of sustainability, (2) provide decision support in a transparent way and (3) be replicable for other researchers. To meet these requirements, we combine different methodological approaches: story-and-simulation (SAS) scenarios, multi-criteria decision-making (MCDM), information modeling and co-simulation. We show in this paper how the combination of these methods can lead to an integrated approach for sustainability evaluation of energy scenarios with automated information exchange. Our approach consists of a sustainability evaluation process (SEP) and an information model for modeling dependencies. The objectives are to guide decisions towards sustainable development of the energy sector and to make the scenario and decision support processes more transparent for both decision makers and researchers.