Managing complex Cyber-Physical Energy Systems (CPES) requires solving various optimization problems with multiple objectives and constraints. As distributed control architectures are becoming more popular in CPES for certain tasks due to their flexibility, robustness, and privacy protection, multi-objective optimization must also be distributed. For this purpose, we present MO-COHDA, a fully distributed, agent-based algorithm, for solving multi-objective optimization problems of CPES. MO-COHDA allows an easy and flexible adaptation to different use cases and integration of custom functionality. To evaluate the effectiveness of MO-COHDA, we compare it to a central NSGA-2 algorithm using multi-objective benchmark functions from the ZDT problem suite. The results show that MO-COHDA can approximate the reference front of the benchmark problems well and is suitable for solving multi-objective optimization problems. In addition, an example use case of scheduling a group of generation units while optimizing three different objectives was evaluated to show how MO-COHDA can be easily applied to real-world optimization problems in CPES.

This paper presents a scaling study on the planning phase of a multi-energy system (MES), which is becoming increasingly prominent in the energy sector. The research aims to investigate the interactions and challenges associated with integrating heat and electrical systems and scaling their components. In this context, interaction between these two domains are investigated and the size of the distributed energy resources in the MES is scaled to examine the impact of sizing on the integrating networks and their controlling system. To achieve this, the paper uses sensitivity analysis and a meta-modeling technique, both incorporated in a toolbox for scaling analysis. These methodologies are validated through simulations, and the results obtained from the simulations can contribute to the advancement of MESs and their implementation in laboratory and field testing.

Metadata play a crucial role in adopting the FAIR principles for research software and enables findability and reusability. However, creating high-quality metadata can be resource-intensive for researchers and research software engineers. To address this challenge, we developed the Software Metadata Extraction and Curation Software (SMECS) which integrates the extraction of metadata from existing sources together with a user-friendly interface for metadata curation. SMECS extracts metadata from online repositories such as GitHub and presents it to researchers through an interactive interface for further curation and export as a CodeMeta file. The usability of SMECS was evaluated through usability experiments which confirmed that SMECS provides a satisfactory user experience. SMECS supports the FAIRification of research software by simplifying metadata creation.