Investigation of bio-mimetic strategies for synthesizing complex networks representative for future energy systems

Abstract

The establishment of electrical distribution grids is important for conducting analyses and studies aimed at integrating new energy resources, assessing network resilience, and developing innovative smart grid methods and concepts. However, these grids are often safeguarded by data protection and privacy protocols, necessitating the creation of synthetic grid models. These models must incorporate realistic features and structures to behave like real networks. This study introduces a framework capable of generating such realistic models on a large scale for both low and medium voltage distribution electrical grids. Two bio-mimetic algorithmic approaches are employed: an ant colony optimization (ACO) and a slime mold expansion algorithm. The generation of these models relieson open-source geo-referenced and demographic data, alongside generated datasets for electrical loads. Example models for Austria and Germany are provided in this study.The results are validated both topologically, using graph theory, and electrically through power flow studies. The proposed methodology relies on bio-mimetic algorithmic approaches, presenting a broad range of application across scientific and industrial sectors. Within the context of Biomedical Engineering, a brief example of the applied methodology is given in the discussion, where grid-like structures of the extra and inter-cellular matrices of organoid models can serve as the starting point of the proposed algorithms. We suggest an approach to the nutritional flow within the models, mirroring the energy flow in the electrical grid models.