Impact of Heterogenous Parameters on the Key Performance Indicators of Energy Communities

The progressive expansion of predominantly renewable, decentralised energy sources and the increasing spread of electromobility pose new challenges for our electricity grid. In this context, ICT's Energy&IT group is particularly dedicated to researching innovative approaches and solutions relating to energy communities. We are therefore particularly proud of the successful collaboration with Siemens AG Austria in smart grid research, which made this diploma thesis possible.

Abstract:

Decarbonizing electrical power generation is an essential part of mitigating climate change. Energy communities offer private individuals and companies the opportunity to actively participate in this. Within an energy community, locally generated renewable energy can be distributed, traded, stored and consumed. For successful planning and long-term operation of energy communities, a sound understanding of the processes and influencing factors is indispensable.

This thesis therefore deals with an in-depth analysis of the influence of heterogeneous parameters on the key performance indicators of energy communities. For this purpose, a simulation framework based on the BIFROST platform was developed, with the help of which it is possible to define, simulate and evaluate different scenarios. Relevant technical, ecological and social parameters as well as environmental influences were identified and their impact on technical, economic and ecological key performance indicators was simulated.

The scenarios defined in this thesis show a predominantly technical, ecological and social advantage, while financial profits depend strongly on the current market situation. The topology of an energy community contributes significantly to its success. A focus on the expansion of individual self-sufficiency shows disadvantages with regard to the effectiveness of energy communities. When designing modern low-voltage grids, it is therefore important to weigh up whether community measures are preferable to individual measures.

Supervisors: Dipl.-Ing. Daniel Hauer, Dipl.-Ing. Stefan Wilker, Ao.Univ.Prof. Dr. Thilo Sauter