Bachelor's thesis Nils Heyermann

 

Energy supply of urban fabric types

Pareto-Front Copyright: EBC Pareto-Front and associated decision matrix

The global challenge of reducing emissions of greenhouse gas emissions requires action in all areas of energy conversion. A significant portion of the final energy needs have buildings, which is why a detailed analysis can prove useful. In order to simplify the consideration of complex neighborhoods, energetic urban space types can be used.

Within the scope of this Bachelor thesis, a study will be carried out to determine how the energy supply of selected energetic urban fabric types can be optimized. First, the urban fabric types must be modeled in the form of representative city districts. They contain both the location of the roads and buildings as well as building-specific data, such as the annual heating energy demand and the number of inhabitants. The optimization goals are the CO2 emissions and the annual costs related to a depreciation period. The modeling and optimization is carried out using the internal Python packages “PyCity“, “PyCity_Calc“ and “PyCity_Opt“.

On the basis of the optimization results, a Pareto front is created, which represents the course of the annual costs over the greenhouse gas emissions. Furthermore, a decision matrix is created. This contains possible energy system configurations and shows percentage changes of the target functions from an associated reference system. By means of this review of the results, decision-makers are to be given the opportunity to select a proposal for a CO2 and cost optimal solution without extensive prior knowledge and specially simulated simulations.

The primary goal is to provide a quick and easy way to demonstrate the optimization potential of an existing system in a climate and cost-effective manner.