Master's Thesis Ralf Növer

 

Optimization of city quarters concerning the grid service

Copyright: EBC

Wind and solar energy sources pose various challenges when being integrated into the power supply system due to their intermittent energy production. The volatility these sources add to the power grid represent an extra stress on the grid.

The aim of this research is to examine to what extend city quarters can be used to relieve the electrical grid. To achieve this aim, firstly, the concept of “grid service“ is defined, quantified and implemented into an existing mixed integer optimization model. The model allows to calculate optimal distributions of thermal and electrical storages, heat generators (boiler, CHPs, heatpumps and electrical heater) as well as photo-voltaic-systems within city-quarters. Furthermore, it is possible to extend the models with local heat networks and thus linking different thermal energy consumers. Next to exploiting aforementioned energy systems for the removal of external loads the model can alternatively be used to reduce annual costs and CO2-emissions. The model is applied to an existing city-quarter for validation.

However, higher loads inside the electrical cables occur when city-quarters provide control energy. This is taken into account by formulating linear constraints which prevent the cables from overcurrents. The target function is validated using a simple case study of three domestic buildings. In the same manner, the optimal configuration for the considered energy system is analyzed. The combination of combined heat and power units as well as electrical heater result in the greatest benefits when providing energy flexibility.

Furthermore, a multi objective optimization is performed. A model containing five buildings is used for this part of the investigation. It is possible to generate three independent Pareto curves by combining two target functions in each of the considered cases. The simulation shows that an increasing supply of control energy results in higher annual costs as well as in a lower energy efficiency. However, the provided flexibility is not remunerated in this simulation. Consequently, by changing the future market environment it might be possible to supply energy flexibility economically.

In the last section, all three Pareto curves are plotted in a three-dimensional graph. These curves then define an area which can be interpreted as a two-dimensional Pareto front. By means of this Pareto front, the location of several equally optimal solutions of the inspected model can be located.