## 2 Methodology
### Heating and Cooling Demand Profiles  
In order to analyze the proposed cycle, real heating data of an existing geothermal district heating network in the German Molasse Basin [24] and estimated cooling demand are applied. The VDI 4655 method [38] is applied to estimate the annual system performance. In this method, 10 typical days are analyzed. This procedure is distinguished from seasonal, i.e., summer (S), winter (W) and transition (T), and from the typical days, i.e., workdays (W) and Sundays (S). In this category, Saturdays are selected as the workday. Furthermore, fine (F) and cloudy (C) days are differentiated to reach acceptable results. However, in the summer days, no distinction between fine and cloudy days (X) is made. These 10 typical days categories are; WWF, WWC, WSF, WSC, TWC, TSC, TSF, SWX, SSX. According to the test reference years (TRY), Germany is divided into various climate zones. As shown in Table 3, according to this climate zones in the mentioned category \"VDI 4655\", the number of typical days during the year changes. In this work, for the purpose of the annual simulation, the 10 typical days are simulated for TRY13 which is associated to the southern German Molasse Basin. The annual performances of the system are calculated by weighting these typical days considering their frequency. In order to conduct a quasi-stationary simulation, the heating and cooling demands for each typical day category are required. The profiles of corresponding heating demand for different typical days categories are developed depending on operational data of a real geothermal heat plant in the German Molasse Basin. The corresponding mean values of heating demands and the ambient temperatures for different typical days categories are illustrated in Table 4, respectively [18]. It is seen that the ambient temperature takes the lowest values in winter days and the highest values occur in the summer days. It is clear that the ambient temperature in cloudy days is basically lower than the fine days. For heating demand values which are listed in Table 4, it is observed that the heating demand has higher values in winter days in comparison to the summer and transition days. In addition, the heating demand in cloudy days, especially in the afternoon is considerably higher than the one in fine days. Furthermore, comparing the heat demand on Sundays and workdays shows that the heating demand is generally lower on Sundays and has a small variation. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\mathbf{(UA)}_{\mathbf{design}}\) (\(\mathbf{kW/K}\)) & \(\mathbf{(\dot{m})}_{\mathbf{design}}\)(\(\mathbf{kg/s}\)) \\ \hline Domestic heat exchanger & 313.2 & 92.64 \\ Boiler & 723.5 & 13.37 \\ Generator & 71.24 & 17.11 \\ Solution heat exchanger & 37.11 & 17.11 \\ Absorber & 343.9 & 263.8 \\ Condenser 1 & 128.6 & 110.4 \\ Condenser 2 & 514.4 & 1110 \\ Dephlegmator & 3.066 & 12.39 \\ Evaporator for heating & 24.18 & 1.74 \\ Evaporator for cooling & 1014 & 2.082 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The calculated parameters of heat exchangers in design mode. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Climate** & \multicolumn{8}{c}{**Number of Typical Days (Frequency of the Different Typical Days)**} \\ \cline{2-11}
**Zone** & **WWF** & **WWC** & **WSF** & **WSC** & **TWC** & **TSC** & **TSF** & **TWF** & **SWX** & **SSX** \\ \hline... & & & & & & & & & & \\ TRY12 & 23 & 57 & 2 & 16 & 91 & 18 & 8 & 27 & 104 & 19 \\ TRY13 & 29 & 91 & 6 & 19 & 72 & 10 & 15 & 37 & 73 & 13 \\ TRY14 & 22 & 115 & 5 & 25 & 81 & 15 & 11 & 42 & 42 & 7 \\... & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test reference years according to VDI 4655 [38]. The cooling demand is not available for specific real application; therefore, it is calculated with the following assumption.