## 3 Case Study  
A systematic approach for the selection of an appropriate DG energy supply system requires a detailed knowledge of heat, cooling, and electricity user demands. The detail level affects the model complexity and one of the factors which plays against the model compactness is the number of time intervals considered. These time periods are defined by the number of different energy demands that have to be covered, and the periodicity considered in the model (hourly, weekly, monthly). Long time periods, such as weeks or months, can be considered for industrial applications, characterized by quite constant energy demands that are fairly independent of environmental conditions. Hourly energy demand data is very important when tertiary sector/residential energy systems are analysed, where the influence of environmental conditions is quite important. In this last case, one solution to contain the model complexity is to represent the whole year through some typical days [32]. In the current optimization case study, the whole year has been subdivided into 24 typical days made up of 24 h each: 12 typical days refer to working days, while the remaining 12 refer to non-working days, so that each month is represented by one working and one non-working day. All values related to each single time interval are weighted through the parameter \(wgt\), which consider their weights in the overall year. The grouping through typical days can be done for all variables, with the exception of the variables related to the thermal storages (either thermal or cooling) for which the whole year has to be considered (detailed explanation can be found in Section 2). This case study is made up of nine tertiary sector users located close to each other in the centre of a small city (60,000 inhabitants) in the northeast of Italy. The users considered are all owned by the public service and this gave us the possibility to access the energy demand data. The users considered are the town hall, a theatre, a library, a primary school, a retirement home, the archive, a hospital, a secondary school and a swimming pool (Figure 2). Figure 2: Plan of the possible path of the DHCN. 1—town hall; 2—theatre; 3—library; 4—primary school; 5—retirement home; 6—archive; 7—hospital; 8—school; 9—swimming pool; C—central unit site. The heterogeneous choice of the buildings under consideration, characterized by different kinds of energy demands, allows us to consider the achieved results not affected by a specific user profile. Furthermore, a similar mix of users is expected to be easily recognized in a lot of other small and medium-sized towns in Europe. Figure 2 shows the location of the nine buildings involved in the study and of the central unit together with solar field. In addition, the possible path of the DHCN is outlined in red. The determination of the possible paths is the result of a preliminary study which considers:  
* layout of the roads which connect the buildings;
* position of the underground utilities (waterworks, sanitation, gas network, etc. ); and
* location of the boiler rooms of the buildings. The users are close to each other and the maximum distance, between user 1 and the central unit, is about 2.5 km. The users considered require thermal energy for space heating and for sanitary hot water, which is conventionally produced by boilers. The thermal energy for space heating is required at a temperature of about 65-70 \({}^{\circ}\)C. The cooling energy is required only for space cooling typically during the summer season and it is produced by compression chillers. In the optimizations, the design values of the temperatures for the operation of the DHN and DCN have been fixed at 70 \({}^{\circ}\)C and 12 \({}^{\circ}\)C, respectively. At the moment, the electric energy is bought from the grid and covers both the electricity demands and the electricity required to power the mechanical chillers. Table 1 reports the annual energy consumptions and peak power of the nine users.