## 5 Case Study  
The proposed approach for quantitatively assessing the market potential of a given distribution network has been applied to a single primary substation (40 MVA transformer), suited in Sardinia (Italy), that has been modelled by a synthetic network with the approach proposed by the authors in [34]. The resulting model, provided in [34], is constituted of five feeders, two of rural ambit (feeder R1 and R2), one dedicated feeder that connects the biggest PV and CHP power plants (feeder D3) and two urban feeders (feeders U4-U5), one of them passive (Figure 2). The annual energy demand of loads of the network is 81.4 GWh/y. The total installed DG capacity power is aimed at reproducing the realistic scenario of DG derived from the analysis of open data concerning the real territory served by the PS. In fact, since the vast majority of DG is based on RES or high-efficiency CHP, open data exist on the position and size of those generators due to public incentive programs. Thus, the production by the local generation has been associated with each territorial portion, according to such information, by considering the rated power capacity of the generators and the production potential of the site. In the proposed case, about 26 MW of PV, 2.5 MW of WIND and 5.2 MW of CHP have been estimated to be installed along the network. The DG position is chosen and distributed for creating a critical scenario: the PVs are located at the end of only one rural feeder. The WIND turbines are connected at the end of the two rural feeders. The CHPs are supposed to be concentrated at the beginning of one rural and one urban feeder. Furthermore, the dedicated feeder includes two known existing plants, one 9.6 MW PV and one 2.7 MW CHP. Twelve typical day profiles for each kind of customer (i.e., residential, commercial, industrial and agricultural) and production technology (i.e., PV, CHP and WIND) are used for the daily demand and production representation, differentiated between weekdays, Saturdays and holidays and by season. In particular, typical production profiles are derived by combining the technical skills of the specific power plants with the historical meteorological data of the region/area on which is located the network. The PV production has been correlated to the latitude that strongly impacts solar radiation. The wind production profiles, derived by historical data, have been differentiated between coastal and mountain installations, and the CHP production profiles have been related, as the demand, to the climatic zone subdivision. The PV, WIND and CHP power plants connected to the test network produce 35.03 GWh/y, 3.87 GWh/y and 20.35 GWh/y, respectively. More details about the feeders' data and the load and generation profiles can be found in [34]. In the proposed approach, the network behaviour in the market is described in terms of flexibility and bids differentiated between upward and downward. Table 2 reports the quantity/price pairs considered in the simulations for all DERs involved in the market in the Case Study. The Italian regulation subdivides the national territory into market zones. Considering the zonal selling price in the Italian Power Exchange Market (IPEX), hour-by-hour pricing for the energy exported to the grid is defined. On the other hand, the energy purchasing price for importing from the grid is based on the energy national single price (PUN). PUN varies hourly, daily and monthly, and, in Italy, is influenced by renewable production concentrated in the middle hours of the day (e.g., photovoltaic). The hourly PUN values and the zonal selling prices, available on the website of the Italian energy market operator (GME [43]), have been averaged for finding purchasing \(P_{p}\) and selling \(P_{s}\) price profiles for the typical days (i.e., three days for season) to be used in this application (i.e., selling price in the range 17.80 EUR/MWh \(\div\) 78.67 EUR/MWh, and purchasing price in the range 24.60 EUR/MWh \(\div\) 74.83 EUR/MWh).