## 6 Conclusions and Sensibility Analysis for Optimal Scenario  
In all the analysed scenarios, when the integral value of the investment related to the new heat supply system is considered, the economic performance indicators are below the profitability limit [13]. This conclusion is highlighted by all the calculated indicators:  
* NPV \(<\) 0
* GPP and PP \(>\) analysed system lifetime (20 years)
* IRR \(<\) 4%  
This aspect was predictable due to the current high investment cost of the new technologies, but also due to small installed capacities, which do not allow significant compensation from high investments, with savings in energy costs and/or revenues resulting from \"selling\" the heat injected into the UPB centralized heat supply system. To financially support the implementation of projects through which new technologies are implemented, which use renewable sources, the application of financial levers, such as non-reimbursable grants, are practised at the European level that allows the amortization of the investment in an acceptable period of time during the life of the objective. Considering this fact, the technical-economic calculation was used the share of the investment that returns as the investment effort of UPB, while the difference (up to the value of the total investment) being ensured by the WEDISTRICT H2020 project, Grant agreement ID: 857801, which aims to demonstrate that District Heating and Cooling systems can be built on a combination of renewable energy sources and waste heat recovery solutions. Under these conditions, in all three analysed scenarios, the proposed solution is efficient from the technical-economic point of view, a conclusion resulting from the determined values of the economic performance indicators. * NPV \(>\) 0
* GPP and PP \(<\) analysed system lifetime (20 years)
* IRR \(>\) 4%  
The scenario in which the maximum values of the technical-economic indicators are obtained is Scenario 2, namely income, the \"virtual receipts\" of annual costs, related to \"virtual sales\" of the entire amount of heat produced by the implementation of the proposed scheme (Table 5). Figure 2: Payback period evolution for all scenarios. To eliminate the degree of uncertainty of the prediction of certain quantities that intervene in the calculation of economic indicators for the entire analysed period (20 years), it is opportune to conduct a sensitivity analysis on the economic efficiency indicators to the variation of quantities that are relatively difficult to predict [14]. In this sense, the variation of NPV, IRR, PP were analysed for the optimal solution from a technical-economic point of view, depending on the following:  
* discounting rate (4, 6, 8%)
* fuel price (natural gas), (\(\pm\)5, \(\pm\)10%): to determine the extent to which the decrease in fuel price no longer allows recovery of the investment (pessimistic scenario--the limit of economic profitability of the proposed solution)
* electricity price: \(\mathrm{e_{p}}\) (\(\pm\)5, \(\pm\)10%)
* the price of the delivered heat: \(\mathrm{q_{p}}\) (\(\pm\)5, \(\pm\)10%)
* the value of \(\mathrm{CO_{2}}\) certificates: \(\mathrm{CO_{2p}}\) (30-35 EUR)
* annual maintenance expenses (biannual growth of \(\pm\)1%)  
Only one size varied, the others remaining constant, at a value equal to that of the reference option. The sensitivity analysis was done only for the selected solution as the optimal one. The increase in the discounted net revenue of the optimal solution was influenced by the increase in the annual revenue, depending on the following:  
* the amount of heat injected into the centralized system
* the fluctuation of the related thermal energy price
* the value of carbon certificates