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Sustainability 2022, 14, 10125 32 of 39 In order to understand the reasons for such variations (Figure 18), the reader is encouraged to first analyse and compare subcategories “a” and “c”. Both of them are set up with reductions in the electricity sold price (Table 8); however, only subcategory “c” is set up with increases in the prices of gas and electricity bought. Thus, it is possible to note that subcategory “a” bought less electricity than “c”, even though “c” has higher utility prices. One reason for this is that, as the prices are higher, not only for electricity, but also for gas, the optimization is conducted to a solution where less gas is supplied to ICEs. In fact, the solutions for “c” received, on average, 10% less gas for ICEs than the solutions for “a” (Table 9). A consequence for this is less self-produced electricity within the EC, which leaves no choice if one cannot buy more electricity. Subcategory “b” has no changes in the price of electricity sold; it has them only in the prices of gas and electricity bought (Table 8). In this scenario, the optimizer still finds advantages in selling more electricity and, in fact, scenario SE30b sells more electricity than all the other scenarios. When it comes to the electricity bought, subcategory “b” found a middle term between “a” and “c”, i.e., solutions in “b” suggest more gas to ICEs with respect to “c” but, at the same time, less gas to ICEs with respect to “a”. That is why scenario “b” bought more electricity than scenario “a” and less than scenario “c”. 5. Conclusions The aim of the present research study was to apply a sharing electricity (SE) option to an energy community (EC) previously studied by our research group and to evaluate the effects on the performance of the EC from a technical, economical, and environmental viewpoint. In the mentioned previous study, the EC shares thermal energy among its users; however, each user is connected individually to the electric grid. In the present study, users have no direct connection with the electric grid; instead, they are connected to a distribution substation (DS) which manages the exchanges of electricity between users and the connection with the electric grid. The optimization was performed through a Mixed Integer Linear Programming (MILP) model running in the X-press software and written in the Mosel language. The EC com- prises nine tertiary sector buildings connected through a DHCN, in a small city in the northeast of Italy. The model optimization allowed the definition of the optimal solution for three types of scenarios: conventional solution (CS), Energy Community Solution (ECS)—without sharing electricity—and Sharing Electricity Solution (SES). The optimal configuration for all scenarios was determined in a way that minimizes the total annual cost of the entire EC. The CS scenario has been added to represent the reality of most current cases and to serve as a reference when analysing the other two scenarios. The scenarios ECS and SES provided a substantial reduction in the total annual costs and total annual CO2 emissions when compared to the CS scenario. ECS allowed reductions of 45.4% and 30.4% in the total annual cost and total annual emissions, respectively, while SES provided reductions of 47.3% and 32.9% in the same parameters. The effect of the sharing electricity implementation is evaluated by comparing scenar- ios ECS and SES. The results revealed improvements both in the total annual costs (−3.4%) and total annual emissions (−3.6%) when the SE is applied to the EC, which represent reductions of 80 k€/y and 280.1 t CO2/y, respectively. Moreover, the SE implementation allowed the following: • Reduction by 84.6% of the total electricity bought from the grid; • Reduction of 32.4% of the total electricity sold to the grid, which indicates a higher con- sumption of self-produced electricity within the EC, corroborated by higher installed capacity and electricity consumption of HPs; • Reduction of emissions at local level, i.e., emissions due to combustion of gas were reduced by 9%; • Reductions on the annual amortization cost (−6.7%), total investment costs with components (−5.8%), and total annual costs with the DHCN (−7.2%);PDF Image | Optimal Sharing Electricity and Thermal Energy
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