# Assessment of Batteries' Contribution for Optimal Self-Sufficiency in Large Building Complexes
## 3 Results  
By following the methodology as previously explained, the dependency of each hotel's self-sufficiency to the PV production and storage capacity is studied, leading to the optimal solution of each individual model. Then, the average value of self-sufficiency is calculated and analyzed, concluding with the exploration of correlations between hotel attributes and capital expenditure. To study the effect of PV power on the system's self-sufficiency, the impact of PV power on self-sufficiency without storage for each hotel is depicted in Figure 11.  
Without a storage system, the self-sufficiency of all hotels converges to 70%. Such behavior is explained by being attributed to the variety in energy demand for the five hotels. The hotel that consumes the most energy is associated with the trendline having the shallowest incline since a certain rise in PV installation contributes less to the total annual self-sufficiency. This means that a storage system is required for all hotels that aspire to achieve nearly total independency with an economically viable PV installation. Applying the indicative value of 3000 kW PV power, the dependency of the self-sufficiency to the system's storage capacity is depicted in Figure 12.  
Figure 10: Computation of system losses after each hour. For this PV power value, some hotels successfully achieve a 100% self-sufficiency rate, meaning they can cover all their energy demands through a proper mixture of PV power and storage capacity. On the other hand, the rest of the hotels struggle to surpass an 80% self-sufficiency rate. This indicates that even with a significant storage capacity, they are unable to fully meet their energy requirements for the given PV power and need to supplement their power supply with electricity from the grid or other sources. To achieve the desired level of self-sufficiency for all hotels with optimal values of PV power and storage capacity, the average percentage of self-sufficiency for a significant range of the decision variables is computed and a three-dimensional scatter plot is designed, as shown in Figure 13.  
Figure 13 highlights that for a specific range of PV power and capacity values, the self-sufficiency overpasses 99%, converging to 100%. A percentage of 99% is considered a satisfactory percentage of independency from the grid. Another important conclusion is that for 99% annual self-sufficiency, an average increase in PV power implies a decrease in average storage capacity. In Figure 14, the green curve consists of the set of points for which the annual self-sufficiency is equal to 99%, as can be seen from points A and B.  
Figure 11: Dependency of self-sufficiency to PV power for the 5 hotels. Figure 12: Dependency of self-sufficiency to storage capacity for a fixed PV power value. In the same range, the optimal solution is the one that requires the minimum capital expenditure requested for the installation of the total system. The process described in Figure 13 was repeated for each separate hotel, isolating all sets of the decision variables yielding at least 99% of annual self-sufficiency. Since the total CAPEX is dependent on the PV power and storage capacity, with the former having the largest impact on CAPEX due to the greater value of EUR /kW, the optimal solution within the feasible range is determined by giving priority to finding the smallest PV power value within a specified range, while simultaneously seeking the lowest possible capacity value that meets the requirement of at least 99% self-sufficiency, in combination with the specific PV power value. Figure 14: Approximation of the separating line between region where self-sufficiency < 99% and self-sufficiency > 99%. Figure 13: Three–dimensional scatter plot of the average annual self–sufficiency for a range of PV power and storage capacity values.