## 6 Conclusions and Remarks  
The results obtained from the analysis of the aforementioned cases offer us important lessons about the mitigation of GHG emissions by integrating a BESS managed from a purely economic perspective. The proposed approach is based on the solution of an optimization problem in which the number of possible combinations varies with the available wind speed profile. As NL becomes negative, the management signal of BESS is directly set to the charging process (\(g_{(i,k)}=+1\)). In the contrary case, when NL is positive, the optimization approach has to determine whether the BESS should be discharged (\(g_{(i,k)}=-1\)) or disconnected (\(g_{(i,k)}=0\)) to reduce the daily NL-peak. Figure 31: TVMS-BPSO evolution for diurnal pattern strength equal to 0. Figure 32: TVMS-BPSO evolution for diurnal pattern strength equal to 0.1 and 0.2. Figure 33: X. TVMS-BPSO evolution for diurnal pattern strength equal to 0.3 and 0.4. Let \(T\) be the number of hours during which NL is positive (Case I Section 4.1), the number of decision variables to be determined is defined as \(2^{T}\). In Case I, the wind speed profile was so low that no energy surplus was observed. However, the initial SOC was high, so that the proposed approach had to determine how to use that stored energy in order to reduce NL-peak. Under these circumstances, \(T\) = 24 and the number of possible combinations is 16,777,216. That is why GA and TVMS-BPSO require some iterations to get a near-optimal solution (Figures 5 and 31-33). Conversely, as wind speed increases, as studied in Case II and Case III, energy surplus increases and the number of combinations is reduced, making the management problem easier to solve. This is why an extremely fast convergence is observed in Figures 14 and 23, for Case II and Case III, respectively. With respect to TVMS-BPSO performance, its important capabilities for global exploration and local exploitation offer a high quality solution similar to that obtained from GA implementation (Table 13). Regarding GHG emissions, the highest reduction was observed in Case I, in which wind power generation was very low, but available energy from BESS was optimally allocated. As Table 3, Table 7, and Table 11 were calculated using the wind-diesel system as reference, Case I presents the highest percentage of reduction. As long as wind speed increases, the diesel generator must be committed to its minimum capacity so that GHG emissions cannot be totally eliminated, and this is important for the optimal sizing of HES. In the presence of an extremely high wind speed, when the wind turbine is disconnected, the reduction of GHG emissions highly depends on how BESS is managed, which can be observed in the results of Case III (Table 11), specifically. Another relevant result is that THC emissions do not always increase with the partial operation of the diesel unit: in Case II, THC emissions were reduced, perhaps because the diesel generator was disconnected in some operational circumstances. Conceptualization, J.M.L.-R., J.M.Y., and J.A.D.-N.; Investigation, J.M.L.-R.; Methodology, J.M.Y. ; Supervision, J.S.A.-S. and J.A.D.-N.; Validation, J.M.Y. and J.S.A.-S.; Writing--Original draft, J.M.L.-R. and J.S.A.-S.; Writing--Review & editing, J.A.D.-N. This work was funded by Ministerio de Economia, Industria y Competitividad of Spanish Government under project number EN2016-77172-R, by Government of Aragon and the European Union, T28_17R, building Aragon from Europe. The authors declare