## 6 Conclusions and Future Work  
In this paper, the HRESs were composed of PVs, WTs, and battery units. The reliability of the HRESs was obtained using the \(LPSP^{max}\) concept. The fitness function of the algorithms was based on the TAC minimization subject to the \(LPSP^{max}\) and other constraints. In contrast to the EDE and SSA optimization schemes, the TLBO used for unit sizing did not require any algorithm specific parameters for execution. Due to this advantage, TLBO was used in both hybrid EESAs. From this paper, the following key points are concluded. 1. Hybrid EESAs were developed for better exploration and exploitation of the search space. EESAs accepted inputs, including solar irradiation, ambient temperature, wind speed, and user's load data. 2. The PV-WT-battery hybrid system was found with the best optimal configuration of RESs with reduced TACs as compared to the PV-battery and WT-battery systems. The TACs achieved by EESA were $64,430, $61,970, $59,200, and $54,171 at \(LPSP^{max}\) values of 0%, 0.5%, 1%, and 3%, respectively. In the PV-WT-battery hybrid system, the algorithms EESA, TLBO, EDE, and SSA were ranked as 1 st, 2 nd, 3 rd, and 4 th, respectively, based on their average TAC values. EESA performed better than other algorithms due to the better search on more promising areas of the  
Figure 12: Convergence process of the EESA algorithm for three scenarios at different \(LPSP^{max}\) values. solution space. On the other hand, TLBO's performance was found better compared to the EDE and SSA schemes because it neither required any algorithm specific parameter, nor its calibration to obtain the optimal results. * The PV-battery system provided the second most economical results. The TACs achieved were $104,640, $96,840, $92,120, and $71,790 at \(LPSP^{max}\) values of 0%, 0.5%, 1%, and 3%, respectively. In this scenario, EESA and TLBO performed equally and were placed in the first category. EDE and SSA achieved second and third rankings based on their average TACs, respectively. * The third scenario: The WT-battery system was the most expensive case, due to the high price of WTs. The TACs $124,750 and $118,690 were achieved by EESA at \(LPSP^{max}\) values of (0%, 0.5%) and (1%, 3%), respectively. Here, all algorithms achieved the same optimal results due to a fewer number of decision variables compared to the PV-WT-battery hybrid system, thus being ranked equally. * The trade-off analysis between TAC and \(LPSP^{max}\) was also evaluated. It was found that when the \(LPSP^{max}\) values were increased from 0%, TACs were minimized and vice versa. In the future, the hybrid algorithms proposed will be compared to a non-algorithm specific parameter based algorithm, i.e., Jaya, and also other schemes that require algorithm specific parameters, including particle swarm optimization and wind driven optimization. All authors discussed and agreed on the idea and scientific contribution. [PERSON]. ([PERSON]), [PERSON] and [PERSON] performed simulations and wrote simulation sections. [PERSON]. ([PERSON]), [PERSON] and [PERSON] did mathematical modeling in the manuscript. [PERSON] and [PERSON] contributed in manuscript writing and revisions. This research received no external funding. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: