# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 7 Conclusions  
This study offered a new meta-heuristic optimization method, called the Heap-based optimizer (HBO) technique, for design four different scenarios of a stand-alone hybrid power model based on PV, WT, Biomass system, and battery storage unit. The major objective function is minimizing the COE with increasing the reliability LPSP, which fulfils the required load of an isolated society in Alrashda village, Dakhla Oasis in the New Valley Governorate, Egypt. The operation of the suggested off-grid system relies on the meteorological data of wind speed, radiation and temperature per hour which obtained from NASA Surface Meteorology and Solar Energy website for 20 years for the selected region. The result obtained by the proposed methodology HBO which is based on utilizing MATLAB software was compared with other optimization methods CFA, GWO, STOA techniques. The simulation results clearly showed that the suggested optimization method HBO is an effective method in identifying the optimal capacities of the generating and energy storage units and ensured good execution in the different scenarios of proposed the hybrid system. The results from the analyses presented in this study show the following:  
* The obtained results indicated that the HBO technique showed the optimal convergence between the investigated algorithms in reaching the best solution. * The HBO method has achieved the best optimal solution for Case-1 scenario. This case is a combination of PV/WT/biomass/Bat units, the best optimal solution has achieved after 27 iterations with the minimal COE of 0.121171 $/kWh, NPC of $ 3,559,143, and LPSP of 0.026789, followed by the results obtained from the CFA technique with the minimal COE of 0.1213225 $/kWh, NPC of $ 3,563,603, and LPSP of 0.0267184 after 44 iterations, followed by the results obtained from GWO, and STOA methods
* The HBO technique has achieved the best optimal solution for Case-2 scenario. This case is consisted of PV/biomass/Bat units, the best optimal solution has achieved after 49 iterations with the minimal COE of 0.1311804 $/kWh, NPC of $3,853,160, and LPSP of 0.0298557, followed by the results obtained from the CFA method with the minimal COE of 0.1315446 $/kWh, NPC of $3,863,857, and LPSP of 0.0291707 after 32 iterations, followed by the results obtained from GWO, and STOA techniques. * Based on the results of parametric and non-parametric statistical measurements performed for a more accurate comparison of the four optimization methods, the proposed HBO in all studied cases showed the best results compared to other optimization methods. * STOA has achieved the best optimal solution for Case-3, and Case-4 with COE of 0.105673 and 0.332497 $/kWh, and NPC of $3,103,938 and $9,766,441, respectively. * By comparing the NPC of the four suggested cases, it finds that Case-3 achieved the lowest NPC, followed by the Case-1. Although the third scenario which based on WT/biomass/Bat units is the least NPC, but it is not the optimal and efficient system for use. As the design of this case is based on batteries and biomass generators only, which have the highest yearly sharing of the capital cost, operating and maintenance cost. * The hybrid power system which is consists of PV/WT/biomass/Bat units would be an appropriate solution with minimal investment cost for rural communities, small industries, isolated wells and isolated farming areas where grid access is too costly or even impossible. Conceptualization, H.A.E.-S., S.K. and H.S. ; Data curation, H.A.E.-S., S.K., A.M.E. and F.J.; Formal analysis, S.K., H.S., A.M.E. and F.J.; Investigation, H.A.E.-S., S.K., H.S. and F.J.; Methodology, H.A.E.-S., S.K. and H.S. ; Project administration, M.T.-V.; Resources, S.K., H.S., M.T.-V., A.M.E. and F.J.; Software, S.K., H.S. and M.T.-V.; Supervision, S.K., M.T.-V. and F.J.; Validation, S.K., H.S., M.T.-V. and A.M.E.; Visualization, M.T.-V. and F.J.; Writing--original draft, H.A.E.-S., S.K. andH.S.