Due to the sustainability and emission-free property of hybrid renewable energy sources (RESs), they became challenging alternative sources to conventional energy production facilities. However, cost of energy (COE) and the intermittent nature of RESs are two adverse factors complicating the problem of system appropriate sizing. This study develops a generalized mathematical model to find the optimal PV/wind/battery system sizes for remote areas. The proposed methodology is applied on a remote area in Egypt-Sinai called Ras-Shaitan. The purpose of the optimization process is to meet the load demand while minimizing the COE under different loss of power supply probability (LPSP). The grey wolf optimizer (GWO) is employed to decide the number of units among photovoltaic, wind and battery banks to achieve the best minimum objective value. To validate the solution quality generated by the GWO, the widely used HOMER software, the particle swarm optimizer (PSO) and genetic algorithm (GA) as a well-known meta-heuristic algorithms, are applied to the same and their results are compared with those obtained by the GWO. Further validations are made, in which, a new algorithm called wild horse optimizer (WHO) is also applied as a new algorithm and its results compared to aforementioned methods. Moreover, different solutions are offered according to the LPSP. The optimal system offered by HOMER has COE of 0.118 $/kWh, while GWO resulted in a save of about 17% of the COE. The GWO has a better convergence trend and best statistics measures than the PSO.

由于混合可再生能源 (RES) 的可持续性和无排放特性，它们成为传统能源生产设施的具有挑战性的替代来源。然而，能源成本 (COE) 和 RES 的间歇性是使系统大小合适的问题复杂化的两个不利因素。本研究开发了一个广义数学模型，以寻找偏远地区的最佳光伏/风能/电池系统尺寸。提议的方法应用于埃及-西奈半岛的一个名为 Ras-Shaitan 的偏远地区。优化过程的目的是在满足负载需求的同时最小化不同电源损耗概率（LPSP）下的COE。灰狼优化器（GWO）用于决定光伏、风能和电池组之间的单元数量，以达到最佳的最小目标值。元启发式算法应用于相同的情况，并将其结果与 GWO 获得的结果进行比较。进行了进一步的验证，其中还应用了一种称为野马优化器（WHO）的新算法作为新算法，并将其结果与上述方法进行了比较。此外，根据 LPSP 提供了不同的解决方案。HOMER 提供的最佳系统的 COE 为 0.118 $/kWh，而 GWO 节省了约 17% 的 COE。GWO 比 PSO 具有更好的收敛趋势和最佳的统计量度。