By taking facts such as oil depletion, increasing number of population and energy demand into account, alternative electric generation scheme called renewable energy has entered into a new phase. These new energy sources are environmentally clean, exhaustible and friendly with affordable cost, and high reliability. Nowadays, energy generators such as photovoltaic (PV), wind turbine (WT), and geothermal energies are among the commonly used renewable sources. In this article, grey wolf optimization (GWO) methodology is proposed for minimizing the total annual cost of hybrid of wind and solar renewable energy system. Here, determining the optimal number of solar panels, WTs, and batteries which can satisfy the desired load is the main objective of this research. The obtained result shows that the proposed methodology finds optimal solution of sizing of the hybrid system with relatively lower total annual cost and fast convergence rate. To check whether the obtained result was feasible, GWO results are compared with the results of PSO, iteration method and by the work of other scholars in literature. Here the superior capabilities of GWO algorithm have been seen. It is hoped that this research would be beneficial and can be benchmark for researchers of the field.

中文翻译：

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用于优化风能和太阳能可再生能源系统规模的灰狼优化器

考虑到石油枯竭、人口数量增加和能源需求等事实，称为可再生能源的替代发电计划进入了一个新阶段。这些新能源环境清洁、可排放、友好、成本低廉、可靠性高。如今，光伏（PV）、风力涡轮机（WT）和地热能等能源发电机是常用的可再生能源之一。在本文中，提出了灰狼优化 (GWO) 方法，以最小化风能和太阳能混合可再生能源系统的年总成本。在这里，确定能够满足所需负载的太阳能电池板、WT 和电池的最佳数量是本研究的主要目标。所得结果表明，该方法以较低的年总成本和较快的收敛速度找到了混合系统规模的最优解。为了检验所获得的结果是否可行，将 GWO 结果与 PSO、迭代方法的结果以及文献中其他学者的工作进行了比较。这里已经看到了 GWO 算法的优越性能。希望这项研究是有益的，可以成为该领域研究人员的基准。