The assessment of the performance of grid hybrid frameworks depends primarily on the costs and reliability, associated with reduced greenhouse gas (GHG) emissions of the system. In this work, with objectives based on the minimization of two optimization features, namely loss of power supply probability (LPSP) and cost of energy (COE), multi-objective optimization of a grid-connected PV/wind turbine framework was implemented in the Faculty of Engineering in Gharyan, Libya, with the aim of providing adequate electricity, while optimizing the system’s renewable energy fraction (REF) was the third objective. This research also aimed to estimate the resulting amount of power produced by the hybrid system and mathematical models were submitted. The results showed the share of the total energy supplying the electricity demand for each part of the network. This study subsequently explored the interrelationship of the grid and the proposed hybrid system in relation to the capacity of the network to sell or obtain electricity from the hybrid system. In addition, multi-objective bat algorithm (MOBA) findings were divided into three dominant regions: the first region was the economically optimal solution (lowest COE), the second region was the conceptual perspective of utilizing renewable energies (highest REF), and the final region was the optimal solution with optimal environmental effects (lowest GHG emissions).

网格混合框架性能的评估主要取决于成本和可靠性，以及与系统减少的温室气体（GHG）排放有关的信息。在这项工作中，目标是基于最小化两个优化功能（即供电概率损失（LPSP）和能源成本（COE）），并在其中实现了并网光伏/风轮机框架的多目标优化。利比亚加里安工程学院的目标是提供充足的电力，同时优化系统的可再生能源比例（REF）是第三个目标。这项研究还旨在估计混合系统产生的发电量，并提交了数学模型。结果表明，在网络的每个部分中，电力总需求量占电力需求的比例。这项研究随后探讨了电网与拟议的混合系统之间的相互关系，该关系与网络从混合系统出售或获取电力的能力有关。此外，多目标蝙蝠算法（MOBA）的发现被分为三个主要区域：第一个区域是经济上最优的解决方案（最低COE），第二个区域是利用可再生能源的概念观点（最高REF），最终区域是具有最佳环境影响（最低温室气体排放量）的最佳解决方案。