This paper develops a recent methodology based on social spider optimizer (SSO) to determine the optimal sizing of a hybrid renewable energy sources (RESs) integrated microgrid (MG). It comprises photovoltaic (PV), wind turbine (WT), battery, diesel generator (DG), and inverter. The cost of energy (COE) is proposed as fitness function. The objective of the proposed SSO is to determine three design variables which are number of PV panels, number of WT, and number of battery autonomy days such that COE is minimized. Additionally, an energy management strategy is presented. Loss of power supply probability (LPSP) is considered to confirm the reliability of operation. The selection of SSO is due to its simplicity in construction and requiring less controlling parameters. The proposed MG is installed in a remote area at Aljouf region in the northern of Kingdom of Saudi Arabia. Annual data of irradiance, wind speed, and temperature are recorded. The SSO results are compared to Harris Hawks optimizer (HHO), Grey Wolf Optimizer (GWO), Multi-Verse Optimizer (MVO), Antlion Optimizer (ALO), and Whale Optimization Algorithm (WOA). The results obtained show that the proposed approach provides the best optimal configuration of hybrid RESs compared to HHO, GWO, MVO, ALO and WOA with COE of 0.1349 $/kWh and LPSP of 0.01714. Moreover, sensitivity analysis with sizing different topologies of MG including PV/Battery/DG, WT/Battery/DG, and PV/WT/Battery/DG is presented. The best COEs are obtained via SSO achieving 0.2180 $/kWh for the first topology and 0.2161$/kWh for the second architecture. Furthermore, sensitivity analysis is also presented to investigate the effect of design variables on COE. The experimental results confirm the superiority of the proposed approach in designing reliable and costless microgrid.

中文翻译：

---

基于最新方法的社会蜘蛛优化器，用于优化 Aljouf 地区混合光伏/风/电池/柴油集成微电网的规模

本文开发了一种基于社交蜘蛛优化器 (SSO) 的最新方法，以确定混合可再生能源 (RES) 集成微电网 (MG) 的最佳规模。它包括光伏（PV）、风力涡轮机（WT）、电池、柴油发电机（DG）和逆变器。能量成本（COE）被提议为适应度函数。提议的 SSO 的目标是确定三个设计变量，即 PV 电池板的数量、WT 的数量和电池自主天数，以使 COE 最小化。此外，还介绍了能源管理策略。电源丢失概率 (LPSP) 被认为是确认操作的可靠性。选择 SSO 是因为它结构简单，需要的控制参数较少。拟建的 MG 安装在沙特阿拉伯王国北部 Aljouf 地区的一个偏远地区。记录辐照度、风速和温度的年度数据。将 SSO 结果与 Harris Hawks 优化器 (HHO)、Grey Wolf Optimizer (GWO)、Multi-Verse Optimizer (MVO)、Antlion Optimizer (ALO) 和 Whale Optimization Algorithm (WOA) 进行比较。获得的结果表明，与 HHO、GWO、MVO、ALO 和 WOA 相比，所提出的方法提供了混合 RES 的最佳优化配置，COE 为 0.1349 $/kWh，LPSP 为 0.01714。此外，还介绍了对 MG 的不同拓扑结构（包括 PV/电池/DG、WT/电池/DG 和 PV/WT/电池/DG）进行尺寸调整的敏感性分析。最佳 COE 是通过 SSO 获得的，第一种拓扑结构为 0.2180 $/kWh，第二种架构为 0.2161 $/kWh。此外，还提出了敏感性分析来研究设计变量对 COE 的影响。实验结果证实了所提出的方法在设计可靠且无成本的微电网方面的优越性。