The optimized results of the Egypt MEDN 15-Bus.

Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. The key implications of integrating RDGs include the improvement of voltage profiles and the minimization of power losses. Various optimization techniques, namely Salp Swarm Algorithm (SSA), Marine Predictor Algorithm (MPA), Grey Wolf Optimizer (GWO), Improved Grey Wolf Optimizer (IGWO), and Seagull Optimization Algorithm (SOA), have been applied to achieve optimal allocation and sizing of RDGs in radial distributed systems (RDS). The present paper is structured in two phases. In the initial phase, the Loss Sensitivity Factor (LSF) is employed to identify the most suitable nodes for integrating RDGs. In the second phase, within the selected candidate nodes from the first phase, the optimal location and capacity of RDGs are determined. Additionally, a comprehensive comparison of the proposed optimization methods is conducted to select the most effective solutions for the allocation of units of RDGs. The efficacy of the utilized techniques is validated through testing on two distinct networks, namely the IEEE 33 and 69 buses RDS in MATLAB, with attainments compared against other techniques. Moreover, a larger RDS system of 118- bus IEEE system has been considered in order to enhance its power quality indices. Moreover, a real case of study from Egypt of 15 bus has been considered and evaluated with considering the applied techniques. The results show the enhancement of the voltage profile and decreasing the power losses of the tested system with the DG systems with the superiority of the MPA and SSA algorithms.近年来的研究重点强调了将可再生分布式电源（renewable distributed generations, RDGs，如光伏（photovoltaic, PV）与风力发电机组（wind turbines, WTs））接入配电系统的重要性，以解决分布式发电（distributed generation, DG）配置相关的各类问题。接入RDGs的核心效益在于优化电压分布并降低功率损耗。目前已有多种优化算法被应用于辐射状配电系统（radial distributed systems, RDS）中RDGs的最优配置与容量整定，包括樽海鞘群算法（Salp Swarm Algorithm, SSA）、海洋捕食者算法（Marine Predictor Algorithm, MPA）、灰狼优化算法（Grey Wolf Optimizer, GWO）、改进型灰狼优化算法（Improved Grey Wolf Optimizer, IGWO）以及海鸥优化算法（Seagull Optimization Algorithm, SOA）。本文研究分为两个阶段：第一阶段采用损耗灵敏度因子（Loss Sensitivity Factor, LSF）筛选出最适宜接入RDGs的节点；第二阶段在第一阶段筛选出的候选节点范围内，确定RDGs的最优安装位置与装机容量。此外，本文还对所提各类优化方法开展了全面对比，以遴选出适配RDGs配置的最优方案。通过在MATLAB平台下的IEEE 33节点与69节点辐射状配电系统上开展测试，验证了所提算法的有效性，并将测试结果与其他算法的结果进行了对比。为进一步验证算法在电能质量指标优化方面的性能，本文还选取了118节点的IEEE大型辐射状配电系统进行测试。同时，本文还选取了埃及某15节点的实际配电系统作为研究案例，对所提算法进行了评估。实验结果表明，接入RDGs后被测系统的电压分布得到显著优化、功率损耗明显降低，其中海洋捕食者算法与樽海鞘群算法的优化效果最优。