The MG technical parameters [49, 50].

Recently, global interest in organizing the functioning of renewable energy resources (RES) through microgrids (MG) has developed, as a unique approach to tackle technical, economic, and environmental difficulties. This study proposes implementing a developed Distributable Resource Management strategy (DRMS) in hybrid Microgrid systems to reduce total net percent cost (TNPC), energy loss (Ploss), and gas emissions (GEM) while taking the cost-benefit index (CBI) and loss of power supply probability (LPSP) as operational constraints. Grey Wolf Optimizer (GWO) was utilized to find the optimal size of the hybrid Microgrid components and calculate the multi-objective function with and without the proposed management method. In addition, a detailed sensitivity analysis of numerous economic and technological parameters was performed to assess system performance. The proposed strategy reduced the system’s total net present cost, power loss, and emissions by (1.06%), (8.69%), and (17.19%), respectively compared to normal operation. Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) techniques were used to verify the results. This study gives a more detailed plan for evaluating the effectiveness of hybrid Microgrid systems from a technical, economic, and environmental perspective.

近年来，通过微电网（MG）统筹可再生能源资源（RES）的运行模式在全球范围内引发广泛关注，该方案为破解技术、经济与环境层面的多重困境提供了独特路径。本研究提出在混合微电网系统中应用改进型分布式资源管理策略（DRMS），以降低总净百分比成本（TNPC）、能量损耗（Ploss）与气体排放（GEM），并将成本效益指数（CBI）与供电中断概率（LPSP）设定为运行约束条件。本文采用灰狼优化器（GWO）求解混合微电网各组件的最优配置规模，并对比了采用与不采用所提管理策略时的多目标优化函数结果。此外，针对多项经济与技术参数开展了详细的敏感性分析，以全面评估系统运行性能。相较于常规运行模式，所提策略分别将系统总净现值成本、功率损耗与气体排放量降低了1.06%、8.69%与17.19%。本文同时采用萤火虫算法（FA）与粒子群优化算法（PSO）对仿真结果进行了验证。本研究从技术、经济与环境三维视角，为混合微电网系统的效能评估提供了更为系统详尽的分析框架。