Optimizing Real-Time Scheduling for Post Islanding Energy Management Using African Vulture Optimization Algorithm on Hybrid Microgrids Environment

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Microgrids (MG) are small-scale energy systems that use distributed energy storage and sources. Hybrid microgrids are transforming energy management by incorporating various energy resources like wind, solar, and battery storage. Effective scheduling of this resource is vital to minimize the costs and maximize energy autonomy. Advanced scheduling algorithm optimizes the operation of hybrid microgrids, which dynamically adjusts the energy consumption and generation to satisfy the demand while ensuring power balancing. This scheduling strategy has been instrumental in improving the sustainability and resilience of MGS, which paves the way for an environmentally friendly and more reliable energy future. They can operate on islanded or grid-connected modes. The optimization of hybrid MG scheduling is paramount in the field of post-island management to ensure effective energy sustainability and distribution. Using metaheuristic approaches like simulated annealing or genetic algorithms allows the finetuning of scheduling parameters to increase energy utilization while reducing environmental impact and costs. Therefore, the study presents a Real-Time Scheduling for Post Islanding Energy Management using African Vulture Optimization Algorithm (RTSPIEM-AVOA) in Hybrid microgrid environment. The RTSPIEM-AVOA approach is utilized to improve its functioning by determining the most efficient scheduling of the installed generation unit. The AVOA can handle complex optimization issues while avoiding local optima solutions because of the balance among the exploitation and exploration stages. A microturbine (MT) system, battery storage, a fuel cell (FC), photovoltaic (PV), and wind turbine (WT) make up the suggested MG system. This work looks at three scenarios: PV and WT operating at normal generation, PV and WT operating at their maximum power, and WT operating at its rated power. Consider the two objective functions of minimizing pollutant emissions and reducing operational costs. According to the experimental results, the RTSPIEM-AVOA technique outperforms other models in microgrid scheduling by efficiently optimizing it to satisfy the community's changing needs while transitioning to a greener and more sustainable energy future.