Innovative hybrid energy storage systems with sustainable integration of green hydrogen and energy management solutions for standalone PV microgrids based on reduced fractional gradient descent algorithm

• Developing of a novel multi-level HESS combining supercapacitors and dual battery modules to enhance performance and lifespan of standalone PV microgrids under different weather conditions high irradiation and partial shading conditions. • Significant extension of battery life achieved by reducing charge/discharge stress through hybridization of supercapacitors and dual battery modules. • Application of LOA, OOA, and RFGD optimization algorithms for efficient energy management and reduced hydrogen consumption in fuel cells. • Optimizing of hydrogen consumption supports reduction in hydrogen consumption by 15 %, which directly enhances the efficiency of green ammonia production, contributing to local industrial sustainability. • Comparison of proposed HESS with conventional systems and utilizing of fuel cells to replace diesel generators, enhancing sustainability and system efficiency. This paper investigates innovative solutions to enhance the performance and lifespan of standalone photovoltaic (PV)-based microgrids, with a particular emphasis on off-grid communities. A major challenge in these systems is the limited lifespan of batteries. To overcome this issue, researchers have created hybrid energy storage systems (HESS) along with advanced power management strategies. This study introduces innovative multi-level HESS approaches and a related energy management strategy designed to alleviate the charge/discharge stress on batteries. Comprehensive Matlab Simulink models of various HESS topologies within standalone PV microgrids are utilized to evaluate system performance under diverse weather conditions and load profiles for rural site. The findings reveal that the proposed HESS significantly extends battery life expectancy compared to existing solutions. Furthermore, the paper presents a novel energy management strategy based on the Reduced Fractional Gradient Descent (RFGD) algorithm optimization, tailored for hybrid systems that include photovoltaic, fuel cell, battery, and supercapacitor components. This strategy aims to minimize hydrogen consumption of Fuel Cells (FCs), thereby supporting the production of green ammonia for local industrial use. The RFGD algorithm is selected for its minimal user-defined parameters and high convergence efficiency. The proposed method is compared with other algorithms, such as the Lyrebird Optimization Algorithm (LOA) and Osprey Optimization Algorithm (OOA). The RFGD algorithm exhibits superior accuracy in optimizing energy management, achieving a 15 % reduction in hydrogen consumption. Its efficiency is evident from the reduced computational time compared to conventional algorithms. Although minor losses in computational resources were observed, they were substantially lower than those associated with traditional optimization techniques. Overall, the RFGD algorithm offers a robust and efficient solution for enhancing the performance of hybrid energy systems.