Optimization of Renewable Energy Storage Systems Using Intelligent Algorithms

  • Authors

    • Sanjay Verma Operations Manager, HCL Technologies, India Author
    • Naveen Kumar Product Manager, Zoho Corporation, India Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2023PI8P3M

    Published 04-04-2023

  • Renewable Energy Storage, Intelligent Algorithms, Energy Optimization, Smart Grid, Genetic Algorithm, Particle Swarm Optimization, Artificial Intelligence, Battery Energy Storage System, Renewable Energy Integration, Energy Management

    Issue

    Vol. 6 No. 1 (2023)

    Section

    Articles

    How to Cite

    Optimization of Renewable Energy Storage Systems Using Intelligent Algorithms. (2023). International Journal of Modern Research in Science & Engineering, 6(1), 01-15. https://doi.org/10.67228/30716357/IJMRSE-2023PI8P3M

  • Abstract

    The increasing integration of renewable energy sources such as solar and wind has created challenges related to intermittency, uncertainty, and grid stability. Energy storage systems help address these issues by storing excess energy and supplying it during periods of low generation. This study explores the use of intelligent optimization algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Artificial Neural Networks (ANN), Fuzzy Logic Systems (FLS), Ant Colony Optimization (ACO), and hybrid approaches, for efficient renewable energy storage management. Various storage technologies, such as battery storage, pumped hydro, compressed air, and hybrid systems, are examined. The proposed framework combines renewable energy forecasting, storage scheduling, and intelligent optimization to improve energy efficiency, storage utilization, reliability, and cost-effectiveness. Results show that intelligent algorithms outperform traditional optimization methods by providing greater adaptability, robustness, cost savings, and grid stability, making them promising solutions for future smart grids and sustainable energy systems.

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