Numerical Simulation and Design of Power Fluctuation Controlling of Hybrid Energy Storage System Based on Modified Particle Swarm Optimization

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Published: Sep 30, 2022
DOI: https://doi.org/10.17762/ijrmee.v9i3.378

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Nand Kishor Gupta
Associate Professor, Department of Electrical and Electronics Engineering, Poornima University, Jaipur , India
Dr. Bharat Bhushan Jain
Professor, Department of Electrical Engineering, Jaipur Engineering College, Jaipur, Rajasthan, India
Ashish Raj
Assistant Professor, Department of Electrical and Electronics Engineering, Poornima University, Jaipur, India
Satish Kumar Alaria
Assistant Professor, Department of Computer Science &Engineering, AIET, Jaipur, India

Abstract

While intermittent renewable energy sources like wind and solar have high volatility, hybrid energy storage is crucial to microgrid power balancing and smoothing out renewable energy's power fluctuations. Microgrids are now a highly effective and adaptable new distribution power grid that can be seamlessly integrated with existing power systems thanks to the microgrid industry's recent rapid growth. The amount of power generated via renewable resources is rising. The generation of photovoltaic (PV) electricity may fluctuate due to changes in cloud cover and weather-related variations in light intensity. These can have a significant impact on a severe Microgrid voltage fluctuation problem in a system with a high PV penetration. However, the battery energy storage system (BESS) is a piece of technology that may be employed to reduce PV volatility and increase the Microgrid's adaptability. An enhanced particle swarm optimization (I-PSO) is created in this study. By maximizing both BESS active and reactive power, battery consumption may be improved, overcharging and discharge prevented, and voltage fluctuation is reduced.

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How to Cite
Nand Kishor Gupta, Dr. Bharat Bhushan Jain, Ashish Raj, & Satish Kumar Alaria. (2022). Numerical Simulation and Design of Power Fluctuation Controlling of Hybrid Energy Storage System Based on Modified Particle Swarm Optimization. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(3), 88–95. https://doi.org/10.17762/ijrmee.v9i3.378
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Vol. 9 No. 3 (2022): July-September (2022) Issue
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