Energy Storage Optimization in Renewable Energy Systems using Particle Swarm Optimization

¹ Moscow State University of Civil Engineering, 129337, Yaroslavskoe shosse, 26, Moscow, Russia
² Department of EEE, GRIET, Bachupally, Hyderabad, Telangana, India.
³ Department of Mechanical Engineering, KG Reddy College of Engineering and Technology, Chilkur(Vil), Moinabad(M), Ranga Reddy(Dist), Hyderabad, 500075, Telangana, India.
⁴ Centre of Research Impact and Outcome, Chitkara University, Rajpura - 140417, Punjab, India
⁵ Uttaranchal University, Dehradun - 248007, India
⁶ Lovely Professional University, Phagwara, Punjab, India,
⁷ Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh - 174103 India
⁸ Department of Computer Engineering & Applications, GLA University, Mathura - 281406 (U.P.), India
⁹ Department of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq

^* Corresponding author: nidziyen@mgsu.ru

Abstract

This research examines the application of Particle Swarm Optimization (PSO) to optimize energy storage optimizations with the objectives of improving energy generation, cost-efficiency, system dependability, and environmental sustainability. The optimisation of solar panel and energy storage capacities was conducted using empirical data from various microgrid locations: Site 1, which had a capacity of 90 kW solar and 40 kW wind, Site 2, which had a capacity of 50 kW wind and 80 kW solar, Site 3, which had a capacity of 60 kW wind and 110 kW solar, and Site 4, which had a capacity of 45 kW wind and 85 kW solar. The findings suggest that energy generation increased significantly by 15% to 25% across all sites following optimization. Furthermore, significant decreases in the levelized cost of energy (LCOE) between 10% and 14% were noted, providing confirmation of the economic feasibility. Increased grid stability of 17% to 24% during periods when microgrids were operating under stable conditions demonstrates that PSO-optimized configurations are dependable. The positive environmental effects of solutions derived from PSO were apparent, as evidenced by the conservation of carbon emissions and ecological footprints, which decreased by 7% to 15%. The sensitivity analysis validated the optimized configurations' robustness, establishing their ability to withstand changes in parameters. In summary, the utilization of PSO to optimize energy storage optimizations showcases its capacity to enhance the efficiency, dependability, cost-effectiveness, and environmental impact of these systems. This advances the possibility of constructing microgrids that exclusively utilize sustainable renewable energy sources.