Optimizing Energy Storage and Hybrid Inverter Performance in Smart Grids Through Machine Learning
Journal Title: Information Dynamics and Applications - Year 2024, Vol 3, Issue 3
Abstract
The effective integration of renewable energy sources (RES), such as solar and wind power, into smart grids is essential for advancing sustainable energy management. Hybrid inverters play a pivotal role in the conversion and distribution of this energy, but conventional approaches, including Static Resource Allocation (SRA) and Fixed Threshold Inverter Control (FTIC), frequently encounter inefficiencies, particularly in managing fluctuating renewable energy inputs and adapting to variable load demands. These inefficiencies lead to increased energy loss and a reduction in overall system performance. In response to these challenges, the Optimized Energy Storage and Hybrid Inverter Management Algorithm (OESHIMA) has been developed, employing machine learning for real-time data analysis and decision-making. By continuously monitoring energy production, storage capacity, and consumption patterns, OESHIMA dynamically optimizes energy allocation and inverter operations. Comparative analysis demonstrates that OESHIMA enhances energy efficiency by 0.25% and reduces energy loss by 0.20% when benchmarked against conventional methods. Furthermore, the algorithm extends the lifespan of energy storage systems by 0.15%, contributing to both sustainable and cost-efficient energy management within smart grids. These findings underscore the potential of OESHIMA in addressing the limitations of traditional energy management systems (EMSs) while improving hybrid inverter performance in the context of renewable energy integration.
Authors and Affiliations
Kavitha Hosakote Shankara, Mallikarjunaswamy Srikantaswamy, Sharmila Nagaraju
Keywords
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- EP ID EP752496
- DOI https://doi.org/10.56578/ida030301
- Views 10
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