Abstract
This study examines the role of artificial intelligence in optimizing smart grid operations under high renewable energy penetration. Using a simulation model with wind and solar datasets, results show that reinforcement learning algorithms improve load forecasting accuracy by 23% and reduce energy curtailment by 17%. The study emphasizes that AI-driven smart grids hold promise for sustainable energy transitions, but governance frameworks are needed to address ethical and equity challenges.
Keywords:
- Keyword: Smart Grids
- Keyword: Renewable Energy
- Keyword: Artificial Intelligence
- Keyword: Grid Stability
How to Cite:
Qi, W., (2025) “AI-Driven Smart Grids: Balancing Renewable Integration and Grid Stability”, Journal of Health Geography and Public Health (JHGPH) . doi: https://doi.org//JHGPH.5