The efficient incorporation of substantial quantities of renewable energy into the power grid is crucial for ensuring a sustainable future and promoting environmentallyfriendly smart cities. The current methods for integrating solar power rely on imprecise weather forecasting and inadequate load distribution, resulting in an overall grid performance that is both unreliable and inefficient. Therefore, it is necessary to adopt a more comprehensive perspective to address these issues. Artificial Intelligence (AI) is one such technology that is applied in high-penetration renewable physical infrastructure. AI-powered smart grids provide the real-time monitoring and control of renewable energy sources, such as solar and wind, to ensure the most efficient use of energy. Smart algorithms optimize battery lifespan, enhancing efficiency and lowering maintenance expenses. AI-powered predictive maintenance models are utilized to identify possible problems in physical infrastructure components prior to system failures. Smart buildings utilize AI to control heating, ventilation, and air conditioning (HVAC) systems by analyzing occupancy trends, weather forecasts, and user preferences. Although AI has mostly good effects on high-penetration renewable physical infrastructure, it also presents problems like as data security concerns, ethical considerations, and the requirement for experienced people to handle and advance these intricate systems. Tackling these obstacles is essential to fully harness the capabilities of AI in enhancing the implementation and effectiveness of widespread renewable energy solutions.
