Abstract: The integration of renewable energy sources into electrical power systems presents significant challenges due to their intermittent and variable nature. Artificial Intelligence (AI) has emerged as a powerful tool to address these challenges by enabling smarter, more adaptive management of renewable energy generation and grid operations. This research paper investigates the application of AI techniques—including machine learning, deep learning, and predictive analytics—in optimizing the integration of renewable energy sources such as solar, wind, and hydroelectric power into existing electrical grids.AI-driven models improve forecasting accuracy for renewable energy output and load demand, enhancing grid stability and operational efficiency. Techniques such as neural networks and support vector machines are employed to predict short-term and long-term energy generation, while reinforcement learning algorithms enable dynamic energy management and storage optimization. Furthermore, AI aids in fault detection, real-time grid monitoring, and demand response management, helping to mitigate the impact of renewable energy variability.
The paper also discusses challenges related to data quality, computational complexity, and the need for scalable AI solutions that can operate in real-time within complex power systems. Case studies and simulations demonstrate the effectiveness of AI approaches in improving renewable penetration while maintaining power quality and system reliability. This study underscores the transformative potential of AI in accelerating the adoption of renewable energy technologies, promoting sustainable energy systems, and enabling the transition towards a low-carbon, smart grid future.
Keywords: Artificial intelligence, renewable energy integration, machine learning, power systems, energy forecasting, grid stability, smart grid.
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