A Review on the Role of AI in Optimizing Renewable Energy Grid Management

The integration of artificial intelligence (AI) within the renewable energy sector, particularly in solar energy, is transforming the way energy is generated, managed, and integrated into the grid. This study examines AI’s potential to significantly enhance the efficiency, scalability, and cost-effectiveness of solar power systems. Leveraging AI-driven forecasting systems, engineers can improve grid stability, optimize demand response, and manage load controls. By utilizing advanced algorithms, AI can predict solar energy generation with high accuracy, enabling better energy planning and enhanced grid reliability. Through data analytics, AI transforms raw data into actionable insights, optimizing solar farms for maximum output, enabling predictive maintenance, and increasing overall system efficiency. Such optimizations not only reduce costs but also minimize dependency on fossil fuels, contributing to lower greenhouse gas emissions. In hybrid renewable energy systems, AI algorithms such as hybrid Long Short-Term Memory (LSTM) with reinforcement learning (RL), RL with simulated annealing (RL-SA), and convolutional neural networks with particle swarm optimization(CNN-PSO) demonstrate improved performance for demand forecasting and load balancing. These methods, combined with AI-driven demand-side management and demand response (DR) strategies, enable near real-time decision-making, further enhancing energy utilization and system sustainability. This study also explores the role of AI in distributed energy resources (DERs) and prosumer-driven transactive energy models, emphasizing the benefits of relieving grid stress and achieving cost efficiency. Additionally, the paper addresses challenges, including data privacy, infrastructure integration, and the need for highly specialized skills for demand response management (DRM) schemes. Lastly, the integration of blockchain technology into DR schemes and the implementation of AI in home energy management systems are analyzed to showcase recent advancements in smart grid applications. This research concludes by discussing potential future developments in explainable AI, reinforcement learning, and edge computing, highlighting their roles in bolstering the resilience and sustainability of renewable energy systems. These emerging technologies present opportunities for creating robust, adaptable, and environmentally friendly energy solutions.