AI-Driven Real-Time Optimization of Distributed Renewable Energy Grids for Demand-Response and Stability in Smart Cities

Abstract

The rapid integration of distributed renewable energy resources such as solar photovoltaic systems and wind turbines has transformed conventional power grids into complex, decentralized energy networks. While this transition supports sustainability goals, it introduces significant challenges related to intermittency, grid stability, and demand–supply imbalance, particularly in urban smart city environments. This paper presents an artificial intelligence–driven real-time optimization framework for distributed renewable energy grids aimed at enhancing demand-response capability and operational stability. Machine learning algorithms, including deep neural networks and reinforcement learning, are employed to forecast energy generation, predict demand patterns, and dynamically control distributed energy resources. The proposed framework is evaluated using a simulated smart city microgrid incorporating renewable generation, energy storage systems, and flexible loads. Performance metrics such as frequency deviation, voltage stability, energy loss reduction, and demand-response efficiency are analyzed under varying operating conditions. Results demonstrate that the AI-driven approach significantly improves grid resilience, reduces peak demand stress, and enhances renewable energy utilization compared to conventional rule-based control strategies. The findings confirm the potential of artificial intelligence as a critical enabler for intelligent energy management in future smart cities, supporting reliable, efficient, and sustainable power systems.