Integrated Renewable Energy Monitoring and Adaptive Load Optimization Using Smart Grid and Intelligent Control Algorithms

Increasing renewable penetration introduces variability that affects voltage regulation, peak demand management, and operational cost in smart grids. This study presents an integrated renewable energy monitoring and adaptive load optimization framework that combines IoT-based real time sensing, short term forecasting, multi objective optimization, model predictive control, and deep reinforcement learning within a unified architecture. The proposed system models power balance, storage dynamics, and operational constraints while minimizing energy cost, renewable curtailment, peak demand, and voltage deviation. A distributed microgrid with photovoltaic generation, wind resources, battery storage, and flexible loads was simulated using OpenDSS. Performance was evaluated against rule based control, standalone model predictive control, and standalone reinforcement learning under varying renewable penetration levels and disturbance scenarios. Results indicate higher renewable utilization, reduced peak demand, tighter voltage regulation, and up to 14% operational cost reduction with the hybrid controller. Stress testing under forecast errors and communication delays demonstrated stable performance within technical limits. The findings show that coordinated predictive control and learning based adaptation improve both economic efficiency and grid stability in renewable rich smart grid environments.