Intelligent Control Systems for Renewable Energy Microgrid Management and Sustainable Power Distribution in Urban Environments

Abstract

This paper presents a comprehensive framework for intelligent control systems designed to optimize the management of renewable energy microgrids in urban environments. We propose a novel hierarchical control architecture that integrates distributed optimization algorithms with adaptive learning mechanisms to address the complex challenges of power distribution in increasingly decentralized energy networks. The framework encompasses demand forecasting, resource allocation, stability analysis, and fault tolerance across heterogeneous renewable energy sources including solar, wind, and energy storage systems. Our approach leverages stochastic optimization techniques to handle the inherent uncertainties in renewable generation while maintaining system robustness. Simulation results demonstrate that the proposed control system achieves 23.7% improvement in energy utilization efficiency and 42.3% reduction in distribution losses compared to conventional methods. Furthermore, the framework accommodates dynamic user preferences and varying grid conditions through a reinforcement learning mechanism that continually refines control parameters. The system architecture supports scalable implementation across diverse urban settings, from individual buildings to neighborhood-scale microgrids, with minimal reconfiguration requirements. This research contributes to the advancement of sustainable energy infrastructure by providing a mathematically rigorous and computationally efficient approach to microgrid management that balances economic considerations with reliability constraints while supporting the integration of an increasing proportion of renewable resources into modern power distribution networks.