Smart Grids and AI Innovations for Efficient Energy Distribution and Consumption

Incorporating smart grid has helped in improving efficiency, reliability and sustainability in energy management systems. But the integrated centralized based energy management systems have major drawbacks in managing the variety and unpredictability of the energy sources and consumption rates. In this paper, a novel approach called FDRL is designed to solve the energy management issue to improve energy distribution and reduce waste. The proposed FDRL framework enables the distribution of decisions to local agents, enabling them to make decisions in real-time regarding energy management without compromising privacy. Simulation results indicate that the proposed FDRL framework is better than the centralized and other decentralized approaches with an accuracy of 98%, precision at 98.5%, recall at 97.5%, and F1 score at 97%. In addition, some performance parameters suggest that the energy efficiency ratio is 92%, the total energy loss is 150 kWh, and the renewable energy usage rate is 60%. Therefore, the study demonstrates FDRL as a potential approach to address energy management better in smart grids and therefore enhance energy systems sustainability and resilience. This work complements the existing body of knowledge on AI applications in energy management, and offers a roadmap for future research and deployment in actual smart grid scenarios. This study adds to the current literature on AI applications within energy management, and offers a foundation for the future research and application in practical settings of smart grids.