TEN-L: A Graph-Based Evolutionary Learning Model for Adaptive Renewable Integration in Smart Grids

Sustainable energy management is achieved through seamless power distribution, satisfying user demands. The swift integration of renewable energy sources sustains the sustainability of smart grid (SG) architectures. This article introduces a Temporal Evolution Network-Learning (TEN-L) model that aims to achieve the aforementioned sustainability in smart grids by integrating renewable resources. The model addresses the rising energy demand driven by environmental impacts, resource depletion, and power outages. TEN-L employs a graph-based evaluation method and an evolutionary optimization to enhance sustainability and distribution efficiency while reducing power losses. The model evaluates the relationship between sustainability factors and distribution efficiency over time, while adjusting the integration of renewable energy sources to accommodate fluctuating demand. By optimizing energy source selection and distribution parameters, TEN-L enhances the reliability and sustainability of smart grid operations. This proposed model achieves a 12.27% higher demand response and an 11.63% higher distribution efficiency for the average hours considered.