Collaborative planning model of digital economy and renewable energy microgrid based on double-layer optimization

Abstract

With the growing importance of renewable energy integration and digital energy management, optimizing the coordination between microgrids and the digital economy has become a critical challenge. This study proposes a collaborative planning model based on a double-layer optimization (DLO) framework that combines Non-dominated Sorting Genetic Algorithm II (NSGA-II) with elite strategy in the upper layer and Gurobi solver in the lower layer. The model is designed to jointly optimize investment cost, carbon emissions, and renewable energy utilization. Experimental evaluations using public datasets showed that the proposed model achieved an average accuracy of 0.92 and a root mean square error of 0.28 under low-load conditions. The system maintained high performance under different data volumes, with carbon emissions reduced to 15 kgCO²/h, optimization time of 4.2 seconds, and energy conversion efficiency reaching 90%. These results demonstrate that the DLO framework improves both computational efficiency and environmental sustainability, providing a practical tool for intelligent microgrid planning and carbon-conscious decision-making.