Evaluation of the new model of renewable energy economic growth under the background of big data

In the era of big data, this paper proposes a novel evaluation model for renewable energy (RE) economic growth, namely, the “big data-driven time-series production simulation and absorption evaluation model.” Unlike traditional methods that rely on static load curves and average output assumptions, this model integrates multi-source heterogeneous data (historical meteorology, real-time generation, and grid operation) and employs a priority dispatch rule with dynamic constraint correction. The experimental results show that the proposed model achieves an RE absorption value of 1582 MW and a curtailment ratio of 8.32%, outperforming traditional algorithms (1336 MW and 14.32%, respectively). To link these technical indicators to economic growth, this paper establishes a conversion framework: (1) higher RE absorption reduces fossil fuel consumption; (2) lower curtailment ratio improves power supply stability, reducing industrial interruption losses; and (3) expanded RE deployment creates green employment opportunities in equipment manufacturing and maintenance sectors. Based on this framework, the proposed model contributes to economic growth through enhanced energy security, cost savings, and job creation. The core contribution of this paper is to provide a computable bridge between RE absorption performance and economic growth indicators.