Optimization of Multi-Energy Grid Integration and Energy Storage in Low-Carbon Power Systems Based on the TCM-MBZOA Algorithm: A Case Study of Yunnan Province

With the rapid transition toward green and low-carbon energy systems, efficient scheduling of power systems is crucial for improving energy utilization and reducing carbon emissions. However, existing converter stations still rely heavily on single-source energy models, which limit the integration of renewables and lead to high carbon footprints. To address this limitation, this paper proposes a multi-source coordinated optimization strategy based on a bi-level programming model and an improved tent chaotic mapping-memory backtracking zebra optimization algorithm (TCM-MBZOA). The outer model maximizes the annual revenue of third-party energy storage operators, while the inner model minimizes operational costs, carbon emissions, and renewable energy curtailment within multiple virtual power plants. The TCM-MBZOA enhances the algorithm’s performance through Tent Chaotic Mapping for diverse initialization, a Memory-Backtracking Strategy for adaptive exploration, and an Adaptive T-Distribution for improved convergence. Applied to a real converter station in Yunnan Province, the proposed method achieves a 10.51% reduction in total energy consumption and significantly improves photovoltaic utilization and energy storage efficiency, outperforming benchmark algorithms. The results demonstrate the practical effectiveness and engineering feasibility of the proposed strategy in enabling low-carbon, high-efficiency operation of modern power systems.