Abstract: [Objectives]　To improve the new energy consumption capacity of the power grid, reduce operational costs for power generation companies, and address issues such as increased network losses and reduced new energy consumption when large-scale renewable energy is integrated into the distribution networks, an economic scheduling strategy for active distribution networks with the inclusion of the coordinated scheduling of data center (DC) and energy storage is proposed.[Methods]　By utilizing the spatiotemporal adjustability of DC and the role of energy storage devices in alleviating the imbalance between grid supply and demand, the coordinated scheduling of DC and energy storage is fully leveraged to achieve peak shaving and valley filling. The search performance of the sparrow search algorithm is improved by incorporating Tent Chaos initialization and multi-population competition mechanisms into the algorithm. In addition, considering the impact of time-of-use pricing on the demand response mechanism, the model is optimized using an improved sparrow search algorithm under constraints such as energy storage costs and operational costs, aiming to achieve the optimal matching of flexible loads and new energy and minimize the total operational costs of the distribution networks.[Results]　Simulations and comparisons are conducted using the improved IEEE 30 and IEEE 33 node systems as examples. The verification results show that the proposed model can effectively reduce the overall operational costs of the distribution networks and improve the new energy consumption rate.[Conclusions]　The economic scheduling method for the distribution networks effectively improves the economic efficiency of scheduling and increases the consumption capacity of new energy.

Key words: energy storage devices, data center (DC), economic scheduling, improved sparrow search algorithm, demand response, new energy consumption