【目的】 在各行业数字化转型的浪潮下，数据中心产业蓬勃发展，并呈现出集群化和广域互联的新态势。鉴于数据中心普遍存在高能耗、高碳排放问题，旨在基于数据中心计算负载的时空灵活性，引导其积极参与电网低碳调度，以达成数据中心与电网节能降碳及经济运行目标。【方法】 提出了一种高精度的动态碳排放核算方法，该方法计及了网络损耗与碳排放时空差异，通过功率迭代溯源算法精准计算出关键的“节点碳势”信号，并基于该信号提出了互联数据中心与电网协同双层调度模型。其中，上层模型以运行成本、碳排放及可再生能源弃用量最小为优化目标；下层模型以最小化电-碳综合成本为优化目标。调度模型通过上下层之间的碳势和负载调整信号进行交互迭代，寻找系统最优的调度策略。【结果】 基于改进IEEE 39节点系统的案例分析表明，在所提协同调度策略下，数据中心在高碳势时段，主动将在线负载转移至低碳势节点处理，并延迟处理本地的离线负载；在低碳势时段，则会承接更多在线负载并集中处理先前延迟的离线负载任务，节能降碳效果显著。【结论】 所提双层优化模型，可有效引导数据中心实现经济与低碳的协同，为数据中心绿色转型和电网低碳调度提供新的技术工具。

[Objective] Under the wave of digital transformation in various sectors， the data center industry is booming and showing a new trend of clustering and wide-area interconnection. Given the high energy consumption and high carbon emissions that are common in data centers， this paper aims to guide them to actively participate in the low-carbon dispatching of the power grid based on the spatio-temporal flexibility of their computing loads， in order to achieve the goals of energy conservation， carbon reduction， and economic operation for both data centers and the power grid. [Methods] This paper proposes a high-precision dynamic carbon emission accounting method， which takes into account the spatio-temporal differences in network losses and carbon emissions. It accurately calculates the key "nodal carbon potential" signal through a power iteration traceability algorithm and， based on this signal， proposes a coordinated two-layer dispatching model for interconnected data centers and the power grid， where the upper-layer model aims to minimize the operation cost， carbon emissions， and renewable energy curtailment， while the lower-layer model aims to minimize the integrated electricity-carbon cost. The dispatching model interacts and iterates through the carbon potential and load adjustment signals between the upper and lower layers to find the optimal dispatching strategy for the system. [Results] The case study based on the improved IEEE 39-bus system shows that under the proposed coordinated dispatching strategy， data centers actively transfer online loads to low-carbon potential nodes for processing during high-carbon potential periods and delay the processing of local offline loads. During low-carbon potential periods， they take on more online loads and concentrate on processing previously delayed offline loads， achieving significant energy conservation and carbon reduction effects. [Conclusions] The two-layer optimization model proposed in this paper can effectively guide data centers to achieve economic and low-carbon coordination， providing a new technical tool for the green transition of data centers and the low-carbon dispatching of the power grid.