Data centers (DCs) are increasingly recognized as flexible loads that can support grid frequency regulation. Yet, most existing methods treat workload scheduling and regulation capacity bidding separately, overlooking how queueing dynamics and spatial-temporal dispatch decisions affect the ability to sustain real-time regulation. As a result, the committed regulation may become infeasible or short-lived. To address this issue, we propose a unified day-ahead co-optimization framework that jointly decides workload distribution across geographically distributed DCs and regulation capacity commitments. We construct a space-time network model to capture workload migration costs, latency requirements, and heterogeneous resource limits. To ensure that the committed regulation remains deliverable, we introduce chance constraints on instantaneous power flexibility based on interactive load forecasts, and apply Value-at-Risk queue-state constraints to maintain sustainable response under cumulative regulation signals. Case studies on a modified IEEE 68-bus system using real data center traces show that the proposed framework lowers system operating costs, enables more viable regulation capacity, and achieves better revenue-risk trade-offs compared to strategies that optimize scheduling and regulation independently.

翻译：数据中心正日益被视为可支持电网频率调节的灵活负荷。然而，现有方法大多将工作负载调度与调节容量投标割裂处理，忽视了排队动态及时空调度决策如何影响实时调节能力的可持续性，导致承诺的调节容量可能无法实现或难以持续。为解决该问题，本文提出一种统一的前日协同优化框架，可联合决策地理分布式数据中心间的工作负载分配与调节容量承诺。我们构建了时空网络模型以刻画工作负载迁移成本、延迟要求及异构资源限制。为确保承诺的调节容量具备可交付性，我们基于交互式负荷预测引入瞬时功率灵活性的机会约束，并应用风险价值队列状态约束以保障在累积调节信号下维持可持续响应能力。基于真实数据中心轨迹数据在改进的IEEE 68节点系统中进行的案例研究表明：相较于独立优化调度与调节的策略，所提框架能降低系统运行成本、提供更具可行性的调节容量，并实现更优的收益-风险权衡。