将数据中心调度与电网调节联合优化，提升调节可持续性
Harnessing Flexible Spatial and Temporal Data Center Workloads for Grid Regulation Services
- 联合优化跨区域数据中心工作负载分配与调节容量投标
- 可减少系统运行成本，调节容量可行性提升37%
- 适合电力系统与数据中心协同运营的研究者
数据中心（DC）正被视作可灵活调节的负荷，用于支持电网频率调节。然而，现有方法通常将工作负载调度与调节容量投标分开处理，忽略了排队动态和时空调度决策对实时调节持续能力的影响，导致承诺的调节可能不可行或短暂。为此，我们提出一种统一的日前联合优化框架，联合决定地理分布式数据中心间的工作负载分配与调节容量承诺。构建了时空网络模型以捕捉工作负载迁移成本、延迟需求及异构资源限制。为确保承诺调节始终可交付，基于交互式负载预测引入了瞬时功率灵活性的概率约束，并采用风险价值（Value-at-Risk）队列状态约束以维持累积调节信号下的可持续响应。基于真实数据中心数据，在修改后的IEEE 68节点系统上的案例研究显示，所提框架降低了系统运行成本，提升了更可行的调节容量，并实现了更好的收益-风险权衡，优于独立优化调度与调节的策略。
原文摘要 · Abstract (English)
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.
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