利用灵活的空间和时间数据中心工作负载为电网调节服务
Harnessing Flexible Spatial and Temporal Data Center Workloads for Grid Regulation Services
AI总结：
本文提出统一框架，通过联合优化数据中心工作负载分配和调节容量承诺，提升电网调节服务的可行性和经济性。
摘要：
数据中心（DCs）日益被认可为可以支持电网频率调节的灵活负载。然而，大多数现有方法将工作负载调度和调节容量招标分开处理，忽略了排队动态和时空调度决策如何影响维持实时调节的能力。因此，承诺的调节可能变得不可行或短暂。为了解决这个问题，我们提出了一种统一的提前一天联合优化框架，该框架共同决定工作负载在地理分布数据中心之间的分配和调节容量承诺。我们构建了一个空间-时间网络模型，以捕捉工作负载迁移成本、延迟要求和异构资源限制。为了确保承诺的调节仍然可交付，我们基于交互式负载预测引入了基于即时电力灵活性的机会约束，并应用价值在风险队列状态约束以在累积调节信号下保持可持续响应。在使用真实数据中心跟踪数据对修改后的IEEE 68节点系统进行案例研究时，所提出的框架降低了系统运行成本，使调节容量更加可行，并在收入-风险权衡方面优于单独优化调度和调节的策略。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
