Harnessing Flexible Spatial and Temporal Data Center Workloads for Grid Regulation Services

TL;DR

This paper presents a unified optimization framework for data centers to jointly schedule workloads and commit regulation capacity, improving grid support reliability and economic efficiency.

Contribution

It introduces a space-time network model and chance constraints for integrated workload and regulation capacity optimization in geographically distributed data centers.

Findings

Reduces system operating costs

Enables more viable regulation capacity

Improves revenue-risk trade-offs

Abstract

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.