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

TL;DR

Problem: Data centers offer flexible loads for grid frequency regulation, but prior work treats workload scheduling and regulation capacity bidding separately, risking infeasible responses. Approach: a day-ahead spatio-temporal co-optimization using a space-time network to jointly schedule workloads across data centers and regulation capacity $R_{l,t}$, with chance constraints on instantaneous power and VaR constraints on cumulative energy to guarantee deliverability under non-Gaussian signals. Contributions: (i) unified optimization that couples workload placement with regulation bidding, (ii) Gaussian Envelope-based convex reformulations for instantaneous power constraints, (iii) VaR-based queue constraints for cumulative energy, and (iv) case studies on a modified IEEE 68-bus system showing reductions in total cost and improved revenue-risk trade-offs. Impact: enables scalable, reliable, and economically attractive DC participation in grid regulation by exploiting space-time flexibility.

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.

Figures (4)

• Figure 1: Illustration for co-optimization model.
• Figure 2: The load across the testing dataset under different coordination models. The curve defines the final cost and frequency regulation capacity.
• Figure 3: 68-bus NY-NE ISO system with 8 data centers. The arrows show active virtual links between different DCs, real-time coordination under different coordination solutions for 100% latency loss and 25% latency loss, as green links and orange links.
• Figure 4: Regulation capacity distribution and revenue–risk tradeoff under different signal modeling approaches.