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A network of spatially distributed data centers can provide operationalflexibility to power systems by shifting computing tasks among electricallyremote locations. However, harnessing this flexibility in real-time through thestandard optimization techniques is challenged by the need for sensitiveoperational datasets and substantial computational resources. To alleviate thedata and computational requirements, this paper introduces a coordinationmechanism based on contextual regression. This mechanism, abbreviated asAgentCONCUR, associates cost-optimal task shifts with public and trustedcontextual data (e.g., real-time prices) and uses regression on this data as acoordination policy. Notably, regression-based coordination does not learn theoptimal coordination actions from a labeled dataset. Instead, it exploits theoptimization structure of the coordination problem to ensure feasible andcost-effective actions. A NYISO-based study reveals large coordination gainsand the optimal features for the successful regression-based coordination.

Agent Coordination via Contextual Regression (AgentCONCUR) for Data Center Flexibility