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

TL;DR

This paper presents AgentCONCUR, a regression-based coordination mechanism for data center task shifting that reduces data and computational needs while ensuring cost-effective operations in power systems.

Contribution

It introduces a novel regression-based coordination method that leverages optimization structure and public data, avoiding the need for labeled datasets.

Findings

Large coordination gains demonstrated in NYISO study

Identification of key features for effective regression-based coordination

Method ensures feasible and cost-effective task shifts

Abstract

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