Characterisation and Quantification of Data Centre Flexibility for Power System Support

TL;DR

This paper develops an integrated optimization model to quantify and characterise the flexibility of data centres for power system support, considering operational constraints and workload management.

Contribution

It introduces a novel operational and flexibility assessment model that quantifies data centre flexibility in terms of magnitude and duration, aiding grid support services.

Findings

Upward flexibility is driven by workload deferral and cooling reduction.

Downward flexibility relies on cooling system increase and UPS charging.

Flexibility potential varies with time and operational constraints.

Abstract

The rapid growth of data centres poses an evolving challenge for power systems with high variable renewable energy. Traditionally operated as passive electrical loads, data centres, have the potential to become active participants that provide flexibility to the grid. However, quantifying and utilising this flexibility have not yet been fully explored. This paper presents an integrated, whole facility optimisation model to investigate the least cost operating schedule of data centres and characterise the aggregate flexibility available from data centres to the power system. The model accounts for IT workload shifting, UPS energy storage, and cooling system. Motivated by the need to alleviate the increasing strain on power systems while leveraging their untapped flexibility potential, this study makes two primary contributions: (i) an operational optimisation model that integrates IT scheduling, UPS operation, and cooling dynamics to establish a cost optimal baseline operation, and (ii) a duration-aware flexibility assessment that, for any given start time and power deviation, computes the maximum feasible duration from this baseline while respecting all operational, thermal, and recovery constraints. This method characterises the aggregate flexibility envelope. Results reveal a clear temporal structure and a notable asymmetry in flexibility provision: upward flexibility (electricity load reduction) is driven by deferring IT workload, which allows for a secondary reduction in cooling power. Downward flexibility (electricity load increase) relies on increasing power consumption of the cooling system, supported by the TES buffer, and charging the UPS. This framework translates abstract flexibility potential into quantified flexibility magnitude and duration that system operators could investigate for use in services such as reserve, frequency response, and price responsive demand.