Online Electricity Purchase for Data Center with Dynamic Virtual Battery from Flexibility Aggregation

TL;DR

This paper presents an online strategy for data centers to purchase electricity efficiently by aggregating flexible loads into virtual batteries and using Lyapunov optimization, reducing costs under uncertainty.

Contribution

It introduces a hierarchical decision framework and an online algorithm that leverages demand flexibility and virtual batteries for cost-effective energy procurement.

Findings

The proposed algorithm achieves bounded cost loss compared to offline optimal.

Virtual batteries simplify large-scale data center management.

Simulation confirms effectiveness under uncertain renewable and price conditions.

Abstract

As a critical component of modern infrastructure, data centers account for a huge amount of power consumption and greenhouse gas emission. This paper studies the electricity purchase strategy for a data center to lower its energy cost while integrating local renewable generation under uncertainty. To facilitate efficient and scalable decision-making, we propose a two-layer hierarchy where the lower layer consists of the operation of all electrical equipment in the data center and the upper layer determines the procurement and dispatch of electricity. At the lower layer, instead of device-level scheduling in real time, we propose to exploit the inherent flexibility in demand, such as thermostatically controlled loads and flexible computing tasks, and aggregate them into virtual batteries. By this means, the upper-layer decision only needs to take into account these virtual batteries, the size of which is generally small and independent of the data center scale. We further propose an online algorithm based on Lyapunov optimization to purchase electricity from the grid with a manageable energy cost, even though the prices, renewable availability, and battery specifications are uncertain and dynamic. In particular, we show that, under mild conditions, our algorithm can achieve bounded loss compared with the offline optimal cost, while strictly respecting battery operational constraints. Extensive simulation studies validate the theoretical analysis and illustrate the tradeoff between optimality and conservativeness.