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

TL;DR

The paper tackles data-center electricity procurement under uncertain prices, renewable output, and flexible loads by introducing a two-layer control framework that aggregates TCLs and deferrable tasks into a single virtual battery. An online policy is derived via Lyapunov optimization, solving a per-slot drift-plus-cost problem to determine $R_e(t)$, $R_b(t)$, $G_e(t)$, $G_b(t)$, and $B_e(t)$ without requiring statistical knowledge. Theoretical results show the long-run cost is within $\mathcal{O}(1/V)$ of the offline optimum $Y^{OPT}$ while guaranteeing battery feasibility, with a tunable parameter $V$ balancing cost and storage capacity. Numerical simulations corroborate the cost reduction and demonstrate the trade-offs between optimality and conservativeness as $V$ varies. The approach offers a scalable and practical mechanism for green-powered, cost-efficient data-center operation under uncertainty, with potential extension to dissipative virtual-battery models.

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.

Figures (3)

• Figure 1: Electricity flows in a data center with virtual batteries.
• Figure 2: Average energy cost with different $V$
• Figure 3: Variation of SoC under different settings of $V$

Theorems & Definitions (5)

• Remark 1
• Lemma 1
• Theorem 1
• Remark 2
• Lemma 2