Distributed Real-Time Energy Management in Data Center Microgrids

TL;DR

This paper proposes a real-time, distributed energy management algorithm for data center microgrids that minimizes long-term costs while handling uncertainties in prices, renewable outputs, and workloads, enhancing sustainability and reliability.

Contribution

It introduces a novel stochastic programming model and a Lyapunov-ADMM based algorithm for efficient, real-time energy management in data center microgrids, considering multiple operational factors.

Findings

Significant reduction in operational costs demonstrated through simulations

Effective handling of uncertainties in electricity prices and renewable outputs

Improved energy efficiency and reliability in data center microgrids

Abstract

Data center operators are typically faced with three significant problems when running their data centers, i.e., rising electricity bills, growing carbon footprints and unexpected power outages. To mitigate these issues, running data centers in microgrids is a good choice since microgrids can enhance the energy efficiency, sustainability and reliability of electrical services. Thus, in this paper, we investigate the problem of energy management for multiple data center microgrids. Specifically, we intend to minimize the long-term operational cost of data center microgrids by taking into account the uncertainties in electricity prices, renewable outputs and data center workloads. We first formulate a stochastic programming problem with the considerations of many factors, e.g., providing heterogeneous service delay guarantees for batch workloads, interactive workload allocation, batch workload shedding, electricity buying/selling, battery charging/discharging efficiency, and the ramping constraints of backup generators. Then, we design a realtime and distributed algorithm for the formulated problem based on Lyapunov optimization technique and a variant of alternating direction method of multipliers (ADMM). Moreover, the performance guarantees provided by the proposed algorithm are analyzed. Extensive simulation results indicate the effectiveness of the proposed algorithm in operational cost reduction for data center microgrids.