Distributed Real-Time Energy Management in Data Center Microgrids

Abstract

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.

Online Energy Management and Heterogeneous Task Scheduling for Smart Communities with Residential Cogeneration and Renewable Energy

“…where b max j is the battery capacity of residence j. The constraint Equations (4) and (5) means that the electricity discharged or charged from the battery has an upper bound in a practical model.…”

“…Li et al [4] proposed a real-time scheduling policy by applying modified Lyapunov optimization to separate and sequentially determine the joint load scheduling and storage control. Yu et al [5] proposed a real-time and distributed algorithm by applying the Lyapunov optimization method and an alternating direction method of multipliers to solve a stochastic programming problem with many practical constraints. Liu et al [6] proposed a parallel distributed optimization algorithm to minimize the cost of microgrids and the utility company by applying game theory.…”

Fog-Assisted Operational Cost Reduction for Cloud Data Centers

“…However, the objective function in P1 is not strictly convex and dual decomposition could not be used. Otherwise, the Lagrangian would be unbounded below [11]. In this paper, we intend to solve P3 based on the PJ-ADMM [15], which could be used to generate a distributed and parallel algorithm.…”

“…In [9], Xu et al investigated the problem of reducing the peak power demand and energy cost of data centers using partial execution. In [10] [11], Yu et al studied the problem of reducing the energy/operational cost for geo-distributed data centers in smart microgrids with the consideration of electricity selling/buying, energy storage, load balancing, renewable energies, and dynamic server provisioning or partial execution. In addition, the operational cost of cloud data centers could be offset partially by economical compensation obtained from the participation of demand response programs [12].…”

Optimal Real-Time Operation Strategy for Microgrid: An ADP-Based Stochastic Nonlinear Optimization Approach

“…The two stage stochastic optimal energy and reserve management is proposed in [30], and the slidingwindow based online algorithm is proposed for real-time energy management in [31]. In [32], [33], distributed energy management algorithms, e.g., alternating direction method of multipliers, are applied in the online optimization of microgrid.…”