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Datacenters as Controllable Load Resources in the Electricity Market
Conference proceeding

Datacenters as Controllable Load Resources in the Electricity Market

Rui Wang, Nagarajan Kandasamy, Chika Nwankpa, David R. Kaeli and IEEE
2013 IEEE 33RD INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS)
01 Jan 2013

Abstract

Computer Science Computer Science, Hardware & Architecture Engineering Engineering, Electrical & Electronic Science & Technology Technology
Datacenters, being major consumers of power, can play an important role in the efficient operation of electrical grids. This paper develops an optimization framework to allow datacenters to operate as controllable load resources within the demand dispatch regime, a demand response (DR) program in which incentives are designed to induce lower electricity use not just during times of high prices but also when the reliability of the local grid is jeopardized or when the electricity supply and demand are unbalanced. Assuming the availability of geographically distributed and virtualized datacenters situated in multiple regional electrical markets, the basic idea is to migrate the workload in the form of virtual machines (VMs) between these centers to maximize the expected payoff. The proposed framework addresses issues specific to the demand dispatch of datacenters such as timeliness of VM migrations and the impact of geographic distance on migration times. It also explicitly incorporates risks that may cause the load curtailment operation to be ultimately unsuccessful and result in monetary losses to datacenter operators; specifically, variability in network bandwidth that can cause uncertainty in VM migration times as well as the uncertain payoff when participating in DR markets. A set of case studies involving datacenters participating in an economic DR program is used to validate the framework.

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28 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#9 Industry, Innovation and Infrastructure

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
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