Data Center Control Strategy for Participation in Demand Response Programs

Cupelli, Lisette; Schütz, Thomas; Jahangiri, Pooyan; Fuchs, Marcus; Monti, Antonello; Müller, Dirk

doi:10.1109/tii.2018.2806889

Cited by 70 publications

(30 citation statements)

References 23 publications

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“…erefore, they proposed policies to deliver datacenter demand response for peak shaving, regulation services, and frequency control programs. Cupelli et al [28] combined the datacenter heating, ventilation, and air-conditioning equipment and delayed IT load and battery storage systems to coadjust the demand response projects.…”

Section: Combining Multiple Methods To Participate In Demandmentioning

confidence: 99%

Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions

Wang

Luo

2020

Mathematical Problems in Engineering

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Modern smart grids have proposed a series of demand response (DR) programs and encourage users to participate in them with the purpose of maintaining reliability and efficiency so as to respond to the sustainable development of demand-side management. As a large load of the smart grid, a datacenter could be regarded as a potential demand response participant. Encouraging datacenters to participate in demand response programs can help the grid to achieve better load balancing effect, while the datacenter can also reduce its own power consumption so as to save electricity costs. In this paper, we designed a demand response participation strategy based on two-stage decisions to reduce the total cost of the datacenter while considering the DR requirements of the grid. The first stage determines whether to participate in demand response by predicting real-time electricity prices of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by reverse auction. Then, the datacenter selects the tasks of the winning users to postpone processing them with awards. Experimental results show that the proposed strategy could help the datacenter to reduce its cost and effectively meet the demand response requirements of the smart grid at the same time.

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Section: Combining Multiple Methods To Participate In Demandmentioning

confidence: 99%

Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions

Wang

Luo

2020

Mathematical Problems in Engineering

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“…Recently, Cioara et al (2018) conducted a simulation-based experiment combining workload shifting, thermal storage facilities, and battery storage within a data center to provide power demand flexibility to demand response markets. Cupelli et al (2018) used a model predictive control approach, integrating the thermal characteristics of a specific data center testbed to simulate data center optimization as a response to dynamic prices and simulated workload profiling requests using thermal buffering and workload shifting. Also, Arnone et al (2017) performed a simulation based on a real data center in order to show demand response participation options.…”

Section: Demand Response With Data Centersmentioning

confidence: 99%

“…Compared to the benefit this is still reasonably efficient. Recently, Cupelli et al (2018) developed the flexible optimizer for data center operations (FLODO) framework. To evaluate the performance of their framework in a price-based demand response scenario, they also considered the EPEX Day Ahead market in Germany and found that the participation in this market reduces the electricity costs of the considered testbed data center by 3.86%.…”

Section: Epex Day Ahead Marketmentioning

confidence: 99%

Sim2Win: How simulation can help data centers to benefit from controlling their power profile

2020

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To support the grid and integrate renewables, demand response schemes reward the power flexibility of energy consumers. Data centers can profit from this by using power management techniques on all levels of data center architecture: infrastructure, hardware, workload, applications. Even though lately, demand response with data centers has been well researched, most works focus on just one or two techniques and one or two valorization options. This leaves data centers stranded that are not represented by the specific combinations of assumptions and techniques presented in research, and thus a huge potential remains barely touched. To address this challenge, the goal of the presented work is to provide data centers with a framework that can be flexibly instantiated by each data center to assess its individual demand response potential. To achieve this goal, this work presents Sim2Win, a data center simulation framework that can replay any set of different power

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“…Li et al [11] proposed a mixed integer programming (MIP) solution for DC electric demand management that combines both the impacts of locational marginal prices and the power management capability of the DC. Cupelli et al [12] presented a framework for the optimal operation of the DC that leverages their heating, ventilation, and air conditioning unit, delay‐tolerant information technology workload and battery storage system for participating in DR programs. Tran et al [13] proposed a two‐stage Stackelberg game model for geo‐distributed DCs to provide the DR.…”

Section: Introductionmentioning

confidence: 99%

Risk‐based stochastic day‐ahead operation for data centre virtual power plants

Zhang

et al. 2019

IET Renewable Power Generation

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Data centres (DCs) have rapidly expanded in recent years due to increasing demand for cloud services. The costs incurred by electricity and water consumption contribute to a major portion of DC operating costs. This article addresses the day-ahead operation of a DC in the context of a virtual power plant (VPP) under the grid-connected mode. The day-ahead operation problem is formulated as a two-stage stochastic mixed integer linear programming (MILP) model that considers workload schedules among server clusters, water consumption, and uncertainties of onsite renewable energy and electricity prices. In addition, the conditional value at risk (CVaR) is utilised to manage the risk caused by various uncertainties that challenge DCs, especially electricity prices. The impacts of the risk level, the water price, and uncertainty on the day-ahead operation of the DC are studied. The numerical test results show that the proposed model can efficiently generate optimal dayahead schedules.

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Data Center Control Strategy for Participation in Demand Response Programs

Cited by 70 publications

References 23 publications

Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions

Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions

Sim2Win: How simulation can help data centers to benefit from controlling their power profile

Risk‐based stochastic day‐ahead operation for data centre virtual power plants

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