Data centers to offer ancillary services

Ghamkhari, Mahdi; Mohsenian‐Rad, Hamed

doi:10.1109/smartgridcomm.2012.6486023

Cited by 53 publications

(23 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, large consumers, such as data centers, are also eligible to register as load resources to offer ancillary services [5], [28]. In this paper, our focus is on a data center that offers spinning reserve [29].…”

Section: Offering Ancillary Servicesmentioning

confidence: 99%

“…It may also participate in wholesale electricity markets, including the day-ahead market (DAM) and real-time market (RTM) [3], [4]. Another option for data centers is to enroll in ancillary service (ANS) programs [5]- [7]. Data centers may also fully or partially operate by local renewable (REN) power generators such as wind turbines [8] and/or solar panels [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Portfolio Optimization of Data Centers

Ghamkhari

Wierman

Mohsenian‐Rad

2017

IEEE Trans. Smart Grid

Self Cite

View full text Add to dashboard Cite

Abstract-Data centers have diverse options to procure electricity. However, the current literature on exploiting these options is very fractured. Specifically, it is still not clear how utilizing one energy option may affect selecting other energy options. To address this open problem, we propose a unified energy portfolio optimization framework that takes into consideration a broad range of energy choices for data centers. Despite the complexity and nonlinearity of the original models, the proposed analysis boils down to solving tractable linear mixed-integer stochastic programs. Using experimental electricity market and Internet workload data, various insightful numerical observations are reported. It is shown that the key to link different energy options with different short-and long-term profit characteristics is to conduct risk management at different time horizons. Also, there is a direct relationship between data centers' servicelevel agreement parameters and their ability to exploit certain energy options. The use of on-site storage and the deployment of geographical workload distribution can particularly help data centers in utilizing high-risk energy choices, such as offering ancillary services or participating in wholesale electricity markets.

show abstract

Section: Offering Ancillary Servicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Portfolio Optimization of Data Centers

Ghamkhari

Wierman

Mohsenian‐Rad

2017

IEEE Trans. Smart Grid

Self Cite

View full text Add to dashboard Cite

show abstract

“…Aikema et al [2] presented simulation results for a datacenter participating in the New York ancillary service market. A selective approach is applied in [9], where participation as an ancillary services provider is determined by expected profits (the compensation is weighed against the SLA penalties).…”

Section: Related Work 21 Control and Energy-aware Datacentersmentioning

confidence: 99%

“…The most similar work to the control approach we present in this paper is by Ghamkhari and Mohsenian-Rad [9]. These authors propose a method to schedule non-critical jobs based on predicted power output and datacenter load, aiming to allow a solar-powered datacenter achieve a net-zero operational energy on a daily basis.…”

Section: Related Work 21 Control and Energy-aware Datacentersmentioning

confidence: 99%

Holistic multiobjective planning of datacenters powered by renewable energy

2015

View full text Add to dashboard Cite

Energy efficiency is a major concern to datacenter operators, because the large amounts of energy used by parallel computing infrastructures increases costs and affects the electricity grid. Datacenter power consumption can be reduced by applying intelligent control techniques to dynamically adjust power demand, but this is hampered by conflicting objectives. For instance, the workload can be controlled to adjust power, but at the expense of service quality. Or, the cooling infrastructure demand can be manipulated without affecting workloads, but at the risk of shifting the datacenter temperature outside the acceptable limits. This paper proposes a multiobjective, evolutionary approach to solving the problem of energy-aware task scheduling in datacenters. Our approach takes into account three problem objectives (power consumption, temperature, and quality of service) when both computing and cooling infrastructures are holistically controlled. We report the two best solutions to each of these problem objectives, as well as the selected trade-off solutions between them.

show abstract

“…For example, considering owner-operated data center, [21], [22] optimize data center resource management for ancillary services. [23] leverages both resource management techniques and ESD charging/discharging for data center demand response with the goal of shaving its peak power usage.…”

Section: Related Workmentioning

confidence: 99%

A Contract design approach for colocation data center demand response

Ahmed¹,

Islam²,

Ren³

2015

2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

View full text Add to dashboard Cite

Demand response programs maintain transmission stability in power grid through reducing electricity use during peak period, making grid more eff cient and robust. While numerous demand response programs are currently being deployed by utility companies, we focus on emergency demand response program, which is critical to ensure reliability during emergency situations. As a key participant in such program, we consider a critical type of data center: multi-tenant colocation data center (or colocation), where multiple tenants mange their own servers in shared space but typically lack incentives to reduce energy for demand response. To enable multi-tenant data center demand response, we propose a contract-based mechanism, called Contract-DR, which offers f nancial incentives to tenants to shed energy during emergency situations, reducing the usage of costineffective and environmentally-unfriendly diesel generation. We conduct theoretical analysis to prove the optimality of Contract-DR and also validate it through a trace-based study.

show abstract

Data centers to offer ancillary services

Cited by 53 publications

References 10 publications

Energy Portfolio Optimization of Data Centers

Energy Portfolio Optimization of Data Centers

Holistic multiobjective planning of datacenters powered by renewable energy

A Contract design approach for colocation data center demand response

Contact Info

Product

Resources

About