Pith. sign in

REVIEW 1 cited by

Leveraging energy storage to optimize data center electricity cost in emerging power markets

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1606.01536 v1 pith:QDSXNLUK submitted 2016-06-05 cs.DC cs.SYeess.SYmath.OC

classification cs.DCcs.SYeess.SYmath.OC
keywords dataenergystoragecenterscenterdeviceselectricitymarkets
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Energy storage in data centers has mainly been used as devices to backup generators during power outages. Recently, there has been a growing interest in using energy storage devices to actively shape power consumption in data centers to reduce their skyrocketing electricity bills. In this paper, we consider using energy storage in data centers for two applications in a joint fashion: reducing peak demand charges and enabling data centers to participate in regulation markets. We develop an optimization framework that captures the cost of electricity, degradation of energy storage devices, as well as the benefit from regulation markets. Under this frame- work, using real data Microsoft data center traces and PJM regulation signals, we show the electricity bill of a data center can be reduced by up to 20%. Furthermore, we demonstrate that the saving from joint optimization can be even larger than the sum of individually optimizing each component. We quantify the particular aspects of data center load profiles that lead to this superlinear gain. Compared to prior works that consider using energy storage devices for each single application alone, our results suggest that energy storage in data centers can have much larger impacts than previously thought possible.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Ubiquitous Intelligence Via Wireless Network-Driven LLMs Evolution

    cs.NI 2025-09 conditional novelty 4.0 of 10

    A position paper that reframes edge AI as a co-evolution loop in which wireless networks feed real-world experiences to LLMs and LLMs optimize the network in return.

Pith tools