A Privacy-Preserving Multi-Time-Scale Tie-Line Power Smoothing Method for Multiple Data Centers.
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| Title: | A Privacy-Preserving Multi-Time-Scale Tie-Line Power Smoothing Method for Multiple Data Centers. |
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| Authors: | Luo, Quanyong1 (AUTHOR), Yu, Jiexiao1,2 (AUTHOR) yjx@tju.edu.cn, Feng, Xiangwei1,2 (AUTHOR) |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 11, p2708. 26p. |
| Subject Terms: | *Data centers, *Federated learning, *Reinforcement learning, *Data security, *Renewable energy sources |
| Abstract: | As renewable penetration in data-center power supply increases, stochastic renewable output can cause tie-line power fluctuations between data centers (DCs) and the utility grid. This paper proposes a privacy-preserving multi-time-scale tie-line power smoothing method for multiple DCs. A two-stage first-order low-pass filter decomposes tie-line fluctuations into high- and low-frequency regulation targets. Server task shifting tracks the high-frequency target, while uninterruptible power supply (UPS) regulation compensates the low-frequency residual under practical energy and power constraints. Second, a federated adaptive proximal policy optimization (Fed-AdaPPO) framework is developed. Proximal policy optimization (PPO) provides stable policy optimization in the continuous action space, and the upper confidence bound (UCB)-guided adaptive exploration improves task-shifting exploration. Critically, only Critic gradients are aggregated across DCs; Actor networks, raw workload data, and user-sensitive information remain local. This design reduces the risk of exposing local state-action mappings. Results show that coordinated server-cluster and UPS regulation reduces the standard deviation of tie-line power by at least 33.4% while maintaining service quality and data privacy. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194588096 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Privacy-Preserving Multi-Time-Scale Tie-Line Power Smoothing Method for Multiple Data Centers. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Luo%2C+Quanyong%22">Luo, Quanyong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Jiexiao%22">Yu, Jiexiao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> yjx@tju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Feng%2C+Xiangwei%22">Feng, Xiangwei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2708. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Data+centers%22">Data centers</searchLink><br />*<searchLink fieldCode="DE" term="%22Federated+learning%22">Federated learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+security%22">Data security</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As renewable penetration in data-center power supply increases, stochastic renewable output can cause tie-line power fluctuations between data centers (DCs) and the utility grid. This paper proposes a privacy-preserving multi-time-scale tie-line power smoothing method for multiple DCs. A two-stage first-order low-pass filter decomposes tie-line fluctuations into high- and low-frequency regulation targets. Server task shifting tracks the high-frequency target, while uninterruptible power supply (UPS) regulation compensates the low-frequency residual under practical energy and power constraints. Second, a federated adaptive proximal policy optimization (Fed-AdaPPO) framework is developed. Proximal policy optimization (PPO) provides stable policy optimization in the continuous action space, and the upper confidence bound (UCB)-guided adaptive exploration improves task-shifting exploration. Critically, only Critic gradients are aggregated across DCs; Actor networks, raw workload data, and user-sensitive information remain local. This design reduces the risk of exposing local state-action mappings. Results show that coordinated server-cluster and UPS regulation reduces the standard deviation of tie-line power by at least 33.4% while maintaining service quality and data privacy. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194588096 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19112708 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 2708 Subjects: – SubjectFull: Data centers Type: general – SubjectFull: Federated learning Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Data security Type: general – SubjectFull: Renewable energy sources Type: general Titles: – TitleFull: A Privacy-Preserving Multi-Time-Scale Tie-Line Power Smoothing Method for Multiple Data Centers. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Luo, Quanyong – PersonEntity: Name: NameFull: Yu, Jiexiao – PersonEntity: Name: NameFull: Feng, Xiangwei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 11 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
