Synergising Hierarchical Data Centers and Power Networks: A Privacy-Preserving Approach

In the era of digitization, data centers have emerged as integral contributors sustaining our interlinked world, bearing responsibility for an increasing proportion of the world's energy consumption. To facilitate the their fast rollout while progressing towards net-zero energy systems, the syn...

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Vydáno v:IEEE transactions on smart grid Ročník 16; číslo 6; s. 5083 - 5098
Hlavní autoři: Liu, Junhong, Teng, Fei, Hou, Francis Yunhe
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.11.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Bi-linear terms
Cloud computing
Combinatorial analysis
Computer centers
Costs
Couplings
Data centers
Data models
distributed non-convex optimization
edge-fog-cloud computing
Energy consumption
Generators
Internet of Things
Lagrangian functions
Linear programming
Lyapunov methods
Lyapunov optimization
Mixed integer
Nonlinear programming
Optimization
Peak load
Privacy
Quadratic programming
Relaxation method (mathematics)
Resource allocation
Servers
Smoothness
Time-frequency analysis
ℓ₁–surrogate Lagrangian decomposition
ISSN:1949-3053, 1949-3061
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Shrnutí:In the era of digitization, data centers have emerged as integral contributors sustaining our interlinked world, bearing responsibility for an increasing proportion of the world's energy consumption. To facilitate the their fast rollout while progressing towards net-zero energy systems, the synergy of hierarchical data centers (cloud-fog-edge) and power networks can play a pivotal role. However, existing centralized co-dispatch manners encroach on the privacy of different agents within the integrated systems, meanwhile suffering from the combinatorial explosion. In this research, we propose a near-optimal distributed privacy-preserving approach to solve the non-convex synergy (day-ahead co-dispatch) problem. The synergy problem is formulated as a mixed integer quadratically constrained quadratic programming considering both communication and energy conservation, where Lyapunov optimization is introduced to balance operating costs and uncertain communication delays. To mitigate impacts of the highly non-convex nature, the normalized multi-parametric disaggregation technique is leveraged to reformulate the problem into a mixed integer non-linear programming. To further overcome non-smoothness of the reformulated problem, the customized <inline-formula> <tex-math notation="LaTeX">\ell _{1}- </tex-math></inline-formula>surrogate Lagrangian relaxation method with convergence guarantees is proposed to solve the problem in a distributed privacy-preserving manner. The effectiveness, optimality, and scalability of the proposed methodologies for the synergy problem are validated via numerical simulations. The results also indicate that computing tasks can be migrated within the hierarchical data centers, demonstrating the flexible resource allocation capabilities of the hierarchical architecture, further facilitating peak load balancing in the power network.
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2025.3603107