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

TL;DR

This paper introduces a distributed, privacy-preserving approach for optimizing the synergy between hierarchical data centers and power networks, addressing energy efficiency and operational costs while ensuring data privacy.

Contribution

It proposes a novel mixed integer non-linear programming formulation and a customized Lagrangian relaxation method for scalable, privacy-preserving co-dispatch in hierarchical data systems.

Findings

Effective in balancing costs and communication delays.

Demonstrates flexible resource allocation and peak load balancing.

Validated through numerical simulations showing scalability and optimality.

Abstract

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 $\ell_1-$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. Simulation results also indicate that computing tasks can be delayed and migrated within the hierarchical data centers, demonstrating the flexible resource allocation capabilities of the hierarchical data center architecture, further facilitating peak load balancing in the power network.