What question did this study set out to answer?

To create a risk-aware scheduling method that optimizes power-computing-network-storage systems in data centres while integrating renewable energy effectively.

June 20, 2026Open Access

A Risk-Aware Coordinated Optimisation Scheduling Method for Coupled Power-Computing-Network-Storage Systems in Remote Data Centres Based on Graph Attention, Green Affinity and CVaR

Key Points

To create a risk-aware scheduling method that optimizes power-computing-network-storage systems in data centres while integrating renewable energy effectively.
Constructed a hierarchical model for multi-type computing tasks, classifying them based on different constraints.
Developed a graph-attention model for mapping migration signals related to energy resources and grid conditions.
Introduced a CVaR metric for tail risk analysis, forming a stochastic linear optimisation model to enhance decision-making.
Reduced expected curtailment volume from 176.939 MWh to 0 MWh.
Lowered CVaR curtailment risk from 694.085 MWh to 0 MWh.
Increased green computing power by 9.283 percentage points, improving the collaborative score by 4.885 points.

Abstract

With the rapid expansion of artificial intelligence infrastructure and cloud computing services, data centres are evolving from rigid electricity loads into flexible resources capable of contributing to renewable energy integration, grid regulation and cross-regional computing power allocation. Addressing the shortcomings in existing research regarding the differences between various types of computing tasks, the mechanisms of green migration under network constraints, and the characterisation of curtailment risks for renewable energy, this paper proposes a risk-aware collaborative optimisation and scheduling method for a power–computing–network–storage coupled system across remote data centres. Firstly, a hierarchical model of multi-type computing tasks is constructed, classifying data centre loads into fixed real-time tasks, online inference tasks, long-duration AI training tasks, and opportunistic elastic tasks, to characterise the differences between these tasks in terms of latency, time-shift, migration, and completion volume constraints. Secondly, a graph-attention-inspired green affinity prior is proposed, mapping grid topological distance, renewable energy availability, data centre PUE, and energy storage regulation capacity into interpretable migration signals, thereby guiding flexible computing power to migrate towards nodes with abundant green electricity and favourable grid support conditions. Subsequently, we introduce the CVaR metric to quantify the tail risk of renewable energy curtailment, establishing a multi-scenario stochastic linear optimisation model that incorporates DC power flow, unit output, renewable energy utilisation, campus energy storage, task SLAs, and cross-node migration constraints. A 24 h simulation based on the IEEE 10-machine, 39-node system demonstrates that the proposed method can reduce the expected curtailment volume from 176.939 MWh to 0 MWh, lower the CVaR curtailment risk from 694.085 MWh to 0 MWh, and increase the proportion of green computing power by 9.283 percentage points compared to the fixed-load baseline, whilst improving the five-tier collaborative score by 4.885 points.

Read Full Paperexternally

AI에게 질문

Bookmark

View Full Paper