What is it about?
This study proposes a new scheduling strategy for hydrogen energy data centers (HEDCs)—a next-generation clean energy infrastructure combining data computing and hydrogen storage. These centers can flexibly adjust their electricity use and thus help balance renewable energy in the power grid. However, their operation faces complex uncertainties such as fluctuating electricity prices and variable renewable energy supply. To tackle this, the authors develop an Efficiency-Preferred Non-Convex Set Interval Optimization (NSIO) approach. This model precisely handles uncertain and nonlinear factors in data center operation while maintaining low computational complexity. By applying NSIO to a modified IEEE 24-bus system, the study shows that the proposed method can reduce electricity costs and improve renewable energy utilization by 2.3%–20% compared to existing uncertain optimization methods.
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Why is it important?
Hydrogen energy data centers are emerging as a key enabler for the low-carbon digital economy. They not only consume large amounts of electricity but can also act as flexible energy storage units. Yet, most previous optimization methods for uncertain systems were either too conservative (robust optimization) or too computationally intensive (stochastic optimization). The NSIO framework fills this gap by balancing efficiency and stability—it achieves globally optimal solutions under non-convex uncertainty and explicitly models hydrogen–electricity interactions. This research provides a practical optimization tool that helps both power system operators and data center managers achieve win–win outcomes: reduced operational costs and higher renewable energy absorption.
Perspectives
The proposed NSIO framework marks a methodological breakthrough for uncertain optimization in complex energy–compute systems. Its mathematical explicitness and high efficiency make it suitable for real-world applications involving large-scale, distributed, or uncertain environments. In future work, this model can be extended to multi-regional energy–compute clusters, carbon-aware data center networks, and AI-driven predictive control systems. The ultimate vision is to build intelligent, low-carbon energy infrastructures that integrate computing, hydrogen storage, and renewable generation for a sustainable digital future.
Professor/Clarivate Highly Cited Researcher/Associate Editor of IEEE TSG/TII/TSTE Yang Li
Northeast Electric Power University
Read the Original
This page is a summary of: Flexible Load Scheduling of Hydrogen Energy Data Centers: An Efficiency-Preferred Non-Convex Uncertain Optimization Approach, IEEE Transactions on Power Systems, January 2025, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tpwrs.2025.3600683.
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