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Interval optimization coupled with statistical feasibility robust optimization for hydrogen data center microgrid scheduling
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DOI：10.1016/j.ejor.2026.08.043.png)
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Abstract
En 中文
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SFRIO integrates statistical feasibility and interval optimization for H2-DCMGs.
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SGET uncertainty sets reduce KKT-induced bilinear constraints.
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Mean RE curtailment is reduced by 79.17%–87.19% against benchmarks.
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Average solution time drops from over 30,000 s to several seconds.
Abstract
The growing power demand of data centers poses new challenges for its energy-efficient and robust operation. This paper investigates the scheduling of hydrogen-enabled data center microgrids with integrated renewable energy (RE) and hydrogen energy storage systems, under uncertainties in both renewable output and the equivalent meteorological index. To address these challenges, we propose a statistical feasibility-based robust interval optimization (SFRIO) framework that jointly minimizes operational cost and RE curtailment. The framework relaxes over-conservative constraints in traditional robust optimization by introducing statistical feasibility, and employs interval optimization with Karush-Kuhn–Tucker (KKT) reformulation. To reduce the computational burden associated with the dual variables in the KKT conditions while guaranteeing statistical feasibility, we further develop a statistical guarantee-based externally tangent (SGET) algorithm that constructs uncertainty sets involving fewer dual-related constraints. Numerical experiments validate that the proposed SFRIO framework, combined with the SGET algorithm, achieves improved solution quality and significantly reduced computational complexity. Simulation results show that the proposed method reduces the mean RE curtailment by 79.17%–87.19% compared with SO, RO, and SFRO, and reduces the average computational time from over 3×104 seconds to several seconds.
Keywords:
OR in energy
Energy management
Data center microgrid
Statistical feasibility
Robust interval optimization
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2.2W
Citations:
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