What is it about?
This paper presents a data-driven robust optimization framework for operating hydrogen-data center microgrids. It introduces statistical feasibility into the traditional robust optimization approach to reduce conservatism. Specifically, the authors propose the Statistical-Guarantee-based Vertex Link (SGVL) algorithm to build polyhedron uncertainty sets for forecasting errors—especially for the uncertain outside temperature that affects data center cooling. By using these polyhedron sets within a rolling robust optimization framework, the method improves both the economic performance (reducing operation cost by 0.29% to 0.64%) and computational efficiency (cutting solving time by 7%–13%) of the dispatch process while keeping the data center temperature within safe limits.
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Why is it important?
As data centers increasingly rely on renewable energy, ensuring efficient and reliable microgrid operations becomes critical. Uncertainty in external conditions—such as outside temperature—can lead to overly conservative dispatch strategies that hurt economic performance. This work is important because it relaxes these conservatism issues by integrating statistical feasibility into robust optimization. The proposed polyhedron uncertainty sets not only reduce computational complexity compared to traditional ellipsoid sets but also allow for controlled, minor violations that improve overall cost efficiency without compromising safety. This balance is vital for advancing sustainable and low-carbon operations in hydrogen-powered data centers.
Perspectives
From my perspective, this paper represents a significant stride in bridging theoretical robust optimization with real-world operational efficiency. I appreciate the innovative use of the SGVL algorithm to construct polyhedron uncertainty sets, which smartly trims down unnecessary conservatism and computational overhead. The approach allows operators to achieve better economic outcomes while still safeguarding critical temperature constraints in data centers. Such advancements are essential as we push towards more sustainable, renewable-powered infrastructures, and this work could pave the way for more agile and cost-effective energy management solutions in the data center industry.
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: Statistical Feasibility Robust Optimization With Polyhedron Uncertainty Set for Hydrogen-Data Center Microgrid Operations, IEEE Transactions on Automation Science and Engineering, January 2025, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tase.2024.3514101.
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