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A Python-Based Data-Center Backup-Power Case for Sustainability-Oriented Engineering Education
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DOI：10.3390/su18199823.png)
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Abstract
En 中文
Engineering education benefits from computational cases that connect system assumptions, computational analysis, and engineering judgment in authentic contexts. This study documents the design of a Python-based engineering education case that recasts a data-center backup-power comparison between vanadium redox flow batteries (VRFBs) and diesel generators (DGs) into a structured computational activity. Guided by backward alignment, the case combines sizing and life-cycle-cost equations, parameter sensitivity, and supply–demand power-ratio analysis under three dispatch modes. In the 4 h benchmark, the VRFB configuration reduced modeled life-cycle cost by USD 1.09–1.84 million across reliability tiers, while payback ranged from 10.3 to 11.5 years. In the separate dispatch-mode demonstration, supply–demand power ratios were 0.55–0.88 for constant-power operation, 0.92–1.00 for electrical-led operation, and 0.92–1.07 for rectangular electrical-led operation. The case is designed as an 8-contact-hour activity within a 48-contact-hour undergraduate engineering course, using a supplied Python template and fixed parameter set. By documenting an adaptable design framework, proposed implementation conditions, and methodological boundaries, this work may inform the development of sustainability-oriented engineering education cases in other energy, thermal-management, infrastructure, and process-system contexts.
Keywords:
engineering education for sustainable development
case-based learning
computational modeling
data-center backup power
life-cycle cost
techno-economic analysis
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3.3
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10.7W
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28.4W
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