The era of digitization has taken full throttle, spanning from cloud computing to artificial intelligence and large-scale data analytics. This growing demand has increased the cooling energy consumption of data centers, the backbone of modern information structures, while the push for net-zero emissions pressures the sector to optimize energy use and adopt greener sources. This study explores the advantages of combining free cooling techniques with simple load management strategies for a potential data center in colder regions of Europe, specifically Ireland. Free cooling is managed through the optimization tool XEMS13, while load management distributes tasks across geographically dispersed data centers with contrasting climates, enabling partial load shifting from warmer locations. The results of the economic, energy, and environmental analysis evaluate the viability of the proposed cooling and load management approach.
Exploiting Geographic Load Shifting in Data Center with Optimal Management of Free Cooling / Lazzeroni, Paolo; Lorenti, Gianmarco; Mascitelli, Federico; Canova, Aldo; Repetto, Maurizio. - (2025). (Intervento presentato al convegno 18th International Workshop on Optimization and Inverse Problems in Electromagnetism tenutosi a Lodz (Poland) nel 8-12 Settembre 2025).
Exploiting Geographic Load Shifting in Data Center with Optimal Management of Free Cooling
Lazzeroni, Paolo;Lorenti Gianmarco;Canova, Aldo;Repetto, Maurizio
2025
Abstract
The era of digitization has taken full throttle, spanning from cloud computing to artificial intelligence and large-scale data analytics. This growing demand has increased the cooling energy consumption of data centers, the backbone of modern information structures, while the push for net-zero emissions pressures the sector to optimize energy use and adopt greener sources. This study explores the advantages of combining free cooling techniques with simple load management strategies for a potential data center in colder regions of Europe, specifically Ireland. Free cooling is managed through the optimization tool XEMS13, while load management distributes tasks across geographically dispersed data centers with contrasting climates, enabling partial load shifting from warmer locations. The results of the economic, energy, and environmental analysis evaluate the viability of the proposed cooling and load management approach.Pubblicazioni consigliate
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https://hdl.handle.net/11583/3003294
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