International Journal on Science and Technology

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Green Data Center: Reduced Energy Consumption During Peak Load

Author(s) Mr. Ayush Nautiyal
Country India
Abstract Earlier, Data Centre sustainability has defined as an efficiency problem that can be resolved by building better hardware etc., improving airflow, increasing reliance on renewable energy, and lower the facility overheads. This definition is now changed; the current wave of AI-centric growth is creating a peak load problem more than the annual energy problem. In 2024, data-centres consumed around 415 TWh of electricity which is approximately 1.5% of the global demand, and it is expected to increase to 945 TWh by 2030. [1] As per the estimates by International Energy Agency (IEA), 60% of electricity is consumed by hardware infrastructure in modern data centre, while cooling consumption ranged from 7% in efficient hyperscale data-centre to more than 30% in traditional data-centre. [1]
Peak load plays very significant role because data-centre are designed to handle the highest demand intervals. Requirements to serve the peak load is considered for the requirement to grid connection, transformer, UPS systems, cooling systems etc. even if the average load is lower.
This paper formulates technical framework for reduction of energy consumption during the peak load in green data centre. It assesses dynamic voltage and frequency scaling (DVFS), virtualization, cooling, workload scheduling, demand response, power capping, thermal storage, renewable energy, grid-interactive UPS and high-density thermal architecture. Primary finding suggests that the peak load reduction needs to be referred as portfolio problem: the maximum benefit arise when adaptable IT controls are integrated with facility side thermal optimization and supply side flexibility. The case study referred in the paper support this finding. Google has reported the deployment of demand response for ML workload; AWS reported that mechanical energy consumption reduced by 46% during peak cooling by new designs; Microsoft reported system level avoidance of potential CO2 through grid interactive UPS programme in Ireland; OVHcloud reported reduction of cooling electricity by 50% and water use by 30% through new AI-enabled cooling architecture. (Google, 2025; AWS, 2025; Microsoft, 2022; OVHcloud, 2025). [2]
The contribution of this paper is to set of engineering models for peak shaving, demand-response potential, and emission effects. The paper priorities primary, official, and recent peer reviewed sources. The key conclusion of this paper is that peak load reduction need to be designed across all integrated layers of data centre – IT, facility, and supply layers.
Keywords Keywords: Green data centre, peak load reduction, power usage effectiveness, demand response, DVFS, AI cooling optimization, Peak Shaving
Field Computer > Data / Information
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-05-08
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