Real-time Energy Management of Large-scale Data Centers: A Model Predictive Control Approach

Contrastive research on peak regulation performance of different types of energy storage resources under multi-station integration scenario

“…The load model of data center, literature [8] notes that its power consumption mainly comes from the server, power regulation system and cooling system, while literature [12] will divide the data center load into IT equipment load and air conditioning equipment load, and the former as constant, set the latter as a function related to temperature. In conclusion, based on the above research, the data center load model was established.…”

Real-time power management in virtualized data centers using predictive analytics

“…For instance, Tan et al (2016) demonstrated the use of neural networks to forecast server power usage with high accuracy, while Zhang et al (2018) applied regression models to predict VM power consumption and optimize resource allocation. Yunyun Wu et al (2020) [2] developed a real-time energy management method for largescale data centers using Model Predictive Control (MPC), incorporating renewable energy and dynamic electricity prices, demonstrating its effectiveness in case studies [Wu et al, [2] . Chengjian Wen and Yifen Mu (2015) [3] presented a predictive control approach for managing power and performance in nonlinear virtualized computing systems, achieving a balance between power saving and performance requirements [Wen & Mu, 2015] [3] .…”

Assessment of Carbon-Aware Flexibility Measures From Data Centres Using Machine Learning

“…In recent years, considerable effort has focussed on reducing data centre energy costs, improving energy efficiency and/or reducing carbon emissions, but mostly from an individual data centre perspective [7], [8], [9], [10]. Many challenges still remain in unlocking data centre flexibility, with the most basic approaches relying upon on-site backup generation, uninterruptible power supply units (UPS), or battery energy storage systems.…”

“…Alternatively, flexibility can be obtained from delay-tolerant IT workloads [12], such that, for example, model predictive control can be applied to optimise data centre operation and increase the system-wide renewables share. A real-time energy management system has also been proposed [9], with the objective of minimising data centre energy costs, but the underlying model only considers limited energy performance details and external power system scheduling influences are not considered [13]. Data centre optimisation within a microgrid has also been investigated, while the potential of using data centres to provide a fast frequency response using UPS and flexible IT workloads has also been evaluated [11].…”