Internet Data Centers Research Articles

Mitigating power grid impact from proactive data center workload shifts: A coordinated scheduling strategy integrating synergistic traffic - data - power networks

The widespread adoption of cloud computing has markedly escalated the demand for Internet services, rendering Internet Data Centers (IDCs) a critical component in the consideration for demand response (DR) initiatives and grid energy management. This work proposes a flexible scheduling strategy integrating data, traffic, and power networks (DN-TN-PN) to mitigate the impact of proactive IDC workload transfers on power system balance. The spatial-temporal transfer characteristics of EV charging and discharging loads are utilized to track the proactive spatial-temporal transfer of IDC workloads. Considering the influence of weather factors and traffic congestion on EV travel time, an incentive pricing model for EV users is introduced to alleviate bus load fluctuation and line congestion in the distribution network. Numerical simulations demonstrate that the proposed EV pricing strategy, which accounts for the coupled DN-TN-PN, reduces IDC-induced load fluctuations by over 20 % and decreases transmission line occupancy by approximately 15 %, opening up more opportunities for the implementation of DR programs.

Potential assessment of coordinated regulation of power load of emerging industrial users based on extreme scenarios of electric vehicle aggregators

AbstractWith the advancement of industrial low carbonization and electrification, emerging industrial production technologies have the characteristics of high‐power consumption and significant impact load. In the context of increasing global climate change, frequent extreme weather events have brought serious challenges to the balance of power and electricity in the power system, and have a significant impact on the production scheduling of industrial users, especially industrial users using electrified production technology. First, based on the representative high‐power industrial users of hydrogen reduction steel plants and internet datacentres (IDC), this paper establishes a flexible resource scheduling optimization model for single industrial users, and considers the collaborative relationship between multi‐user flexible resources to establish a collaborative scheduling optimization model for industrial users. Then, considering the charging and discharging characteristics of electric vehicles (EV) in extreme scenarios, the redundant capacity of EVs is aggregated by EV aggregators and sold to industrial users, and a collaborative scheduling optimization model of EV aggregators and industrial users is established. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. Compared with the traditional industrial user production optimization, the proposed model can tap the potential of multi‐agent scheduling operation in extreme scenarios.

A Comprehensive Quantitative Lifecycle Cost and Environmental Impact Analysis Model for Computing Infrastructure

This paper presents a comprehensive quantitative model for quantitative assessment of the lifecycle costs and environmental impacts of computing infrastructure, with a focus on internet data centers (IDCs) and high-performance computing (HPC) facilities. The key innovation lies in the integration of interdisciplinary cost evaluation and carbon emission methods for the establishment of this quantitative model. This framework, which outlines key cost components and carbon emission factors, enables the calculation of total costs, electricity expenses, and greenhouse gas emissions throughout the lifecycle of infrastructure. With IDCs as a case study, the research clarifies the intricate cost structure associated with equipment procurement, energy usage, land acquisition, and operational expenses. This paper provides an in-depth understanding of the cost structure and environmental impact of computing infrastructure in support of sustainable decision-making in its development.•Based on established cost estimation methods, such as Lifecycle Cost Analysis (LCCA) and the Analogous Estimating Method, this study examines costs across construction and operation phases.•The Emission Factor Method is used to quantify environmental impact, emphasizing the significance of regional energy mix and power usage effectiveness (PUE).

Research on cooperative operation optimization method of internet data center alliance considering diversified flexibility

With the rapid development of large-scale internet data center in China, the goal of carbon peak and carbon neutrality has put forward higher requirements for low-carbon economic operation of internet data center. Considering complementary advantages among internet data centers, this paper proposes a cooperative operation optimization method of internet data center alliance based on diversified flexibility. Firstly, a cooperative operation structure of alliance considering electricity and carbon trading between members was built, as well as the diversified flexibility model considering carbon capture and power to gas device. Then, a cooperative optimization operation model of alliance was constructed based on Nash bargaining theory, and the allocation of cooperation benefit among members can be realized based on electrical carbon contribution rate. In order to improve solution convergence, the alternating direction multiplier method is improved by penalty factor updating and correction factor modeling method. Finally, the proposed method is applied to the demonstration project of “East Data and West Computing” in Qingyang, Gansu, China, and the trial run results are consistent with the theory analysis. On the one hand, the diversified flexibility and the electricity and carbon trading between members can effectively reduce the total cost and carbon emissions of alliance. On the other hand, the cooperation benefits distribution strategy based on the electrical carbon contribution rate can effectively enhance enthusiasm of members to participate in alliance. The improved alternating direction multiplier method can achieve distributed optimization of alliance, protect the privacy of each member, and has good convergence and scalability.

Experimental and Numerical Investigation of Airflow Organization in Modular Data Centres Utilizing Floor Grid Air Supply

Under the background of “dual carbon” development goals, the rapid expansion of internet data centers driven by advancements in 5G technology has led to increased energy consumption and elevated heat densities within the server rooms in these facilities. In this study, the modular data center is taken as the research object for the purpose of figuring out a way to improve the thermal environment of the computer room, reduce power consumption, and ensure the safe and stable running of servers. To this end, this study established an airflow organization model for the modular data center and verified this model through experimental methods. Computational Fluid Dynamics (CFD) simulations were employed to investigate the effects of raised floor height, floor opening rate, and cold/hot air channel closure on airflow organization. Furthermore, the efficiency of airflow organization was evaluated using entransy loss metrics. The results show that optimal airflow conditions are achieved when the height of the raised floor is 600–800 mm, the opening rate is 40%, and the combined opening is 40%. Additionally, the closure of either the cold channel or both the cold and hot channels significantly improves airflow performance. Specifically, cold channel closure is recommended for new data centers with underfloor air supply systems, while combined cold and hot channel closure is suitable for data centers with high power density and extended air supply distances.

A multi‐objective interval optimization approach to expansion planning of active distribution system with distributed internet data centers and renewable energy resources

AbstractWith the development of the digital economy, the power demand for data centers (DCs) is rising rapidly, which presents a challenge to the economic and low‐carbon operation of the future distribution system. To this end, this paper fully considers the multiple flexibility of DC and its impact on the active distribution network, and establishes a collaborative planning model of DC and active distribution network. Differing from most existing studies that apply robust optimization or stochastic optimization for uncertainty characterization, this study employs a novel interval optimization approach to capture the inherent uncertainties within the system (including the renewable energy source (RES) generation, electricity price, electrical loads, emissions factor and workloads). Subsequently, the planning model is reformulated as the interval multi‐objective optimization problem (IMOP) to minimize economic cost and carbon emission. On this basis, instead of using a conventional deterministic‐conversion approach, an interval multi‐objective optimization evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the proposed IMOP, which is able to fully preserve the uncertainty inherent in interval‐typed information and allow to obtain an interval‐formed Pareto front for risk‐averse decision‐making. Finally, an IEEE 33‐node active distribution network is utilized for simulation and analysis to confirm the efficacy of the proposed approach.

Data-Driven Flexibility Capability Modeling of Internet Data Center Considering Task Dependency

Data-Driven Flexibility Capability Modeling of Internet Data Center Considering Task Dependency

Scheduling IDC-based virtual power plants considering backup power

The virtual power plant (VPP) can aggregate massive distributed resources to participate in power system operation. Different types of resources have been investigated to construct VPPs. Internet data centers (IDCs) are regarded as flexible power consumers that can potentially cooperate with power systems. This paper exhibits the insight for constructing VPPs based on IDCs while comprehensively considering the operational flexibility of the workloads and power supply devices. On the workloads, this paper models the temporal and spatial flexibility of workloads in detail. On the power supply devices, this paper explores their spare capacity with respect to the power supply reliability requirement. Then, a day-ahead scheduling method of the IDC-based VPPs is established based on the operational flexibility modeling. Case studies validate the proposed approach. Moreover, because of the coupling of workload dispatch and power supply device operation, the synergy effect does not equal the sum of its parts.

Energy-aware coordinated operation strategy of geographically distributed data centers

As digital technology continues to advance rapidly, the substantial operational electricity costs associated with the extensive deployment of globally distributed internet data centers have become a significant concern. Addressing this challenging issue is crucial for the future expansion of internet data centers. In this paper, a coordinated strategy is proposed that leverages the spatial–temporal flexibility of internet data centers and their interaction with the power system. Firstly, an energy-aware internet data center loading model is introduced that incorporates multiple coupled scheduling methods to maximize the utilization of internet data centers' flexibility potential. Secondly, the proposed internet data centers loading model is integrated into a coordinated optimization model aimed at achieving higher profits through cost reduction in response to varying electricity prices across multiple regions. To facilitate the coordinated operation of geo-graphically internet data centers, an incentive profit-sharing mechanism based on Nash Bargaining theory is designed to fairly distribute coordination profits based on the contribution ratio of each data center in coordination. Through comprehensive case studies utilizing real-world datasets, the results demonstrate that the internet data center loading model, considering multiple coupled scheduling methods, can enhance internet data centers’ flexibility potential, resulting in a significant 11.73% reduction in total cost, with the coupling relationship accounting for 5.05% of this reduction. Furthermore, the proposed profit-sharing mechanism effectively fosters coordination and enthusiasm among geographically distributed internet data centers.

Multi-source self-supervised domain adaptation network for VRLA battery anomaly detection of data center under non-ideal conditions

As the key component of uninterruptible power supply, the anomaly detection of valve-regulated lead-acid (VRLA) batteries is the reliable guarantee for the operation of internet data centers. Although deep neural networks (DNNs) have been investigated in battery anomaly detection, the key problem of feature learning under non-ideal conditions, i.e., data scarcity and class-imbalance, is not addressed very well. In this article, a novel DNN called multi-source self-supervised domain adaptation network (MSSSDAN) is proposed for VRLA battery anomaly detection of data center under non-ideal conditions. Firstly, multi-dimensional collaborative self-supervised learning (MDCSSL) is proposed to address the class-imbalanced problem by performing auxiliary feature learning task considering the temporal dependency of different channels. Secondly, a novel transfer learning (TL) framework, multi-source adaptive transfer learning (MSATL) is developed to solve the domain shift problem by adaptively minimizing the distribution discrepancy of different domains. Finally, the outperformance of MSSSDAN is verified on the VRLA battery dataset collected from Tencent data center. The testing results demonstrate that MSSSDAN can effectively solve the problem of battery anomaly detection under non-ideal conditions.

Combined Heat and Power Dispatch Against Cold Waves Utilizing Responsive Internet Data Centers

Combined Heat and Power Dispatch Against Cold Waves Utilizing Responsive Internet Data Centers

Dual-drive coordinated operation strategy for internet data centers by exploiting spatiotemporal flexibility

With the rapid expansion of computational infrastructure, the electricity consumption of geographically dispersed Internet Data Centers (IDCs) has imposed a substantial influence on the power grid. Geographically distributed IDCs, which present a versatile electricity demand with significant operational flexibility, hold potential for mitigating cost issues and balancing regional supply and load demand. To exploit this flexibility, this paper has developed a dual-drive coordinated operational strategy, which integrates locational electricity prices and unit carbon emission costs based on the dynamic carbon emission intensity (DCEI) of various regions as interaction signals to improve coordination effectiveness. The upper-level determines the consumption behaviour of IDCs, the lower-level provides feedback on dual-drive signals factoring in IDCs' involvement in grid operation, whilst an iterative algorithm is suggested to solve the bi-level model. Case studies based on real-world datasets demonstrate that the proposed operation strategy can achieve arbitrage through multi-regional carbon-electricity markets, significantly reduce operating costs, and support the rapid development of IDCs. Additionally, this strategy facilitates optimal matching of power load in IDCs with renewable energy sources, thereby mitigating imbalances in regional renewable energy supply and demand.

Energy efficiency optimization analysis for a cooling composite air conditioning system for internet data centers

In this study, a validated mathematical model for a composite air conditioning system was established based on onsite test data. The system consists of a refrigerant pump driven two-phase cooling loop (RPTCL) and vapor compression refrigeration (VCR) cycles. The system can be switched to refrigerant pump (RP) mode, compressor mode, or hybrid mode, according to the outdoor temperature. By analyzing, the inadequacy of the existing Internet Data center (IDC) air conditioning system operation strategy was confirmed, and subsequently, five optimization strategies were proposed. The optimization strategies took two identical composite air conditioning systems as optimization unit, and under the premise of meeting the demand for cooling capacity, maximizing the operating time of RP and hybrid mode, which with higher energy efficiency, through complementary cooling capacity, to reduce the energy consumption. Meanwhile, when the cooling supply was higher than the demand, paired with optimization unit’s ON/OFF ratio control, the unit’s energy consumption was further reduced. The most optimization strategy provided 525.6 MWh of cooling energy to meet demand while consuming a minimum of 182.5 MWh. The optimization strategy also achieved a minimum of 12.1% and a maximum of 52.5% energy savings for IDCs in other climate zones of China.

Joint optimization of operational cost and carbon emission in multiple data center micro-grids

As the internet data centers are mushrooming, the energy consumption and carbon emission of data centers are increasing rapidly. To cut down the electricity cost and carbon emission of the data centers, we proposed an optimization method to reduce the electricity cost and carbon emission in geo-distributed multiple data centers. In the proposed method, the carbon tax is considered in the overall operation cost to optimize the carbon emission. The spatial and temporal flexibility of computational workload is fully utilized by considering the difference in renewable energy power output, local electricity and carbon emission of multiple geo-distributed data centers to achieve a better performance. Furthermore, the nonlinear characteristics of the power loss of uninterruptible power supply (UPS) are considered in the optimization. To verify the proposed optimization method, simulation of six cases is carried out with realistic data, and results have proved the proposed method can reduce the operational costs by 4.93%–12.7% and decrease carbon emissions by up to 10%.

Optimal operation of Internet Data Center with PV and energy storage type of UPS clusters

With the development of green data centers, a large number of Uninterruptible Power Supply (UPS) resources in Internet Data Center (IDC) are becoming idle assets owing to their low utilization rate. The revitalization of these idle UPS resources is an urgent problem that must be addressed. Based on the energy storage type of the UPS (EUPS) and using renewable sources, a solution for IDCs is proposed in this study. Subsequently, an EUPS cluster classification method based on the concept of shared mechanism niche (CSMN) was proposed to effectively solve the EUPS control problem. Accordingly, the classified EUPS aggregation unit was used to determine the optimal operation of the IDC. An IDC cost minimization optimization model was established, and the Quantum Particle Swarm Optimization (QPSO) algorithm was adopted. Finally, the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.

Distributionally robust planning for data center park considering operational economy and reliability

Digitization has dramatically facilitated the development of internet data centers (IDCs), which have become necessary demand response resources for distribution networks. To improve equipment utilization, integrated coordination of IDCs, photovoltaics, energy storage systems, and flexible substations is essential. This also ensures power supply reliability and the operational economy of IDC park. This paper proposes a distributionally robust planning method for an IDC park considering operational economy and reliability. First, a coordinated planning model of an IDC park is proposed with multiple resources, in which the workload dispatch of the IDC is incorporated under fault conditions. The reliability index of the IDC park is designed to quantify its reliability cost. Then, considering the fluctuation of distributed generators and workload, a distributionally robust planning model is established. A planning scheme for the IDC park under the worst scenario probability distribution is obtained. Furthermore, the influence of uncertainties on the planning scheme is analyzed. Finally, a practical data center park is used for case studies. The established planning scheme effectively balances the operational economy, reliability, and planning conservatism.

Supply Restoration of Data Centers in Flexible Distribution Networks With Spatial-Temporal Regulation

The reliable operation of Internet data centers (IDCs) is crucial to digitization transformation of society. However, the extreme fault on the IDC-located distribution network is fatal to IDC operation. The evolution of flexible distribution networks (FDNs) can satisfy the deep de-pendence of IDC on high-quality and reliable power supply. In FDNs, AC/DC feeders with multiple voltage levels can be flexibly interconnected and adjusted by flexible stations (FSs). It is promising to flexibly enhance operation per-formance of crucial IDCs after fault isolation in FDNs. In this paper, a coordinated supply restoration method of IDCs in FDNs with spatial-temporal regulation is proposed to minimize IDC data-dropping loss under complex faults. First, a spatial-temporal transfer strategy of IDC work-loads is precisely formulated and an FS-based network reconfiguration strategy is proposed. Then, a mixed-integer second-order conic programming model is established for efficient solving. Spatial-temporal coordination among the source, network, storage, and demand sides can be realized without violating physical feasibility. Finally, the proposed method is validated on the modified IEEE 33-node and modified IEEE 123-node test cases. Case studies show that total outage loss is effectively reduced with sufficient utili-zation of available power. Operational performance of FDN is also enhanced under extreme faults.

Research on collaborative scheduling of internet data center and regional integrated energy system based on electricity-heat-water coupling

Aiming at the carbon peaking and carbon neutrality goals, the water resource and energy management of internet data center involves many aspects, including the interaction among internet data center, power grid, water network and heat consumer. As an important demand response resource for the power grid, the interaction between both can improve the flexibility of the power grid, and ensure the power supply reliability and operation economy of internet data center. Existing references assume that regional integrated energy system can obtain all data information and scheduling permissions of internet data center, ignore the autonomous decision-making ability of both, resulting in its application has some constraints. The operation pattern switchover of internet data center and data allocation among multiple internet data center s are ignored, it is not possible to fully play the flexibility characteristics of internet data center. The study of interaction between internet data center and power grid simplifies the power grid as an ideal power source, ignoring its operational constraints. Internet data center configured with renewable energy, electric energy storage device and water-cooling system is taken as one of the research objects in this paper, and regional integrated energy system formed by the coupling of distribution network and water network as the other. The water-energy coupling and cooperative operation between above two are studied for solving the above problem. The proposed method has been validated in the demonstration project of the “East Data and West Computing” big data industrial park in Qingyang, Gansu, China. The results of example show that water-energy demand response and waste heat reuse can help to reduce the operational cost of internet data center, improve scheduling flexibility of regional integrated energy system, and enhance the capacity of renewable energy utilization. The operational cost of internet data center and regional integrated energy system is sensitive to emergency capacity of electric energy storage device, cost coefficient of the power demand response of electric energy storage device and water demand response of reservoir, data load flexible scheduling strategy, which is consistent with the operational status of the demonstration project.

Evaluation of seismic response of server cabinets through shaking table tests

Telecommunication system is one of the critical lifeline systems for national life and post-earthquake emergency response. Telecommunication equipment is the necessary components to preserve communication functionality of telecommunication system. The severe damage and functionality loss of data center buildings resulting from damage to telecommunication equipment are frequently reported following earthquakes. Thus, there is an increasing need to understand the performance of telecommunication equipment during earthquakes.Seismic investigation of three server cabinets in internet data center buildings are presented in this study. The seismic damage and dynamic characteristics of server cabinets were evaluated and explained in terms of the natural frequency, stress, acceleration, dynamic amplification factor, and deformation by triaxial shaking table tests. The input acceleration time histories were generated based on the required response spectra in YD 5083 code and AC 156 code, with seismic intensities ranging from 0.1 g to 1.2 g. Based on the test results, the effects of different mass distributions inside the cabinets and earthquake inputs were investigated. The seismic damage limit states of server cabinets were identified based on observed damage phenomenon. The corresponding seismic fragility models of server cabinets were developed based on shaking table test data. The results show that the Power Distribution Units inside the cabinets still worked normally and the output voltage was normal for all loading cases although the frame of cabinets suffered severe seismic damage. The normal functionality of server cabinets including more electronic devices and equipment should be carefully checked by shaking table tests.

Comparative energy analysis of cooling energy performance between conventional and hybrid air source internet data center cooling system

The widespread need for data processing has led to an increase in internet data centers (IDCs), contributing to the generation of high heat loads and high energy usage. This study discusses a simulation model of a newly proposed cooling system applied to data centers for five selected climatic areas in South Korea. Using experimental data of the newly proposed system, performance correlations are developed by regression analysis method to estimate performance curve coefficients for energy modelling and simulation to predict the annual energy performance of the system and compare the performance with that of a conventional cooling system. The results showed an overall annual average energy efficiency of approximately 14.1% of the hybrid cooling system over the conventional cooling system. The mean seasonal coefficient of performance (SCOP) for the hybrid system and the conventional system is approximately 6.7 and 4.9 respectively for all cases in the winter period while a SCOP of 3.2 and 3.7 respectively for the summer period.