Key Takeaways & Executive Findings
- •• • Traditional models (PUE, RC) that ignore server layout, cold aisle containment, and load time-series cause instantaneous load discrepancies of 12.56%–34.21%, directly undermining peak load and ramp-rate analyses for grid planning and demand response. • • The CFD-based model reveals a nonlinear dynamic coupling among IT load, temperature field, and cooling power, demonstrating that data center load is dominated by internal thermophysical dynamics rather than a linear mapping of instantaneous IT load. • • Compared to static PUE models, the proposed model captures thermal inertia-induced power delays and dynamic fluctuations; unlike second-order RC gray-box models, it quantifies the decisive influence of physical parameters such as server layout on non-uniform temperature fields and local hot spots. • • The model enables deep coupling with grid planning models via constraints, boundaries, and variable transfer, supporting second-level (or even millisecond-level) transient simulations integrated into long-term planning—a recent hotspot in equipment planning research.
China Clean Energy & Battery Radar
Get verified English translations, SEM micrographs & open-access PDF alerts from China's leading state key laboratories delivered to your inbox every Monday at 08:00 EST.
Abstract
Data center expansion has driven a surge in energy consumption, and prediction deviations for high-density loads introduce risks to power grid planning and operation. Existing models exhibit significant limitations: they neglect temperature non-uniformity and the nonlinear impact of rack layout on airflow; rely on static power usage effectiveness (PUE) without accounting for the coupling between load fluctuations and cooling dynamic response; and linearize cooling systems, ignoring IT equipment–cooling system–thermal environment interactions. This paper proposes a spatiotemporal thermodynamic dynamic load model for data centers based on computational fluid dynamics (CFD). Spatially, a physical model incorporating cold aisle containment, server power, and layout is constructed; CFD simulations capture temperature non-uniformity and establish a collaborative IT power–temperature field–cooling power model. Temporally, IT load time-series fluctuations, thermal inertia response, and cooling power dynamics are coupled to quantify the impact of physical parameters on overall load at short time scales. Simulation results for a typical day show that traditional models, by neglecting these key parameters, may cause instantaneous load discrepancies of 12.56%–34.21%. Data center load characteristics are strongly coupled with internal thermal environment dynamics, and the proposed model provides a high-fidelity thermodynamic reference for data centers participating in grid interaction as flexible resources.
1. Introduction
Existing power system studies predominantly model data center loads using power usage efficiency (PUE), reducing the instantaneous load to a linear function of IT power. This simplification fails to represent the dynamic interactions among IT equipment, cooling systems, and the thermal environment. Spatially, data centers exhibit non-uniform temperature fields with local hot spots; rack layout and server distribution govern rack-level thermal recirculation, and over-simplified temperature treatment in cooling energy calculations introduces significant errors. Temporally, server power fluctuations induce cooling capacity variations, and instantaneous cooling power is real-time dependent on local temperatures. Static annual PUE cannot capture the coupling between IT load time-series fluctuations and cooling system dynamic response.
Cooling systems account for approximately 40% of total data center power consumption, making their accurate modeling critical for overall load model fidelity. National standards mandate CFD for airflow organization verification. Traditional mathematical-analytical energy models oversimplify temperature and struggle to capture thermal dynamics. While thermodynamic gray-box models using resistance-capacitance (RC) networks improve physical interpretability and temporal dynamics, they assume uniform temperature distribution and neglect spatial non-uniformity from complex airflow—particularly the nonlinear driving mechanism of local hot spots on cooling energy. This paper systematically introduces CFD, a high-fidelity simulation technology well-validated in thermodynamics and fluid mechanics, into power system data center load modeling to accurately reflect the impact of server layout, ambient temperature, and server load time-series on energy consumption.
Loading authentic research manuscript (Pages 1–5)...
ZHOU Niancheng, XU Ying, CHI Yuan, XU Luona, ZHOU Yiyao, LUO Yongjie (2026). Spatiotemporal Thermodynamic Dynamic Load Modeling and Characteristic Analysis of Data Centers for Power System Applications. Power Automation Equipment. https://doi.org/10.16081/j.epae.202607004
Research & Educational Purpose Only: The translations, structured abstracts, analytical annotations, and data reports provided by SinoGreenTechare intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoGreenTech claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What is the quantified load discrepancy between the proposed CFD-based model and traditional models, and what physical parameters drive this gap?
The proposed model reveals instantaneous load discrepancies of 12.56%–34.21% compared to traditional models. This gap is driven by the neglect of server layout, cold aisle containment, and load time-series in conventional PUE or RC models. These factors determine the non-uniform temperature field and local hot spots, which nonlinearly affect cooling power.
How does the model handle the trade-off between computational cost and accuracy for power system planning studies?
The CFD model provides a high-fidelity thermodynamic reference with physical completeness. It can be coupled with grid planning models via constraints, boundaries, and variable transfer, enabling second-level (or millisecond-level) transient simulations integrated into long-term planning. This approach balances accuracy and computational feasibility for planning studies.
What are the key limitations of existing RC gray-box models that this work addresses?
RC gray-box models assume uniform temperature distribution and fail to account for spatial non-uniformity caused by complex airflow organization. They also neglect the nonlinear driving mechanism of local hot spots on cooling energy. The proposed CFD model captures these effects, providing a more accurate representation of IT load–temperature–cooling power coupling.
How does thermal inertia affect the dynamic response of data center loads, and how is it modeled?
Thermal inertia induces power delays and dynamic fluctuations in cooling systems. The proposed model couples IT load time-series fluctuations, thermal inertia response, and cooling power dynamics to quantify short-time-scale impacts. This contrasts with static PUE models that cannot capture such transient behavior.
What is the industrial relevance of the 40% cooling power share in data centers, and how does the model improve upon it?
Cooling systems consume approximately 40% of total data center power. Traditional models use fixed COP or PUE, ignoring real-time local temperature effects. The CFD-based model captures the nonlinear coupling between IT power, temperature field, and cooling power, enabling more accurate prediction of cooling energy and overall load.
Related Chinese Research & Cross-Citations
Impedance Modeling and Parameter Optimization Method for Wind Farms Considering Station-Level Control
Large-scale wind farms integrated into weak grids are susceptible to broadband oscillations, a problem that existing impedance models and control parameter optimization methods fail to address systematically because they neglect station-level control and frequency coupling effects. This paper proposes a station-level control strategy based on available power allocation and an adaptive compass search (ACS) algorithm for optimizing station control parameters. A sequence impedance method incorporating frequency coupling effects establishes the aggregated wind farm impedance, and the grid-connected multiple-input multiple-output (MIMO) system is decoupled into positive- and negative-sequence single-input single-output (SISO) impedance models. A high-precision wind farm impedance model that accounts for station-level control is constructed, and the Nyquist criterion evaluates the suppression effect of station control on broadband oscillations. The ACS algorithm optimizes station control parameters to enhance the adaptability of wind farm impedance to external grid impedance, thereby reducing oscillation risk. RT-LAB platform simulations validate the impedance modeling method and control parameter optimization. Results demonstrate that considering station-level control yields a high-precision wind farm impedance model. Compared with genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), and sparrow search algorithm (SSA), ACS is more suitable for optimizing wind farm control parameters. Under specific weak-grid conditions, ACS-optimized station control parameters effectively improve grid-connected stability of the wind farm.
Active and Reactive Power Coordinated Voltage Support Control Method for High-Inertia Energy-Storage Synchronous Condenser
The high-inertia energy-storage synchronous condenser (SC-HI-ES) can provide reactive power support by adjusting excitation current and active power support by actively varying rotor speed. However, the absence of a prime mover makes its control capability difficult to quantify, preventing coordinated active and reactive power support for grid voltage. This paper analyzes the factors influencing SC-HI-ES power control capability, derives rotor current equations considering stator and rotor circuit constraints, and establishes the power controllable range (PCR) under rotor current constraints and speed variations. The relationship between the PCR and voltage support power demand is parsed, and a coordinated active-reactive power voltage support control method is proposed. Case studies validate the method's effectiveness. Results show that the proposed method enhances voltage support and avoids rotor current over-limit. Compared with methods ignoring speed variation or controlling only reactive power, the proposed method dynamically calculates PCR-based power demand under current speed, and through coordinated active and reactive power control, maximally satisfies the point of common coupling voltage target while ensuring rotor current does not exceed allowable values. This provides effective technical support for SC-HI-ES application in new-type power systems. Frequency control capability characterization and frequency-coordinated control are identified as future research directions.
Hardware-in-the-Loop Test Platform for Photovoltaic High-Frequency Controllers Based on UREP + FPGA
Real-time constraints prevent CPU-based electromagnetic transient simulators from accurately sampling high-frequency PWM signals at microsecond step sizes, leading to insufficient HIL test precision and even instability of the closed-loop system. This paper introduces a PWM averaging technique to construct a novel UREP (electromagnetic transient real-time simulator) + FPGA hardware-in-the-loop test platform. To achieve accurate PWM acquisition, the FPGA's nanosecond-level clock resolution increases the number of sampling points within a single switching period from a few points under microsecond simulation steps to several thousand points. The FPGA implements fixed-step PWM averaging, converting discrete switching states into continuous duty cycles that serve as control signals for the UREP-side inverter average model. This technique avoids frequent topology updates and state matrix reconstructions triggered by switching events, reduces high real-time computational resource consumption, improves numerical stability of the in-loop system, and enhances HIL test accuracy under large-step real-time constraints. Simulation cases and industrial-grade controller HIL tests verify the platform's feasibility and accuracy. The platform provides an efficient, reproducible verification scheme for photovoltaic controller control strategies and offers a feasible technical path for domestic electromagnetic transient real-time simulation platforms to conduct high-frequency controller HIL tests.
Design and Implementation of a Distributed Simulation Architecture for Modern Power Systems Based on Data Distribution Service
The integration of high-penetration renewable energy and power electronic converters has intensified the dynamic complexity of modern power systems, imposing stringent demands on simulation accuracy and computational efficiency. This paper proposes a distributed simulation architecture based on Data Distribution Service (DDS) that leverages dispersed computing resources to enhance scalability and efficiency while preserving numerical fidelity. The power system model is mathematically decoupled into multiple independently solvable basic subsystems, and a generic data transmission interface is designed using DDS. A time-consumption balancing scheme groups and distributes these subsystems across multiple devices, and a two-layer synchronization strategy enables efficient parallel simulation. The architecture is validated on a two-area four-machine system, the IEEE New England 10-machine 39-bus system, and the WECC 29-machine 179-bus system. Compared with centralized simulation, the average error on the two-area four-machine system remains below 1.05%. Simulation efficiency improvement reaches approximately 10% on the 10-machine 39-bus system and about 48% on the 29-machine 179-bus system. The results confirm high accuracy across different system scales and demonstrate that efficiency gains become more pronounced as system size increases, validating the architecture's scalability and compatibility. The proposed framework offers a promising pathway for large-scale power system simulation and supports future integration with edge computing and cross-platform deployment.
Heterogeneous Weighted Graph Partitioning and Decoupling Optimization Strategy for Electromagnetic Transient Parallel Simulation of AC/DC Distribution Networks
The increasing penetration of power electronic devices in AC/DC distribution networks imposes stringent computational demands on electromagnetic transient (EMT) parallel simulation. Conventional transmission-line delay decoupling methods are ill-suited to the strong electrical coupling characteristic of such networks. This paper proposes a non-delay decoupling parallel simulation acceleration framework based on heterogeneous weighted graph partitioning. An empirical computational cost evaluation model for each parallel decoupling stage is established, and a heterogeneous weighted graph model is constructed to precisely characterize the simulation computational complexity of AC/DC distribution network components, mapping matrix dimensions of device mathematical models to graph node weights. A multi-objective graph partitioning scheme is formulated that simultaneously balances partition computational overhead and minimizes the number of tie-line variables, complemented by an optimal partition number screening strategy. Simulation validation is conducted on three large-scale AC/DC distribution network composite test cases: IEEE 34-node, IEEE 123-node, and European Low Voltage (European LV) systems, all retrofitted with DC sections. Results demonstrate that the proposed empirical computational cost model achieves a simulation time fitting goodness-of-fit R² > 0.97, indicating high predictive accuracy. Under optimal partition configuration, the proposed method attains parallel speedup ratios of 12.11–15.77, significantly outperforming conventional natural partitioning schemes and effectively enhancing the EMT parallel simulation efficiency of AC/DC distribution networks.
Hierarchical Hybrid Non-Delay Decoupling Parallel Method for Electromagnetic Transient Simulation of DC-Collector Offshore Wind Farms
Real-time electromagnetic transient (EMT) simulation of DC-collector offshore wind farms is constrained by microsecond time steps, high model order from cascaded power electronic converters, and the inability of conventional decoupling methods to handle complex series-parallel topologies without introducing artificial delays. Existing non-delay decoupling methods, such as multi-area Thevenin equivalence (MATE) and compensation method, rely on branch tearing and are ill-suited for systems with numerous common-bus partitions, leading to excessive link variables and singular admittance matrices. This paper proposes a hierarchical hybrid non-delay decoupling parallel method that integrates MATE and its dual (node-tearing) approach through a layered architecture. The method constructs a mixed equivalent model tailored to DC-collector offshore wind farms, enabling flexible selection between unified and hierarchical solution modes for link variables based on operating conditions. A complete non-delay decoupling simulation workflow is established and validated on a DC series-parallel grid-connected offshore wind farm test case implemented in MATLAB. Results demonstrate that the proposed method reduces solution matrix dimensions and achieves significant speedup without compromising accuracy. For a 96-turbine wind farm, the hybrid decoupling model achieves an 11.99x speedup over the detailed model, compared to 8.77x for series-only decoupling and 6.22x for parallel-only decoupling. Mean absolute errors (MAE) for key variables remain below 0.1011, and root mean square errors (RMSE) below 0.6318, confirming high fidelity. The method enhances parallel simulation performance and offers a generalizable solution for real-time EMT simulation of large-scale offshore wind farms.