Key Takeaways & Executive Findings
- •• • Average simulation error remains below 1.05% on the two-area four-machine system, ensuring that distributed decoupling does not compromise numerical fidelity for transient stability studies. • • Simulation efficiency improvement reaches approximately 10% on the IEEE New England 10-machine 39-bus system, demonstrating tangible speedup even at moderate scales. • • On the WECC 29-machine 179-bus system, the efficiency gain escalates to about 48%, confirming that the architecture's advantage grows with system size and complexity. • • The DDS-based dynamic discovery mechanism enables automatic interconnection of subsystems, allowing seamless integration of new subsystems without prior knowledge of the complete system, which reduces deployment friction and supports incremental expansion.
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
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.
1. Introduction
Modern power systems are undergoing a structural transformation driven by high-penetration renewable generation and widespread deployment of power electronic converters. These elements introduce high dimensionality, multi-timescale coupling, and strong nonlinearities that render conventional centralized simulation frameworks increasingly inadequate. Centralized approaches, which rely on monolithic modeling and solving, face exponential growth in computational complexity and are constrained by limited local computing resources. Attempts to mitigate these issues through model simplification often sacrifice accuracy, while high-performance computing solutions such as GPU acceleration or cloud-based task scheduling remain tethered to a centralized solution paradigm, struggling with heterogeneous multi-area coupling and resource dependencies.
Distributed simulation architectures offer a promising alternative by harnessing dispersed computing resources and enabling flexible integration of domain-specific solvers. However, existing distributed frameworks still exhibit critical shortcomings: insufficient consideration of scalability for modern power system modeling, complex subsystem interface design, and slow simulation speeds. This work addresses these bottlenecks by proposing a complete distributed simulation architecture based on Data Distribution Service (DDS). The power system model is mathematically decoupled into independently solvable basic subsystems, and a generic DDS-based communication interface is designed for data exchange. A time-consumption balancing scheme distributes combined subsystems across multiple devices, while a two-layer synchronization strategy ensures coordinated parallel solving. The architecture is validated on systems ranging from a two-area four-machine model to the WECC 29-machine 179-bus system, demonstrating high accuracy and increasing efficiency gains with system scale.
Loading authentic research manuscript (Pages 1–5)...
CHEN Yuying, WEN Jianfeng, YAO Wei, JIANG Lin (2026). Design and Implementation of a Distributed Simulation Architecture for Modern Power Systems Based on Data Distribution Service. Power Automation Equipment. https://doi.org/10.16081/j.epae.202603019
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 measured simulation error when using the distributed architecture compared to centralized simulation, and does it remain within acceptable bounds for transient stability analysis?
On the two-area four-machine system, the average error ε2 remains below 1.05% relative to centralized simulation. This level of accuracy is sufficient for transient stability studies, as it falls within typical engineering tolerances for dynamic security assessment.
How does the simulation efficiency improvement scale with system size, and what are the quantified gains on the IEEE 10-machine 39-bus and WECC 29-machine 179-bus systems?
The efficiency improvement rate is approximately 10% on the IEEE New England 10-machine 39-bus system and about 48% on the WECC 29-machine 179-bus system. This demonstrates that the distributed architecture yields greater speedup as system scale and complexity increase, validating its scalability for large-scale power system simulation.
What synchronization strategy is employed to ensure data consistency and temporal alignment among distributed subsystems, and how does it affect simulation reliability?
A two-layer synchronization strategy is implemented to coordinate subsystem solving. This strategy ensures both synchronization and data reliability across distributed subsystems, preventing numerical divergence and maintaining temporal consistency without introducing excessive communication overhead.
How does the DDS-based communication interface facilitate scalability and integration of new subsystems, and what are the practical implications for cross-platform deployment?
The DDS-based interface uses a dynamic discovery mechanism that enables automatic interconnection of subsystems. New subsystems can be integrated by simply extending DDS data topics and communication interfaces, without requiring prior knowledge of the complete system. This design supports flexible expansion and is compatible with future cross-platform and cross-software deployment, as well as integration with edge computing for hierarchical resource allocation.
What are the limitations of the current architecture, and what future work is planned to address them?
Current limitations include the need for further optimization of communication efficiency and resource allocation in heterogeneous environments. Future work will focus on cross-platform and cross-software deployment, integration with edge computing, and hierarchical refinement of the architecture to enhance computational resource allocation and communication efficiency in practical application scenarios.
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.
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.
Eigenvalue Computation Techniques for Small-Signal Stability Analysis of Large-Scale New-Type Power Systems: A Review and Outlook
The escalating 'double-high' penetration of power electronics in new-type power systems has rendered conventional electromechanical transient small-signal stability analysis inadequate, necessitating electromagnetic transient (EMT) small-signal stability assessment. Eigenvalue analysis, grounded in rigorous theoretical foundations, is widely applied but faces two critical bottlenecks when scaled to large systems: the construction of EMT linearized state-space equations and the solution of high-order state-matrix eigenvalues. This review examines the urgent demand for EMT model eigenvalue analysis in large-scale new-type power systems. It systematically surveys research status and challenges across three domains: equilibrium-point modeling of EMT models, linearized state-space modeling, and efficient computation of critical eigenvalues. For equilibrium-point modeling, the paper evaluates Park transformation, double Park transformation, shifted frequency analysis (SFA), time-scale transformation, and Floquet-theory-based trajectory linearization. For linearized state-space modeling, it assesses component-level and network-level linearization strategies. For eigenvalue computation, it reviews partial eigenvalue algorithms, sparse matrix techniques, and model-order reduction methods. Key challenges include the inability of Park transformation to handle asymmetric or single-phase systems, the computational burden of Floquet transition matrix eigendecomposition, and the poor scalability of dense eigenvalue solvers for systems exceeding thousands of states. The paper concludes by identifying future research directions, including structure-preserving linearization, GPU-accelerated sparse eigenvalue algorithms, and data-driven model reduction, to enable practical EMT small-signal stability analysis for systems with 18.4 billion kW of installed renewable capacity by 2030.