Abstract
The increasing penetration of inverter-based resources (IBRs) is transforming power system dynamics, challenging traditional analysis tools based on Root Mean Square (RMS) phasor-domain modeling. Electro-Magnetic Transient (EMT) modeling offers detailed time-domain insights essential for understanding fast converter-grid interactions, protection behavior, and stability phenomena. This paper provides a comprehensive review of advanced practices for integrating RMS and EMT models, co-simulation methodologies, best practices, and emerging research directions. It draws upon guidance from NESO, IEEE, CIGRÉ, NERC, and recent academic literature to propose a structured approach for bridging scalability and fidelity.
1. Introduction
Power systems are undergoing rapid transformation with the accelerated deployment of inverter-based resources (IBRs) such as photovoltaic (PV) plants, battery energy storage systems (BESS), and wind farms. These resources are interfaced with the grid through fast-acting power electronic converters, whose dynamics differ fundamentally from synchronous machines. RMS-based models, while efficient for large-scale planning and stability studies, often fail to capture critical fast phenomena such as sub-synchronous oscillations, converter control interactions, and fault responses [1], [2]. EMT models, operating at microsecond time steps, can represent these dynamics with high fidelity, but are computationally intensive [3]. The real challenge lies not in choosing between RMS and EMT, but in creating a consistent and scalable integration framework between them [4].
2. RMS and EMT Modelling Fundamentals
RMS (phasor-domain) models represent electrical quantities as fundamental frequency phasors, suitable for slow or quasi-steady phenomena. They are computationally efficient, making them ideal for wide-area studies, long-term planning, and system-level stability analysis [5]. EMT models, by contrast, solve instantaneous voltages and currents in the time domain with small time steps (typically 10–20 μs). This allows them to capture detailed switching behavior, control loops, and protection dynamics that RMS cannot [6], [7]. As power systems become dominated by fast converter controls, EMT analysis is increasingly essential for operational security and model validation.
3. Hybrid RMS–EMT Integration Methodologies
Several methodologies have been developed to couple RMS and EMT domains. These include RMS–EMT co-simulation, hybrid solvers using dynamic phasors, and domain decomposition techniques [8]–[12]. RMS–EMT co-simulation couples phasor solvers with EMT simulators, allowing different parts of the network to be modeled at different fidelities. Dynamic phasor-based hybrid solvers model selected subsystems in EMT while maintaining phasor representations elsewhere. Domain decomposition techniques (e.g., Aitken–Schwarz DDM) enable parallel computation and improved convergence between domains [13].
4. Best Practices for High‑Fidelity Integration
To ensure meaningful hybrid simulations, parameter consistency between RMS and EMT models is critical. This includes aligning network data, transformer impedances, short-circuit ratios, control parameters, and initialization conditions [14]. Using real-code controllers in EMT ensures behavioral equivalence with actual equipment [15]. Proper initialization procedures avoid artificial transients, and benchmarking RMS results against EMT under both small- and large-signal conditions ensures cross-domain consistency [16]. Clear documentation, version control, and multi-team governance are also essential [17].
5. Acceleration and Co‑Simulation Techniques
Because EMT simulations are computationally demanding, several acceleration methods have emerged. Parallel and distributed EMT simulation frameworks leverage HPC to scale EMT to larger systems [18]. Multiscale methods dynamically switch between EMT and reduced models to save computation while preserving accuracy [19]. Adaptive step-size solvers and model reduction techniques further improve efficiency [20]. Real-time co-simulation frameworks mitigate latency between RMS and EMT solvers through extrapolation and buffering techniques [21].
6. Practical Use Cases and Lessons Learned
Practical applications of hybrid RMS–EMT frameworks include protection and fault studies, oscillation damping analysis, and wide-area disturbance investigations. Operators use EMT to analyze critical zones during major events while relying on RMS for system-wide dynamics [22]. Hybrid methods are also key in grid code compliance testing, especially for inverter-based generation under weak grid conditions [23].
7. Research Directions and Future Challenges
Active research focuses on scalable hybrid simulation algorithms, automated model partitioning, standardized real-code interfaces, and AI-assisted co-simulation orchestration [24]–[27]. As grids evolve toward ultra-high IBR penetration, EMT will increasingly shift from a specialized analysis tool to a routine component of planning and operational studies.
8. Conclusion
RMS and EMT models offer complementary strengths. RMS provides scalability, while EMT delivers physical accuracy. Bridging these domains requires disciplined parameter alignment, robust co-simulation techniques, and governance frameworks. Adopting best practices and leveraging emerging hybrid methods can enable reliable, future-ready power system studies.
References
[1] NESO. Guidance Notes for Electro-Magnetic Transient (EMT) Models – V2.0, Sep 2025.
[2] IEEE PES Task Force. Modelling and Dynamic Performance of Inverter-Based Resources in EMT Tools, IEEE Trans. Power Delivery, 2023.
[3] CIGRÉ Technical Brochure 851, Electromagnetic Transient Modeling and Simulation for Large Power Systems with High Penetration of Power Electronics, 2023.
[4] NERC. EMT Analysis in Operations Planning, 2025.
[5] K. Huang et al., Heterogeneous Multiscale Methods for EMT-Phasor Co-Simulation, arXiv:2302.09341, 2023.
[6] CIGRÉ WG C4.60. Generic EMT-type Modelling of Inverter-Based Resources for Long Term Planning Studies, 2021.
[7] IEEE PES, Large-Scale EMT Model Examples for High-Share IBR Systems, 2025.
[8] IPST 2021 Conference Paper: RMS–EMT Co-simulation via Line Interface.
[9] IPST 2017 Paper: Hybrid EMT and Dynamic Phasor Simulation.
[10] Aitken-Schwarz DDM for EMT–Phasor Coupling, arXiv:2102.02507, 2021.
[11] NREL ParaEMT Framework Documentation, 2023.
[12] CIGRÉ B4.82. Guidelines for Real-Code in EMT Models, 2025.