New Algorithms Push Toward High-Fidelity EMT Modeling for Grid Data Analysis

In an era where electric grids are undergoing rapid transformations, the need for advanced modeling techniques has never been more pressing. Traditional methods fall short in capturing the intricate dynamics of modern grids, which are increasingly integrating renewable energy sources, distributed energy resources, and power electronics-based devices.

 

Electric grids are integrating renewable energy sources like solar. Image used courtesy of Pexels/Tom Fisk 

 

To address this issue, the industry has placed a lot of attention on electromagnetic transient (EMT) domain analysis. 

 

What is EMT?

EMT domain analysis is an advanced computational approach used for modeling and simulating power systems to capture fast-changing electrical phenomena. 

 

EMT analysis of a breaker closing in a power system. Image used courtesy of PSCAD

 

Unlike traditional phasor domain simulations, which are generally used for steady-state and slow-changing dynamic analyses, EMT simulations focus on capturing high-frequency transients, harmonics, and other non-linear behaviors that are increasingly prevalent in modern electric grids. These simulations are characterized by their small-time steps and high-fidelity models, which make them computationally intensive but also more accurate in representing the complex interactions within the grid.

 

The Need for EMT 

Traditional grids were primarily composed of synchronous generators and passive loads, making them relatively straightforward to model and manage. However, the modern grid is becoming increasingly complex due to the integration of renewable energy sources like solar and wind, the proliferation of distributed energy resources, and the adoption of power electronics-based devices such as inverters and electric vehicle chargers. These elements introduce fast transients and non-linearities that can't be adequately captured by traditional modeling techniques.

 

Simulation results for different input modes of a universal converter model. Image used courtesy of Sidwall et al.

 

EMT domain analysis is also particularly crucial for understanding and mitigating faults related to inverter-based resources. Inverters are key components in renewable energy systems, converting DC power from solar panels or wind turbines into AC power that can be fed into the grid. 

However, they are also susceptible to various types of faults and disturbances, which can propagate through the grid and lead to larger systemic issues. By using EMT simulations, grid operators can gain a more accurate understanding of these rapidly unfolding events, allowing for better planning, control, and fault mitigation strategies.

Moreover, regulatory bodies like the North American Electric Reliability Corporation (NERC) are recognizing the necessity of EMT simulations for ensuring grid reliability. As grids continue to evolve, the stakes for managing them reliably are higher than ever. Failures can result in significant economic losses and pose risks to national security. Therefore, EMT domain analysis is not just a technological advancement; it's an essential tool for ensuring the resilience and reliability of modern electric grids.

 

ORNL Unlocks High Fidelity Modeling

One challenge facing EMT is that traditional modeling techniques have proven inadequate for capturing the fast transients and complex interactions that occur in grids with a high penetration of such resources. To address this, the Oak Ridge National Laboratory (ORNL) has been pursuing work toward developing high-fidelity EMT models that can accurately simulate these rapidly unfolding events.

In collaboration with Southern California Edison, ORNL recently integrated new algorithms and models into an EMT domain analysis tool. One of the significant milestones of this research was the successful replication of a 2018 transmission fault that led to a large solar plant reducing its output. This achievement is crucial because it validates the effectiveness of EMT domain analysis in identifying and understanding faults that can have a cascading impact on grid stability and reliability.

The high-fidelity models developed will help utilities gain a deeper understanding of the physical dynamics of power electronics, thereby enabling better grid management and planning.