Adaptive RAS/SPS System Settings for Improving Grid Reliability and Asset Utilization through Predictive Simulation and Controls

Demonstrating the value of calculating RAS parameters in an adaptive way, and the impact of the DOE OE Advanced Grid Modeling program on the industry planning and operation practice.

POC: Xiaoyuan Fan

Electric-Grid-Modernization

Background

Given the increasing penetration of renewable energy, demand response and smart controllers in today’s power grid, atypical power flow patterns such as reverse flows, loop flows, and stochastic dynamic behavior are being observed in real-time operation. These new patterns may invalidate the existing protection relay settings, especially Remedial Action Scheme (RAS), also known as Special Protection Scheme (SPS), which when invalidated can potentially cause cascading failures if the operational issues caused by the new challenges are not fully understood and addressed.

Understanding the logic and modeling practice of RAS/SPS is critical for the power industry to better prepare for the new challenges. However, the relay settings are traditionally done using offline studies, which is a very time-consuming process due to the lack of computational power. Currently, there is no automation tool that can assist planning and protection engineers with adaptive settings of such RAS/SPS systems to respond to unknown grid conditions. Thus, the relay settings are typically over-conservative, e.g., causing unnecessary flow curtailment or generation tripping that can affect revenue of generator owners and economic operation of the entire power network as a whole.

Several challenges are identified in the industry preventing the RAS settings from being determined in an adaptive/online manner. One key issue is that the computational speed is not fast enough in today’s commercial tools to perform a full-scale study to calculate RAS parameters such as the arming level and validate the control performance in a preventive way. 

The development of faster computational techniques at PNNL, including parallel dynamic simulation and massive contingency analysis powered by HPC techniques, provides a feasible and low-cost way to validate preventive controls and adjust parameter settings in near real time to improve security and efficiency.