PNNL

Abstract

The 100% renewable energy targets from the policymakers for the future grids and the studies to enable this goal have recently drawn a significant amount of interest. The high renewable penetration made future large interconnected grids (LIGs) more volatile and hard to understand using historical observations. This led to the need to study future LIGs using a wide range of future year operating conditions and contingencies. However, existing planning tools are not sufficient for the dynamic security assessment (DSA) of these future LIGs due to a lack of detailed modeling capabilities and computational limitations when processing a wide range of scenarios. This paper addresses these two challenges by proposing; 1) a framework that can generate dynamic simulation data for a wide range of scenarios. This dynamic simulation data is generated by respecting the constraints of production cost models and their respective AC power flow dynamic simulation models at an hourly resolution. The framework also models the cascading behavior of LIGs; 2) a machine learning-based approach for fast scanning DSA data. The proposed simulation framework is used to generate 1.485 terabytes of dynamic simulation data for the 2028 WECC system containing 4455 scenarios. The proposed machine learning framework used the 2028 WECC system to demonstrate its effectiveness and speed in identifying the critical scenarios based on their total count of voltage and frequency violations.