Addressing inertia and grid strength of operating high renewable grids

The power grid architecture is undergoing substantial transformation as efforts are made to phase out generators that produce greenhouse gases and replace them with renewable energy sources. While inverter-based renewable (IBRs) energy resources supply clean energy to electricity customers, they also pose challenges to grid strength and system inertia. The grid strength is weakened with IBRs connected, potentially leading to voltage stability and reliability issues, especially during large events such as Fault-Induced Delayed Voltage Response (FIDVR). Therefore, studies focus on FIDVR events, using IBRs to mitigate FIDVR, and assessing grid strength with IBRs.

Another challenge introduced by IBRs is decreasing system inertia. Low inertia reduces the system's ability to suppress frequency variations when there are power changes. To help operators avoid low inertia situations and maintain system stability, an online power system inertia monitoring system based on the Bath County Pumped Storage Hydropower (PSH) plant turn-off operations has been developed. This approach utilizes PSH turn-off event signatures for detection and employs improved inertia estimation for more accurate results. Additionally, the second-largest PSH plant is studied, providing supplementary estimates for EI inertia estimation. To provide continuous inertia estimates, the feasibility of using ambient data for system inertia estimation is explored.

The complexity of an IBR-connected grid necessitates high-resolution data, which challenges network communication and data storage. To address this, novel data compression methods are proposed to compress high-resolution synchrophasor and point-on-wave data.

The studies in this thesis evaluate grid strength and inertia with increasing IBR penetration using PSS/e simulations and field PMU measurements. These efforts aim to ensure stable system operation for large interconnection power systems, such as the Eastern Interconnection (EI).