Electric Reliability Council of Texas

In 2024, Texas operators observed 23-Hz oscillations in real power measurements close to a large electronic load (LEL). Oscillations emerged when the load's power consumption reached approximately 320 MW level and subsided as the active power demand decreased. The paper aims to analyze the event and reproduce the oscillations using electromagnetic transient (EMT) simulations. In the first stage, a representative feedback system is developed, and frequency-domain analysis is conducted to examine the phenomenon and identify its key influencing factors. Next, detailed EMT simulations are performed to further validate the proposed analytical approach. The results show that the feedback system effectively captures and characterizes the critical features of the 23-Hz oscillation incident. In addition, the EMT simulations successfully reproduce the real-world event, with the simulated results closely matching the fault recorder data.

The increasing number of Large Loads, such as data centers and cryptocurrency miners, is introducing new reliability challenges for Bulk Power Systems (BPS). As seen in recent industry events, these loads have become significant contributors to customer-initiated load reduction, oscillations, and frequency transients. While these Large Loads form the basis for our digital economy and support modern infrastructure, it is important that they do not adversely impact the BPS reliability. This paper investigates various BPS events that primarily involve Large Loads and develops a taxonomy of root-causes related to equipment design, control systems, and the software operating these loads. This taxonomy is utilized to guide further research into solutions for these issues, and the paper proposes both facility-level and grid-level mitigation techniques to address the identified challenges.

Dynamic phenomena linked to inverter-based resources (IBRs) have gained global attention. Several IBR-induced dynamics have caused bulk power system-connected wind or solar power plants to trip, and some have even led to widespread outages. In addition, many oscillations have been observed involving IBR power plants. In 2023, the IEEE Power & Energy Society (PES) IBR Subsynchronous Oscillations (SSO) task force published a journal article, “Real-World Subsynchronous Oscillation Events in Power Grids With High Penetrations of Inverter-Based Resources,” in which 19 IBR oscillation events were examined for their causation. Earlier in 2020, another PES task force article, “Definition and Classification of Power System Stability-Revisited & Extended,” authored by prominent academics, introduced converter-driven stability as a new category of stability. The international power grid industry community also took action by publishing the CIGRE Green Book, Power System Dynamic Modelling and Analysis in Evolving Networks (led by Babak Badrzadeh and Zia Emin) in 2024. In August 2024, the Energy Systems Integration Group (ESIG) released a practical guide led by Nick Miller, “Diagnosis and Mitigation of Observed Oscillations in IBR-Dominant Power System: A Practical Guide.” The goal of the guide is to assist practicing engineers in making initial judgments and conducting detailed analyses about oscillations. When addressing the classification of stability and oscillations, the guide emphasizes a causality-based taxonomy for grouping, such as voltage control-induced oscillations, synchronization-induced oscillations, and frequency or active power control-induced oscillations.

This paper presents a stochastic framework based on the bow-tie methodology and root-cause analysis to assess correlated triggering risk events, combined with Monte Carlo simulation of cascading barrier failures. The example framework models root causes—including low renewable output, high thermal outages, and severe transmission constraints—and quantifies their combined impact when the Capacity Available for Operating Reserves (CAFOR) falls below 1,500 MW. Preventive and mitigative barriers, consistent with ERCOT’s Energy Emergency Alert (EEA) actions, are simulated using Monte Carlo techniques with correlated reliability parameters. Results show that winter months peak near 3% involuntary load curtailment at the 95th percentile, while summer months remain at ~0.6–0.8%. The framework demonstrates a data-driven basis for evaluating resource adequacy and prioritizing reliability investments in increasingly weather-dependent grids.

This paper presents a risk segmentation approach for scheduling generation in power systems with high penetration of renewable energy resources (RERs). This approach aims to facilitate risk-adjusted participation of RERs in the day-ahead market (DAM). It borrows from debt securitization techniques to define risk tranches and develops separate bid curves of RERs by the tranches of different grades of risk. Assigning a higher price to a tranche with greater risk can prevent a renewable asset owner from incurring losses when the asset cannot produce the day-ahead committed amount of energy and must buy energy from the real-time market (RTM). In the proposed approach, the system operator utilizes the risk-segmented bids from RERs and generation reliability constraints to solve a risk-adjusted unit commitment (UC) problem. To a system operator, this can be treated as a deterministic DAM dispatch. The stochasticity of this UC problem is embedded in the renewable bid curves. The resulting increase in DAM revenues due to additional DAM commitment benefits the renewable asset owners. The loads can also benefit from additional RER commitment in DAM as it lowers DAM energy prices. The paper will focus on using this risk-adjusted strategy for wind energy resources. The approach is illustrated on a synthetic 6,700- bus model of the 2030 Electric Reliability Council of Texas (ERCOT) system that has a significant penetration of wind resources.