Jin Tan

As higher shares of renewable energy are integrated into power systems, synchronous generators (SGs) are increasingly being replaced by inverter-based resources (IBRs). This shift reduces the grid’s physical inertia, an essential property that helps stabilize frequency during disturbances. To safely integrate these sources, grid operators need real-time tools to monitor system frequency stability and assess the dynamic frequency support provided by IBRs. Traditional inertia monitoring methods, developed for SG-dominated systems, are no longer adequate, as IBRs respond with fundamentally different controls, known as fast frequency response.To address this gap, we introduce a probing signal-based approach that enables the real-time estimation of both system inertia and frequency response using existing IBR infrastructure. By injecting small, controlled power perturbations through IBRs and analyzing the resulting frequency dynamics, this method provides accurate, noise-resilient measurements without requiring hardware upgrades or major grid disturbances. Importantly, it can be applied at both the system and device level, revealing the specific contributions of different IBR control strategies to grid frequency stability.We validate this approach through extensive field-testing on the island of Kaua‘i, Hawaii, demonstrating its feasibility under real-world operating conditions, including during a unique period of 100% renewable operation. The results reveal that IBRs can deliver fast and effective frequency support, but their dynamic contributions significantly vary depending on the control strategies and the operating conditions. By isolating and quantifying these effects, the method provides visibility into both system-level and device-level behavior that is not captured by static models. These findings highlight the need for dynamic, real-time monitoring tools as power systems evolve toward high levels of inverter-based generation.

Hybrid power plants (HPPs), which combine variable renewable generation and battery energy storage systems (BESSs), are emerging as critical flexibility resources for renewable-rich grids. Yet HPP real-time dispatch remains difficult in island power systems, given both renewable power uncertainty and BESS state-of-charge (SoC) management, where day-ahead (DA) forecast errors can invalidate DA schedules and purely observation-driven online control can be myopic. This paper proposes a short-term-forecast-augmented surrogate Lyapunov drift-plus-penalty algorithm for operator-oriented HPP dispatch. The method embeds imperfect look-ahead information directly into the Lyapunov decision rule by replacing the conditional-expectation drift-plus-penalty objective with a tractable forecast-based surrogate under a bounded-first-moment forecast error condition. The algorithm is embedded in a system-level dispatch framework that couples day-ahead unit commitments, real-time observations, and short-term forecasts. The framework also integrates expert knowledge informed operational flexibility and a generic incidence-matrix representation of heterogeneous HPP portfolios. Numerical studies on a realistic 30-day island power-system case with photovoltaic (PV) uncertainty show that the framework eliminates loss of load, PV curtailment, reserve shortage, and transmission-line violations under imperfect forecasts. Its average operating cost is only 0.37% above an ideal perfect forecast benchmark. Robustness tests show penalty-free operation for short-term forecast error standard deviations up to 40%. These results indicate that the proposed forecast-aware Lyapunov control can bridge online optimization and reliable real-time operation of renewable-rich island grids.

This report describes some examples of the operation of two grid-forming (GFM) inverter-based photovoltaic-battery hybrid power plants connected to the electric system of the island of Kauai in Hawaii. Both plants are photovoltaic power plants with battery energy storage and are connected to the networked transmission system operated by Kauai Island Utility Cooperative (KIUC), along with several other power plants including an oil-fired plant, diesel generators, small hydroelectric plants, a biofuel plant, and several other photovoltaic plants. While many studies have examined the potential benefits of GFM operation to enhance grid stability in regions with high levels of inverter-based resources (IBRs), nearly all grid-connected IBRs have historically been operated using grid-following (GFL) controls. At the time of the events described in this report, the two GFM plants in Kauai were the only known IBR plants in the U.S. that operated in grid-forming mode while in parallel with a transmission system. A primary goal of this report is to demonstrate the feasibility, potential benefits, and challenges of operating IBR plants in GFM mode. In particular, this report describes two major grid stability events that, along with related studies by the authors and others, indicate significant benefits of GFM operation. This report also compares the performance of the two GFM plants to a specification for GFM IBRs developed by the UNIFI Consortium. The benefits described here are enabled in part because the two plants in question include both PV and battery energy storage (BESS). For two of the PV-BESS hybrid facilities described in this report, the solar inverters operated in GFL mode, while the battery inverters operated in GFM mode, known as AC-coupling. The other hybrid facilities use DC-coupled inverters that deliver both PV and BESS energy to the grid through the same power electronic device. Battery inverters that operate in GFM mode while grid-connected are now available from several vendors for utility-scale plants. In addition, some vendors offer GFM inverters that accept DC-coupled PV and battery inputs simultaneously. However, while GFM operation of PV inverters is feasible, GFM-capable inverters for utility-scale PV-only applications are not widely available currently.

The process of energy decarbonization in island power systems is accelerated due to the swift integration of inverter-based renewable energy resources (IBRs). The unique features of such systems, including rapid frequency changes resulting from potential generation outages or imbalances due to the unpredictability of renewable power, pose a significant challenge in maintaining the frequency nadir without external support. This paper presents a unit commitment (UC) model with data-driven frequency nadir constraints, including either frequency nadir or minimum inertia requirements, helping to limit frequency deviations after significant generator outages. The constraints are formulated using a linear regression model that takes advantage of real-world, year-long generation scheduling and dynamic simulation data. The efficacy of the proposed UC model is verified through a year-long simulation in an actual island power system using historical weather data. The alternative minimum inertia constraint, derived from actual system operation assumptions, is also evaluated. Findings demonstrate that the proposed frequency nadir constraint notably improves the system's frequency nadir under high photovoltaic (PV) penetration levels, albeit with a slight increase in generation costs, when compared to the alternative minimum inertia constraint.

As extreme weather increasingly threatens power system reliability, efforts to model these impacts remain hindered by fragmented, event-specific physical models and datasets that lack high resolution. Consequently, system vulnerabilities are left unaddressed. This lack of foresight represents a blind spot for the rapidly increase in integrating renewable energy in the Western Electricity Coordinating Council (WECC) region, which faces increasing but understudied risks from hurricanes and severe storms. To address this gap, this work proposes a data-driven framework that leverages existing weather databases to construct an extremeweather impact factor matrix. This matrix is then spatially transformed and applied to normal weather events to generate the synthesis of high-resolution extreme weather scenarios with customizable trajectories and intensities. A synthetic hurricane with different intensity levels targeting Southern California is simulated using the WECC 240-bus system. Simulations reveal spatially disproportionate grid impacts. Although the event targets Southern California, expansive cloud cover reduces Northern California’s solar generation by more than 78%, compared to a 45% reduction in Southern California. Simultaneously, strong wind triggers safety shutdowns, significantly degrading southern wind production during the peak hurricane impact period. Infrastructure fragility results indicate that maximum physical damage occurs not within the storm’s eye but within a radius of 100–250 km from the eyewall. Within this critical zone, transmission lines, wind and solar plants emerge as significantly vulnerable by sustaining damage even during tropical storms, whereas substations exhibit resilience to Category 2 hurricanes. These results within the framework can be beneficialfor system stress-testing and planning, machine learning training with more extreme scenarios, and operations under extreme weather.

The Stability-Augmented Optimal Control of Hybrid PV Plants with Very High Penetration of Inverter-based Resources (SAPPHIRE) project team developed and demonstrated advanced modeling, control, and monitoring tools to address the stability challenges of operating power systems with high shares of inverter-based resources (IBRs). At the system level, SAPPHIRE created a stability-constrained scheduling framework that incorporates frequency-stability-constrained alternating current optimal power flow formulations. This framework allows operators to co-optimize cost and stability requirements in both island and bulk power systems. At the plant level, the project advanced grid-forming (GFM) control technologies for hybrid photovoltaic plants (HPPs), enabling them to enhance small-signal stability by flexibly transitioning between operating modes. The team also developed complementary modeling of battery degradation and secondary frequency regulation to ensure economic and reliable operation while an HPP is providing grid services. Meanwhile, SAPPHIRE delivered significant innovations in real-time monitoring techniques to enhance grid stability. For example, the project team developed measurement-based real-time inertia estimation for both ambient data and probing signal approaches. These methods were validated through simulations and power-hardware-in-the-loop experiments at the National Laboratory of the Rockies' Flatirons Campus. We established and successfully applied a new oscillation event analysis framework to investigate a real 19-hertz oscillation event on Kaua'i Island. By combining field data, full electromagnetic transient (EMT) modeling of island grids, and EMT-based small-signal stability analysis techniques, the project identified the root causes and validated mitigation strategies, including converting grid-following to grid-forming inverters. The project team successfully conducted the following three field demonstrations in Hawaii: Grid stability enhancement with GFM HPPs on Kaua'i Island showed improved frequency quality after inverter conversion to GFM mode by leveraging 2 years of continuously collected data from the island's grid. Real-time inertia estimation was implemented and demonstrated using a photovoltaic (PV)-battery plant to inject inertia probing signals to Kaua'i Island. The Kaua'i Island Utility Cooperative grid operated with extremely high solar and hydro energy (100%) for 3 consecutive hours, with 88.5% from solar PV and 11.5% from hydropower, demonstrating secure system performance enabled by GFM HPPs. We delivered 13 peer-reviewed journal papers, 16 conference papers, and 32 invited presentations, and we contributed to 4 industry-led technical reports from the North American Electric Reliability Corporation, the Energy Systems Integration Group, and the Institute of Electrical and Electronics Engineers Power System Dynamic Performance Committee. We also generated two software records for inertia-monitoring tools. Overall, SAPPHIRE demonstrated that HPPs equipped with advanced GFM controls can provide essential stability services, mitigate oscillation risks, and enable reliable operation of islanded grids powered entirely with major energy from solar and water. These outcomes provide actionable pathways for utilities, system operators, and industry stakeholders as they plan for high-IBR futures.

T-PSH is an innovative structure of C-PSH that features two turbines, one for pumping and one for generation, replacing the single turbine in C-PSH. These turbines are housed in separate chambers, with an additional set of penstocks connecting the pump turbine to the hydraulic system. This structure enables T-PSH to operate in an HSC mode, allowing for simultaneous pumping and generation, which provides greater operational flexibility compared to C-PSH. To model T-PSH's HSC mode, a user defined turbine and governor system model is required, including the shared penstock model and additional pump turbine model. This specification will focus on the details of the user-defined turbine and governor model, which leverages the HYGOV model.

This presentation reports a twin circuit theory that enables us to interpret power system dynamics from a electric circuit perspective. Our proposed twin circuit theory can roughly prove the dissipating energy flow (DEF) method, and shows the positive impacts of well-designed grid-forming (GFM) inverters on power systems stability.