Active Power Control on Wind Turbine Machines (CRADA Final Report)
This project will evaluate the effectiveness of active power control by wind turbine machines.
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This project will evaluate the effectiveness of active power control by wind turbine machines.
This project at the Pacific Northwest National Laboratory (PNNL) enhances our understanding of how aquatic life interacts with marine renewable energy (MRE) and hydropower systems through the development and testing of advanced sensor technology. The Marine Sensor Fish (MSF) and its smaller variants, the Sensor Fish Mini (SF Mini) and Flexible Sensor Fish (FSF), measure critical environmental stressors, such as shear forces, pressure changes, and strike impacts, that are essential for assessing the risks posed by energy devices, including tidal turbines, hydropower installations, and other MRE infrastructure. Rigorous field testing has shown that these tools are effective in capturing detailed data on turbulence, pressure variations, strike impacts, and other stressors near these devices, providing valuable insights into the conditions faced by aquatic species. Economically, these tools offer renewable energy developers a practical solution for streamlining environmental assessments and ensuring regulatory compliance, ultimately reducing costs associated with evaluating and mitigating ecological impacts. This research also benefits the public by promoting the growth of renewable energy sources that protect aquatic ecosystems, advancing both sustainable energy production and environmental stewardship.
The goal of this project was to develop and validate two complementary, cost-effective remote sensing technologies to monitor avian fatalities at utility-scale solar facilities: fixed platform (Animal Activity Monitoring-AAM) and aerial-based (Uncrewed Aircraft Systems-UAS). This project used these features with machine learning to automate the detection of avian carcasses and nests at solar facilities.
This presentation describes version 3 of the grid-forming (GFM) specifications for inverter-based resources developed by the UNIFI (Uniform Interoperability for Grid-Forming Inverters) Consortium. It summarizes the requirements for GFM inverters, the four categories of GFM capability, and the tests designed to assess GFM capability. The tests including time-domain tests similar to those in use by some industry entities and frequency-domain tests that are largely new. This presentation also describes ongoing efforts to use the UNIFI GFM specifications as the starting point for a new IEEE standard called IEEE P2800.1.
This research project will address the most pressing uncertainties in modeling and measuring the electric power grid effects of geomagnetic disturbances (GMDs) and the E3 portion of nuclear electromagnetic pulse (EMP). The primary goal is to help decision-makers in the electric power sector have the knowledge and tools they need to most effectively mitigate GMD effects on the North American electric grid, with a secondary focus on EMP response. Primary tasks will involve comprehensive modeling,
The primary objective of the Accelerating Resilience of the Community through Holistic Engagement and Use of Renewables (ARCHER) initiative was to identify and incorporate the unique variations in energy burden, social vulnerability, living conditions, and access to essential services that differ across communities. By accounting for these localized factors—down to the neighborhood level—the project supports more targeted and effective investments in community resilience. The framework seeks to establish practical planning guidance, methods, and performance measures for community energy resilience, integrate community-level and electric utility system resilience planning, and assess its effectiveness through comparison with conventional and operational planning approaches. A key component of the project was its data exchange platform, which is used to evaluate and demonstrate the tools, methodologies, and planning approaches developed through ARCHER. This open-source platform enables developers and vendors of distribution and outage management systems to build upon the research by incorporating its concepts into their own tools and workflows. This capability is enabled by the transparent availability of data, functional requirements, and the underlying information model. The project yielded several important insights. First, meaningful engagement with communities is essential to achieving comprehensive resilience outcomes. Second, resilience planning is most effective when electric grid considerations and broader community needs are addressed in a coordinated manner. Third, the use of platforms that allow for real-time input from communities can enhance utility responsiveness during restoration activities. Fourth, a structured and systematic planning approach can successfully translate ARCHER concepts into practice. Fifth, the development of an integrated metric that reflects both grid performance and community impacts provides a more holistic basis for evaluating resilience. Finally, incorporating community engagement and equity considerations into grid operations is critical, particularly during severe weather events that result in extended outages.
The project developed hybrid physics-based and machine-learning methods for near-real-time detection of balance-of-system faults (e.g., string, combiner, and tracker outages) in utility-scale Photovoltaic plants, achieving over 50% true positive rates with under 10% false positives and significantly reducing engineering setup time. In the extended phase, the scope expanded to plant-level underperformance analysis and industry benchmarking through the SUPER.epri.com platform. SUPER standardizes data processing and performance metrics across more than 9 GWac and 120+ plants, enabling robust comparisons and insights into loss rates, inverter downtime, and capacity degradation.
This Evaluation Plan defines practical, commissioning-oriented test protocols for in-field assessment of grid-forming (GFM) inverter-based resources (IBRs) deployed under the UNIFI Consortium Field Demonstration. The demonstration’s overarching objective is to validate multi vendor GFM technologies at impactful unit/plant scale, assess interoperability and grid-service performance, and evaluate the applicability of the UNIFI Specifications for Grid-forming Inverter-based Resources in real utility environments, including integration with operational systems and protection schemes.
Assessing the availability of key wind resources requires augmenting observations to support the implementation of wind energy infrastructure. However, observations are limited, necessitating the development of high-resolution, long-term gridded datasets. This study presents a robust, dynamically downscaled climatological dataset, offering 20 years of hourly wind data at a 4 km spatial resolution across North America, and evaluates its performance against observations, including meteorological towers and automated surface-observing system (ASOS) stations, as well as coarse-resolution reanalysis data (the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5)). Results demonstrate that the downscaled high-resolution wind data outperform ERA5 in regions of complex terrain and coastal areas, with improved overlap coefficients for wind data distributions and reduced root mean square errors (RMSEs) for hub-height and near-surface diurnal wind patterns. The downscaled simulation also captures the synoptic drivers of seasonal wind direction patterns reasonably well, indicated by high wind rose similarity indices. This study also provides an analysis of interannual variability, utilizing the dataset's full 20-year period, and model uncertainty, generated by varying model initial conditions and physics parameterizations across 1-year ensemble members, which are key considerations for wind resource assessment in wind farm development.
The primary hazard of a leak from a hydrogen system is due to the immediate or delayed ignition of the fuel leading to a jet flame or explosion. Therefore, understanding the hydrogen ignition probability is critical for analyzing the risk of hydrogen systems. This report reviews the current understanding of hydrogen ignition mechanisms and methods for modeling their probability. The stoichiometry, ignition strength, and ignition source temperature are all important characteristics that can affect both the probability of ignition and the outcome of the subsequent combustion event. A brief review of diffusion ignition demonstrates that ignition probability models must account for seemingly spontaneous ignition of hydrogen in addition to scenarios where the ignition source is readily identified. State-of-the art models for both immediate and delayed ignition probabilities are presented, including different physical aspects of the scenarios (e.g., flow rate, ignition source characteristics) that are considered in the different modeling approaches. Current models often fail to account for the unique properties of hydrogen compared to other fuels, and most lack rigorous validation with hydrogen as a fuel. A fault tree framework is proposed to systematically evaluate the probability of ignition by integrating various ignition mechanisms and their uncertainties. Furthermore, this type of framework could enable additional insights into the most important mechanisms and would enable uncertainty quantification in risk assessment modeling. Recommendations for future research include the need for experimental validation of ignition models and the development of comprehensive methodologies that incorporate the specifics of hydrogen behavior in real-world scenarios.
This paper reports the effect of low-cost immersed condensers on the performance of a heat pump water heater (HPWH) that does not require a water-circulating pump. The proposed design is based on the immersion of an L-style condenser coil through an opening in the top of the water tank. The novel design aims to eliminate the need for the water-circulating pumps, thereby substantially improving efficiency and reducing costs and maintenance of the HPWH system. Comprehensive computational fluid dynamics (CFD) simulations and experimental tests were performed, and the results showed that the CFD simulations were close to the experimental data. Further results confirm that the L-style condenser can introduce buoyancy-driven flow and eliminate temperature stratification in the studied HPWH water tank, thereby improving the heat transfer coefficient and coefficient of performance of the HPWH. The testing results further showed that the L-style condenser enabled more than 53% higher coefficient of performance and 400% higher heat transfer coefficient compared with the straight vertical condenser, and achieved much faster water heating. Furthermore, the effects of immersed condensers with different circuits on the HPWH performance were evaluated and compared. All results indicate that the current energy-saving and inexpensive HPWH technology is technically valid, and the benefits of performance improvement and low cost are attractive in future HPWH design.
Artificial intelligence and machine learning systems have the potential to influence the future design and implementation of cybersecurity systems for the power grid. These systems may enhance the overall operation of the power system by leveraging and making sense of massive amounts of data. However, we must also understand how AI/ML will need to be protected from cyber threat actors. We discuss the existing insights the NREL team has developed using AI/ML systems and then present resources including ESIF and the Cyber Energy Emulation Platform that can be used to generate training data and insights. We end by offering suggestions on priority research paths for AI in cybersecurity.
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In January 2025, the U.S. Department of Energy (DOE) sponsored a technical workshop with fuel vendors that provided a forum for increased collaboration on backend fuel cycle research. The workshop reviewed observations of thermal annealing of irradiated zirconium alloys in simulated dry storage conditions and discussed implications to the industry’s interest in reducing wet storage to optimize operational flexibility and the expected trend of increasing discharge burnup (and decay heats), which could lead to an increase in dry storage temperatures. One approach to accommodate increased decay heats in dry storage is to increase the regulatory temperature limit during dry storage, which is now 400°C. However, this could lead to thermal annealing of the cladding, which will tend to increase creep rates, creep strains, and ductility while decreasing yield strength. These considerations are compounded by bonding between the pellet and cladding at high burnup, which results in the pellet carrying more load during fuel rod deformations, especially in bending, which is the dominant deformation phenomenon in canister drop analyses. Because the U.S. Nuclear Regulatory Commission (NRC) recommends yield strength as a primary failure criterion for accident analyses in storage and transportation, particularly for canister drop scenarios, the purpose of the workshop was to develop options for alternative failure criterion that could replace yield strength in canister drop analyses. Topics discussed were (1) the regulatory framework, (2) the effect of thermal annealing on microhardness and tensile properties of cladding materials, (3) the effect of thermal annealing on fuel rod failure during bending, bending fatigue, and pinch loading, and (4) an alternative failure criterion for canister drop analyses. This paper provides a summary of the key discussions and outcomes of the workshop