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At least 343 records · Page 19

Enabling Power at Sea: Opportunities for Expanded Ocean Observations through Marine Renewable Energy Integration

The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and in-person interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.

16 TIDAL AND WAVE POWER↗

Powering the Blue Economy: Progress Exploring Marine Renewable Energy Integration With Ocean Observations

The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and inperson interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.

marine renewable energy, powering the blue economy↗

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Inference of Rock Flow and Mechanical Properties from Injection-Induced Microseismic Events During Geologic CO 2 Storage

Monitoring microseismic activities during CO 2 injection into geologic formations is important for ensuring the safety of the storage operations. The resulting data provide insight into the response of the storage formation to CO 2 injection and can be used to infer the underlying rock flow and mechanical properties. In this paper, assimilation of microseismic data is performed for dynamic characterization of the storage formation by using a stochastic simulation model to forecast the microseismic response of a geologic formation during CO 2 injection. Two modeling approaches are adopted to predict the space-time distribution of the injection-induced microseismicity. The first model is based on pore pressure relaxation assumption, while the second model uses coupled flow and geomechanics simulation to establish the complex physical relation between the storage formation properties and the corresponding microseismic responses during CO 2 injection. The stochastic predictive models in each case are used in ensemble data assimilation frameworks to estimate rock properties from the observed microseismic data. Two data assimilation methods are considered: (i) a new ensemble-based stochastic point process filter (EnPPF) that can directly integrate discrete microseismic events, and (ii) a variant of ensemble smoother, known as the ensemble smoother with multiple data assimilation (ES-MDA), which requires continuous representation of microseismic events for assimilation. The two methods are successfully applied to a geologically realistic model of the Farnsworth Field in Texas, with complex geologic flow units and interacting fault systems.

42 ENGINEERING↗

Low-cost fin-tube heat exchanger design for building thermal energy storage using phase change material

Heat transfer in phase change materials (PCMs) is complex because the melting and freezing fronts change as functions of stored or released heat. In prior attempts to optimize heat exchangers (HXs) in one or two dimensions, complex geometry has often been used to maximize the melt and freeze front area. This complex geometry is difficult and hence expensive to construct. This paper proposes a multiple-scale 3D finite element modeling approach to design fin-tube HXs for low-cost latent thermal energy storage applications. Further, the optimal fin and tube designs were determined at three scales (unit-scale, medium-scale, and large-scale) by modeling the melt and freeze front in three dimensions and using measured bulk thermal properties. The finite element model was validated by comparing it with the experimental data for a referenced design of a similar type. The results indicate that commercially available organic PCMs with low conductivity (<0.3 W/m·K) can have charge and discharge times appropriate for building thermal energy storage (i.e., 4–5 h) with fin-tube HX designs at costs <$26/kWh, even when the temperature difference (5.56°C) between the heat transfer fluid and the PCM phase change temperature is small. However, as the HX increases in length, the temperature reduction along the tube limits some larger-scale designs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Dynamical System Scaling of a Thermocline Thermal Storage System in the Thermal Energy Distribution System (TEDS) Facility

The purpose of this study was to develop a process to convert input signals from one facility into another by reflecting geometric and environmental settings. The Dynamic Energy Transport and Integration Laboratory (DETAIL) is one facility in development that aims to emulate the daily interactions among power production industry systems and be capable of receiving real-time data from those systems as inputs. To convert signals and ensure that the temporal sequences and magnitudes reflect the laboratory settings, the ability to scale and project data is essential. To demonstrate this ability, Dynamical System Scaling (a methodology that enables systems to scale and project or extrapolate datasets to desired environments while conserving the observed transient behavior based on first principles) was applied to DETAIL’s thermocline thermal storage system in the Thermal Energy Distribution System. The thermocline system was successfully scaled and a test case was conducted to generate a doubly accelerated energy charge and discharge in reference to past experimental data from the facility. The accelerated data were determined as able to conserve the amount of energy stored and the associated test boundary conditions were charge line maximum temperature, charge line velocity, and thermocline maximum temperature at 354 °C, 0.458 m/s, and 418 °C, respectively. The research results represented a case that required signals to be accelerated without altering the stored energy.

Yoshiura, Ramon (ORCID:0000000319781313)↗

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗

Measurement of the inclusive cross sections of prompt J / ψ and ψ ( 3686 ) production in e + e − annihilation from s = 3.808 to 4.951 GeV

The inclusive cross sections of prompt J / ψ and ψ ( 3686 ) production are measured at center-of-mass energies from 3.808 to 4.951 GeV. The dataset used is 22 fb − 1 of e + e − annihilation data collected with the BESIII detector operating at the BEPCII storage ring. The results obtained are in agreement with the previous BESIII measurements of exclusive J / ψ and ψ ( 3686 ) production. The average values obtained for the cross sections measured in the center-of-mass energy ranges from 4.527 to 4.951 GeV for J / ψ and from 4.843 to 4.951 GeV for ψ ( 3686 ) , where the impact of known resonances is negligible, are 14.0 ± 1.7 ± 3.1 pb and 15.3 ± 3.0 pb , respectively. For J / ψ , the first and the second uncertainties are statistical and systematic, respectively. For ψ ( 3686 ) , the uncertainty is total. These values are useful for testing charmonium production models. Published by the American Physical Society 2025

Ablikim, M.↗

Battery Energy Storage System Evaluation Method

This report describes development of an effort to assess Battery Energy Storage System (BESS) performance that the US DOE Federal Energy Management Program (FEMP) and others can employ to evaluate performance of deployed BESS or solar photovoltaic (PV) +BESS systems. The proposed method is based on actual battery charge and discharge metered data to be collected from BESS systems provided by federal agencies participating in the FEMP's performance assessment initiatives. Long-term (e.g., at least one year) time series (e.g., hourly) charge and discharge data are analyzed to provide approximate estimates of key performance indicators (KPIs).

25 ENERGY STORAGE↗

Carbon Storage Planning Inquiry Tool (CS PlanIT)

The CS PlanIT provides stakeholders with easy access to geographically explore, query, and evaluate multiple data layers for a user-defined area of interest, with the goal to accelerate carbon storage resource, feasibility assessments, and planning efforts. The data within CS PlanIT are arranged and categorized to assess each stage of the Carbon Capture and Storage supply chain, from CO2 (Carbon Dioxide) emission sources, critical energy infrastructure (including transport), to CO2 geologic storage and sink locations. Furthermore, stakeholders can gain insights into community and environmental impacts, as CS PlanIT leverages Environmental Justice and Social Justice (EJSJ) and natural hazard datasets. Additionally, documentation in the form of an interactive ArcGIS story map and data catalog are included within the EDX publication to provide user’s information to better understand and explain CS PlanIT’s layout, functions, and associated data sets.

Justman, Devin M.↗

Hybrid Power Plants: Status of Operating and Proposed Plants, 2022 Edition [Slides]

Falling battery prices and the growth of variable renewable generation are driving a surge of interest in “hybrid” power plants that combine, for example, wind or solar generating capacity with co-located batteries. While most of the current interest involves pairing photovoltaic (PV) plants with batteries, other types of hybrid or co-located plants with wide-ranging configurations have been part of the U.S. electricity mix for decades. This annually updated briefing tracks and maps existing hybrid or co-located plants across the United States while also synthesizing data mined from power purchase agreements (PPAs) and generation interconnection queues to shed light on near- and long-term development pipelines. The scope includes co-located hybrid plants that pair two or more generators and/or that pair generation with storage at a single point of interconnection, and full hybrids that feature co-location and co-control. The focus is on plants with one megawatt (MW) or more of capacity; smaller (often behind-the-meter) projects are also increasingly common, but are not included in this data synthesis. Key findings from the latest briefing include: -At the end of 2021, there were nearly 300 hybrid plants (>1 MW) operating across the United States, totaling nearly 36 gigawatts (GW) of generating capacity and 3.2 GW/8.1 GWh of energy storage. PV+storage plants are by far the most common, dominating in terms of plant number (140), storage capacity (2.2 GW/7.0 GWh), storage:generator ratio (53%), and storage duration (3.2 hours). But there are nearly twenty other hybrid plant configurations as well, including several different fossil hybrid categories (each dominated by the fossil component) as well as wind+storage, wind+PV, wind+PV+storage, geothermal+PV, and others. -Last year was a breakout year for PV+storage hybrids in particular: 67 of the 74 hybrids added in 2021 were PV+storage. By the end of 2021, there were more GW of battery capacity installed in PV+storage hybrids (2.2 GW) than as standalone storage plants (1.8 GW). The difference is even starker in energy terms, with PV+storage plants hosting twice as much battery capacity as standalone storage plants (7 GWh vs. 3.5 GWh, respectively). Much of the battery capacity added in hybrid form in 2021 was a battery retrofit to a pre-existing PV plant. -Data on plants under development from the interconnection queues of all seven ISOs/RTOs plus 35 individual utilities suggest that these hybridization trends are likely to continue. At the close of 2021, there were more than 670 GW of solar plants in the nation’s queues; 285 GW (~42%) of this capacity was proposed as a hybrid, most typically pairing PV with battery storage (PV+storage represented nearly 90% of all hybrid capacity in the queues). For wind, 247 GW of capacity sat in the queues, with 19 GW (~8%) proposed as a hybrid, again most-often pairing wind with storage (wind+storage represented ~4% of all hybrid capacity in the queues). Meanwhile, nearly half of all storage in the queues is estimated to be part of a hybrid plant. While many of these proposed plants will not ultimately reach commercial operations, the depth of interest in hybrid plants—especially PV+storage—is notable.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Marine Hydrokinetic Tidal Turbine

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset is part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with other energy technologies. This dataset contains inputs and outputs from simulations of a floating marine hydrokinetic turbine over approximately half a tidal cycle (~6.6 hours). Inflow conditions were derived from field measurements in Alaska’s Cook Inlet and represent a tidal environment in which the current speed ramps from near 0 m/s to a peak of 3 m/s and back. The original acoustic doppler current profiler dataset is publicly available on the Marine and Hydrokinetic Data Repository. In a full tidal cycle, the flow reverses and the rotor would reorient; this reversal was not modeled. In the Cook Inlet campaign , turbulence intensity was similar in both directions. Two inflow cases are included. In the first case, labeled “raw” in the files, the measured current time series was used directly in the InflowWind module of OpenFAST. Speed and direction were applied as a function of time and elevation, uniformly in the horizontal direction. With full spatial coherence, this approach captures high turbulent variability and results in pronounced power fluctuations, so it is considered a conservative, near-worst-case representation of loading. In the second case, labeled “average” in the files, a 30-minute moving average was applied to extract the slowly varying mean speed. The residual fluctuations about this mean were used to generate spatially varying, full-field turbulence inputs with TurbSim, giving a more physically realistic representation of the inflow across the rotor disk. Two random realizations were used to produce distinct inflow conditions for two OpenFAST simulations representing a two-turbine array. The same turbulence intensity is applied across the full time series, producing larger fluctuations at the start and end, where the mean speed is low. The second case is the more appropriate framework for performance and power assessment but overpredicts turbulence at lower flow speeds and underpredicts it at higher speeds. As the floating platform moves and the rotor changes its x-position, Taylor’s frozen turbulence hypothesis used by InflowWind assumes a constant rather than a time-varying mean velocity, introducing some inaccuracy in the velocity plane sampling. The turbine modeled is the 500-kW Reference Model 1, a horizontal-axis two-bladed hydrokinetic turbine on a four-column floating semisubmersible substructure . Simulations were performed using OpenFAST v4.1 with the Reference Open Source Controller (ROSCO) v2.10. All input files required to reproduce the simulations are included. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel . This unit supports up to 2.5 MW, but NLR has only a single 1.25-MW stack. The datasets report hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. The system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operating current of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The simulated tidal turbine time series data was translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz. Each zip file represents a single tidal electrolysis experiment and is named: {technology}_{inflow method}_{number of 500 kW tidal turbines connected} For instance, “tidal-500kW-RM1_average_2.zip” is a 6-hour experiment using the 500-kW tidal reference model, scaled by 2x (1-MW) to better match the electrolyzer maximum of 1.25MW, fed with the 30-minute moving average current case. Each zip folder contains the following files: A .csv file of raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wave power. A .csv file combines all tidal profiles as "combined_tidal_experiments.csv." A separate experiment, “characterization_200.zip,” shows the MC250 electrolyzer steady-state response with 30-minute load steps over 5 hours and is accessible with this entry.

08 HYDROGEN↗

H-2-SALT: Storing Fossil Energy as Hydrogen in Salt Caverns

Hydrogen storage in subsurface caverns is known as perhaps largest and longest-term energy storage system. While few such systems are currently in service (e.g., Texas; Teesside, UK), they are envisioned as being a pillar of the current energy transition that will allow intermittent power production from renewable sources (e.g., wind, solar) to be balanced with demand at grid-scale. In addition, hydrogen energy storage can allow existing fossil power plants to run more economically either by minimizing startups/shutdowns or by allowing them to take advantage of arbitrage. Finally, stored hydrogen can feed a variety of non-power users such as pipeline gas, heating, transportation, and manufacturing. The H-2-SALT paper study assessed the feasibility of a power-to-hydrogen system utilizing salt cavern storage of hydrogen in bedded salt in central Kansas, where over 750 such caverns have been constructed to date, of which approximately 350 are still in service. Legacy well and salt cavern data were collected to develop a regional geological database for two sites in Central Kansas and adjacent areas for use in more detailed geological and geomechanical site characterization efforts. This study found commercial viability and competitiveness of large-scale, electrolytic H 2 production and storage ($1.78 per kg H 2 ) that can be used for both electrical power supply to the grid during high-priced electricity periods (including co-firing with NG and fuel cell power production) and sale of H 2 for various industries, such as petrochemicals or transportation. The commercial potential of the H-2-SALT system is also relatively high because each of its components operate commercially today. It is recommended that a stratigraphic test well be drilled to collect log data and core samples that can reduce uncertainty surrounding geological and geomechanical properties of the salt beds that will be required for further cavern and energy storage system design studies.

03 NATURAL GAS↗

Hydropower Capital and O&M Costs: An Exploration of the FERC Form 1 Data

This report explores the potential for using responses from the Federal Energy Regulatory Commission’s (FERC’s) “Electric Utility Annual Report,” also known as FERC Form 1, as a cost database for conventional and pumped storage hydropower (PSH). The report outlines the process used to compile an easy-to-access database from the original FERC Form 1 data and discusses the historical cost/performance data. The steps for developing the database include downloading the annual FoxPro files from the FERC website (FERC, 2020), reading them into an Excel format, and reconciling naming issues across years, repeated entries, and shared assets, among others. The cleaned and compiled Form 1 database is available on Oak Ridge National Laboratory’s (ORNL’s) HydroSource website. For this report, the compiled Form 1 database was linked to two other hydropower databases, the National Inventory of Dams and the ORNL Existing Hydropower Assets, that provide additional information on the characteristics and other features of plants in the Form 1 data. Explorations of the plant characteristics, performance, capital costs, and operation and maintenance (O&M) cost data in the combined database were presented separately for conventional hydropower and PSH projects. Form 1 has several advantages as a hydropower cost database. First, the cost data is reported directly by plant owners. Second, Form 1 provides cost breakdowns of capital and operating costs for large conventional and PSH plants enabling more detailed tracking of hydropower costs compared with typical total cost estimates. Third, although the Form 1 data is not reported by all operational hydropower plants in the United States, the available data represents a wide range of plant characteristics and a sizable proportion of the hydropower fleet (61% of PSH plants and 22% of conventional hydropower plants by capacity). The lower proportion of the conventional hydropower fleet is because Form 1 reporting requirements apply only to private utilities that meet given size thresholds, leaving out federally owned facilities and many smaller hydropower plants. Overall, the Form 1 cost database represents a unique, publicly available database on hydropower asset capital and O&M costs. This report leads to a compiled database for the reporting years 1994 to 2020 that helps resolve issues with accessibility and use of the FERC Form 1 data by hydropower plant owners, project developers, technology developers, and regulators.

13 HYDRO ENERGY↗

Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model

In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enforced by a two-dimensional (2D) mathematical model. The 2D model, which includes 6 governing equations and 24 boundary conditions, provides a detailed representation of the electrochemical reactions, mass transport and hydrodynamics occurring inside the redox flow battery. To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method. Our numerical results show that the PINN is able to predict cell voltage correctly, but the prediction of potentials shows a constant-like shift. To fix the shift, the PINN is enhanced by further constrains derived from the current collector boundary. Finally, we show that the enhanced PINN can be even further improved if a small number of labeled data is available.

25 ENERGY STORAGE↗

Availability Estimates of Low-Cost, Dispatch-Constrained Electricity (LDE)

This dataset accompanies the report "Potential Availability of and Supply Curves for Low-Cost, Dispatch-Constrained Electricity", which provides initial estimates of the quantity and availability of the potential low-cost, dispatch-constrained electricity (LDE) resource in the United States under scenarios with high variable renewable energy (VRE) penetrations. The analysis also estimates supply curves that can be used in subsequent analysis of the opportunity to use the LDE. The estimated LDE resource could be used for various applications that value low-cost electricity and can operate at reduced capacity factors, including electrolytic hydrogen production and carbon capture. The analysis accompanying this dataset was performed in 2018-2019 to support an analysis of "The Technical and Economic Potential of the H2@Scale Concept within the United States" ( https://www.nlr.gov/docs/fy21osti/77610.pdf ). This dataset includes the quantity of estimated LDE resource for each hour of the year in 2050, aggregated to both the national level and the ReEDS balancing area level. As described in the accompanying report, the ReEDS model was used to project the evolution of the power sector over time. Links to the mapping of ReEDS balancing areas to states and ReEDS balancing area shape files are provided. This dataset also includes the report figure data.

08 HYDROGEN↗

Qualitative Risk Assessment of Legacy Wells within the Estimated Prairie State Generating Company Area of Review

This report details the digitization of a legacy wellbore database, including data processing assumptions, parameter estimation, and risk assessment methodology. The database, comprising 6,454 documents, was provided by ISGS. It includes valuable data from the Prairie State Generating Company (PSGC) and One Earth Energy (OEE) sites of the CarbonSAFE Phase III – Illinois Storage Corridor project. The report focuses on wells within a 15-mile radius from the Lively Grove #1 (LG#1) well at PSGC site, evaluating subsurface conditions and potential risks. A total of 4,386 wellbores within 15 miles of the LG#1 well were filtered based on depth and formation codes. LG#1 is the stratigraphic well at the PSGC site drilled in 2021. Ninety-four (94) wells penetrating the Maquoketa Shale Group (the primary confining unit) within the estimated area-of-review (AoR) for the PSGC site were evaluated using a qualitative risk assessment (QRA) methodology. The QRA developed by Arbad et al. 2022 focuses on legacy wells within the AoR and categorizes them based on well construction details. The QRA identifies wells that need immediate attention by categorizing them based on penetration depth and protection. Wells within the AoR were categorized into nine groups based on penetrations and protections. These categories range from Type 1 wells, with no documentation, to Type 9 wells, which do not penetrate the primary confining unit or storage reservoir (unit). Well accessibility within the AoR varies based on well status, including Dry & Abandoned (DA), Plugged & Abandoned (PA), Injection (INJ), Oil/Gas Producing (PROD), and Observation (Obs) wells. Accessibility levels were determined by well construction, with DA wells being the least accessible and Observation wells the most accessible, impacting gas leakage detection possibilities. Remedial action priority of wells decreases from Type 1 to Type 9 wells. Type 1 to Type 6 wells with status DA and PA require immediate attention, while Type 7 and Type 8 wells are low priority. A risk matrix used to prioritize corrective actions for legacy wells is proposed to categorize wells within an AoR based on penetrations, protections, and accessibility. The methodology involves data acquisition, well categorization into nine types, and determining CO 2 leakage pathways using well schematics and geospatial mapping. This approach is particularly useful for managing the integrity of legacy wells throughout the lifecycle of a Carbon Capture and Storage (CCS) project. A qualitative risk assessment of 94 wells within the AoR of the PSGC site identified 54 wells with high priority for corrective action due to penetration of the primary containment seal. The assessment utilizes color-coded maps to categorize well types and prioritize corrective actions, providing a comprehensive analysis. Schematics of wells penetrating the primary confining unit were drawn, and leakage pathways were identified. Details of all wells penetrating the confining zone are provided in the appendix, including information on well types, plugging, and casing status.

01 COAL, LIGNITE, AND PEAT↗

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Wind

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production by conducting a statistical analysis of historical wind data over a five-year period (2020-2025) from a single 1.5MW turbine manufactured by General Electric (GE) located at NLR’s Flatirons Campus, to generate an experimental test profile that was deployed on a 1.25-MW proton exchange membrane type MC250 electrolyzer system manufactured by Nel Hydrogen . [1] While the electrolyzer balance-of-plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. The historical wind data provided several metrics, however, the analysis particularly focused on the measured power output by the wind turbine. The power output time series of data for each day was categorized by total energy generation and standard deviation, and the day that represented the highest combination of these two metrics was chosen – December 25th, 2022. This process was then repeated for a moving four-hour window within this day to identify the most statistically variable period. Finally, this four-hour period was scaled by 65% to match the 1.25 MW electrolyzer. The electrolysis system controls hydrogen production by varying DC current applied to the stack, from a maximum of 3000 A to a minimum safe operation of 300 A, or 10%. Because the current – voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical wind profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1 Hz frequency. For more details on the statistical analysis process, see the presentation labeled “ Public Reference Data for Megawatt-Scale Hydrogen Electrolysis” provided with each data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}_{scaling factor}-{electrolyzer ramp rate in amperes/second} For instance, “wind-GE1.5MW_0.65-400.zip” represents the hour-long experiment using historical data from the wind-GE1.5MW turbine, scaled to 65%, with the electrolyzer power supply set to a maximum ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and wind power input. A PDF file detailing the historical wind data statistical analysis used to generate the wind profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_historical_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis [1] nelhydrogen.com/product/mc-series-electrolyser .

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