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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

A Dynamical Model for The Jupiter Thermocline

A new conceptual model for the observed banded wind structure of the giant outer planets is proposed, assuming a smoothly distributed potential vorticity (PV) atop a layer of deeply seated, latitudinally variable stratification. With a vanishing horizontal entropy contrast presumably enforced at the bottom of the flow layer by the underlying convection, the thermal-wind balance of observed cloud-top motions implies a mapping of constant potential temperature surfaces, nearly vertical at upper tropospheric levels, down to a deeper, flatter, but variably sloped 'thermocline.' For a generally colder-poleward distribution of isentropes consistent with the strong equatorial jets of both Jupiter and Saturn, this kind of mapping would also imply a poleward decreasing static stability. The proposed temperature-stability distribution is just the reverse of the Earth's troposphere, where the strongest latitudinal potential temperature gradients are at the bottom instead of the top, with static stability generally increasing toward the pole. The warmer-stabler correlation for Jupiter would be consistent, however, with a nearly monotonic distribution of PV from low to high latitudes, over planetary scales for which the planetary vorticity dominates the relative vorticity of the jets. In this way efficient PV mixing for the isentropically bounded thermocline can account for the dynamical maintenance of the cyclonic flanks of the equatorial jet. Over latitudinal intervals comparable to the internal deformation scale, however, the gradient of the absolute vorticity is dominated by the flow curvature, with the local latitudinal gradient of the static stability proportional to the local potential temperature gradient and therefore the local variation in geostrophic velocity. The resulting correlation of velocities and vorticity gradients implies an alternation of the flow over the internal deformation radius set by the vertical entropy contrast.

Allison, Michael↗
DOE OSTI2023

Supply Chain Cybersecurity Recommendations for Solar Photovoltaics

Solar photovoltaic (PV) cybersecurity is a growing field of research. As deployments of solar PV has increased, cyber risk has also increased. However, utility solar PV installations are not required to comply with North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) unless they meet a minimum generation threshold of 75 Megawatts (MW). Individual residential scale solar PV deployments will not meet that generation threshold and are therefore excluded from NERC CIP requirements. With most solar installations below 75MW, solar PV has been deployed with minimal oversight and highly variable cybersecurity maturity. The resources that make up the digital supply chain can include software, code, data, as well as other digital components. However as clean energy technology advances, cybersecurity threats and vulnerabilities continue to evolve and grow in sophistication. Solar PV faces a unique challenge in which it can be deployed in residential buildings and purchased by a consumer directly. This makes the supply chain of PV a unique challenge, where responsible parties for cybersecurity vary widely depending on the type of solar PV being deployed. Supply chain cybersecurity for solar PV represents a critical area for ensuring safe operations as the U.S. moves towards a clean energy future.

14 SOLAR ENERGY↗
DOE OSTI2016

Energy Storage Requirements for Achieving 50% Solar Photovoltaic Energy Penetration in California

We estimate the storage required to enable PV penetration up to 50% in California (with renewable penetration over 66%), and we quantify the complex relationships among storage, PV penetration, grid flexibility, and PV costs due to increased curtailment. We find that the storage needed depends strongly on the amount of other flexibility resources deployed. With very low-cost PV (three cents per kilowatt-hour) and a highly flexible electric power system, about 19 gigawatts of energy storage could enable 50% PV penetration with a marginal net PV levelized cost of energy (LCOE) comparable to the variable costs of future combined-cycle gas generators under carbon constraints. This system requires extensive use of flexible generation, transmission, demand response, and electrifying one quarter of the vehicle fleet in California with largely optimized charging. A less flexible system, or more expensive PV would require significantly greater amounts of storage. The amount of storage needed to support very large amounts of PV might fit within a least-cost framework driven by declining storage costs and reduced storage-duration needs due to high PV penetration.

14 SOLAR ENERGY↗
DOE OSTI2016

Energy Storage Requirements for Achieving 50% Penetration of Solar Photovoltaic Energy in California

We estimate the storage required to enable PV penetration up to 50% in California (with renewable penetration over 66%), and we quantify the complex relationships among storage, PV penetration, grid flexibility, and PV costs due to increased curtailment. We find that the storage needed depends strongly on the amount of other flexibility resources deployed. With very low-cost PV (three cents per kilowatt-hour) and a highly flexible electric power system, about 19 gigawatts of energy storage could enable 50% PV penetration with a marginal net PV levelized cost of energy (LCOE) comparable to the variable costs of future combined-cycle gas generators under carbon constraints. This system requires extensive use of flexible generation, transmission, demand response, and electrifying one quarter of the vehicle fleet in California with largely optimized charging. A less flexible system, or more expensive PV would require significantly greater amounts of storage. The amount of storage needed to support very large amounts of PV might fit within a least-cost framework driven by declining storage costs and reduced storage-duration needs due to high PV penetration.

14 SOLAR ENERGY↗
DOE OSTI2017

New Best-Practices Guide for Photovoltaic System Operations and Maintenance

Fact sheet summarizing technical report TP-7A40-67553. As solar photovoltaic (PV) systems have continued their transition from niche applications into large, mature markets in the United States, their potential as financial investments has risen accordingly. Mainstream investors, however, need to feel confident about the risk and return of solar photovoltaic (PV) systems before committing funds. A major influence on risk and return for PV is operations and maintenance (O&M) - but O&M practices and costs vary widely across the United States, making these variables difficult for investors to predict. To address this barrier to continued PV investment, the PV O&M Working Group has developed a new best-practices guide for PV O&M.

14 SOLAR ENERGY↗
DOE OSTI2021

A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method

Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Reliability Analysis of Power Grids Considering Component Failures of Variable Energy Resources

This paper proposes an improved model for the reliability assessment of power systems considering component failures of variable energy resources (VER). The inherent intermittency of VER such as solar photovoltaic (PV) and wind farms, along with their susceptibility to component failures, present significant challenges to reliable system operation. These issues, combined with power grid operation and network constraints, complicate the reliable operation of VER-integrated power systems. Here, to address these concerns, this paper introduces a reliability assessment framework that considers VER input variability, its impact on component availability, and their resulting impact on overall system reliability. Stochastic models based on discrete Markov processes are developed to incorporate variable irradiance, wind speeds, and their effects on PV and wind component failure rates. A next-event and state transition-based approach is then developed to integrate the stochastic models into a mixed-timing sequential Monte Carlo simulation framework for composite reliability assessment. Case studies on the RTS-GMLC system demonstrate the effectiveness of the proposed model in evaluating the reliability of VER-integrated systems.

Pandit, Dilip [Sandia National Laboratories (SNL-N↗
DOE OSTI2022

On the operational characteristics and economic value of pumped thermal energy storage

Pumped thermal energy storage (PTES) systems use an electrically-driven heat pump to store electricity in the form of thermal energy, and subsequently dispatch the stored thermal energy to generate electricity using a thermodynamic heat engine. Optimal day-ahead operational scheduling and annual value of a PTES system based on Joule-Brayton thermodynamic cycles and two-tank molten salt hot thermal storage is evaluated in this work. Production cost models, which simultaneously optimize commitment and dispatch schedules for an entire set of generators to minimize the cost of satisfying electricity demand, are employed to determine system-optimal operation and day-ahead energy value of the PTES system within each of six hypothetical near-future grid scenarios intended to approximately represent the U.S. Western Interconnection or the Texas Interconnection. Sensitivity to grid scenario (including the contribution of variable renewable energy sources), thermal storage capacity, relative heat pump and heat engine capacities, and startup/shutdown cycling costs are evaluated. PTES energy value and heat engine annual capacity factor increase strongly as the contribution of variable renewable resources increases, heat pump capacity increases relative to heat engine capacity, or PTES cycling costs decrease. Grid scenarios in which the contribution of variable renewable energy is dominated by solar photovoltaics (PV) vs. wind produce inherently different PTES operational patterns. Annual PTES energy value within PV-dominated scenarios increased with storage capacity only up to approximately seven hours of full-load discharge capacity, whereas that within wind-dominated scenarios exhibited a continual increase with storage duration up to at least 16 hours.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation

Realizing the SETO 2030 mission of reducing solar energy costs to 3-5 c/kWh will require innovative enabling research on effective, cost-efficient integration of local PV within distribution systems. However, the intermittent and variable nature of PVs compels operators to impose conservative hosting capacity constraints. Given the extremely high variability of (intermittent and unpredictable) solar energy generation, relaxing the capacity constraints (which are currently around 15%) and achieving 100% or greater integration of renewables will require a fundamental transformation of the power grid via the utilization of exponentially larger amounts of AMI enabled fine-grained data. To address the challenges in increasing the penetration of renewable energy based DERs, this project envisions an Enhanced System Layer (ESL) at the distribution network level that is reliable, cost-effective and scalable to millions of Distributed Energy Resources (DERs)/devices. This includes developing: 1) Transformative and highly scalable machine learning based predictive analytics tools that plug into distribution system planning and provide real-time situational awareness at the distribution level for short and long-term operational planning. The tools will be built using novel data-driven energy models of millions of active nodes with AMI, 2) Adaptive stochastic analysis and optimization algorithms for real-time grid operations, 3) Dynamic Scenario Analysis using parallel Cloudenabled implementations with < 1 minute computational cycle times.

14 SOLAR ENERGY↗
DOE OSTI2021

Distributed Wind Controls: A Research Roadmap for Microgrids, Infrastructure Resilience, and Controls Launchpad (MIRACL)

The Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) project’s controls research area aims to expand the benefits from distributed wind (DW) generation assets beyond solely providing low cost power directly to consumers. To make distributed wind turbines operate more effectively there is a need for more advanced ways to control them, allowing power companies, businesses, and energy consumers to take advantage of the unique characteristics of wind energy. DW can contribute to the safe and secure operation of grid by providing services such as voltage regulation, frequency regulation, spinning reserves, and even black start capabilities. In the larger DER context where much of the research has focused around solar technologies, the inertia available in wind technologies has generally not been considered. For wind turbines to provide these services in an optimal and expanded way, development and demonstration of control methods and communication interfaces within a grid and microgrid framework are required. In this document, NREL led the literature review in collaboration with SNL, of DER controls-focused integration research to establish a baseline for the controls research under the MIRACL project to identify specific control functions to be focused on throughout this project. This literature review focused primarily on the control functions of variable distributed generation, largely pulling from past solar PV and battery controls works, with a specific focus on applicability for distributed wind energy systems. The goal of this document is to identify a research roadmap based on the open literature and past national laboratory works to inform advanced wind turbine and power electronics control functions for four use cases: 1) distributed wind in isolated systems, 2) grid-connected microgrids (wind-hybrid systems and islanded operation), 3) behind-the-meter distributed wind applications in the power distribution systems, and 4) front-of-the-meter distributed wind applications in the power distribution systems.

17 WIND ENERGY↗
DOE OSTI2019

Validating Performance Models for Hybrid Power Plant Control Assessment

The need for simple, but accurate performance models of wind turbine generators (WTGs), photovoltaic (PV) plants, and battery energy storage systems (BESS) for various hybrid power plant (HPP) studies motivates the present work. Particularly, the development and verification stage of HPP controls requires reduced-order models to minimize the complexity and computation effort of simulation platforms. In this paper, such models are proposed, and the most essential parts of the models are validated through field measurements. The models target power system integration studies involving active and reactive power, as well as frequency and voltage regulation where detailed models, as proposed in the standards, can be cumbersome. Field measurements of two Vestas WTGs, one 1-MW PV plant, and one 1-MW/1-MWh BESS are used for model validation. The results show that the WTG and PV performance models correctly estimate the power generation variability according to fluctuations in wind speed and solar irradiance. The BESS performance model provides satisfactory results related to grid-forming control performance and estimation of state-of-charge. The presented validation work enables using the proposed performance models for power system studies and HPP control design in all model-based design stages, that is, preliminary analysis, design, verification, and validation with a high level of confidence.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Review of Technical Photovoltaic Key Performance Indicators and the Importance of Data Quality Routines

Technical key performance indicators (KPIs) are important metrics used to assess and quantitatively summarize various aspects of photovoltaic (PV) systems, including long-term performance, economic viability, and carbon footprint. Herein, a group of experts of the International Energy Agency's Photovoltaic Power Systems Programme Task 13 collect and describ the most important technical KPIs used in the industry. Thereby, a set of best practices for reliably handling PV system data is presented and the impact of data quality and climatic variability on KPI calculation is investigated. Further, the effective use of technical KPIs allows triggering data-driven and informed decisions to optimize PV systems and providing a comprehensive overview of how PV systems operate across different conditions and climates. With the worldwide growth of the PV industry, more companies operate/own PV systems in different regions, where the climatic and seasonal profiles differ. This requires context-aware evaluation of KPIs, or the judicious application of multiple KPIs, to ensure that each asset is evaluated correctly. Beyond that, there is untapped potential in the utilization of KPIs through geospatial mapping and extrapolation of fleet KPIs. This study demonstrates that the uncertainty in KPI estimation is not well understood and depends on data quality, climatic variability, and system configuration.

14 SOLAR ENERGY↗
DOE OSTI2018

Comparing supply and demand models for future photovoltaic power generation in the USA

We explore the plausible range of future deployment of photovoltaic generation capacity in the USA using a supply-focused model based on supply-chain growth constraints and a demand-focused model based on minimizing the overall cost of the electricity system. Both approaches require assumptions based on previous experience and anticipated trends. For each of the models, we assign plausible ranges for the key assumptions and then compare the resulting PV deployment over time. Each model was applied to 2 different future scenarios: one in which PV market penetration is ultimately constrained by the uncontrolled variability of solar power and one in which low-cost energy storage or some equivalent measure largely alleviates this constraint. The supply-focused and demand-focused models are in substantial agreement, not just in the long term, where deployment is largely determined by the assumed market penetration constraints, but also in the interim years. For the future scenario without low-cost energy storage or equivalent measures, the 2 models give an average plausible range of PV generation capacity in the USA of 150 to 530 GWdc in 2030 and 260 to 810 GWdc in 2040. With low-cost energy storage or equivalent measures, the corresponding ranges are 160 to 630 GWdc in 2030 and 280 to 1200 GWdc in 2040. The latter range is enough to supply 10% to 40% of US electricity demand in 2040, based on current demand growth.

14 SOLAR ENERGY↗
DOE OSTI2016

Residential PV-Energy Storage Testing Collaboration with SunPower: Cooperative Research and Development Final Report, CRADA Number CRD-14-569

Residential scale energy storage devices have shown great promise to add value and improve the ability to integrate distributed and variable generation into the distribution grid. Battery energy storage systems combined with PV and advanced inverters can provide many services. These include extended backup power in case of grid failure, reductions in feeder and system level peak load, system level net load ramp reduction, grid management services both locally (e.g., voltage management) and bulk system (e.g., reserves) level, and improved grid reliability through grid voltage and frequency support. Even though the cost of the energy storage systems are decreasing, one of the main challenges related to the widespread deployment of energy storage, as found by the U.S. Department of Energy (DOE), is utility acceptance of storage technologies. Various interconnection challenges exist when connecting distributed PV-energy storage into the electrical distribution grid in terms of safety, reliability and stability of the electric power systems. Some of the urgent challenges, as seen by the manufacturers and installers are: safety of the battery cells and the packs, performance of the power conversion systems, effect of bidirectional power flow on existing utility equipment and qualification of solutions such as volt-ampere reactive (VAR) support and frequency regulation. Under this project, National Renewable Energy Laboratory (NREL) researchers will work with SunPower to address these challenges by research, testing and analysis at Energy System Integration Facility (ESIF) and energy storage testing laboratory at NREL. NREL's unique power hardware-in-the-loop (PHIL) capability will be utilized to evaluate system-level issues such as reverse power flow and VAR support (if available) for these PV-energy storage systems. Through the analysis of test results, the proposed project will demonstrate use of ESIF capabilities in a way that advances both the DOE and SunPower's goals of moving clean, renewable energy technologies onto the electrical grid.

14 SOLAR ENERGY↗
DOE OSTI2022

Photovoltaic Plant and Battery Energy Storage System Integration at NREL's Flatirons Campus

Although utility-scale solar photovoltaic (PV) power plants are becoming a cost-effective energy resource, there is belief within the energy industry that the increasing penetrations of PV technologies could potentially impact grid reliability. This is due to the variability across timescales, the forecast uncertainty of the solar energy resource, and the impacts on both distribution and transmission systems. This can cause utilities to severely limit PV installations or to assign PV integration costs when considering a least-cost portfolio of resources. As California and other regions in the United States are marching toward fulfilling - and probably exceeding - their renewables portfolio standards goals, grid operators are beginning to face operational challenges that could have implications for existing renewable and nonrenewable generators and that will shape opportunities for future projects. In this work, we focused on developing controls and conducting demonstrations for AC-coupled PV-battery energy storage systems (BESS) in which PV and BESS are collocated and share a point of common coupling (PCC).

14 SOLAR ENERGY↗
DOE OSTI2026

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↗