Search NASA⌕ Search

SEARCH · Search NASA

Results for “solar variability”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development Support for NREL's System Advisor Model (SAM): Cooperative Research and Development, CRADA Number CRD-20-16998 (Final Report)

The variable nature of renewable generation, which depends on the sun shining and wind blowing for example, presents challenges for adequate and least-cost resource planning. The deployment of renewable generation assets is anticipated to increase due to various drivers, such as declining costs, favorable government policies, and increasing procurement by corporations and consumers. High levels of renewable penetration are anticipated to pose flexibility, reliability, and cost challenges to the electrical system. The development and implementation of new features into performance modeling software, such as the National Renewable Energy Laboratory's (NREL) System Advisor Model (SAM), is one way to help think through these challenges and develop appropriate strategies.

14 SOLAR ENERGY↗
DOE OSTI2024

The importance of accounting for landscape position when investigating grasslands: A multidisciplinary characterisation of a Californian coastal grassland

Data from the characterisation of the Point Reyes Field Site, published in AGU Earth's Future under the title: The importance of accounting for landscape position when investigating grasslands: A multidisciplinary characterisation of a Californian coastal grassland. This paper explored the effect of landscape position on the response of a Californian grassland to seasonal changes. All files are csv files. The EMI data contains 8 csv files with a metadata csv explaining the columns. The dataset also includes soil variables including total concentrations calculated from fused samples, then dissolved and measured on ICP-AES for whole-rock elements and ICP-MS for trace elements. Mineral composition was attained using X-ray diffraction at BL 11-3 at SSRL . Data was then run through the High Score database to characterise different mineral phases. total It also includes a table with bulk soil characteristics such as soil pH, cation exchange capacity, and soil textural data. Data from Teros 12 Meter soil moisture, electrical conductivity and temperature sensors are presented in SMS Csv file. While the WL bottom and top files contain data from Piezometers measuring the ground water table. We have included a csv file that contains soil CO2 efflux data from Feb 2021-Oct 2021 in the Point Reyes Grassland Experiment We have included the spatially orientated (easting northing) remotely sensed datasets that were used in the K-means clustering analysis conducted on our site with electrical conductivity, normalised difference vegetation index, elevation, slope, solar radiation, topographic position and wetness index, and a clustering score. Finally there is a list of all the identified grassland species at the site.For more information on flux data, please contact the corresponding author.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Physical properties, internal structure, and the three‐dimensional petrography of CI chondrites

physical properties and the nature of their breccation, we investigated nine samples of the Ivuna and Orgueil CI chondrites ranging in size from 1 mm to 4 cm in approximate diameter. The combined mass of unique material investigated in this work is 113 g. For our investigations, we use ideal gas pycnometry, 3-D laser scanning, x-ray computed microtomography (μCT), and accompanying digital data extraction techniques. We found that the bulk density of the samples ranged from 1.61 to 2.10 g cm −3 . Larger samples tend to have a lower bulk density. Grain density (ranging from 2.44 to 2.55 g cm −3 ) is significantly less variable than the bulk density in our samples and the quantity of porosity (ranging from 14.6% to 33.8%) is the dominant factor in determining the bulk density of CI chondrite material. Our μCT results show that the visible porosity across all sizes of our CI chondrite samples is in the form of cracks, but these cracks can account for less than two-thirds of the porosity in the CI chondrites. Other porosity is not visible, even at μCT resolutions of 2.7 μm voxel edge −1 and we conclude that it is sub-micron in nature. It is not clear if the cracks seen in our samples are indigenous to the chondrites or are a result of terrestrial processes. We also find that the CI chondrites are excellent examples of the fractal-like nature of brecciation, where clasts can be observed at all scales we imaged. The breccias are composed of sub-equant-shaped and sub-rounded-textured clasts like melt-free impact breccias on other solar system bodies. From our μCT volume and digital data extraction, we determine that the Ivuna CI chondrite breccia is organized: the mostly sub-equant clasts within our ~2 cm chunk of Ivuna have a mean diameter of 1.33 mm and their aligned longest axes define a lineation structure. We speculate that the lineation was imparted after fragmentation of the clasts by slight shear on the parent asteroid which could be the result of seismic-related granular flow or mild non-axial impact-related compaction. These data will help to place returned asteroidal material from asteroids 162173 Ryugu and 101955 Bennu and the CI chondrites into a mutual geological context.

CI chondrite↗
DOE OSTI2026

Reconstructing Quasar Spectra and Measuring the Lyα Forest with SpenderQ

Quasar spectra carry the imprint of foreground intergalactic medium (IGM) through absorption features. In particular, absorption caused by neutral hydrogen gas, the "Lyα forest," is a key spectroscopic tracer for cosmological analyses used to measure cosmic expansion and test physics beyond the standard model. Despite their importance, current methods for measuring Lyα absorption cannot directly derive the intrinsic quasar continuum and make strong assumptions on its shape, thus distorting the measured Lyα clustering. We present SpenderQ , a ML-based approach for directly reconstructing the intrinsic quasar spectra and measuring the Lyα forest from observations. SpenderQ uses the Spender spectrum autoencoder to learn a compact and redshift-invariant latent encoding of quasar spectra, combined with an iterative procedure to identify and mask absorption regions. To demonstrate its performance, we apply SpenderQ to 400,000 synthetic quasar spectra created to validate the Dark Energy Spectroscopic Instrument Year 1 Lyα cosmological analyses. SpenderQ accurately reconstructs the true intrinsic quasar spectra, including the broad Lyβ, Lyα, SiIV, CIV, and CIII emission lines. Redward of Lyα, SpenderQ provides percent-level reconstructions of the true quasar spectra. Blueward of Lyα, SpenderQ reconstructs the true spectra to < 5%. SpenderQ reproduces the shapes of individual quasar spectra more robustly than the current state-of-the-art. We, thus, expect it will significantly reduce biases in Lyα clustering measurements and enable studies of quasars and their physical properties. SpenderQ also provides informative latent variable encodings that can be used to, e.g., classify quasars with Broad Absorption Lines. Overall, SpenderQ provides a new data-driven approach for unbiased Lyα forest measurements in cosmological, quasar, and IGM studies.

Hahn, ChangHoon [Arizona U., Astron. Dept. - Stewa↗
DOE OSTI2025

Uncertainty-Based Design: Finite Element and Explainable Machine Learning Modeling of Carbon–Carbon Composites for Ultra-High Temperature Solar Receivers

Design under uncertainty has significantly grown in research developments during the past decade. Additionally, machine learning (ML) and explainable ML (XML) have offered various opportunities to provide reliable predictable models. The current article investigates the use of finite element modeling (FEM), ML and XML predictions, and uncertain-based design of carbon-carbon (C-C) composites for use in ultra-high temperatures. A C-C composite concentrating solar power (CSP) as a microvascular receiver is considered as a case study. These C-C composites are fiber composites with directly integrated carbonized microchannels to form a lightweight, high-absorptivity material that includes an embedded microvascular network of channels. The topology of these microchannels is engineered to optimize heat transfer to a supercritical carbon dioxide (sCO2) heat transfer fluid. The mechanical characterization of C-C composites is highly challenging. Thus, designing every component made of C-C composites for ultra-high temperature applications needs an uncertainty-based analysis. As a part of a comprehensive project on the development of a novel carbonized microvascular C-C composite, this paper explores C-C composite sensitivity analysis, FEM, ML prediction, and XML analysis. The resulting composite can then be carbonized and coated with an oxidation-resistant coating to form a thermally efficient and mechanically robust C-C composite. An ANSYS 3-D-FE model was used to analyze the CSP’s stress/strain. To consider the variability in the mechanical and thermal properties of C-C composites, various mechanical properties are considered as the ANSYS FEM’s input. A synthetic dataset from 730 ANSYS runs was produced to feed into the ML and XML algorithms for uncertainty analysis and prediction. The ML and XML algorithms could accurately predict the CSP stresses/strains.

Daghigh, Vahid (ORCID:0000000298941620)↗
DOE OSTI2025

Dynamic security assessment of systems powered only by grid-forming power plants with uncertain dispatch using polynomial vectors

A modern challenge in power engineering is to perform the dynamic security assessment (DSA) of grids that are 100% powered by inverter-based resources (IBRs). Addressing this challenge is difficult because: (i) the dispatch of IBRs can be uncertain as a result of the variability of renewable resources and (ii) they have hard current control limits that cannot be neglected, contrasting synchronous machines. To address this problem, this paper sets forth a framework to conduct DSA of bulk power systems that are 100% powered by grid-forming IBRs. Furthermore, the framework considers that IBR operational conditions are unknown but bounded by a zonotope which is also expressed as a polynomial vector for uncertainty propagation via Dormand–Prince integration. The framework is applied to modified versions of the WSCC 9-bus and IEEE 39-bus grids.

14 SOLAR ENERGY↗
DOE OSTI2024

Cooperation in Transmission Expansion Planning: Enhancing Grid Reliability and Efficiency Under a Changing Climate

Electricity grids are challenged to maintain reliability during more intense and frequent extreme weather events due to climate change. This challenge is exacerbated by multi-sector electrification and power sector decarbonization through increased reliance on variable renewable energy, which necessitates the expansion of transmission infrastructure. However, transmission expansion planning is often complicated by intertwined planning authorities and jurisdictions, and allocation of large capital investment needs. These factors cause authorities to manage transmission investments individually (i.e., only/mostly intraregional planning), which can lead to suboptimal transmission networks. This study investigates the potential benefits of cooperative transmission expansion planning (i.e., both intraregional and interregional planning that optimizes transmission investments across the entire physical system). Using sectoral and economic optimization, and machine learning models, it analyzes the impact of different levels of cooperation among transmission planning regions within U.S. Western Interconnection in 2019 and 2059 via an iterative investment process. Furthermore, it examines the effects of future climate change on transmission cooperation by simulating historical heat waves from 2019 under conditions of 2059. The results indicate that cooperative transmission planning leads to lower wholesale electricity prices, decreased energy outages, and reduced greenhouse gas emissions. However, the advantages of collaboration diminish during widespread heat waves, despite remaining beneficial especially for regions like California Independent System Operator with substantial solar installations. The study underscores the importance of transmission cooperation in reducing costs and enhancing reliability, emphasizing the need for strategic investments in storage to address challenges posed by future extreme weather events with varying spatial scales.

Capacity Expansion Model↗
DOE OSTI2020

Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization

The identification and prediction of the daily soiling profiles of a photovoltaic site is essential to plan the optimal cleaning schedule. In this article, we analyze and propose various methods to extract and generate photovoltaic soiling profiles, in order to improve the analysis and the forecast of the losses. New soiling rate extraction methods are proposed to reflect the seasonal variability of the soiling rates and, for this reason, are found to identify the most convenient cleaning day with the highest accuracy for the investigated sites. Also, we present an approach that could be used to predict future soiling losses through the implementation of stochastic weather generation algorithms whose ability to identify in advance the best cleaning schedule is also successfully tested. The methods presented in this article can optimize the operation and maintenance schedule and could make it possible, in the future, to predict soiling losses through analysis based only on environmental parameters, such as rainfall and particulate matter, without the need of long-term soiling data.

14 SOLAR ENERGY↗
DOE OSTI2022

Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Laboratory-based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Additionally, there is a growing concern on characterizing the non-uniformity of field exposure. Therefore, data from field surveys are critical to assess the performance of component lifetimes. Using a documented field survey protocol, 19 field surveys were conducted. The focus of this survey strategy is to investigate spatial continuity in degradation modes. By combining field survey data with real-time satellite weather data, stressor / response models have been trained. Generalized additive Models (GAM) model was created to predict the value of degradation based on measured predictors. Two different GAM constructions were testing using different implementations of basis splines. The model includes variables on the environmental stressors of the system and the location of each measurement in the PV mounting structure. The incorporation of hierarchical structure into the models allowed for material specific degradation rates, while maintaining the assumption of a global trend. The model performed well with an adjusted R2 of 0.975 for yellowness index prediction.

backsheet↗
DOE OSTI2025

Selection of Global Climate Model Data for Downscaling With Generative Machine Learning and Use in the Power Planning for Alignment of Climate and Energy Systems Project

The range of results from climate models and scenarios is important to the understanding of uncertainty in power planning analysis. A U.S. Department of Energy-funded analytic project called Power Planning for Alignment of Climate and Energy Systems is developing data and analytic methods to reflect the effects of climate change on key variables for power system planning, as part of the Grid Modernization Lab Consortium. This project will select and prepare global climate model results for use in power system planning models. A related report (Evaluation of Global Climate Models for Use in Energy Analysis) assesses the performance of various global climate models from the Coupled Model Intercomparison Project Phase 6 data archive for their historical skill with respect to energy system performance and for their future projections under multiple climate change scenarios. Building from that report, we describe the selection of a climate scenario (Shared Socioeconomic Pathway [SSP] 2-4.5) and five climate models: TaiESM1, EC-Earth3-CC, GFDL-CM4, EC-Earth3-Veg, and MPI-ESM1-2-HR. We describe the model selection criteria, which were based on the quality of the match between model results under historical conditions and on the representation of the range of future values for several variables. These results will be downscaled via an open-source generative machine learning method called Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

An Integrated Assessment of a G3 GMD Event on Large-Scale Power Grids: From Magnetometer Data to Geomagnetically Induced Current Analysis

Solar activities can cause geomagnetic disturbances (GMDs) that give rise to geomagnetically induced currents (GICs) which may compromise the reliability of the power system. In order to build more reliable models representing GMD interactions with the power grid, the power system’s detailed electrical model must be considered along with fluctuations in the earth’s magnetic and induced surface electric fields. Here, this study investigates the impact of incorporating spatially varying magnetic fields into surface electric field models on GMD risk metrics. A spatially independent magnetic field model and a spatially varying model are compared through simulations. To perform this analysis, the earth’s magnetic field disturbances are transformed into surface electric fields using respective one-dimensional earth conductivity models. Then, the modeling impact of these electric fields is studied using a 2,000-bus grid for Texas and a 25,000-bus grid for the northeast and mid- Atlantic regions of the United States. Simulation results reveal that the inclusion of spatially varying magnetic fields results in considerable differences in GMD risk metrics, highlighting the importance of accounting for spatial variability when assessing GMD risks in the power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Techno-economic comparison of sCO 2 cycles for particle-based CSP at design-point conditions

In this work, we compare the techno-economic performance of supercritical carbon dioxide power cycles integrated in a particle CSP system. We model four core cycle configurations: simple (with optional bypass), recompression (with optional bypass), partial cooling, and turbine split flow, which each demonstrate different benefits in a CSP system, such as high efficiency, low cost, or large HTF temperature differences. We parametrically sweep cycle design variables for each configuration. The set of power cycle performance results are then combined with a design point particle CSP system model which calculates the system specific cost. The simple cycle and turbine split flow cycles have the best performance in the baseline results, with system specific costs of 5,912 and 5,899 $\$$/kWe respectively. In addition to the baseline set of results, we also vary key parameters and costs in a sensitivity study. The cycle designs with the best system performance limit their efficiency to ~45 %, despite demonstrating higher maximum efficiencies, due to the rapid increase in cost of recuperation as efficiencies rise. The simple cycle has strong performance in the analysis and is on average only 1.4 % worse than the optimal configuration. Lowered turbine inlet temperatures from the sensitivity study improve performance by reducing the PHX and turbine cost. Decreasing the inlet temperature from 700 to 625°C results in an >8 % decrease in system specific cost. Future work should expand sensitivity analyses to colder turbine inlet temperatures and calculate system performance by simulating annual performance with off-design solar and cycle component models.

14 SOLAR ENERGY↗
DOE OSTI2020

Blackstart of Power Grids with Inverter-Based Resources: Preprint

This paper presents the findings of our investigation into inverter-based resource- (IBR-) driven blackstart of electric grids. Four potential black-start configurations with different setups are presented. To evaluate the technical feasibility of IBR-driven black start in the four configurations, a behavioral model of inverters that mimics current-limited inverter operation is developed using variable resistors in the MATLAB Simulink/Simscape environment. The inverter model is connected to an induction motor through transformers and a transmission line to simulate its startup. Simulation results show that even with the limited current supply capability of inverters because of their physical constraints, IBRs can black-start a motor under certain conditions. Results also show that by using soft-start techniques, such as ramped supply voltage, inrush currents can be reduced, thereby expanding the conditions under which IBRs can provide black-start support. Simulation results with different scenarios lead to discussions and key takeaways that could be valuable for further IBR-driven black-start research.

14 SOLAR ENERGY↗
DOE OSTI2020

On the Accuracy of the ALMA Flux Calibration in the Time Domain and across Spectral Windows

A diverse array of science goals requires accurate flux calibration of observations with the Atacama Large Millimeter/submillimeter array (ALMA); however, this goal remains challenging due to the stochastic time-variability of the “grid” quasars ALMA uses for calibration. In this work, we use 343.5 GHz (Band 7) ALMA Atacama Compact Array observations of four bright and stable young stellar objects over seven epochs to independently assess the accuracy of the ALMA flux calibration and to refine the relative calibration across epochs. The use of these four extra calibrators allows us to achieve an unprecedented relative ALMA calibration accuracy of ~3%. On the other hand, when the observatory calibrator catalog is not up to date, the Band 7 data calibrated by the ALMA pipeline may have a flux calibration poorer than the nominal 10%, which can be exacerbated by weather-related phase decorrelation when self-calibration of the science target is either not possible or not attempted. We also uncover a relative flux calibration uncertainty between spectral windows of 0.8%, implying that measuring spectral indices within a single ALMA band is likely highly uncertain. We thus recommend various methods for science goals requiring high flux accuracy and robust calibration, in particular, the observation of additional calibrators combined with a relative calibration strategy, and observation of solar system objects for high absolute accuracy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Grassland productivity responds unexpectedly to dynamic light and soil water environments induced by photovoltaic arrays

Abstract Agrivoltaic (AV) systems are designed to coproduce photovoltaic (PV) energy on lands simultaneously supporting agriculture (food/forage production). PV infrastructure in agroecosystems alters resources critical for plant growth, and water‐limited agroecosystems such as grasslands are likely to be particularly sensitive to the unique spatial and temporal patterns of incident sunlight and soil water inherent within AV systems. However, the impact of resource alteration on forage production, the primary ecosystem service from managed grasslands, is poorly resolved. Here, we evaluated seasonal patterns of soil moisture (SM) and diurnal variation in incident sunlight (photosynthetic photon flux density [PPFD]) in a single‐axis‐tracking AV system established in a formerly managed semiarid C 3 grassland in Colorado. Our goals were to (1) quantify dynamic patterns of PPFD and SM within a 1.2 MW PV array in a perennial grassland, and (2) determine how aboveground net primary production (ANPP) and photosynthetic parameters responded to the resource patterns created by the PV array. We hypothesized that spatial variability in ANPP would be strongly related to SM patterns, typical of most grasslands. We measured significant reductions in ANPP directly beneath PV panels, where SM and PPFD were both low. However, in locations with significantly increased SM from the shedding and redistribution of precipitation by PV panels, ANPP was not increased. Instead, ANPP was greatest in locations where plants were shaded in the afternoon but received high levels of PPFD in the morning hours, when air temperatures and vapor pressure deficits were relatively low. Thus, contrary to expectations, we found relatively weak relationships between SM and ANPP despite significant spatial variability in both. Further, there was little evidence that light‐saturated photosynthesis ( A sat ) and quantum yield of CO 2 assimilation (ϕCO 2 ) differed for plants growing directly beneath (lowest PPFD) versus between (highest PPFD) PV panels. Overall, the AV system established in this semiarid managed grassland did not alter patterns of ANPP in ways predictable from past studies of controls of ANPP in open grasslands. However, our results suggest that the diurnal timing of low versus high periods of PPFD incident on plants is an important determinant of productivity patterns in grasslands.

14 SOLAR ENERGY↗
DOE OSTI2023

Model-Free Probabilistic Forecasting of Nodal Voltages in Distribution Systems

As the penetration of distributed energy resources (DERs) into distribution systems increases, so does the interest in forecasting relevant system variables to help mitigate the associated challenges. One such challenge is the more frequent occurrence of excessive voltages in distribution systems with higher shares of DERs. Accurate and reliable estimates together with forecasts of system states (i.e., nodal voltages) will therefore play a key role in improving the utilization of these variable and uncertain sources while mitigating potential operational risks. Whilst recent literature has explored machine learning (ML) methods for voltage estimation and their extrapolation for a short-time period into the future, few have taken uncertainty quantification into account, and these methods have not yet been translated into operations. This paper discusses the advantages offered by probabilistic voltage forecasts and proposes a non-parametric Bayesian method suitable for forecasting nodal voltages at short-term time horizons while accounting for uncertainties in load and distributed photovoltaic (PV) generation. We demonstrate the value of the proposed Gaussian process (GP) model for a case study using historical forecasts and observation data.

distribution system↗
DOE OSTI2024

Data for: A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial-Aquatic Interfaces

The investigation of how climate change and water level fluctuations impact variable and interconnected ecosystems, like the interfaces between terrestrial and aquatic environments, requires the collection and integration of many data types. We describe an integrative and autonomous environmental monitoring approach that uses environmental sensors and data loggers to monitor surface water, groundwater, soil, and vegetation changes and generate essential data for predictive models. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including a large-scale flood manipulation experiment, collectively generating over three million observations per month. Such sensor networks hold great promise for tracking and comprehending environmental changes where land and water intersect. The sensor system and overall approach to sensor management that we have designed is intended to be widely accessible for research teams spanning in size from an individual investigator to large multi-institution projects. This dataset shows example data generated by the sensor network described above. Data output for data loggers connected to groundwater water quality sondes measuring dissolved oxygen, pH, oxidative redox potential (ORP), groundwater elevation, groundwater salinity and temperature, and groundwater elevation; replicate soil moisture and conductivity probes installed at 10 and 30 cm below the ground surface; rainfall and solar radiation; and mean sap flow from 8 replicate probes. Sensors are installed at upland forest (UP), wetlands (W), and transitional locations between these ecosystems (TR). Example datasets are from Goodwin Islands in the Chesapeake Bay and Portage River along the Lake Erie coastline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Modular, Crushed-Rock Thermal Energy Storage Pilot Design (Final Report)

The goal of this project was to design a next-step pilot to advance near-term energy storage integrated with a fossil plant to provide a facility capable of being viable and effective in a market with growing penetration of variable renewable energy (VRE). Thermal energy storage (TES) represents an ideal technology for this purpose. The completed effort included a feasibility study to prepare for the Phase II pre-front end engineering design (pre-FEED) for implementing a crushed-rock TES system integrated with a natural gas combined cycle (NGCC) plant. The crushed rock storage technology, which is being developed by Brenmiller Energy, is a modular TES system termed bGen™, which can accommodate both thermal and electrical inputs and output steam, hot water, or hot air. For this application, the estimated efficiency is 80% thermal to thermal. For the feasibility study, the Brenmiller technology was designed to operate on a slipstream from NYPA’s Eugene W. Zeltmann Power Project (Zeltmann) NGCC plant in Astoria, New York. The projected size of the system will be up to 4 MWe with at least 4 hours of storage duration, or 16 MWh-e total. The study also included a techno-economic evaluation of a 200 MWh commercial-scale demonstration. Prior to this project, EPRI had reviewed Brenmiller’s technology, which is being built to demonstrate bGen™ at 1.7 MWe on a solar plant (Rotem) and has been designed for an NGCC facility in Italy, assessing it at technology readiness level (TRL) 5. Brenmiller is also conducting a separate 1-MWth pilot with NYPA that pairs a bGen™ module with a microturbine for a combined-heat-and-power (CHP) application to improve efficiency and provide flexibility. The next-step pilot being designed as part of this project would represent a 5-fold increase in scale, versus Rotem, and would show the technology’s ability to provide effective and economical energy storage, bringing the technology to TRL 6. This pilot would represent the next-to-last demonstration scale before the technology could be commercial ready at GWh-e scales in the 2030 timeframe. The main objective of the work completed by the Electric Power Research Institute, Inc. (EPRI), Brenmiller Energy (Brenmiller), New York Power Authority (NYPA), and United E&C (formerly AECOM) was to perform a Phase I feasibility study on the integration of a crushed-rock thermal energy storage (TES) with a fossil plant. Under this project, the EPRI-led team successfully completed a feasibility study to prepare for the potential future Phase II pre-front end engineering design (pre-FEED) to implement a crushed-rock TES system integrated with a natural gas combined cycle (NGCC) plant. Specific deliverables under this project included the Technology Maturation Plan, Conceptual Study, Techno-Economic Assessment, Technology Gap Assessment, Project Plan for Phase II (submitted as Phase II Renewal Application), Commercialization Plan, and the Final Report (this document). This Final Report includes a compilation of the various summary reports that were prepared during the 12-month schedule of the Phase I project execution under award DE-FE0032017.

03 NATURAL GAS↗