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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Final Technical Report: Impact of Wildfires on Solar Generation, Reserves, and Energy Prices

This study quantifies the potential impacts of wildfires on the California grid, specifically the impacts under three different wildfire scenarios: a "no wildfire" scenario and two wildfire-specific scenarios, "2020 wildfire" and "enhanced wildfire" (with both wildfire scenarios assuming spatial distribution of the wildfire location the same as the 2020 fires). The study combines each scenario with two solar energy infrastructures: one corresponding to the current solar installation and the other to future (midcentury) solar installation. The analysis is conducted using state-of-the-science modeling tools: primarily WRF-Chem for atmospheric modeling with chemistry to account for impacts of wildfires on solar radiation and PLEXOS for electricity market simulations. The analysis suggests that wildfires can significantly attenuate solar radiation at both short downwind distances as well as far from the emissions sources. Although the study is limited in scope, and the analysis studies only a few of the many factors that can affect photovoltaic generation and electricity markets, it indicates that wildfire smoke is an important factor that needs to be carefully considered and analyzed in planning the future grid.

electricity market↗
DOE OSTI2021

Distributed Solar and Storage Adoption Modeling

The National Renewable Energy Laboratory (NREL) is analyzing the rapidly increasing role of energy storage in the electrical grid through 2050 through its Storage Futures Study. In one phase of the study, NREL used the laboratory's Distributed Generation Market (dGen) model to examine the various future distributed storage capacity adoption scenarios, results, and implications.

agent-based modeling↗
DOE OSTI2020

Impacts of Price Formation Efforts Considering High Renewable Penetration Levels and System Resource Adequacy Targets

Future markets with very high penetrations of renewable energy could have many low- to zero-cost periods, which would reduce energy revenues for the generation fleet. This can impact the ability of resources that are needed for long-term reliability to recover operating and capital costs. This paper explores the impact of an alternative pricing mechanism, relaxed minimum pricing, on revenue sufficiency on existing and future resource mixes, including those with high penetrations of wind and solar. This study evaluates eight scenarios encompassing three different sensitivity categories: (1) one alternative pricing mechanism versus traditional LMP pricing, (2) high versus low renewable penetration levels, and (3) using a resource mix that has been adjusted to a preset resource adequacy target versus one that contains a full set of resources. Results show renewable penetration has a greater impact on pricing and resulting profits than does the adjusted resource mix, the two pricing methods have modest variations in profits, and prices under the higher renewable penetration case were higher than under low renewable penetration. These conclusions are not intended to be direct predictions for future outcomes but rather to lead to additional research on the impacts of pricing on investment incentives and future resource adequacy targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

The National Climate Database (NCDB): An Unbiased 100-Year Dataset for PV Modeling

In this study, we develop a statistical technique to downscale the future projection of solar irradiance for photovoltaics (PV) energy-related applications. A set of Regional Climate Model (RCM)-based projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) are used as inputs to statistical methods to generate high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). The main steps of the statistical downscaling method include (1) regridding RCM output (0.22 degree and daily resolutions) to handle the modeled-observed data sets on a common grid, (2) correcting bias of RCM GHI using satellite-derived observation, and (3) implementing temporal and spatial downscaling to generate GHI at 8-km and hourly resolution. Basically, complex physical processes and interactions between solar radiation and various atmospheric constituents lead solar irradiance to be highly variable and uncertain. Underrepresentation of clouds from the RCM parameterizations is the main source of error and uncertainty in modeling solar irradiance. Thus, we adapt and use the high-quality satellite-derived data from the National Solar Radiation Database (NSRDB) to analyze the bias and error of RCM GHI as well as estimate the statistical parameters for spatial and temporal downscaling. This presentation will summarize the comprehensive analysis conducted to produce and assess the results under two climate scenarios (RCP4.5 and RCP8.5). We will also present a detailed validation demonstrating the strengths of the downscaling method, a summary of the 100-year dataset from 2001-2100, and future extension of this research.

bias correction↗
DOE OSTI2024

Detection of astrophysical neutrinos at prospective locations of dark matter detectors

We study the prospects for detection of solar, atmospheric neutrino, and diffuse supernova neutrino background (DSNB) fluxes at future large-scale dark matter detectors through both electron and nuclear recoils. We specifically examine how the detection prospects change for several prospective detector locations [Sanford Underground Research Facility (SURF), SNOlab, Gran Sasso, China Jinping Underground Laboratory (CJPL), and Kamioka] and improve upon the statistical methodologies used in previous studies. Because of its ability to measure lower neutrino energies than other locations, we find that the best prospects for the atmospheric neutrino flux are at the SURF location, while the prospects are weakest at CJPL because it is restricted to higher neutrino energies. On the contrary, the prospects for the DSNB are best at CJPL, due largely to the reduced atmospheric neutrino background at this location. Including full detector resolution and efficiency models, the CNO component of the solar flux is detectable via the electron recoil channel with exposures of ∼ 10 3 ton-yr for all locations. These results highlight the benefits for employing two detector locations, one at high and one at low latitude. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2023

Hybrid renewable energy systems: the value of storage as a function of PV-wind variability

As shares of variable renewable energy (VRE) on the electric grid increase, sources of grid flexibility will become increasingly important for maintaining the reliability and affordability of electricity supply. Lithium-ion battery energy storage has been identified as an important and cost-effective source of flexibility, both by itself and when coupled with VRE technologies like solar photovoltaics (PV) and wind. In this study, we explored the current and future value of utility-scale hybrid energy systems comprising PV, wind, and lithium-ion battery technologies (PV-wind-battery systems). Using a price-taker model with simulated hourly energy and capacity prices, we simulated the revenue-maximizing dispatch of a range of PV-wind-battery configurations across Texas, from the present through 2050. Holding PV capacity and point-of-interconnection capacity constant, we modeled configurations with varying wind-to-PV capacity ratios and battery-to-PV capacity ratios. We found that coupling PV, wind, and battery technologies allows for more effective utilization of interconnection capacity by increasing capacity factors to 60%–80%+ and capacity credits to close to 100%, depending on battery capacity. We also compared the energy and capacity values of PV-wind and PV-wind-battery systems to the corresponding stability coefficient metric, which describes the location-and configuration-specific complementarity of PV and wind resources. Our results show that the stability coefficient effectively predicts the configuration-location combinations in which a smaller battery component can provide comparable economic performance in a PV-wind-battery system (compared to a PV-battery system). These PV-wind-battery hybrids can help integrate more VRE by providing smoother, more predictable generation and greater flexibility.

14 SOLAR ENERGY↗
DOE OSTI2024

Pinellas County Clear Sky Assessment Process: Prioritizing Solar + Storage for Resilient Facilities & Communities

The Clear Sky Tampa Bay project was a 15-month collaborative research effort to support solar + storage deployment for community resilience in Florida. The Clear Sky Decision Support Toolkit is a collection of resources designed to support users in conducting solar + storage prioritization and feasibility screening assessments at critical facilities. The Tampa Bay Regional Planning Council worked with four local governments in the region to test and apply the Toolkit. This case study series describes how each partner government used the Toolkit and highlights key insights and lessons learned that other users could follow to replicate the process. Pinellas County applied the Clear Sky Decision Support Toolkit to improve interagency coordination and gather data to analyze solar + storage projects for its critical facilities. This case study examines energy resilience strategies for the Pinellas County Solid Waste-to-Energy Plant. The Toolkit's modular assessment process developed institutional capacity and knowledge within the county and will assist in the research and application of future solar energy projects.

14 SOLAR ENERGY↗
DOE OSTI2025

Comprehensive Evaluation of Agrivoltaics Research: Breadth, Depth, and Insights for Future Research

Agrivoltaics integrates agricultural production with solar energy generation to address challenges related to land use, food security, and renewable energy development. This study provides the most comprehensive evaluation to date of global agrivoltaic research, aiming to classify the literature, identify strengths and gaps, and guide future work. We systematically screened over 3000 English-language publications through 2023 for relevant agrivoltaic publications. A total of 670 studies were categorized in the InSPIRE Data Portal across five agrivoltaic activities and multiple hierarchical themes, including physical, biological, technological, social, and crosscutting domains. We found that research was concentrated on crop production, microclimate dynamics, and PV performance, with gaps in areas like human health, wildlife, policy, and standardized methodologies. Although the U.S. emphasizes animal grazing and habitat-based systems in practice, most U.S.-based studies focused disproportionately on crop production. The analysis revealed uneven geographic and topical representation and highlighted a lack of integrated, interdisciplinary approaches. This study concludes that while agrivoltaic research has grown rapidly, more coordinated efforts could support standardized data collection, address overlooked ecological and social impacts, and align research focus with real-world system implementation, ultimately improving the scalability and successful deployment of agrivoltaic systems.

14 SOLAR ENERGY↗
DOE OSTI2024

Low‐Temperature Synthesis of Stable CaZn 2 P 2 Zintl Phosphide Thin Films as Candidate Top Absorbers

Abstract The development of tandem photovoltaics and photoelectrochemical solar cells requires new absorber materials with bandgaps in the range of ≈1.5–2.3 eV, for use in the top cell paired with a narrower‐gap bottom cell. An outstanding challenge is finding materials with suitable optoelectronic and defect properties, good operational stability, and synthesis conditions that preserve underlying device layers. This study demonstrates the Zintl phosphide compound CaZn 2 P 2 as a compelling candidate semiconductor for these applications. Phase‐pure, ≈500 nm‐thick CaZn 2 P 2 thin films are prepared using a scalable reactive sputter deposition process at growth temperatures as low as 100 °C, which is desirable for device integration. Ultraviolet‐visible spectroscopy shows that CaZn 2 P 2 films exhibit an optical absorptivity of ≈10 4 cm −1 at ≈1.95 eV direct bandgap. Room‐temperature photoluminescence (PL) measurements show near‐band‐edge optical emission, and time‐resolved microwave conductivity (TRMC) measurements indicate a photoexcited carrier lifetime of ≈30 ns. CaZn 2 P 2 is highly stable in both ambient conditions and moisture, as evidenced by PL and TRMC measurements. Experimental data are supported by first‐principles calculations, which indicate the absence of low‐formation‐energy, deep intrinsic defects. Overall, this study shall motivate future work integrating this potential top cell absorber material into tandem solar cells.

14 SOLAR ENERGY↗
DOE OSTI2024

Prospects for measuring the time variation of astrophysical neutrino sources at dark matter detectors

We study the prospects for measuring the time variation of solar and atmospheric neutrino fluxes at future large-scale xenon and argon dark matter detectors. For solar neutrinos, a yearly time variation arises from the eccentricity of Earth’s orbit and, for charged current interactions, from a smaller energy-dependent day-night variation due to flavor regeneration as neutrinos travel through Earth. For a 100-ton xenon detector running for ten years with a xenon-136 fraction of ≲ 0.1 % , in the electron recoil channel a time-variation amplitude of about 0.8% is detectable with a power of 90% and the level of significance of 10%. This is sufficient to detect time variation due to eccentricity, which has amplitude of ∼ 3 % . In the nuclear recoil channel, the detectable amplitude is about 10% under current detector resolution and efficiency conditions, and this generally reduces to about 1% for improved detector resolution and efficiency, the latter of which is sufficient to detect time variation due to eccentricity. Our analysis assumes both known and unknown periods. We provide scalings to determine the sensitivity to an arbitrary time-varying amplitude as a function of detector parameters. Identifying the time variation of the neutrino fluxes will be important for distinguishing neutrinos from dark matter signals and other detector-related backgrounds and extracting properties of neutrinos that can be uniquely studied in dark matter experiments. Published by the American Physical Society 2024

Zhuang, Yi (ORCID:0000000277138724)↗
DOE OSTI2026

Enhancing Building Resilience: Maintaining Energy Efficiency and Thermal Comfort During Power Outages in Cold Climates

The increasing frequency and intensity of extreme weather events, such as heatwaves and cold snaps, present significant challenges to building energy performance and occupant comfort. Highly correlated with climate events are widespread long duration power interruptions that may affect thousands of buildings and millions of customers. This study evaluates the impact of building energy performance and occupant thermal comfort in medium-sized office buildings in a cold climate region. Using energy models representing pre-1980 and 2019 vintages, simulations were conducted to assess energy performance under typical weather conditions and occupant thermal comfort during power interrupted extreme cold snap and heatwave climate events under both current 2020s and future 2050s weather conditions. The results show a projected 33% increase in cooling energy demand and a 19% reduction in heating energy by 2050. Findings reveal that older buildings are more susceptible to cold discomfort during cold snaps, while modern airtight buildings are more vulnerable to overheating during heatwaves. Various passive energy efficiency measures, such as improved infiltration control, thermal windows, solar-controlled windows, and cool envelopes, were evaluated for their ability to mitigate thermal discomforts. Solar controlled windows and weatherstripping contribute to reducing cold thermal discomfort by 21% during a power-interrupted cold snap. Solar-controlled windows were found to reduce hot thermal discomfort by 34% during a future power-interrupted heatwave. The study highlights the importance of targeted retrofitting strategies to enhance thermal resilience, especially during power outages, to ensure occupant safety and comfort during extreme climate events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Method for Spatiotemporal Solar Power Profile Estimation for a Proposed U.S.–Caribbean–South America Super Grid under Hurricanes

Solar photovoltaic (PV) generation technology stands out as a scalable and cost-effective solution to enable the transition toward decarbonization. However, PV solar output, beyond the daily solar irradiance variability and unavailability during nights, is very sensitive to weather events like hurricanes. Hurricanes nucleate massive amounts of clouds around their centers, shading hundreds of kilometers in their path, reducing PV power output. This research proposes a spatiotemporal method, implemented in MATLAB R2023b coding, to estimate the shading effect of hurricanes over a wide distribution of PV solar plants connected to a high-voltage power infrastructure called the U.S.–Caribbean–South America super grid. The complete interconnection of the U.S., the Caribbean, and South America results in the lowest power valley levels, i.e., an overall percentual reduction in PV power output caused by hurricane shading. The simulations assess the impact of hurricanes in 10 synthetic trajectories spanning from Texas to Florida. The Caribbean would also experience lower power valleys with expanded interconnectivity schemes. The U.S.–Caribbean–South America super grid reduces Caribbean variability from 37.8% to 8.9% in the case study. The proposed spatiotemporal method for PV power profile estimation is a valuable tool for future solar power generation expansion, transmission planning, and system design considering the impact of hurricanes.

14 SOLAR ENERGY↗
DOE OSTI2023

Impact of Wildfires on Solar Generation, Reserves and Energy Prices

Wildfire seasons in the Western U.S. become more prolonged and intense in recent years bringing significant variability and uncertainty of solar generation. It greatly challenges the bulk power system and electricity market operation managed by California Independent System Operator (CAISO) as California leads both the wildfire records and the solar power integration. This study presents a screening-level analysis of the impact of wildfires on solar generation, operating reserve and energy prices applying historical real-world wildfire and market operation data. To the best of the authors knowledge, it is a first-of-its-kind study and will lay the foundation for market impact quantification and wildfire mitigation strategies design based on projected wildfire activities in future years.

electricity price↗
DOE OSTI2025

Agrivoltaic Grazing Systems for a Sustainable Future: A Multi‐Disciplinary Review & Gap Analysis

Solar photovoltaics (PV) is the fastest growing source of electricity in the world, however, its expansion has raised concerns about the displacement of agriculture and the degradation of rangeland ecosystems. This has prompted research and development of multi-land-use systems that co-prioritize energy production alongside agriculture, commonly referred to as agrivoltaics. Currently, agrivoltaic grazing (also called solar grazing) is one of the most common forms of these systems. However, peer-reviewed research on these systems is limited. This review synthesizes research on agrivoltaic grazing systems, drawing upon literature addressing agrivoltaics broadly and considering relevant literature in adjacent fields of study. Based on this review, we identify six key gaps and priority directions for future research. These include foundational fieldwork to characterize the layered ecological impacts of solar PV and grazing and understand PV-livestock integration from both an animal welfare and a PV infrastructure perspective. This will facilitate the development of biogeochemical and economic models that improve our predictive capacity and ability to compare system designs. We also identify gaps in understanding the human aspects of these systems and emphasize the importance of utilizing collaborative research methods and increasing research on the social dimensions of agrivoltaic grazing systems.

14 SOLAR ENERGY↗
DOE OSTI2023

Subordinated Gaussian Processes for Solar Irradiance

Traditionally the power grid has been a one-way street with power flowing from large transmission-connected generators through the distribution network to consumers. This paradigm is changing with the introduction of distributed renewable energy resources (DERs), and with it, the way the grid is managed. There is currently a dearth of high fidelity solar irradiance datasets available to help grid researchers understand how expansion of DERs could affect future power system operations. Realistic simulations of by-the-second solar irradiances are needed to study how DER variability affects the grid. Irradiance data are highly non-stationary and non-Gaussian, and even modern time series models are challenged by their distributional properties. We develop a subordinated non-Gaussian stochastic model whose simulations realistically capture the distribution and dependence structure in measured irradiance. We illustrate our approach on a fine resolution dataset from Hawaii, where our approach outperforms standard nonlinear time series models.

MATHEMATICS AND COMPUTING,SOLAR ENERGY↗
DOE OSTI2024

Neural Networks to Find the Optimal Forcing for Offsetting the Anthropogenic Climate Change Effects

Abstract Of great relevance to climate engineering is the systematic relationship between the radiative forcing to the climate system and the response of the system, a relationship often represented by the linear response function (LRF) of the system. However, estimating the LRF often becomes an ill-posed inverse problem due to high-dimensionality and nonunique relationships between the forcing and response. Recent advances in machine learning make it possible to address the ill-posed inverse problem through regularization and sparse system fitting. Here, we develop a convolutional neural network (CNN) for regularized inversion. The CNN is trained using the surface temperature responses from a set of Green’s function perturbation experiments as imagery input data together with data sample densification. The resulting CNN model can infer the forcing pattern responsible for the temperature response from out-of-sample forcing scenarios. This promising proof of concept suggests a possible strategy for estimating the optimal forcing to negate certain undesirable effects of climate change. The limited success of this effort underscores the challenges of solving an inverse problem for a climate system with inherent nonlinearity. Significance Statement Predicting the climate response for a given climate forcing is a direct problem, while inferring the forcing for a given desired climate response is often an inverse, ill-posed, problem, posing a new challenge to the climate community. This study makes the first attempt to infer the radiative forcing for a given target pattern of global surface temperature response using a deep learning approach. The resulting deeply trained convolutional neural network inversion model shows promise in capturing the forcing pattern corresponding to a given surface temperature response, with a significant implication on the design of an optimal solar radiation management strategy for curbing global warming. This study also highlights the technical challenges that future research should prioritize in seeking feasible solutions to the inverse climate problem.

Ren, Huiying↗
DOE OSTI2025

Final Report for CSP Tower Public Opinion and Education Project

As part of the CSP Plant Optimization Study for the California Power Market (DE-EE0009809) the project wanted to understand the public’s opinion of the technology and and explore the types of community engagement that is needed to support such development. The initial objectives for the public perception activities were twofold. The primary objective was to gather public opinion and feedback from the communities living near the two operating solar power tower plants, Ivanpah and Crescent Dunes, and to distill lessons learned from the community engagement conducted before, during, and after the plants were developed to inform future development. This included outreach to nearby airports. The other objective was to gauge public opinion about large-scale solar, specifically CSP towers, to start educating the public on the benefits and to begin building relationships with communities of interest, initially targeting the Kingman, Arizona area. The objectives shifted after the first exploratory trip to Ivanpah and Kingman, however, as it became apparent that it would be challenging to gather public opinion from the community surrounding Ivanpah that could be useful for other community profiles, and the company wasn’t ready to address the concerns in Kingman. Another area was chosen, therefore, to represent those where future development is possible. The recently published Lawrence Berkeley National Lab Perceptions of Large-Scale Solar Project Neighbors Study exemplified public perception polling based on social science and served as the foundation for the survey questions taken to the field. The intent was to ensure that people knew their input was valued and that the time they spent was valuable for the participant as well. It has been noted in the literature that in-person interaction has greater benefits than activities online or via mail, as well as limits the expense.

14 SOLAR ENERGY↗