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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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Public outreach fact sheet about the Los Angeles 100% Renewable Energy Study (LA100), which is the most comprehensive, detailed study done to model an entirely renewable-based power system as complex as LA's.
This document describes high-level findings from the Los Angeles 100% Renewable Energy Study (LA100), a partnership between the Los Angeles Department of Water and Power (LADWP) and the National Renewable Energy Laboratory (NREL) to analyze potential pathways LA can take to achieve a 100% clean energy future. This is an excerpt of the LA100 Executive Summary (NREL/TP-6A20-79444-ES), which provides additional background and detail.
In 2020, the Joint Institute for Strategic Energy Analysis (JISEA) completed several ongoing studies, published many reports and journal articles, and continued remote dialogue and analysis with research partners. The 2021 annual report captures the work completed the past year, highlights key findings across diverse studies, and shows the people behind the research, offering a great window into JISEA for anyone wanting to know more about the institute.
Solar photovoltaics (PV) are the fastest growing renewable energy technology for clean, inexpensive, and sustainable electricity generation. Along with numerous technical roadmaps to improve system cost, performance and reliability, the PV industry should also plan to handle large volumes of silicon panel waste, which is initially estimated to be ~13 million metric tons (MT) by 2050 in the U.S. alone. Understanding the magnitude of material needs and how material flows throughout the PV panel life cycle could respond to design, operational and different end-of-life (EOL) circular pathways will help transition into a circular, resource-conserving economy. Herein, we introduce a dynamic material flow analysis (DMFA) framework based on electricity generation to quantify time-series stocks and flows of bulk PV materials (e.g., solar glass and aluminum frames) throughout the life cycles of utility-scale silicon PV systems in the U.S. in the period 2000-2100. We apply the model to a range of scenarios to understand how material demands depend on selected PV-related parameters, different material circularity strategies, and recent module design trends (e.g., bifacial, frameless). We found that float glass and aluminum in PV installations would likely reach 100 million MT and 12 million MT by 2100, respectively, in the baseline scenario. The most influential parameters for PV installation and subsequent waste reduction are found to be module lifetime, module efficiency, annual degradation, and material reduction. Module recycling and component remanufacturing were found to be the most effective material circularity strategies for waste minimization. Panel reuse has negligible savings on waste under current module efficiencies compared to replacements with newer generations with higher efficiency. Ongoing trends to produce larger power frameless modules could save 10 million MT of glass and ~9 million MT of aluminum. Our results enable advanced planning for future materials needs and provide insight into potential opportunities to minimize waste.
The Solar Energy Innovation Network (SEIN) helps communities discover transformative ways of adopting solar energy. Teams develop their ideas in real-world contexts, yielding results that can unlock tomorrow's solar markets.
This presentation was given as part of a panel session at GRC 2023 entitled: Do Non-Conventional Geothermal Developments Need a Specialized Legal Framework? The presentation includes an overview an NREL publication titled, "Topics and Considerations for Developing State Geothermal Regulations," in addition to specific considerations as related to EGS/AGS technologies with relevant state and U.S. Federal statute examples.
The Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates for vehicle technologies as well as fuels, and it details the assumptions used to calculate those costs, such as gas and electricity prices, discount rates, and vehicle miles traveled. The 2024 update added more biofuels pathways to align with pathways used in the Biomass Scenario Model.
Energy analysts at the National Renewable Energy Laboratory (NREL) use a broad set of expertise and cutting-edge tools to capture the complexities of our interconnected energy system. Decision-makers rely on these insights to drive cost savings, enhance grid reliability, support long-term innovation, and bolster America's energy workforce and global competitiveness. This fact sheet highlights some of NREL's high-impact analyses, data, and tools, largely focusing on power grid analysis.
Non-hardware costs are majority of the cost of producing solar photovoltaic (PV) electricity. Here we use matched data on patents and over 125,000 residential PV installations to estimate the effects of three learning mechanisms in reducing PV costs: learning by doing, searching, and interacting. While previous work in this area has focused predominantly on learning by doing, we find that learning by searching and interacting are also significant mechanisms to facilitate non-hardware cost reductions. Including these two mechanisms reduces the effect of learning by doing in explaining non-hardware cost reductions by 43%. Our results suggest that prior work may overemphasize the role of learning by doing and the policies that help generate learning by doing. Analysis of the supplier-network between installers and their suppliers shows that concentrated supplier networks are associated with lower non-hardware costs, although there are key differences between installer-panel and installer-inverter manufacturer networks. An important implication is that policies for reducing non-hardware costs need to take a more complete view of how different learning mechanisms engender cost reductions. They should particularly consider the important role of learning in supplier networks in cost reductions—an effect that until now has largely been missing in analyses of solar non-hardware costs.
This report is part of a series investigating the potential for utility-scale energy storage in South Asia. The first part of the work is focused on opportunities and barriers for energy storage within existing policy and regulatory frameworks in the region. This report applies the previously-developed Energy Storage Readiness Assessment to evaluate the policy and regulatory environment for energy storage in India. Forthcoming reports will expand this analysis to other countries in South Asia. These evaluations are designed to provide insights on the opportunities and barriers for energy storage growth and deployment in each country. The second part of this work consists of techno-economic analysis to understand the drivers of energy storage investments in South Asia. Using NREL’s power system planning and operational models of South Asia, this analysis will identify potential storage applications and growth under various cost, policy, and demand growth scenarios. In addition, the regulatory and policy barriers and incentive mechanisms identified in the first task will be incorporated into the modeling task to understand their impact on energy storage deployment and operation. Together these studies will inform the applications and value of energy storage on South Asia power systems and policy and regulatory pathways to realize this value.
In agricultural and industrial equipment, both new and remanufactured systems are often available for warranty coverage. In such cases, it may be challenging for equipment manufacturers to properly trade-off between the system reliability and the cost associated with a replacement option (e.g., replace with a new or remanufactured system). To address this problem, we present a reliability-informed life-cycle warranty cost (LCWC) analysis framework that enables equipment manufacturers to evaluate different warranty policies. These warranty policies differ in whether a new or remanufactured system is used for replacement in the case of product failure. The novelty of this LCWC analysis framework lies in its ability to incorporate real-world field reliability data into warranty policy assessment using probabilistic warranty cost models that consider multiple life cycles. First, the reliability functions for the new and remanufactured systems are built as the time-to-failure distributions that provide the best-fit to the field reliability data. Then, these reliability functions and their corresponding warranty policies are used to build the LCWC models according to the specific warranty terms. Finally, Monte Carlo simulation is used to propagate the time-to-failure uncertainty of each system, modeled by its reliability function, through each LCWC model to produce a probability distribution of the LCWC. The effectiveness of the proposed reliability-informed LCWC analysis framework is demonstrated with a real-world case study on a transmission used in some agricultural equipment.
Depending on a range of technical, economic, policy, regulatory, and market-related factors, the adoption of DPV may have a net positive or negative financial impact on utilities and ratepayers. This guidebook provides a methodological approach for quantifying the net financial impact of DPV on utilities (in the form of “net revenue losses”) and ratepayers (in the form of “net tariff impacts”). Written for technical staff at utilities, regulatory bodies, energy ministries, research institutes, and civil society organizations who wish to better understand the financial impacts of DPV in their jurisdiction, this guidebook can be used to inform important decisions regarding DPV compensation, tariff design, and regulatory cost allocations, among other aspects. It is focused exclusively on customer-sited DPV systems and adopts a primarily cost-based (as opposed to an equally valid value-based) perspective to understanding DPV financial impacts. The approach for calculating net revenue losses can be applied in nearly any utility jurisdiction, regardless of institutional arrangements, business models, or regulatory paradigms.
Since 2000, corn ethanol production in the USA has increased significantly – from 1.6 to 15 billion gallons (6.1 to 57 billion liters) – due to supportive biofuel policies. In this study, we conduct a retrospective analysis of the changes in US corn ethanol greenhouse gas emission intensity, sometimes known as carbon intensity (CI), over the 15 years from 2005 to 2019. Our analysis shows a significant decrease in CI: from 58 to 45 gCO 2 e/MJ of corn ethanol (a 23% reduction). This is due to several factors. Corn grain yield has increased continuously, reaching 168 bushels/acre (10.5 metric tons/ha, a 15% increase) while fertilizer inputs per acre have remained constant, resulting in decreased intensities of fertilizer inputs (e.g., 7% and 18% reduction in nitrogen and potash use per bushel of corn grain harvested, respectively). A 6.5% increase in ethanol yield, from 2.70 to 2.86 gal/bushel corn (0.402 to 0.427 L kg -1 corn), and a 24% reduction in ethanol plant energy use, from 32 000 to 25 000 Btu/gal ethanol (9.0 to 6.9 MJ L -1 ethanol) also helped reduce the CI. The total GHG emission reduction benefits through the reduction in the CI and increased ethanol production volume are estimated at 140 million metric tons (MMT) from 2005 to 2019 in the ethanol industry. Displacement of petroleum gasoline by corn ethanol in the transportation fuel market resulted in a total GHG emission reduction benefit of 544 MMT CO 2 e during the period 2005 to 2019.
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Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations. To make advancements in all the aforementioned scientific areas, many technical aspects need to be more thoroughly studied and deeply understood in parallel discrete event simulation (PDES). The unique dynamics inherent in a discrete event modeling approach, by their very nature, intersect and influence the entire stack of the computing system, including (a) the unique nature of the instruction sets exercised in PDES workloads without a predominance of high-precision floating point operations, (b) virtual time-constrained multi-threaded execution of many logical processes per processor, (c) extremely variable and difficult to predict network traffic characteristics, (d) interfaces and inter-dependencies with machine learning and artificial intelligence codes at higher software layers, and (e) highly challenging load balancing needs, especially in effectively accounting for accelerated/extremely heterogeneous computing in current and future high-performance computing systems. Efficient and accurate parallel execution of PDES workloads is also dominated by challenges in dealing with their asynchronous concurrency fundamentally present at the model level. Conservative synchronization, optimistic/speculative synchronization, and their hybrid schemes open new questions in fundamental computer science with respect to reversibility of computation and prediction (lookahead) of behaviors inherent within model codes. On the implementation front, there are relatively few scalable, general-purpose parallel discrete event simulators in the world, and even fewer have been studied on emerging hardware platforms. To enable scientific advances using PDES, the research needs in computer science must also be pursued and met in the intersection of the algorithmic and hardware-aware aspects of scalable PDES engines. This report is aimed at capturing a computer science-oriented view of this important area of research in PDES, presenting a sample of important applications with their inherent discrete event technology elements. Needs are outlined in core areas of parallel discrete event research as well as cross-cutting directions in computer science research that positively impact scientific advancements across several important application areas. A selection of priority research opportunities in advanced computing for PDES is identified to serve as reference for key research topics and their order of importance for scientific advancements.
Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations.
A machine readable collection of documented wind siting ordinances at the state and local (e.g., county, township) level throughout the United States. The data were compiled using the Infrastructure Continuous Ordinance Mapping for Planning and Siting Systems (INFRA-COMPASS) tool, which leverages Large Language Models (LLMs) to automate the collection of local codes and ordinances applicable to energy infrastructure. URLs for the ordinance source documents are included in the Wind Ordinances spreadsheet. The GeoPackage file included below contains the jurisdiction shapes for each ordinance. Note that the GeoPackage file is formatted for ingestion by NREL's reVX setbacks tool and therefore does not contain any of the state-level regulations. NOTE: This data was collected with the help of generative AI. The Large Language Models used for this effort make mistakes. Always validate the data for critical use cases. This data is an update to a previously developed database of wind ordinances found in OEDI Submission 5733: see the "U.S. Wind Siting Regulation and Zoning Ordinances 2022" link below. INFRA-COMPASS version used for collection: v0.8.2 LLMs used for collection: GPT-4.1, GPT-4.1 mini, GPT-4.1 nano, GPT-4o mini
Costs for renewable energy technologies have declined rapidly in the past decade and their use for residential, commercial, and utility scale electricity has therefore grown exponentially. As the energy mix diversifies, energy analysis becomes increasing important, requiring renewable energy to integrate with other reduced emission energy sources, such as small modular nuclear reactors and carbon capture and utilization. Energy analysis can also explore the expansion of clean energy into new sectors, such as industrial processes and electrification. The U.S. National Renewable Energy Laboratory (NREL) and the Joint Institute for Strategic Energy Analysis (JISEA), which is a partnership of NREL and universities, study the integration and application of clean energy technologies into existing and new sectors. This presentation includes an overview of NREL and JISEA and summarizes innovations in renewable energy technologies and energy systems analysis.