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DOE OSTI2024

High-Fidelity Multiphysics Modeling of a Heat Pipe Microreactor Using BlueCrab

Researchers who are actively developing nuclear microreactors are planning to employ innovative designs and features using traditional commercial modeling tools that may be inadequate for their design and licensing activities. The codes developed under the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) program provide flexibility in terms of geometry modeling and multiphysics coupling and are particularly well suited for modeling novel microreactor concepts. To test the maturity of these codes, this paper introduces a conceptual heat pipe microreactor (HP-MR) designed to gather various technologies of interest to microreactor developers such as control drums, heat pipes, and hydride moderators. Here, the objective of this effort is to demonstrate NEAMS tools capability to perform high-fidelity multiphysics simulations, using coupled neutronics (via the Griffin code), heat conduction (via the BISON code), heat pipe modeling (via the Sockeye code), and hydrogen redistribution in hydride metal moderator (via the SWIFT code). Codes are coupled in-memory through the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which permits flexible multiphysics data transfer schemes. The analysis confirmed two key aspects of the HP-MR concept: (1) its ability to follow the power load requested from the heat pipe and (2) its ability to avoid heat pipe cascading failure unless designed with high power close to operating failure limits of its heat pipes. The developed computational model was distributed publicly on the Virtual Test Bed for training purposes to accelerate adoption by industry and to provide a high-fidelity multiphysics solution for benchmarking against other tools. Additional multiphysics analyses including other transients and coupled physics were identified as necessary future work, together with a focus on validating multiphysics behavior against experiments.

Microreactor↗

DOE OSTI2022

Simulating Energy and Security Interactions in Semiconductor Manufacturing: Insights from the Intel Minifab Model

Semiconductor manufacturing is a highly complex. Fabrication plants must deal with re-entrant flows to support multiple types of wafers being produced simultaneously, each with their own deadlines and specifications. The manufacturing process itself depends upon the ability to control and programmatically adjust a variety of environmental conditions including temperature, humidity, and air quality. In addition, wafer fabrication consumes large amounts of electricity. Emerging technologies may help reduce the energy footprint of such facilities but can introduce cybersecurity risks. Therefore, this paper presents a modeling and simulation framework to quantify tradeoffs between operational measures of performance, energy consumption, and cybersecurity controls. We augment the Intel Minifab model, with the Purdue Enterprise Reference Architecture (PERA) for cybersecurity as well as tool-level energy consumption data from a real-world semiconductor manufacturing testbed. In this manner, we intend to provide stakeholders with a systematic, data-driven approach to evaluate emerging risks within the manufacturing process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Outcomes of the DOE Workshop on Atmospheric Challenges for the Wind Energy Industry

The U.S. Department of Energy-funded Mesoscale-to-Microscale Coupling (MMC) project team planned and conducted a virtual Workshop on Atmospheric Challenges for the Wind Energy Industry on October 19 and 20, 2020. The goal of the workshop was to forge a dialog with the community, including industry representatives, on how modeling tools are currently being used, the present active atmospheric modeling research in support of wind energy, and required advancements in capabilities and technology to continue to advance wind energy deployment. The workshop was planned in collaboration with an industry advisory panel that included representatives from wind power plant developers, turbine manufacturers, and companies that provide resource assessment and forecasting services. The format of the workshop included panels from government research sponsors, visionaries from industry, and mixed panels of researchers discussing research status and needs. A shared keynote presentation from the Technical University of Denmark experts anchored the second day of the workshop. An emphasis was placed on understanding the research needs in the offshore environment. In addition, breakout opportunities were provided each day. On the first day, the breakout discussions addressed predesigned questions configured to elicit participants’ thoughts on needed research directions. The second-day breakouts treated three important technical topics through a combination of presentations and group conversations. Each workshop participant chose their breakout preference from among downscaling details, modeling for turbines, and using artificial intelligence for atmospheric modeling. The discussions were robust and productive. The outcomes of the workshop include archiving a series of recommendations from industry and the research community on research directions required to further advance wind energy deployment. Discussions confirmed the need for high-fidelity modeling but that there are specific areas of applicability and other areas where the time and cost of computation is prohibitive. In those cases, the high-fidelity models can inform low-order models that are more practical for real-time or widely deployed applications. Industry must consider the financial cost of performing more expensive modeling approaches, but industry engineers and researchers are using these approaches where there appears to be a return on investment. An emerging type of low-order model is based on machine learning (ML). Participants confirmed that there are many atmospheric phenomena that need to be modeled better, including low-level jets, cold air outbreaks, land-sea induced circulations, diurnal variability, thin stable boundary layers, dynamic changes such as from frontal passage, interaction of wakes and blockage, and more. For the offshore environment, there is wide agreement that some level of ocean-wave-atmospheric coupling is necessary to capture variations in rotor-level winds needed to plan and operate offshore wind plants. Another recurring recommendation is that more observations are needed, particularly for the offshore environment. Those observations should consider the needs for model improvement, both for physically based models and for ML models. Observations must capture atmospheric profiles of variables that are important to understanding and modeling atmospheric and oceanic phenomena that impact boundary layer winds. Models must be validated with data and the uncertainty quantified, particularly those that are sensitive to initial and boundary conditions. Finally, a repeated request was to consider the holistic needs of hybrid plants of wind, solar, and storage resources because those types of plants are likely to be the wave of the future. In addition, industry wishes to understand impacts of the resource under a changing climate for long-term planning.

17 WIND ENERGY↗

NASA NTRS2005

Space Based Ornithology: On the Wings of Migration and Biophysics

The study of bird migration on a global scale is one of the compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties. Space based technology offers new opportunities to shed understanding on the distribution and migration of organisms on the planet and their sensitivity to human disturbances and environmental changes. Migration is an incredibly diverse and complex behavior. A broad outline of space based research must address three fundamental questions: (1) where could birds be, i.e. what is their fundamental niche constrained by their biophysical limits? (2) where do we actually find birds, i.e. what is their realizable niche as modified by local or regional abiotic and biotic factors, and (3) how do they get there (and how do we know?), that is what are their migration patterns and associated mechanisms? Our working hypothesis is that individual organism biophysical models of energy and water balance, driven by satellite measurements of spatio-temporal gradients in climate and habitat, will help us to explain the variability in avian species richness and distribution. Dynamic state variable modeling provides one tool for studying bird migration across multiple scales and can be linked to mechanistic models describing the time and energy budget states of migrating birds. Such models yield an understanding of how a migratory flyway and its component habitats function as a whole and link stop-over ecology with biological conservation and management. Further these models provide an ecological forecasting tool for science and application users to address what are the possible consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration.

Smith, James A.↗

DOE OSTI2022

Battery Energy Storage Scenario Analyses Using the Lithium-Ion Battery Resource Assessment (LIBRA) Model

Meeting aggressive carbon emission goals will entail widespread deployment of renewable sources of electricity. Because these sources are variable, there is a need to develop scalable energy storage technologies. The U.S. Department of Energy is supporting efforts to increase U.S. manufacturing and recycling capabilities for LIBs and to decrease costs of stationary storage batteries. Many factors influence the domestic manufacturing and cost of stationary storage batteries, including availability of critical raw materials (lithium, cobalt, and nickel), competition from various demand sectors (consumer electronics, vehicles, and battery energy storage), resource recovery (recycling), government policies, and learning in the industry, among other factors. Understanding how these factors interact and identifying synergies and bottlenecks is important for developing effective strategies for the LIB stationary energy storage system. We developed the Lithium-Ion Battery Resource Analysis (LIBRA) model as a tool to help stakeholders better understand the following types of questions: What are the roles of R&D, industrial learning, and scaling of demand in lowering the cost of battery energy storage system production? How do the intersections between the EV and stationary storage sectors affect the battery supply chain? For various stationary storage and EV penetration scenarios, what volumes of critical materials might be required and what role can resource recovery play? What does expected demand for both EVs and stationary storage portend for mineral resources and overall mineral scarcity? The LIBRA model is developed using a System dynamics (SD) modeling approach to represent interactions across the segments of the battery materials supply chain. System dynamics models can capture the complex interactions and feedback between the various system components that influence supply and demand. The LIBRA model is comprised of several interacting modules that represent specific portions of the LIB supply chain. The model tracks the buildout of the domestic LIB industry over time (2020 - 2050) and in the context of competing demands for raw materials, recycling, and markets for LIBs. The LIBRA model represents major systemic feedback loops and delays across the supply chain. This report provides a complete documentation for the LIBRA model, including model assumptions, data, scenario analysis results, and sensitivity analysis of the model's input space.

25 ENERGY STORAGE↗

DOE OSTI2023

Turbine-based Power System Tool

Advanced research tools are essential to illustrate the effects of turbine-based power system operations on the electric grid network. This paper presents the development of an open-source tool to visualize the operation of turbine-based electricity generation. The tool exposes power system models, control parameters, and corresponding values of the turbine model and its electrical system, allowing them to customize the simulation according to their needs. The developed tool enables users to simulate power profiles for different turbinebased energy generation such as wind, tidal, and gas turbines. It allows users to investigate the details of the generated profiles of various types of turbine systems at various scales. It can provide valuable insights into model development and facilitate the analysis of integrated power systems. By enabling access to turbine-based operations visualization, the tool aims to bridge the gap between advanced research tools and users, facilitating broader adoption of renewable energy technologies and aiding in developing sustainable power systems.

Kini, Roshan L.↗

DOE OSTI2025

Properties of Electronic Materials

This final technical report summarizes the research conducted under DOE Grant DE-SC0002623, "Properties of Electronic Materials," led by Principal Investigator Shengbai Zhang at Rensselaer Polytechnic Institute. Over the 16-year period, the project employed first-principles computational methods to investigate the structural, electronic, and dynamic properties of a wide range of electronic materials, with applications in energy technologies, optoelectronics, and data storage. Key areas included topological insulators, phase-change materials, graphene and two-dimensional systems, perovskites for photovoltaics, defect engineering in semiconductors, kagome lattices, and ultrafast carrier dynamics. The research resulted in 115 peer-reviewed publications, advancing fundamental understanding of material behaviors at the atomic scale and contributing to innovations in renewable energy, memory devices, and quantum materials. Findings have implications for improving energy efficiency, developing lead-free solar cells, and enabling high-speed data processing. The work has trained numerous graduate students and postdocs, fostering the next generation of computational materials scientists. The original goals were to develop theoretical models and computational tools to predict and optimize electronic properties of materials for energy applications. All objectives were accomplished, with no major departures from planned methodologies. Challenges in computational scaling were addressed through access to high-performance computing resources.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Advanced Fuels Campaign 2019 Accomplishments

The mission of the Advanced Fuels Campaign (AFC) is to perform or support research, development, and demonstration (RD&D) activities to identify and mature innovative fuels and cladding materials and associated technologies with the potential to improve the performance and safety of current and future reactors; increase the efficient utilization of nuclear energy resources; contribute to enhancing proliferation resistance of the nuclear fuel cycle; and address challenges related to waste management and ultimate disposal. AFC pursues its mission objectives using a goal-oriented, science-based approach that seeks to establish a fundamental understanding of fuel and cladding behaviors under conditions that arise during fabrication, normal steady-state irradiation, off-normal transient scenarios, and storage/disposal. This approach includes advancing the theoretical understanding of fuel behavior, conducting fundamental and integral experiments, and supporting the mechanistic, multi-scale modeling of nuclear fuels to inform and guide fuel development projects, advance the technological readiness of promising fuel candidates, and ultimately support fuel qualification and licensing initiatives. In the area of advanced tools for the modeling and simulation of nuclear fuels, the AFC works in close partnership with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, participating with NEAMS in developing mechanistic fuel behavior models and providing experimental data to inform and validate its most advanced tools. Specifically, AFC objectives in thecoming five year horizon include: 1. support the industry-led development of Accident Tolerant Fuel (ATF) technologies with improved reliability and performance under normal operations and enhanced tolerance during hypothetical accident scenarios, with implementation of lead test rods/assemblies of one or more ATF concepts in commercial reactor(s) by or before 2022 and batch reloads of near-term concept(s) in the 2023-2026 timeframe; 2. lead research and development on innovative fuel and cladding technologies for applications to future advanced reactors, especially fast-spectrum reactors, including reactors that utilize both once-through and recycle scenarios; 3. continue the development and demonstration of a multi-scale,science-based approach to fuel development and testing, andcontribute to the establishment of a state-of-the-art R&D infrastructure necessary to accelerate the development of new fuel concepts; and collaborate with NEAMS on the development and validation of multi-scale, multi-physics, and increasingly predictive fuel performance models and codes. 4. collaborate with NEAMS on the development and validation of multi-scale, multi-physics, and increasingly predictive fuel performance models and codes.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Modelling of a Flow-Induced Oscillation, Two-Cylinder, Hydrokinetic Energy Converter Based on Experimental Data

The VIVACE Converter consists of cylindrical oscillators in tandem subjected to transverse flow-induced oscillations (FIOs) that can be improved by varying the system parameters for a given in-flow velocity: damping, stiffness, and in-flow center-to-center spacing. Compared to a single isolated cylinder, tandem cylinders can harness more hydrokinetic energy due to synergy in FIO. Experimental and numerical methods have been utilized to analyze the FIO and energy harnessing of VIVACE. A surrogate-based model of two tandem cylinders is developed to predict the power harvesting and corresponding efficiency by introducing a backpropagation neural network. It is then utilized to reduce excessive experimental or computational testing. The effects of spacing, damping, and stiffness on harvested power and efficiency of the established prediction-model are analyzed. At each selected flow velocity, optimization results of power harvesting using the prediction-model are calculated under different combinations of damping and stiffness. The main conclusions are: (1) The surrogate model, built on extensive experimental data for tandem cylinders, can predict the cylinder oscillatory response accurately. (2) Increasing the damping ratio range from 0–0.24 to 0–0.30 is beneficial for improving power efficiency, but has no significant effect on power harvesting. (3) In galloping, a spacing ratio of 1.57 has the highest optimal harnessed power and efficiency compared with other spacing values. (4) Two tandem cylinders can harness 2.01–4.67 times the optimal power of an isolated cylinder. In addition, the former can achieve 1.46–4.01 times the efficiency of the latter. (5) The surrogate model is an efficient predictive tool defining parameters of the Converter for improved energy acquisition.

backpropagation neural network↗

DOE OSTI2025

Nine Canyon Long-Duration Energy Storage: A Feasibility Study

The Nine Canyon Long Duration Energy Storage (LDES) Feasibility Study explores the technical and economic viability of deploying advanced energy storage technologies at Energy Northwest's (EN) Nine Canyon (9C) Wind Project site in Benton County, Washington. Supported by the Washington State Department of Commerce and the U.S. Department of Energy’s Office of Electricity under its LDES Voucher Program, the study represents a collaborative effort between EN, Pacific Northwest National Laboratory (PNNL), and ARES North America. At the core of this effort is the development of a generalized techno-economic modeling framework and evaluation tool designed to assess the value proposition of LDES projects across a variety of contexts. The modeling tool is technology-agnostic and accommodates user-defined parameters such as rated power, energy duration, round-trip efficiency, capital and operational costs, and dispatch constraints. It also integrates economic inputs, including market prices, energy revenue structures, and financing parameters to evaluate performance through key metrics. The tool provides utilities with a transparent, adaptable platform to support decision-making, investment prioritization, and portfolio planning for various storage technologies. To guide scenario design and interpretation, the study first surveyed the LDES technology landscape, including lithium-ion batteries, flow batteries, non-hydro gravity storage, and thermo-mechanical systems, comparing cost trajectories, technical performance, safety and hazards, materials sourcing and recyclability, and spatial/siting considerations. This literature-grounded review highlights technology trade-offs and reinforces the need to align technology choice with site characteristics, use cases, and project objectives. A companion chapter examines ownership structures (EN ownership, third-party ownership, shared models) and offtake options (energy marketing, capacity/energy PPAs, time-of-use PPAs, block-delivery PPAs, and tolling), where PPAs (power purchase agreements) represent contractual arrangements for buying and selling electricity. The chapter also highlights implications for risk allocation, capital access, operational control, and revenue certainty. The study also evaluates supervisory control and data acquisition (SCADA) and transmission interconnection pathways, options include upgrading the existing SCADA or deploying a dedicated LDES controller, with attention to protection schemes, data telemetry, cybersecurity, and regulatory coordination with BPA. In addition, an ARES-specific geotechnical and hydrology assessment presented in the appendix screens multiple corridors for slope stability, bearing capacity, cut-and-fill magnitude, and stormwater behavior.

25 ENERGY STORAGE↗

DOE OSTI2024

Dashboard for Marine Energy Site Assessment and Monitoring

The marine energy (ME) industry presently relies upon fragmented site assessment solutions that require high resource expenditure for deployment at each site and do not leverage the wealth of readily available tools and information. A wave energy resource assessment dashboard, currently in development, will substantially improve siting, permitting, operations, and maintenance of ME projects by providing an integrated solution that is a one-stop-shop for a developer’s needs. The Site Energy Assessment and MOnitoring Dashboard (SEAMOD) will be of commercial interest to anyone seeking to deploy an ME project and is easily expandable to include tidal and wind energy site assessments. The integrated dashboard is being developed using state-of-the-art database and cloud computing methods and data-assimilative modeling tools that can be coupled with low-cost, rapidly deployable wave buoys and environmental sensing hardware. The combined software and hardware dashboard will reduce wave energy site characterization and wave climate monitoring costs by more than 60 percent and provide assessments that meet international industry standards. To realize a thriving global ME industry, the physical environment at a potential deployment site must be understood, not only for resource characterization, but also for optimization of device and power conversion performance. SEAMOD directly addresses these needs with a commercially marketable product. SEAMOD is a low-cost solution that provides comprehensive ME resource assessments, baseline environmental monitoring, and offshore characterizations required for successful ME development. The key technical objectives for Phase I were a series of software development goals, which when implemented with monitoring solutions, produced an initial proof-of-concept low-cost wave energy resources dashboard. In Phase II, the development of the prototype SEAMOD continued. The basic framework employed was the development of a revised dashboard and monitoring tool customized for ME applications by focusing on IEC site assessment and method requirements. Development was focused on the integration of full hindcast metocean products to provide hindcast resource characterization and environmental information. The final integrated dashboard provides a low-cost solution that delivers comprehensive, scalable, industry-standard energy resource assessments and offshore characterizations required for successful ME development. The integrated dashboard offers visibility of the most recent site modeling, measurements, and historical data. The application and integration of consensus-based standards for wave energy resource assessment, as determined by the International Electrotechnical Commission (IEC), are crucial for the impact and value of SEAMOD. SEAMOD includes monthly, seasonal, and yearly statistics, as well as the total 30-year record, offering temporal resolution of the IEC parameters to aid potential developers in determining the available wave energy resources in their area of interest.

16 TIDAL AND WAVE POWER↗

NASA NTRS2021

Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

Lianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated trees. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition, and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (−30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana-specific traits were also responsible for a significant reduction of tree (−19%) and ecosystem (−7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of lianas on forest functioning, and paves the way for the evaluation of the large-scale impacts of lianas on forest biogeochemical cycles.

Félicien Meunier↗

DOE OSTI2023

Large Language Models (LLMs) for Energy Systems Research

The integration of Large Language Models (LLMs) in energy systems research promises transformative results, as demonstrated in this work, particularly in the realms of information retrieval and legal document analysis. We have developed a chat-based interface, specifically designed to query an extensive corpus of technical reports from the National Renewable Energy Laboratory (NREL). This interface capitalizes on the natural language processing capabilities of LLMs, providing future consumers of NREL research with a user-friendly platform to access and extract valuable information from technical documents, thus enhancing the dissemination of research to the public. In addition to information retrieval, we have employed LLMs to extract renewable energy siting ordinances from a variety of legal documents, a task traditionally driven by significant human labor. This automated extraction not only supports the ongoing development of the high-impact NREL siting ordinance database but also ensures the database's accuracy and comprehensiveness. Crucially, we have augmented the performance of LLMs through the integration of a decision tree framework, resulting in a substantial improvement in extraction accuracy. Comparative analysis with manual efforts has shown that this approach not only rivals but also significantly surpasses human accuracy, heralding increased reliability in legal document analysis for energy systems research. To democratize access to these advancements and foster collaborative research, we introduce the "Energy Language Model" (ELM), an open-source software package. ELM encapsulates the methodologies and tools developed in this work, providing researchers and practitioners with a robust toolkit to conduct similar analyses within their respective domains. Through these contributions, this work underscores the immense potential of LLMs in revolutionizing energy systems research, improving accuracy, efficiency, and accessibility in the field.

automation↗

DOE OSTI2020

A Comprehensive Review and Qualitative Analysis of Micro-Combined Heat and Power Modeling Approaches

Concurrent production of electrical and thermal energy from a Combined Heat and Power (CHP) device is an attractive tool to address the growing energy needs of the planet. Micro CHP (µCHP) systems can reduce a building’s primary energy consumption, reduce carbon footprint, and enhance resiliency. Modeling of the µCHP helps understand the system from multiple perspectives and helps discover errors earlier, improves impact analysis and simulation of system solutions for ease of integration with the building. Consequently, there is a need for analysis of the impact of µCHP modeling approach on its reliability and flexibility. The primary objective of this paper is to review the state-of-the art models in the µCHP space with a focus towards internal combustion engine as the primary mover (PM) and limit the study to system modeling, calibration, and validation methodologies. Based on the analysis, recommendations for further model considerations and refinements are presented.

42 ENGINEERING↗

DOE OSTI2023

Quantifying agricultural productive use of energy load in Sub-Saharan Africa and its impact on microgrid configurations and costs

The use of advanced energy technologies for agricultural purposes—such as irrigation, refrigeration, crop processing, and egg incubation—has the potential to increase crop yield, reduce vulnerability to changing precipitation patterns, increase shelf life, strengthen income and employment opportunities in rural areas, and reduce emissions by displacing fossil fuel-based technologies. These productive uses of energy (PUE) in remote areas could potentially be powered by microgrids that additionally serve otherwise unelectrified communities, most of which are located in rural Sub-Saharan Africa. Here, in this paper, we use high-resolution geospatial data to estimate the end-use electricity demand for a range of agricultural PUE across Sub-Saharan Africa, and we share these data in an open-access mapping tool. Next, we use REopt®, a techno-economic optimization model of energy systems, to determine the cost and system sizing implications of incorporating agricultural PUE into microgrid designs in Kenya and Zambia. We estimate the upper bound of agricultural PUE demand for irrigation, milling, shelling, refrigeration, and egg incubation across Sub-Saharan to be 16.8 TWh/yr. We find that incorporating local agricultural PUE into microgrid system designs increases the required system sizing while having minimal impact on the levelized cost of energy of these systems. Our analysis is the first to demonstrate the PUE potential in the agricultural sector at a 10x10-kilometer resolution across Sub-Saharan Africa and to show, at scale, how site-specific PUE can impact the cost and sizing of microgrids that are otherwise deployed to serve local household and community load.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

An Evaluation of Advanced Tools for Distributed Wind Turbine Performance Estimation

We evaluate various classes of distributed wind turbine performance tools across two sites in the United States. The class of tools ranges from the simple mass conservation model to the coupled Reynolds-averaged Navier-Stokes model, all initiated by the WIND Toolkit data set. The resource estimation at the site is evaluated against measured data at the mast location. Taking a sample 100-kW wind turbine and constant losses, we evaluate Openwind, Continuum, and WindNinja tools and document annual energy production (AEP) and time-series statistics associated with the performance estimation of the wind turbine. Using a methodology that is consistent and unbiased across the three options currently available in the industry, we elaborate results at the two sample locations and discuss the probable sources of discrepancy in the AEP estimates. Two main sources of the discrepancy come from the input WIND Toolkit data and the spatial modeling techniques of the tools to capture atmospheric physics. The discussion includes additional values that these tools may bring into the energy assessment process to enhance the owners' confidence over the distributed wind power systems.

17 WIND ENERGY↗

DOE OSTI2023

Accelerating Low-Income Financing and Transactions (LIFT) for Solar Access Everywhere (Final Technical Report)

The Accelerating Low-Income Financing and Transactions (LIFT) for Solar Access Everywhere project’s goal was to expand Low-to-Moderate Income (LMI) solar access for homeowners and renters. The LIFT project researched and gathered data on 453 LMI community solar project across the country. Following three years of research, the project delivered three groundbreaking research papers in June 2022, focused on 1) customer experience, 2) the growth of community solar programs, and 3) project-level financial best practices for serving LMI communities. These were followed by a user-friendly web-based Toolkit allowing users to interact with project data and key findings in November 2022. The customer experience research examined community solar subscribers’ primary motivations to join and remain satisfied with projects. Our research identified 453 projects across the country that dedicated some portion of the system capacity to LMI households. Seventeen of these projects participated in the LIFT customer experience research, allowing the project team to survey their customers and gain insight into how LMI subscribers feel about community solar and the programs that serve them. Subscribers in our sample indicated that the most critical issue that motivated them to participate in their program, however, was not savings but helping the environment. This was true for both LMI and non-LMI subscribers. Helping the environment was also the most important issue for LMI subscribers to measure how well their program was working for them. LIFT also explored how rapidly community solar has grown since its inception in 2006, publishing results in the Growth of U.S. Community Solar Serving LMI Households report. The results showed that community solar projects serving LMI households are one of the fastest growing segments of the solar industry. The report identifies and recommends ways developers should overcome real or perceived risks to LMI customer acquisition and subscriber management. Through the analysis of community solar project finance research, LIFT showed that most community solar projects serving LMI households are financed in the same ways mainstream community solar projects are financed. The value stacks and financial returns are no different, although LMI inclusion and participation rate varied across programs in our sample, ranging from between 10% and 100%. Based on the findings from the LIFT research, the team built a web-based user-friendly Toolkit, consisting of case studies, project finance best practices, and several tools built around the national dataset of 453 community solar projects that serve LMI households. These allow users to engage with the dataset in multiple ways; to explore the landscape of LMI community solar in the U.S., and to design community solar projects to optimize LMI inclusion, equity, and savings levels. The Toolkit also includes a library of LIFT-generated and LIFT-curated resources for users to learn more about how to best serve LMI communities through community solar. LIFT officially published the Toolkit on October 31, 2022, followed by a launch event (public webinar) on November 17, 2022. The core LIFT partners continue to engage in outreach and dissemination efforts to promote the LIFT Toolkit and research publications. Our driving motivation is to continue enabling solar developers to leverage the findings of this three-year research effort. By implication, the LIFT Toolkit is designed for use by utilities, energy service providers, and financiers or investors as a learning and decision-making tool to rapidly scale project models that optimize LMI inclusion and maximize real household savings.

14 SOLAR ENERGY↗

DOE OSTI2023

MORS Phalax Ad for Systems Analysis

At Lawrence Livermore National Laboratory, applying cutting-edge science and technology to challenging security questions is an everyday thing. Systems Analysis is one way we solve problems—by bringing analytic rigor to missions like strategic deterrence and critical infrastructure defense. Working with the brightest minds in science, technology, and engineering, systems analysts at LLNL bring state-of-the-art analytic tools to bear on problems of national importance. From ensuring the safety, security, and effectiveness of our nation’s nuclear stockpile to shaping the future of inertial fusion energy—if you have a penchant for problem-solving using modeling and analysis tools—we have a place for you.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗