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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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DOE OSTI2023

Engineering-scale Modeling of High-Temperature Creep and Creep Crack Growth in Alloy 316H

This document demonstrates completion of the goals described in the technical narrative of the Department of Energy’s Industry Funding Opportunity Announcement (iFOA) project entitled “Modeling and Simulation Development Pathways to Accelerating KP-FHR Licensing,” which relates to the development and demonstration of capabilities for conducting engineering-scale simulations of Alloy 316H components under high-temperature conditions. This work encompassed two major aspects: integrating and testing constitutive models for the creep response of 316H at high temperatures, and developing tools for modeling creep crack growth (CCG) in 316H. Two classes of constitutive models were used in this effort: phenomenological models based on behavior observed at the engineering scale, and reduced-order models (ROMs) that represent the nonlinear response of mesoscale models that capture the sensitivity to material microstructure and processing. Likewise, the approaches employed for CCG modeling considered both simplified engineering approaches and detailed simulations of creep and damage ahead of the crack tip. These developments, which were performed by utilizing the Grizzly code as well as the open-source libraries it depends on, strengthen Grizzly’s ability to support licensing and safety analyses of high-temperature reactor components.

316H↗
DOE OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Critical Infrastructure Classification via CNN-based Modeling and Image Analysis

With recent advances in the fields of satellite imagery and machine learning we now have the ability to develop explainable deep learning models that enhance critical infrastructure analysis. Funded through Idaho National Laboratory’s (INL) Laboratory Directed Research and Development (LDRD) office we are in the process of developing a deep learning model capable of identifying critical infrastructure facilities and embedded features within those facilities. Utilizing current limit of practice techniques in the machine learning areas of explainability and transfer learning our model, once complete, will have the capacity to be used on multiple different imagery data sets and produce results that not only classify critical infrastructure facilities, but also explain why a critical infrastructure facility was classified as a certain type of facility. These advancements eliminate the ‘black box’ approach deep learning models have had in the past, where a user will have to trust the conclusion of a model without understanding what reasoning when into the model’s classification process. They also expand a model’s usefulness, traditionally a deep learning model will have to use the same data set it was originally trained on. Given the long training times of deep learning models this is impractical in a number of scenarios. By utilizing transfer learning advancements, we are eliminating the need to train our model on the same data set it is then run on to classify critical infrastructure facilities. We are also enabling the analysis and classification of data sets that are potentially too small to be divided into a training and testing data set. Once completed this model can provide a foundation to enhanced critical infrastructure analysis, dependency analysis, and potential disaster relief efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Dataset for 'Ombadi et al. (2023). A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature'

This package contains the main codes, sample input data and main result files to reproduce the analysis and results presented in the article: “Ombadi et al. (2023), A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature”. The folder consists of the following: (1) “Raw data”: a folder that contains sample input data which is used in some of the codes for demonstration purposes. It also contains data that was not pre-processed such as Elevation data; (2) “Results”: this folder contains files of the main results presented in the paper including: “Annual-Max-Series”, “Change-rainfall-extremes”, “Change-snow-fraction”, “Warming levels_By scenario_model_year” and “Masks”. Description of these folders is detailed in the "Readme.rtf" file; (3) Python jupyter notebooks (Extract_Annual Max Series (AMS).ipynb, Elevation-dependent amplification of rainfall extremes.ipynb, Sensitivity_to_global_warming.ipynb) demonstrate the main steps of analysis. Further description of those notebooks is provided in the "Readme.rtf" file; (4) R code for extreme value analysis (Extreme_Value_Analysis.R). The sample and pre-processed dataset in "Raw data" is obtained from publicly available repositories of CMIP6 and ERA5 datasets; see Methods for more detail. This research was supported by Office of Science, Office of Biological and Environmental Research of the US Department of Energy under contract no. DE-AC02-05CH11231 for the CASCADE Scientific Focus (funded by the Regional and Global Model Analysis Program area within the Earth and Environmental Systems Modeling Program) and the iNAIADS Early Career Research Project (funded by the Environmental Systems Science program).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Demonstration of NREL Modeling Capability to Design the Next Generation of Floating Offshore Wind Turbines with Stiesdal and Magellan Wind (Cooperative Research and Development Final Report)

This Technology Commercialization Fund (TCF) CRADA involved demonstration of NREL modeling capability using OpenFAST (formerly known as FAST) to design the next generation of floating offshore wind turbines (FOTW) with Stiesdal’s TetraSpar design. The objective of the project was to enable the design and optimization of next generation FOWT that show promise to make FOWT cost-competitive with other energy technologies by upgrading, verifying, and validating improvements to OpenFAST. This objective was achieved by (1) upgrading OpenFAST to compute floating substructure flexibility and member-level loads, which is critical to enable the design of floating substructures—especially newer designs that are streamlined, flexible, and cost-effective; (2) verifying the new OpenFAST capabilities through model-to-model comparisons and validating the capabilities through comparisons to empirical data generated with wave-tank testing, using TetraSpar data provided by Stiesdal; and (3) making available the upgraded OpenFAST tool to the wind energy community to enable next-generation floating wind designs.

17 WIND ENERGY↗
DOE OSTI2021

Nuclear Science User Facilities: Instrument Scientist Program

The traditional single institution User Facility model prevalent in the DOE complex funds Instrument Scientists (IS) full-time to collaborate with and guide users, disseminate results through publication, build a broad and competent user base, continuously improve instrumentation, enhance data collection methods, and improve data analysis methods. This model focuses the Instrument Scientist on ensuring that visiting researchers obtain the best and most comprehensive data. Instrument Scientists are also expected to establish personal research programs based on the use of the instrument(s) under their charge, which allows for further advances in instrumentation and science. This model ensures that the User Facility offers unique, world-leading instruments and data analysis to the user community, creates high demand among top researchers for the use of these capabilities, and as a result, positively impacts science and technology in the United States.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage (Task 5 Report Field Scale Stress Modeling)

This report describes research accomplishments achieved with funding provided through Department of Energy (DOE) Contract DE-FE0031686, for the project “A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage.” The purpose of this DOE funding is to develop non-invasive methods to obtain important information about subsurface stresses. The overall research goal of the project was to develop and improve methods for determining the spatial distribution of stresses, including magnitude and orientation of the three principal stress components in the subsurface, based on new methods that extract stress information from seismic data combined with well measurements, extended with well tests and logs, and unified in a numerical model that permits computation of the full stress tensor throughout the domains under investigation.

58 GEOSCIENCES↗
DOE OSTI2020

Risk-Based Dynamic Contingency Analysis Applied to Puerto Rico Electric Infrastructure

The U.S. Department of Energy’s (DOE) Office of Electricity and Office of Energy Efficiency & Renewable Energy have funded DOE National Laboratories to perform modeling, analysis, and high-level design of resilience-enhancement options for the power grid of the Commonwealth of Puerto Rico. The Pacific Northwest National Laboratory (PNNL) is one of the national laboratories contributing to the DOE effort. Under this funding, PNNL completed Phase I of their analysis in 2018, which identified high-priority transmission enhancements derived from detailed dynamic cascading analysis of severe contingencies, including a hurricane scenario example. This report describes additional analysis completed under Phase II, which was performed over 2019. The PNNL team applied decades of experience making complex power systems more resilient, reliable, secure, flexible, affordable and sustainable, with partners in government and industry. In this report, PNNL presents the Phase II analysis, in which a risk-based dynamic contingency analysis approach to evaluate impact of several hurricane scenarios was developed. This approach was used to identify high-priority enhancements and test resilience mitigation actions, including the evaluation of high-solar scenarios developed as a result of the 2019 Puerto Rico Integrated Resource Plan (IRP).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

WORKSHOP ON URBAN SCALE PROCESSES AND THEIR REPRESENTATION IN HIGH SPATIAL RESOLUTION EARTH SYSTEM MODELS

It is essential that numerical models used to study physics, chemistry, and biology affecting the Earth system at regional and global scales represent the effects of urban areas on climate and the effects of changing the climate on urban areas. At the same time, it is essential to develop state of the art, simple and accurate urban models to better understand the relevant processes and also toaddress issues related to urban security against the spectra of chemical, biological and radiological (CBR) hazards. Towards bringing these communities together, a 2.5-day international workshop was held at Argonne National Laboratory (ANL) in the Chicago area on May 22-24, 2019. This workshop brought together national and international experts to develop a roadmap for a better understanding of the issues associated with urban areas and at enhancing the capabilities of regional and global Earth System models (ESMs) in representing the atmospheric dynamics and chemistry, unique aspects of the biosphere and land use, and human dimensions of the urban environment. This workshop was especially important to those developing very high-resolution versions of regional and global models. The workshop also discussed existing datasets, including in situ and satellite observations, and the emerging smart city sensing technologies and their possible use in model development (e.g., the NSF-funded Array of Things network currently operating in Chicago and expanding to other cities).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗
DOE OSTI2021

A Multi-variable, Multi-value Hydrodynamic Modeling Approach to Support Marine Energy and Coastal Resilience Applications

Funded by the Marine Energy Seedling Laboratory Program, the study reported on here aimed to develop a multi-variable, multi-value hydrodynamic modeling approach based on the Finite-Volume Community Ocean Model (FVCOM) to support a wide range of marine renewable energy and coastal resilience applications. We migrated the marine hydrokinetic energy module to simulate tidal energy extraction by tidal turbines under wave-current interactions. We explored the particle tracking module in FVCOM and applied it to Sequim Bay, Washington, to illustrate the surface current field and mimic the pollutant transport near the surface. A tracer-water age module was added to FVCOM to examine water exchange through the entrance. Lastly, a user-specified vertical coordinate option was added to FVCOM to improve simulation of the effect of floating turbines on surface currents and scalar transport. The above-described model development has been tested using a simplified model domain and demonstrated in real-world coastal systems. The outcome of this project will provide a useful modeling code and approach for marine renewable energy and coastal resilience research and applications.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Advancing Theory and Modeling Efforts in Heliophysics

Heliophysics theory and modeling build understanding from fundamental principles to motivate, interpret, and predict observations. Together with observational analysis, they constitute a comprehensive scientific program in heliophysics. As observations and data analysis become increasingly detailed, it is critical that theory and modeling develop more quantitative predictions and iterate with observations. Advanced theory and modeling can inspire and greatly improve the design of new instruments and increase their chance of success. In addition, in order to build physicsbased space weather forecast models, it is important to keep developing and testing new theories, and maintaining constant communications with theory and modeling. Maintaining a sustainable effort in theory and modeling is critically important to heliophysics. We recommend that all funding agencies join forces and consider expanding current and creating new theory and modeling programs–especially, 1. NASA should restore the HTMS program to its original support level to meet the critical needs of heliophysics science; 2. a Strategic Research Model program needs to be created to support model development for next-generation basic research codes; 3. new programs must be created for addressing mission-critical theory and modeling needs; and 4. enhanced programs are urgently required for training the next generation of theorists and modelers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Impacts of Renewable Energy and Green Hydrogen Policies on Uttar Pradesh's Power Sector Future

This report is part of a broader program focused on supporting Indian states with long-term power system planning. More information about this program can be found at the National Renewable Energy Laboratory's "Supporting India's States With Renewable Energy Integration" web page at https://www.nrel.gov/international/india-renewable-energy-integration.html. The power sector in Uttar Pradesh, India's most populous state, is poised to transform over the next few decades due to a combination of national and state-level policies impacting both the supply and demand of electricity. The Government of Uttar Pradesh has policies and plans to develop in-state solar PV, pumped storage hydropower, and green hydrogen. Power system policymakers and utilities in Uttar Pradesh are faced with the challenges of planning a system that incorporates increasing amounts of renewable energy and storage resources, meets rising electricity demand due to economic development and green hydrogen production, and satisfies operational and reliability requirements. To support these various objectives, the National Renewable Energy Laboratory (NREL), RMI, and the Uttar Pradesh New and Renewable Energy Development Agency (UPNEDA) evaluated the least-cost pathways for the state's power sector through 2050. NREL developed a capacity expansion model that identifies investment and operational decisions for every year (2024-2050) for all of India, with detailed representation for the state of Uttar Pradesh, which can provide a framework for recurring planning studies. The main insights from this study can also help inform policy development and investment decisions.

08 HYDROGEN↗
DOE OSTI2021

Regulation of Chloroplast Division and Compartment Size (Final Technical Report)

During leaf growth and development, chloroplasts numbers increase as cells expand to maximize energy capture and photosynthetic capacity. Chloroplasts proliferate by dividing in the middle, producing a large population of small organelles of similar size and shape. This process is orchestrated by macromolecular complexes located both inside and outside the chloroplast whose assembly and contractile activity across the two envelope membranes must be coordinated and spatially regulated. Through research supported by our DOE funding, and by exploiting the powerful model system Arabidopsis thaliana, we have significantly advanced our knowledge of how coordination of the division complexes is achieved and how division-site placement is spatially restricted to the middle of the chloroplast. Using chloroplast division mutants, we gained new understanding of how chloroplast size and shape influence chloroplast movement and photosynthetic performance. We identified a gene contributing to natural variation in chloroplast size. Major findings are highlighted.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Role of Renewable Energy, Storage, and Demand Response in Karnataka's Power Sector Future

Karnataka ranks fourth among Indian states in installed capacity of renewable energy, which accounts for over 50% of its resource mix. The state is positioned to play a leading role in India's energy transition and contribute to meeting national targets for renewable energy thanks, in part, to ambitious state goals to develop Karnataka's abundant zero carbon energy resources and plans to phase out new investments in thermal plants. Power system stakeholders in Karnataka are faced with the challenge of planning a generation portfolio that accounts for the variability of wind and solar resources, meets increasing and shifting electricity demand, and satisfies operational and reliability requirements. The objective of this study is to evaluate least-cost pathways for Karnataka's electric power system through 2050. The capacity expansion model developed for this study identifies power system investment and operational decisions for every year (2020-2050) for all of India, with detailed representation of the state of Karnataka. The study led to five high-level results and provides a framework for recurring planning studies to meet changing system conditions and needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Advances on CHP District Energy and Microgrids Deployment: Simplified Tool for Rapidly Deploying Feasibility Analytics for the Non-Technical User (Final Technical Report)

Community energy systems have proven to have the potential to improve cost efficiency, resilience, and decarbonize. However, investing in community energy systems such as community microgrids or district energy systems is a complex decision due to the high initial investment and the uncertainties associated with the long development time and lifecycle of the project. Tools that make feasibility assessments accessible to non-technical users like investors, policymakers, and other stakeholders will result in more feasibility analyses completed, more candidate projects identified, and more community energy systems deployed. The pilot tool developed under this award is named Energy Fellow. Energy Fellow allows technical and non-technical users to complete feasibility analyses for district energy systems and community microgrids. This is the first software tool of its kind designed for non-technical users and available at no cost. Its scope was adjusted to a 25x25-mile region within the Houston area in Texas to make its development compatible with the funding available. However, the findings and models developed make this pilot tool easily scalable to the US. The lessons learned during the design, implementation, and testing stages have helped find trade-off solutions to software and hardware challenges related to implementing 3D models in online tools. Green software strategies has been successfully applied to the design and operations of the tool, and the team has researched the aspects of the (non-technical) user experience that will make commercial developments of this tool even more impactful.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

2022 Component Innovation Awardee: Carter Wind Turbines

Carter Wind Turbines will develop a competitively priced, portable, ultra-lightweight, self-erecting turbine that will expand the viability of wind energy systems for commercial and residential customers in areas far from population centers. The new turbine is a taller version of the Carter Model 300, which was developed with funding from previous Competitiveness Improvement Project (CIP) awards. Compared to conventional turbines, the new assembly is cheaper to ship, can be erected in one day without any cranes or heavy excavation equipment, and is compatible with the wide range of grid configurations and site conditions found in remote areas.

CIP↗
DOE OSTI2020

MN Solar Pathways: Solar Deployment Strategies-Modeling and Case Studies

As part of the Minnesota Solar Pathways project, funded in part by the Department of Energy’s Solar Energy Technologies Office, Clean Power Research (CPR) developed a Solar Deployment Strategies modeling tool to calculate the economic impacts and market incentives created by different solar portfolios and rate structures. CPR developed the tool using Minnesota-specific data and assumptions. The case studies described in this report provide insight into possible pathways toward the state goal of 10% solar, but the emphasis of the report is the demonstration of the tool in use cases that can be expanded and modified in future years as needs change.

14 SOLAR ENERGY↗