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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 OSTI2024

End-Use Savings Shapes Upgrade Package Documentation: LED Lighting, HP-RTU and ASHP-Boiler

Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models over the past 3 years, the objective of this work is to produce national data sets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) upgrade measures, or upgrades. "Measures" refers to energy efficiency variables that can be applied to buildings during modeling. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility upgrade applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. This documentation focuses on an upgrade package of three end-use savings shapes upgrades - Light Emitting Diode (LED) Lighting, Heat Pump Rooftop Unit (RTU) (HP-RTU), and Air-Source Heat Pump (ASHP) Boiler, which we will refer to collectively as the "Interior Lighting and Heat Pump" package. More details on the individual upgrades can be found on the ComStock Measures Documentation page. An upgrade package applies two or more EUSS upgrades to a single building model simulation. Since ComStock is a bottom-up physics-based model, an upgrade package will go beyond aggregating or summing the individual upgrade results and produce novel results by simulating interactions between the upgrades. For example, pairing an envelope upgrade with an electrification upgrade would likely result in higher savings results than the sum of these upgrades individually, and the size of the heating, ventilating, and air conditioning (HVAC) equipment may be reduced if the envelope upgrade reduces the loads significantly.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Modeling Mesoscale Processes of Scalable Synthesis (Final Report)

The main objective of the center is to develop tools that can help for the understanding of equilibrium and non-equilibrium properties of nanoparticles in aqueous electrolytes. To achieve this goal, we need to develop new computational methodologies that can integrate molecular level description such as the electronic structure analysis to mesoscale description such as generalized hydrodynamics. The challenge is to develop systematic coarse graining techniques that can adaptively choose the level of description needed for the particular task. For example, if bonding breaking is involved, then one should use appropriate models from quantum mechanics to accurately model the time scale and molecular mechanism for the breaking process. However, to model transport process or the process of self-assembly, much less detailed models can be used. There are two main components to this task. One is to develop accurate models that bridge different levels of description. The classical example is the QM/MM model that bridges quantum mechanics and molecular mechanics for studying chemical reactions of large molecules. This work was awarded the Nobel prize this year. For our purpose, we also need to develop methods that bridge molecular and continuum levels of description. Another main task is to develop systematic coarse-graining methods that can give rise to accurate mesoscale and macroscale models, starting from more detailed models at the atomic or molecular scale. None of these ideas are new. By now they have been fashionable topics for quite some time. But neither problem is satisfactorily resolved. The main problem is that uncontrolled approximations are used. As a result, we have very little idea about the validity and accuracy of these models. The main purpose of the center is to exploit the Mori-Zwanzig formalism to achieve the goals stated above. The Mori-Zwanzig formalism is a general tool for eliminating degrees of freedom from existing models. What is attractive about this formalism is its generality. The flip side is that it is also somewhat of a tautology. In order to make it a practical tool, one has to make approximations and this is where the trouble comes. Instead of focusing on Mori-Zwanzig formalism, the PI proposes to study the closely related renormalization group formalism (RG). Like Mori-Zwanzig, RG is a general strategy for eliminating small scale degrees of freedom in a model. Unlike Mori-Zwanzig, RG has the advantage that at least for some problems, one can write a PDE (partial differential equation) type of model for the exact RG flow, without any approximation. This gives us a solid starting point for discussing and analyzing approximations. The PI studied approximate RG flows for turbulent diffusion and related problems. However, extending it to more practical problems proved to be hard. The second line that the PI has pursued is to develop special numerical algorithms for studying particularly important events for dynamics processes in meso-scale systems. The PI succeeded in doing so for the problem of understanding phase transition in metallic systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A comparative analysis of YOLOv8 and U-Net image segmentation approaches for transmission electron micrographs of polycrystalline thin films

Metallic thin films offer a platform to experimentally study the dynamics of microstructural evolution, but the required transmission electron microscopy (TEM)-based imaging generates complex images that are challenging to segment and quantify. This work provides a comparative analysis of a new YOLOv8 model and an established U-Net model for bright-field TEM images of polycrystals, employing a framework leveraging physical observables to evaluate performance against two hand-traced benchmark datasets. This methodology obviates the comparison of large, diversely structured, and manually labeled datasets that are required to assess performance on a per-image/per-pixel basis. It is found that the YOLOv8 model, adapted for real-time instance segmentation, has up to 43× faster inferencing (NVIDIA GeForce RTX 4090) compared to U-Net and reconstructs hand-traced grain size distributions (GSDs) with excellent fidelity, finding mean diameter within 3% for grains near an optimal magnification; for grains that deviate from the optimal pixel-diameter, the size of small- (large)-diameter grains is systematically over- (under)-estimated. This is partially mitigated by including scale-aware augmentations during training. Moreover, when the bias is corrected post-inference by a rigid shift in distribution, the YOLOv8 model reproduces ground truth GSDs with exceptional fidelity, with statistical tests indicating <5% probability that the distributions are distinct. Based on ground truth data, calibration curves pertaining to this shift can be constructed for a given model. This issue is not present in the U-Net model’s results, indicating that for quantitative measurements where the true size of objects is of interest, special procedures must be implemented for YOLO-based models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluating the Hybrid Modelling Competition: A Step Towards Developing Good Modelling Practice

Hybrid modelling, a combination of mechanistic and data-driven modelling, is a promis¬ing approach to advance current mathematical models towards improved deci¬sion support tools for today's water-related challenges. Researchers have been develop¬ing guidelines or references for good modelling practices in the water field for mecha¬nistic (Rieger et al., 2012) and data-driven (Zhu et al., 2023) modelling, respectively. However, good modelling practices for hybrid modelling are currently missing (Schneider et al., 2022). Therefore, the International Water Association’s (IWA) hybrid modelling working group initiated the first competition on a data science competition platform (i.e. Kaggle) for water resource recovery modelling at the Watermatex con¬ference in September 2023 in Quebec. The main objective of this competition was to gain insights and experience to create good modelling practices. Further goals were to motivate students, researchers, and practitioners model, foster a vibrant and engaged community, and evaluate the efficacy of com¬petitions in solving modelling challenges within the water domain. Our next goal is that facilities will measure and gather relevant data for future competitions to solve their challenges from a modeller’s perspective.

Schneider, Mariane↗
DOE OSTI2023

Parton labeling without matching: unveiling emergent labelling capabilities in regression models

Parton labeling methods are widely used when reconstructing collider events with top quarks or other massive particles. State-of-the-art techniques are based on machine learning and require training data with events that have been matched using simulations with truth information. In nature, there is no unique matching between partons and final state objects due to the properties of the strong force and due to acceptance effects. We propose a new approach to parton labeling that circumvents these challenges by recycling regression models. The final state objects that are most relevant for a regression model to predict the properties of a particular top quark are assigned to said parent particle without having any parton-matched training data. This approach is demonstrated using simulated events with top quarks and outperforms the widely used $χ$ 2 method.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

WOMBAT (Windfarm Operations and Maintenance cost-Benefit Analysis Tool) WISDEM® [SWR-21-68]

The windfarm operations and maintenance cost-benefit analysis tool (WOMBAT) is software to simulate the operations and maintenance phase of either onshore or offshore windfarms. WOMBAT is a medium-fidelity tool that is designed to model turbine, cable, and substation failures at the subassembly level, but is flexible enough that individual component or asset-level modeling can also be performed. WOMBAT enables users to model technological improvements, the use of a wide range of service equipment from drone repairs and remote resets to heavy lift vessels and crawler cranes, to analyze cost trends with varying trade-offs in conjunction with energy production, availability and a growing range of metrics. This library provides a tool to simulate the operation and maintenance phase (O&M) of distributed, land-based, and offshore windfarms using a discrete event simulation framework. WOMBAT is written around the SimPy discrete event simulation framework. Additionally, this is supported using a flexible and modular object-oriented code base, which enables the modeling of arbitrarily large (or small) windfarms with as many or as few failure and maintenance tasks that can be encoded. Please note that this is still heavily under development, so you may find some functionality to be incomplete at the current moment, but rest assured the functionality is expanding. With that said, it would be greatly appreciated for issues or PRs to be submitted for any improvements at all, from fixing typos (guaranteed to be a few) to features to testing. Also available at: https://pypi.org/project/wombat/

Cooperman, Aubryn↗
DOE OSTI2017

Combustion energy frontier research center (CEFRC) final report (August 1, 2009 – July 31, 2016)

The Combustion Energy Frontier Research Center (CEFRC) was established to tackle the single overarching grand challenge of energy sustainability, energy security and global warming: to develop a “validated, predictive, multi-scale, combustion modeling capability to optimize the design and operation of evolving fuels in advanced engines for transportation applications,” as identified in the DOE report on “Basic Energy Needs for Clean and Efficient Combustion of 21st Century Transportation Fuels”. The challenge is particularly daunting since energy conversion efficiencies and exhaust emissions are governed by coupled chemical and transport processes at multiple length scales ranging from electron excitation to molecular rearrangements to nanoscale particulate formation to turbulent fuel/air mixing. To tackle this challenge, the CEFRC assembled a world-class team of 15 principal investigators, with the objectives to: 1) develop and test theoretical models to predict elementary reaction rates, molecule thermalization rates, chemical bond dissociation energies, and nonequilibrium transport properties using quantum chemistry calculations that account for strong electron correlation and multiple electronic potential energy surfaces; 2) develop automated kinetic mechanism generation, reduction, and error control methods for predicting alternative fuel including biofuel oxidation, fuel droplet decomposition, and NOx and particulate formation; 3) validate and improve the predictions of these models by measuring ignition delay times, species profiles, flame structures, burning limits, turbulence-kinetic coupling, and NOx and soot emissions at high-pressures and near-limit conditions, by using advanced experimental diagnostic techniques including multiple laser techniques, molecular beam sampling and synchrotron photoionization, and by conducting the measurements in high-pressure shock tubes, jet-stirred and flow reactors, flame bombs, counterflow flames, and advanced-design rapid compression ignition instruments; and 4) develop a suite of validated petascale high-fidelity simulation and modeling capabilities to understand and predict chemistry-turbulence-radiation coupling for new fuels in new regimes, including the high pressure, low-temperature combustion in advanced engine and turbine designs, and 5) establish a knowledge highway between researchers and engineers in academia, national laboratories, and industry to facilitate the dissemination and exchange of knowledge on national and international levels, and enrich the talent pool and capabilities of the next generation of combustion scientists and engineers. The technical activities of the CEFRC were conducted through three Disciplinary Working Groups – Chemistry Theory, Experiment and Mechanism, and Reacting Flows, which coordinated the Center’s research on the development of combustion chemistry of Foundation Fuels (C0–C4 hydrocarbons), Alcohols, and Biodiesel through three corresponding Mechanism Thrust Groups. Such a two-dimensional coordinated and tightly interwoven research structure has been proven to be highly effective in assuring the interplay between the developments of the fundamentals of combustion science and the utilization of the various categories of fuels. The Center has accomplished the above goals over the five year period (August 1, 2009 – July 31, 2014) with appropriated funding, followed by two additional no-cost-extension (NCE) years. The research results are documented in 230 journal publications, with six legacy review papers on the study of combustion chemistry using shock tubes, flow reactors, rapid compression machines, and flames, on uncertainty quantification, and on theoretical reaction dynamics and chemical modeling of combustion. A robust outreach program complemented these PI-led research activities, consisting of: 1) a roving post-doc program comprised of a corps of Center-appointed, co- or multi-sponsored post-doctoral fellows with rotating assignments to conduct seed projects initiated by at least two PIs, in residence with these sponsoring PIs, to rapidly pursue new and high-risk, high-payoff interdisciplinary ideas; 2) an annual summer school on combustion heavily attended (~200) by senior graduate students and practicing researchers covering advanced topics on chemical kinetics, fluid mechanics, turbulent combustion, engine combustion, new technologies, etc.; 3) a robust open web-site providing Center and community information as well as the lecture videos and notes of the summer school; and 4) widely distributed biannual newsletters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Impact of Advanced Vehicle Technologies on Energy Consumption for the City of Detroit Using Transportation System Simulations

In developing a transportation system model, the energy impact of the model is extremely important for sustainability and validation. One approach to evaluating the energy impact is to consider the amount of fuel consumed by transportation in the model for different scenarios and technologies. Argonne National Laboratory has collaborated with the Southeast Michigan Council of Governments (SEMCOG) and the Detroit Department of Transportation to develop and validate a transportation system model for Southeast Michigan, focusing on the city of Detroit. The objective of this paper is to analyze the vehicle energy consumption in the Detroit Transportation System model. The focus is on evaluating the SEMCOG road network drive cycles as performed with current and future vehicle technologies.

Islam, Ehsan↗
DOE OSTI2017

Piston geometry effects in a light-duty, swirl-supported diesel engine: Flow structure characterization

This work studied how in-cylinder flow structure is affected in a light-duty, swirl-supported diesel engine when equipped with three different piston geometries: the first two featuring a conventional re-entrant bowl, either with or without valve cut-outs on the piston surface and the third featuring a stepped-lip bowl. Particle image velocimetry experiments were conducted inside an optical engine to measure swirl vortex intensity and structure during the intake and compression strokes. A full computational model of the optical diesel engine was built using the FRESCO code, a recently developed object-oriented parallel computational fluid dynamics platform for engine simulations. The model was first validated against the measured swirl-plane velocity fields, and the simulation convergence for multiple cycles was assessed. Flow topology was studied by addressing bulk flow and turbulence quantities, including swirl structure, squish flux, plus geometric and operating parameters, such as the presence of valve cut-outs on the piston surface, compression ratio and engine speed. The results demonstrated that conventional re-entrant bowls have stronger flow separation at intake, hampering bowl swirl, but higher global swirl than for stepped-lip bowls thanks to a stronger and more axisymmetric squish mechanism and less tilted swirl. Stepped-lip bowls have larger inhomogeneities (tilt and axisymmetry) and higher turbulence levels, but also faster turbulence dissipation toward top dead center. They have weaker squish flux but larger squish inversion momentum as a result of the smaller inertia.

Perini, Federico↗
DOE OSTI2026

Optimizing fluvial flood mitigation strategies: A multi-objective approach for cost-effective and socially-aware infrastructure feasibility analysis

Effective levee planning must balance capital cost, risk reduction, and community priorities. These objectives are rarely optimized together. This study presents a feasibility phase, simulationin-the-loop framework that couples terrain-based flood modeling with a socially aware multiobjective optimizer. Flood risk is measured as Expected Annual Exposed Population (EAEP), obtained by integrating exposure over Annual Exceedance Probability (AEP) nodes, mirroring the Hydrologic Engineering Center's Flood Damage Reduction Analysis (HEC-FDA) expected-annual formulation but with people rather than dollars. Exposure per scenario is computed by overlaying binary inundation masks with a population surface at the tract level. Distributional fairness is encoded through a Group Benefit Share (GBS) constraint that requires high-SVI tracts to receive at least a baseline share of annualized benefits. Capital cost is represented by a height-dependent unit-cost model suitable for screening. This study addresses the two-objective problem, minimize cost and expected annual exposure subject to the GBS constraint, using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and leveraging Pareto front for feasibility phase decision making. Implemented with terrain-based flood modeling, GeoFlood, for rapid scenario evaluation, the framework is demonstrated in Southeast Texas. The results reveal clear trade-offs among cost, risk, and social benefits and identify non-dominated levee height configurations that satisfy the benefit-share floor. The contributions are a scalable decision support method that operationalizes expected annual population-based risk, embeds enforceable benefit-sharing guarantees, and uses lightweight simulation to explore large design spaces before higher fidelity design stages.

Flood mitigation↗
DOE OSTI2023

Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites

Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the “chassis” organism’s metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Kepler Data Analysis: Non-Gaussian Noise and Fourier Gaussian Process Analysis of Stellar Variability

We develop a statistical analysis model of Kepler stellar flux data in the presence of planet transits, non-Gaussian noise, and stellar variability. We first develop a model for the Kepler noise probability distribution in the presence of outliers, which make the noise probability distribution non-Gaussian. We develop a signal likelihood analysis based on this probability distribution, in which we model the signal as a sum of the star variability and planetary transits. We argue that these components need to be modeled together if optimal signal is to be extracted from the data. For the stellar variability model we develop an optimal Gaussian process analysis using a Fourier-based Wiener filter approach, where the power spectrum is non-parametric and learned from the data. We develop high dimensional optimization of the objective function, where we jointly optimize all the model parameters, including thousands of star variability modes, and planet transit parameters. We apply the method to Kepler-90 data and show that it gives a better match to the stellar variability than the existing methods, and robustly handles noise outliers. As a consequence, the planet radii have a higher value than what the existing methods give, including splines and celerite.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Predictive LES Modeling and Validation of High-Pressure Turbulent Flames and Flashback in Hydrogen-enriched Gas Turbines

The overall objective of this project is to make advances in large-eddy simulation modeling of swirl-flame flashback at elevated pressure, where the reactants can be premixed or stratified, and composed of methane enriched with hydrogen. The modeling effort was conducted with a companion experimental validation effort that focused on high-speed measurements of flashback in a model swirl-flame combustor. The modeling effort focused on the development of manifold-based combustion model for flashback simulations. The main complexity in this configuration comes from the flames’ propagation through a stratified fuel-air mixture along with heat loss near the walls. For this purpose, a non-adiabatic flamelet model was developed and tested using canonical flow configurations. Further, extensive validation in premixed configurations is used to establish the model’s accuracy in capturing heat loss and flame propagation. Finally, the model was tested using the UT flame flashback experiment, and was shown to accurately capture the flame flashback process. The end products from this work are a) a full validated flameout-based approach for modeling flame flashback; b) a complete implementation of the model in the open source OpenFOAM framework, which has been made available to both industry and academia; c) an extensive suite of validation studies performed using the OpenFOAM solver. In addition, new techniques for flame modeling, subfilter modeling of scalars, and simulation of low Mach number flows were developed and tested. In the experimental validation effort, flashback was studied in a premixed and stratified swirl flame by using high-speed imaging diagnostics. Fuels tested included CH 4 enriched with H 2 , with H 2 enrichment up to 84% by volume. These flashback experiments are conducted at pressures ranging from 1 to 5 atm. A swirler-based fuel-injection system was used to create the fuel-air stratification in the radial direction. For the high-pressure measurements, an optically accessible elevated pressure chamber was used. The spatial distribution of the equivalence ratio under non-reacting conditions was characterized using planar laser-induced fluorescence with acetone as the fuel tracer. It was observed that fuel-air pockets were distributed across the mixing tube width, although in an average sense, the fuel-air mixture was radially stratified. The global behavior of upstream flame propagation was investigated for different levels of hydrogen-enrichment. For stratified hydrogen-rich flashback, the propagation path of the flame changed from the inner wall to outer wall, which was induced by the faster chemistry of stoichiometric mixtures that are frequently present near the outer wall. This behavior of hydrogen-rich flashback persists even at elevated pressures up to 5 atm, although the propagation of the flame occurs as a wide flame tongue as opposed to the acute-tipped flame structures present in the atmospheric cases. The end product of the experimental effort is greatly improved knowledge of flashback physics and a unique database that can be used for model validation.

08 HYDROGEN↗
DOE OSTI2020

Design Paradigm for Modular Multilevel Converter-Based Generator Rectifier Systems

Modular Multilevel Converters (MMC) are being widely considered for medium to high voltage DC applications. Designing such converters through multi-objective optimization is of interest because such an approach allows the trade-off between competing objectives (for example mass and loss) to be explicitly and quantitatively identified. In this work, an optimization based design paradigm for MMC based generator rectifier systems is proposed. Such development typically requires detailed component design and simulation models for the electric machine and converter which are computationally expensive. As an alternative, the proposed work utilizes an electric machine metamodel, inductor metamodel, and high-speed steady-state simulation model for the MMC to facilitate multi-objective optimization minimizing system metrics of interest while satisfying system constraints. A case study is undertaken to demonstrate the functionality of the proposed design paradigm.

42 ENGINEERING↗
DOE OSTI2019

System Design, Analysis, and Modeling Activities Supporting the DOE Hydrogen Storage Engineering Center of Excellence (HSECoE): Final Project Report

The objective of this project was to identify optimal pathways for successful hydrogen storage system technology through modeling, analysis, and testing support. The National Renewable Energy Laboratory (1) performed vehicle simulations of various system configurations to support the overall systems engineering, (2) led the storage system energy analysis and provided the Hydrogen Storage Engineering Center of Excellence (HSECoE) with results to help guide engineering design, and (3) compiled and obtained media engineering properties for the HSECoE.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Maven: a multimodal foundation model for supernova science

Abstract A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. We present Maven, the first foundation model for supernova science. To construct Maven, we first pre-train our model to align photometry and spectroscopy from 0.5 M synthetic supernovae using a contrastive objective. We then fine-tune the model on 4702 observed supernovae from the Zwicky transient facility. Maven reaches state-of-the-art performance on both classification and redshift estimation, despite the embeddings not being explicitly optimized for these tasks. Through ablation studies, we show that pre-training with synthetic data improves overall performance. In the upcoming era of the Vera C. Rubin observatory, Maven will serve as a valuable tool for leveraging large, unlabeled and multimodal time-domain datasets.

Zhang, Gemma (ORCID:0000000280198082)↗
DOE OSTI2018

Remote Off-grid Microgrid Design Support Tool (ROMDST) - An Optimal Design Support Tool for Remote, Resilient, and Reliable Microgrids Phase II - Final Report

The objective of the project is to develop an advanced optimization-based design support tool for AC or DC microgrids in remote locations, where utility grids may not be accessible. The mathematical model and the interface are being developed such that multiple design objectives and criteria/constraints can be easily enabled or disabled, to deliver a flexible tool relevant to a large, diverse user base, and to facilitate future feature developments. The new tool delivered by this project leverages the team’s extensive expertise in the development, testing, deployment, and commercialization of the state-of-the-art Distributed Energy Resources Customer Adoption Model (DER-CAM) – which is the foundation for the new tool. Phase I of the project leverages the microgrid design and decision support tool DER-CAM. Key Phase I developments include the following adaption to the DER-CAM model: a) AC or DC microgrid architectures, b) multiple economic objectives (e.g. single customer vs community vs utility perspective), c) constraints on fuel availability (e.g. total fuel available during outage periods), d) active and reactive power flow constraints for normal and N-1 contingency cases, e) component part load efficiencies (e.g. power electronic devices), and f) interactive data visualization capabilities (e.g. design topology elements). A component library including components’ default technical and economical characteristics was also developed. In Phase II, the project is focused on tool testing and validation, use for real-world microgrid designs, and outreach. Fully developing this tool will have the following impacts: 1) optimal off-grid microgrid designs that replace existing back of the envelope or non-optimal calculations, reducing capital costs and risk of microgrid deployment; 2) removing barriers to microgrid assessments by lowering microgrid soft costs, as the tool is freely usable; 3) reliable and resilient designs that reduce the cost of critical load shedding due to component outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗