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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE 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↗
DOE OSTI2026

Supporting Resource Adequacy via the ReEDS-India Model [Slides]

ReEDS-India is an open-access tool for mid- and long-term capacity expansion modeling that finds the mix of generation, transmission, and storage technologies that meet the anticipated requirements of the electric sector at least cost.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Integration and Optimization of a Waste Heat Driven Organic Rankine Cycle for Power Generation in Wastewater Treatment Plants

The study focuses on achieving energy self-sufficiency in Wastewater Treatment Plants by proposing a comprehensive model for integrating, sizing, and optimizing an Organic Rankine Cycle system. The Organic Rankine Cycle system is designed to utilize waste heat from the gensets at As Samra Wastewater Treatment Plant in Jordan, where it will contribute to the overall electrical energy supply of the plant. Real data from As Samra Wastewater Treatment Plant is used to model and calculate the available waste heat using TRNSYS® software. The Organic Rankine Cycle model is then developed using ASPEN PLUS® software to explore the impact of operational parameters and determine their optimal values for maximizing the plant's energy profile. An economic analysis is conducted to assess the feasibility of the proposed model, considering system components, installation, operation, and maintenance costs. To optimize the Organic Rankine Cycle system, the study employs the Multi-Output Support Vector Regression technique to capture nonlinear relationships between independent variables (fluid type, turbine inlet pressure, turbine inlet temperature, turbine outlet pressure, and mass flow rate) and dependent variables (pump power input, waste heat input, and turbine specific work). The Osprey optimization algorithm is used to address the multi-objective optimization problem, with the proposed Pareto-based Osprey Optimization Algorithm and the Multi-Objective Particle Swarm Optimization technique being employed to evaluate critical performance and economic parameters such as system thermal efficiency, net power output, and the levelized cost of electricity. The results of the optimization strategies indicate that the M-SVR model's prediction accuracy is significantly improved after parameter optimization, with the model returning high R 2 and low Mean Square Error values of 0.991 and 0.00216, respectively. The Pareto-Based Osprey Optimization Algorithm optimizer identifies the best working fluid as Isobutane/Isopentane in a ratio of 66:34, with optimal turbine inlet pressure and temperature of 15 bars and 218 °C, respectively. In conclusion, the Organic Rankine Cycle model at these optimal conditions achieves a cycle efficiency of 19.93% and an Levelized Cost of Electricity value of 0.0353 USD/kWh.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model

Abstract For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Finding predictive models for singlet fission by machine learning

Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Stochastic machine learning via sigma profiles to build a digital chemical space

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Automatic Building Energy Modeling (AutoBEM)

AutoBEM provides automated building energy modeling for quantifying energy, demand, emissions, and cost reductions for over 1 million buildings per hour. It creates a digital twin energy model of a building from a set of descriptors. Benefits include: Scale - has been used to simulate every building in the U.S. Speed - fastest creation and simulation of energy models for buildings. Accuracy - validated against 15-minute data for 178,000 buildings as well as multiple cities.

New, JoshuaR↗
DOE OSTI2022

An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening

Fecal immunochemical testing (FIT) is an established method for colorectal cancer (CRC) screening. Measured FIT-concentrations are associated with both present and future risk of CRC, and may be used for personalized screening. However, evaluation of personalized screening is computationally challenging. In this study, a broadly applicable algorithm is presented to efficiently optimize personalized screening policies that prescribe screening intervals and FIT-cutoffs, based on age and FIT-history. We present a mathematical framework for personalized screening policies and a bi-objective evolutionary algorithm that identifies policies with minimal costs and maximal health benefits. The algorithm is combined with an established microsimulation model (MISCAN-Colon), to accurately estimate the costs and benefits of generated policies, without restrictive Markov assumptions. The performance of the algorithm is demonstrated in three experiments. In Experiment 1, a relatively small benchmark problem, the optimal policies were known. The algorithm approached the maximum feasible benefits with a relative difference of 0.007%. Experiment 2 optimized both intervals and cutoffs, Experiment 3 optimized cutoffs only. Optimal policies in both experiments are unknown. Compared to policies recently evaluated for the USPSTF, personalized screening increased health benefits up to 14 and 4.3%, for Experiments 2 and 3, respectively, without adding costs. Generated policies have several features concordant with current screening recommendations. The method presented in this paper is flexible and capable of optimizing personalized screening policies evaluated with computationally-intensive but established simulation models. It can be used to inform screening policies for CRC or other diseases. For CRC, more debate is needed on what features a policy needs to exhibit to make it suitable for implementation in practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Scale bridging damage model for quasi-brittle metals informed with crack evolution statistics

Computationally efficient methods for bridging length scales, from highly resolved micro/meso-scale models that can explicitly model crack growth, to macro-scale continuum models that are more suitable for modeling large parts, have been of interest to researchers for decades. In this work, an improved brittle damage model is presented for the simulation of dynamic fracture in continuum scale quasi-brittle metal components. Crack evolution statistics, including the number, length, and orientation of individual cracks, are extracted from high-fidelity, finite discrete element method (FDEM) simulations and used to generate effective material moduli that reflect the material’s damaged state over time. This strategy allows for the retention of small-scale physical behaviors such as crack growth and coalescence in continuum scale hydrodynamic simulations. However, the high-fidelity simulations required to generate the crack statistics are computationally expensive. Thus, steps were taken to produce a flexible constitutive model to reduce the number of costly high-fidelity simulations needed to produce accurate results. A new stress based degradation criterion is introduced for the degradation of individual material zones. This allows for the development of a heterogeneous damage distribution within the bulk material. Then a flow stress model is added to the hydrodynamic simulation to account for plasticity in quasi-brittle materials. As a result, the effective moduli model can be applied to a larger range of materials. The effective moduli constitutive model is used to simulate beryllium flyer plate experiments. The results from the continuum scale simulations using statistics from a single high-fidelity simulation are found to be in excellent agreement with numerical and experimental velocity interferometer data. The same set of crack statistics are used to extrapolate the results of a higher rate flyer plate case using the effective moduli model. In conclusion, the extension of this model to higher rate cases shows promise for further reducing the number of costly high-fidelity simulations needed to generate crack statistics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluation of Composite Structural Materials for Heliostat Cost Reduction

Structures manufactured from steel comprise up to 40% of a CSP heliostat's cost. Composite structures represent a potential opportunity to reduce this cost. A reference heliostat structural model has been created with a reflector area of 25m 2 . The design, constructed of low-carbon steel, pro-vides baseline deflection and stiffness under a 21 m/s operating wind speeds. Wind loads on the tracker structure are determined for both operating and stow conditions. An established roster of suitable metal alternative materials is considered including: glass, basalt, and carbon reinforced polymer (GFRP, BFRP, and CFRP respectively). Three heliostat components are investigated: the pylon, torque tube, and the purlin-strut assembly. Composite material properties are substituted for those of steel, and the beams are re-sized to match the original steel components' deflection under given wind loads. Weight and cost changes resulting from this resizing are evaluated. It is found that GFRP and BFRP represent a 3X–6X cost premium for the same operating deflection character-istics as steel across all three investigated component classes; with weight reduction only achieved for the purlin-strut assembly. While CFRP components can achieve approximately 25–75% weight savings depending on the application, this comes with a 9X–14X cost increase over the steel base-line for tube-type structures and roughly 5X cost increase when replacing c-channel structures. This work does not rule out the possibility of cost savings when the heliostat design and kinematics to take advantage of composites' specific properties.

14 SOLAR ENERGY↗
DOE OSTI2025

Improved PV System Control Strategies to Reduce Power Management Costs in Nanogrids

An improved PV system control method is proposed to reduce nanogrid operation costs in this paper. A model including it various components such as photovoltaic (PV) systems, energy storage systems (ESSs), gateways, and household loads is considered with the constraints of the ESS, PV irradiance from real data, and household load. We designed an optimal economic dispatch strategy with an improved PV system control method. Combining the proposed optimal economic dispatch and PV system control strategies, it can improve the control performance for both transient and steady-state responses thereby enabling the maximum power to be extracted from the PV. Consequently, the PV power is maximized, which allows the ESS to use less power and sell the surplus to external power sources, which means the proposed method decreases the nanogrid operation costs. Furthermore, this performance is verified via nanogrid simulations and PV experimental kit.

PV system control↗
DOE OSTI2020

Rotational transition, domain formation, dislocations, and defects in vortex systems with combined sixfold and twelvefold anisotropic interactions

We introduce a phenomenological model for a pairwise repulsive interaction potential of vortices in a type-II superconductor, consisting of superimposed sixfold and twelvefold anisotropies. Using numerical simulations we study how the vortex lattice configuration varies as the magnitudes of the two anisotropic interaction terms change. A triangular lattice appears for all values, and rotates through 30 ° as the ratio of the sixfold and twlevefold anisotropy amplitudes is varied, in agreement with experimental results. The transition causes the vortex lattice to split into domains that have rotated clockwise or counterclockwise, with grain boundaries that are “decorated” by dislocations consisting of fivefold and sevenfold coordinated vortices. We also find intradomain dislocations and defects, and characterize them in terms of their energy cost. We discuss how this model could be generalized to other particle-based systems with anisotropic interactions, such as colloids, and consider the limit of very large anisotropy where it is possible to create cluster crystal states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Application of a Distributed Element Roughness Model to Additively Manufactured Internal Cooling Channels

Abstract Design for cooling effectiveness in turbine blades relies on accurate models for dynamic losses and heat transfer of internal cooling passages. Metal additive manufacturing (AM) has expanded the design space for these configurations, but can give rise to large-scale roughness features. The range of roughness length scales in these systems makes morphology resolved computational fluid dynamics (CFD) impractical. However, volumetric roughness models can be leveraged, as they have computational costs orders of magnitude lower. In this work, a discrete element roughness model (DERM), based on the double-averaged Navier–Stokes equations, is presented and applied to additively manufactured rough channels, representative of gas turbine blade cooling passages. Unique to this formulation of DERM is a generalized sheltering-based treatment of drag, a two-layer model for spatially averaged Reynolds stresses, and explicit treatment of dispersion. Six different AM rough surface channel configurations are studied, with roughness trough to peak sizes ranging from 15% to 60% nominal channel passage half-width, and the roughness Reynolds number ranges from Rek = 60 to 300. DERM predictions for spatially and temporally averaged mean flow quantities are compared to previously reported direct numerical simulation results. Good agreement in the mean velocity profiles, stress balances, and drag partitions are observed. While DERM models are typically calibrated to specific deterministic roughness morphologies at comparatively small roughness Reynolds numbers, the present more generalized DERM formulation has wider applicability. Here, it is demonstrated that the model can accommodate random roughness of large scale, typical of AM.

Engineering↗
DOE OSTI2021

Geothermal Operational Optimization with Machine Learning

The Geothermal Operational Optimization with Machine Learning (GOOML) project has developed a generic and extensible component-based system modeling framework to study complex geothermal fields using a data-driven approach. Through building a digital twin of a geothermal steam field with the GOOML modeling framework, operators can analyze historical and forecasted power production, explore possible steam field configurations, and optimize real world operations, all in a cost-effective digital environment. The GOOML modeling software is based on a historical data-assimilation framework that uses first-principal thermodynamics to model steam field components using historical data, and a forecast framework that uses machine-learning-driven models of steam field components to predict future operations. This modeling framework creates countless new opportunities for digital exploration of steam field design and operations. To date, digital twins have been developed for several steam fields in New Zealand and the United States. These digital twins have been validated by comparing hindcast predictions against historical production data. Field design and operations have been explored using genetic optimization and reinforcement learning. Initial results show compelling and often surprising opportunities for improved design and operation of fields with 2 to 5 percent improvements in annual energy production. GOOML is driving a step-change in geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and a first-of-its-kind intelligent geothermal systems model.

40 EE - Geothermal Technologies Office (EE-4G)↗
DOE OSTI2022

High-Temperature Ceramic-Carbonate Dual-Phase Membrane Reactor for Pre-combustion Carbon Dioxide Capture (Final Scientific/Technical Report)

Arizona State University, in collaboration with University of South Carolina, worked on a project aimed at development of a new high temperature, high pressure CO 2 perm-selective membrane reactor for water-gas-shift reaction (WGS) with simulated gasifier syngas to produce a high concentration H 2 stream with CO 2 capture. The membrane reactor is made of a CO 2 semi-permeable ceramic-carbonate dual-phase (CCDP) membrane with high CO 2 perm-selectivity/permeance and thermal/mechanical stability for application in WGS reaction. The objectives of this project were to (1) synthesize the chemically/thermally stable tubular CCDP membranes with CO 2 permeance and selectivity (with respect to H 2 , CO or H 2 O) larger than 6.5×10-7 mol/m2·s·Pa and 500, respectively; (2) establish CCDP membrane reactor setup and study high pressure CO 2 permeation and WGS reaction with CO 2 capture using the setup; and (3) identify conditions for WGS in the CCDP membrane reactor that produce CO 2 and H 2 streams with purity of >99% and >90% respectively at CO conversion >95% and overall carbon capture >90%. The work in this project included both membrane development and membrane reactor process study. The membrane development efforts were focused on investigating a H 2 S resistant and highly oxygen-ionic conducting metal oxide material and membrane for CO 2 separation, fabrication of tubular samaria-doped-ceria/molten-carbonate CCDP membrane with high mechanical strength, and experimental and modeling study of high-pressure CO 2 permeation of the CCDP membranes. Mathematical models were developed to describe WGS in the CCDP membrane reactor without a catalyst or packed with a commercial high temperature WGS catalyst. Experiments on WGS in the CCDP membrane reactor with the commercial WGS catalyst, guided by the model analysis, were performed to identify optimum conditions for achieving the CO conversion, carbon capture, and the purity of the H 2 and CO 2 streams mentioned above. At 30 atm feed pressure, 750°C operation temperature, space velocity of 250 h-1, and with steam sweep, a single-stage CCDP membrane reactor with average CO 2 permeation flux of 0.5 cm3(STP)/min.cm2 can achieve CO 2 conversion of 95% and overall carbon capture of 94%, and produce CO 2 and H 2 streams with dry-based purity of >99% and 92% respectively. The project also included process design and techno-economic analysis (TEA) for a CCDP membrane reactor process for WGS reaction with CO 2 capture for a 550 MW coal-fired IGCC power plant, and its comparison with the conventional fixed-bed reactor system for WGS with follow-up CO 2 capture by an amine absorption process. The target performance for the reactor for WGS with CO 2 capture includes CO conversion >95%, hydrogen stream purity >90%, CO 2 stream purity >95%, and total carbon capture >90%. The CCDP membrane developed in this project can achieve the performance target, without subsequent CO 2 capture process at the optimum conditions identified in this project. The outcome of the process design and TEA analysis shows that the membrane reactor for WGS with in-situ CO 2 capture has an operating cost about 40% lower than that for the conventional fixed-bed reactor with a separate amine absorption process for CO 2 capture. However, the capital cost of the membrane reactor process is about twice that of the conventional process because of the higher cost of the CCDP membrane. Modeling analysis shows that a membrane reactor using a CCDP membrane with higher CO 2 permeance (about three times the current value) can deliver the targeted performance for WGS reaction with CO 2 capture at a much higher space velocity and lower membrane surface area to catalyst volume ratio, leading to a smaller catalyst amount and/or membrane area and hence significantly reduced membrane reactor capital costs.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics

Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. Here, we propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reductions than diseased models. In all cases, our workflow coupling Dakota’s MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport

Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. Here, the framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Cost-Effectiveness of Local Distribution Tied Solar within KyMEA

KyMEA is a Kentucky joint action agency which provides electric transmission and energy services to eleven municipal utilities. One of KyMEA's member cities, Frankfort, has set a goal to reach 100% renewable generation for the city load by 2023 and 100% carbon free city operations and 100% renewable generation community-wide by 2030. The aim of this research was to determine if the Frankfort city government could reduce its power costs by implementing distribution tied solar while also avoiding any cross subsidization between other KyMEA member utilities or other Frankfort Plant Board customers. The project simulated the KyMEA generation portfolio and transmission system from KyMEA's cost perspective using the NREL Engage capacity expansion model. Running in operate mode, the authors used Engage to compare KyMEA's costs to operate the baseline system to its costs to operate the system with Frankfort meeting some of its load with distribution-tied PV. The cost perspective of Frankfort Plant Board, meanwhile, was simulated using Python. The simulations indicated that it would be cost-beneficial to KyMEA, i.e., that no additional costs would be borne by other members, and that it would be cost-neutral to Frankfort Plant Board for Frankfort city government to implement distribution tied utility-scale PV. The new load attributes resulting from the distribution-tied PV lowered KyMEA's cost to serve load by improving system load factor, reducing system transmission and energy costs, and increasing revenues from market energy sales.

14 SOLAR ENERGY↗