Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Generative Design”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,279 records · Page 71

Sensitivity analysis of small modular reactor mPower with ATHLET

Small modular reactor (SMR) possesses the characteristics of less radioactive source term, simpler structure, modular design and features like passive safety as well as low risk of proliferation. Developed and designed by Babcock and Wilcox, the mPower reactor is one of pronouncing small modular reactors that adopts the integrated design of the pressurized water reactor, steam generator and a built-in internal control rod drive mechanism. In this paper, the code ATHLET (Analysis of fossil-hydraulics of Leaks and Transients) is used to model the mPower reactor. At the same time, the sensitivity analysis of the core structure and geometric parameters of the pipeline loop, as well as the steady-state simulation and post-exothermic simulation are carried out depending on the given scenarios. In addition, the sensitivity analysis of pressure, temperature and mass flow in the secondary loop is performed. The results of this study will mitigate some of the frequent misunderstanding in the field and will help to better understand the design of SMR and provide basic research data for further research. However, there are limitations in the current research. Considering that the mPower is still in the conceptual design, the established model is only based on the limited public data while detailed parameters are still in myth, which leads to certain level of uncertainties in the calculation. Besides, the research is mainly on the influence of sensitivity analysis under steady state conditions. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗

Resilient Cooperative Voltage Control for Distribution Network with High Penetration Distributed Energy Resources

Here, this paper considers the problem of designing a resilient distributed voltage control algorithm for distribution systems with high penetration of distributed energy resources in the presence of an unknown cyber-attack. The purpose of the attack is to force the system to violate the operating voltage limit by intercepting its communication channels and inserting exogenous signals to perturb and/or modify the information being exchanged. We first review the cooperative voltage control proposed in our previous work and provide a new stability analysis for it. Next, we present a resilient cooperative voltage control algorithm by introducing a virtual system interconnected with the original system such that the voltage can be maintained within the operational limit under unknown attacks. The resiliency of the proposed algorithm is demonstrated via simulations on the IEEE 8500-node system when subjected to an attack which consists of corrupting the data being exchanged in the communication network between two generation units.

cooperative control↗

A compact design for an electric driven hydraulic gear machine capable of multiple quadrant operation

This paper presents an innovative design of an integrated electro-hydraulic (EH) flow generation unit able to operate in multiple quadrants. The proposed unit is composed of a hydraulic machine which can operate in either pumping or motoring mode, and an electric machine, which can operate as electric motor or generator. The adopted morphological integration of the two unit fulfills the goals of maximizing compactness, power to weight ratio, energy efficiency and quietness. Furthermore, the design results from a numerical optimization procedure that includes detailed simulation of both the electric machine and the hydraulic machine, using simulation tools previously developed by the authors, but combined for the first time in this work. After describing the optimization procedure, the paper focuses on the design integration aspects of the novel unit. A 9 kW prototype of the integrated unit was also fabricated and tested to verify its performance. Tests results show a measured overall energy efficiency up to 69%, demonstrating the potentials of the proposed concept, despite some limitations present in the first prototypes which could be addressed in future design refinements.

42 ENGINEERING↗

Highly Symmetric, Self-Assembling 3D DNA Crystals with Cubic and Trigonal Lattices

The rational design of nanoscopic DNA tiles has yielded highly ordered crystalline matter in 2D and 3D. The most well-studied 3D tile is the DNA tensegrity triangle, which is known to self-assemble into macroscopic crystals. However, contemporary rational design parameters for 3D DNA crystals nearly universally invoke integer numbers of DNA helical turns and Watson–Crick (WC) base pairs. Here, in this study, 24-bp edges are substituted into a previously 21-bp (two helical turns of DNA) tensegrity triangle motif to explore whether such unconventional motif can self-assemble into 3D crystals. The use of noncanonical base pairs in the sticky ends results in a cubic arrangement of tensegrity triangles with exceedingly high symmetry, assembling a lattice from winding helical axes and diamond-like tessellation patterns. Reverting this motif to sticky ends with Watson–Crick pairs results in a trigonal hexagonal arrangement, replicating this diamond arrangement in a hexagonal context. These results showcase that the authors can generate unexpected, highly complex, pathways for materials design by testing modifications to 3D tiles without prior knowledge of the ensuing symmetry. This study expands the rational design toolbox for DNA nanotechnology; and it further illustrates the existence of yet-unexplored arrangements of crystalline soft matter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

High-entropy 1D halide perovskite piezoelectrics found by megalibrary synthesis and rapid nonlinear optical screening

Piezoelectric molecular crystals offer excellent compositional and structural tunability and sustainable processability. However, their discovery is slow, primarily due to the serial synthesis and screening processes used. Here, we report an approach that combines massively parallel megalibrary synthesis with scanning second harmonic generation (SHG) microscopy for rapid screening of piezoelectric molecular crystals. Megalibraries consisting of more than 1,000,000 compositionally distinct but positionally encoded TMCM x TMA (1–x) Cd y Pb (1–y) ClzBr (3–z) (TMCM: trimethylchloromethylammonium, TMA: tetramethylammonium; 0 ≤ x ≤ 1, 0 ≤ y ≤ 1, 0 ≤ z ≤ 3) nanocrystals were synthesized. The megalibraries were rapidly screened by SHG microscopy to identify notable noncentrosymmetric structures, which were then tested for piezoelectricity, facilitating discovery of a high-entropy noncentrosymmetric material with a large d 33 (TMCM 0.75 TMA 0.25 Cd 0.75 Pb 0.25 Cl 1.5 Br 1.5 , 42.8 picocoulombs per newton). Furthermore, this approach enabled systematic investigation of the Curie temperature (T C )–composition relationship in the TMCMCdCl z Br (3–z) system, facilitating reverse design of materials with targeted T C . Our work establishes a powerful approach to accelerate the discovery and design of unusual piezoelectrics for next-generation electronics and optics.

Li, Jun [Northwestern University, Evanston, IL (Un↗

Generative adversarial networks for ensemble projections of future urban morphology

As city planners design and adapt cities for future resilience and intelligence, interactions among neighborhood morphological development with respect to changes in population and resultant built infrastructure's impact on the natural environment must be considered. For deep understanding of these interactions, explicit representation of future neighborhoods is necessary for future city modeling. Generative Adversarial Networks (GANs) have been shown to produce spatially accurate urban forms at scales representing entire cities to those at neighborhood and single building scale. Here we demonstrate a GAN method for generating an ensemble of possible new neighborhoods given land use characteristics and designated neighborhood type.

Dumas, Melissa↗

Non‐Equilibrium Synthesis Methods to Create Metastable and High‐Entropy Nanomaterials

Stabilizing multiple elements within a single phase enables the creation of advanced materials with exceptional properties arising from their complex composition. However, under equilibrium conditions, the Hume–Rothery rules impose strict limitations on solid-state miscibility, restricting combinations of elements with mismatched crystal structures, atomic radii, valence states, or electronegativities. This severely narrows the accessible compositional space for creating new inorganic materials. In this review, we highlight how non-equilibrium synthesis methods, featuring ultrafast heating and quenching, can overcome these thermodynamic barriers, enabling integration of immiscible elements into metastable and high-entropy nanostructures. The resulting materials benefit from both kinetic trapping and stabilization by high configurational entropy, leading to enhanced phase stability. These materials can exhibit unique structural and functional properties that are needed for advancing catalysis, energy storage, thermoelectrics, and sensing. Furthermore, the ability of non-equilibrium methods to generate unconventional compositions and structures expands the material design space dramatically, offering rich datasets for AI-guided materials discovery. When combined with their inherent high-throughput and scalable characteristics, these approaches enable rapid, iterative optimization and accelerate the development and industrial production of next-generation inorganic materials.

high-entropy materials↗

The development of a Sustained High Power Density (SHPD) facility

A Sustained High Power Density (SHPD) facility is being planned within the U.S. as a non DT device which can be a bridge to a Compact Fusion Pilot Plant (CFPP) DT operated facility with the future capacity to produce a levelised cost of electricity at a rate competitive with current power generating systems. To accomplish this a review of ITER cost data and recent next step physics/engineering studies that promoted technology development, physics enhancements and innovated configuration improvements that increase operating availability have been injected into a proposed PPPL SHPD design. Much of the physics and engineering design basis for this effort centers on a series of ST studies carried out in recent years attempting to maximize the low aspect ratio (AR) ST performance within realistic engineering constraints of this compact fusion device. One issue found in the low AR design is the lack of volt-seconds generated by a small central solenoid that resides in the available space left after sizing the TF structure to support the high current density/HTS TF coil. Although not required in a larger pilot plant device, a solenoid wrapped around the plasma side of the SC TF coil was considered for the small low aspect ratio SHPD device. Adding a solenoid to the plasma side of the TF imposes a major change to the device configuration – a change which has been found to have cost and assembly issues when compared with an OH solenoid located at the machine center. Finally, the inclusion of a liquid metal first wall divertor system, the integration of local pairs of outboard DCLL blanket segments and a summary of physics performance conditions will be presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A red-light-powered silicon nanowire biophotochemical diode for simultaneous CO 2 reduction and glycerol valorization

A bias-free photochemical diode, in which a p-type photocathode is connected to an n-type photoanode to harness light for driving photoelectrochemical reduction and oxidation pairs, serves as a platform for realizing light-driven fuel generation from CO 2 . However, the conventional design, in which cathodic CO 2 reduction is coupled with the anodic oxygen evolution reaction (OER), requires substantial energy input. Here we present a photochemical diode device that harnesses red light (740 nm) to simultaneously drive biophotocathodic CO 2 -to-multicarbon conversion and photoanodic glycerol oxidation as an alternative to the OER to overcome the above thermodynamic limitation. The device consists of an efficient CO 2 -fixing microorganism, Sporomusa ovata, interfaced with a silicon nanowire photocathode and a Pt–Au-loaded silicon nanowire photoanode. This photochemical diode operates bias-free under low-intensity (20 mW cm –2 ) red light irradiation with ~80% Faradaic efficiency for both the cathodic and anodic products. Finally, this work provides an alternative photosynthetic route to mitigate excessive CO 2 emissions and efficiently generate value-added chemicals from CO 2 and glycerol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

AICCA: AI-Driven Cloud Classification Atlas

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data.

97 MATHEMATICS AND COMPUTING↗

Nuclear data uncertainty propagation applied to the versatile test reactor conceptual design

We report the Versatile Test Reactor (VTR) currently under development is a 300 MWth sodium-cooled fast reactor (SFR) fueled with ternary metal alloy fuel, which aims to accelerate the testing of advanced nuclear fuels, materials, instrumentation, and sensors in high flux environments that are necessary to license the next generation of advanced reactor concepts. To support the VTR design process, uncertainties associated with the nuclear data has been propagated through the reactor core neutronics calculation to global parameters of interest, such as the core multiplication factor, kinetic parameters, and various reactivity feedback coefficients, following the sensitivity based uncertainty propagation approach. By folding the sensitivity coefficients, separately computed by the generalized perturbation theory code PERSENT and Monte Carlo code Serpent 2, with the variance-covariance matrices from COMMARA-2.0, we obtain the reaction-wise, isotope-wise, and overall uncertainties for each response of interest due to nuclear data uncertainty. With Serpent 2, the statistical error of the uncertainty is obtained by propagating the statistical error of the sensitivity coefficients through the same process using a newly developed uncertainty propagation method. From both codes, the overall top uncertainty contributors are found to be the cross section of Fe-56 elastic scattering, Na-23 elastic scattering, and U 238 inelastic scattering. The large contributions of the Fe-56 elastic scattering cross sections to global parameters are due to its relatively large relative uncertainty of 5–10% in nuclear data and the large volume of Fe-containing reflector assemblies in the fairly compact VTR core design. Both codes agreed well for the overall uncertainty estimates of all responses of interest, except the delayed neutron fraction, prompt neutron generation time, and the coolant density feedback coefficient, where Serpent 2 yielded a much larger value than PERSENT due to the large statistical error of sensitivity coefficients. The calculated uncertainties are also compared to those associated with other SFR cores. Another outcome of this study is a variance-covariance matrix of reactivity coefficients, which can be used in the subsequent uncertainty propagation to the system level to investigate the impact of identified uncertainties on system responses in the safety analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model

Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here, we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as the target to enable the discovery of protein materials with superior mechanical properties.

59 BASIC BIOLOGICAL SCIENCES↗

Performance Testing of an Integrated Magnetic Power Take-Off

A wave energy converter (WEC) and the power take-off (PTO) generator system can be represented by using a mass-spring-dampener model. By incorporating a negative stiffness spring within the PTO, the overall stiffness of the PTO can be lowered allowing the impedance of the PTO to be more closely matched with the WEC. By designing for impedance matching the WEC can greatly enhance its power generation capability. This project has involved the design, fabrication and testing of a new type of linear-stroke length and rotary stroke length adjustable negative stiffness magnetic springs for use within a wave energy converter (WEC). The magnetic springs were studied by using 3-D finite element analysis with the objective of creating high energy density and a long linear stroke length. After the construction and testing of both a proof-of-principle adjustable linear and rotary magnetic spring prototype the rotary (torsional) magnetic spring was selected for scaling up analysis. The selected scaled-up proof-of-principle magnetic spring had a peak torque of 850 Nm with a ±45 degree stroke length. By translating the inner rotor relative to the outer rotor, the stiffness could be adjusted to be either negative or positive stiffness. During this project a magnetic lead screw was also studied, and it was shown that by combining the magnetic lead screw with a linearly translating magnetic spring a very long rotary stroke length could be attained. However, as the magnetic lead screw increased complexity and reduced overall energy density relative to a rotary (torsional) spring, this design approach was not further pursed. Dynamometer testing of the variable stiffness magnetic springs was first completed by Portland State University and following this the magnetic spring performance was independently verified by Sandia National Laboratory (Sandia). A WEC analysis when using a variable stiffness magnetic spring was completed by using the WecOptTool. WecOptTool is an open-source WEC optimization software developed by Sandia that supports efficient power take-off (PTO) and control optimization. Wave condition data from the Oregon PacWave test-site was used in this study. The analysis showed that a WEC with a tunable stiffness value could consistently achieve about 80% of its maximum theoretical power production. The use of a tunable stiffness WEC, rather than zero-stiffness or a constant stiffness WEC was also shown to lead to a smaller maximum PTO force requirement. The variable stiffness magnetic spring was integrated into an experimental WEC developed by Sandia, called a Wave-Bot. Sandia successfully completed water-tank testing of the Wave-Bot at the Navy’s Carderock, Maryland, wave-basin test site. The wave-basin testing helped to experimentally demonstrate the operating capabilities and increased power generation capability of a WEC containing an integrated variable stiffness magnetic spring. A WEC capacity factor analysis was also completed. The capacity factor was defined as the ratio of annual average WEC generator power to the maximum (RMS) generated power. This capacity factor provided a means of identifying the ratio of potential revenue to cost. It was calculated that if a tunable variable stiffness magnetic spring has RMS constraints it could operate with a capacity factor above 30%. This is comparable to a wind turbine’s capacity factor.

16 TIDAL AND WAVE POWER↗

Experimental Investigation of Vapor Formation in Liquid CO2 Flow Through a Converging-Diverging Nozzle

Carbon dioxide is an attractive working fluid for many cycles, including for pumped thermal energy storage (PTES). A challenge with some proposed sCO2 PTES cycles is the operation of sCO2 machinery outside the typical bounds of experience, with local phase change from the liquid state being particularly unknown. Presently, there is insufficient data in the literature regarding multiphase CO2 to adequately design a multiphase-tolerant turbine, so generation of foundational data is required. This experimental study investigates the flow characteristics of sub-sonic liquid CO2 undergoing expansion and phase change in a converging-diverging nozzle. The nozzle is instrumented to measure static pressure, unsteady pressure, temperature, and density. The static pressure transducers are located at 27 axial locations to accurately characterize the pressure profile in the nozzle. High-accuracy RTDs are located at the entrance and exit of the nozzle, and three dynamic pressure transducers are strategically located to capture any unsteady phenomena. During testing, values of mass flow and nozzle inlet pressure are swept to vary the pressure drop and fluid properties. The measured total pressure drop in the nozzle is compared to a homogenous model and the Lockhart-Martinelli correlation method, with the latter predicting loss quite closely. The resulting data set is valuable for validating multiphase numerical models in a simple geometry before implementation of these models in turbomachinery design.

25 ENERGY STORAGE↗

Design of Efficient Molecular Electrocatalysts for Water and Carbon Dioxide Reduction Using Predictive Models of Thermodynamic Properties

Objectives: 1) To design and synthesize water soluble transition metal complexes that function as electrocatalysts for the reduction of water to hydrogen or carbon dioxide to formate 2) To experimentally measure thermochemical properties, such as hydride donor ability, and use these quantities to optimize catalyst performance in solutions of varying pH 3) To incorporate results from detailed mechanistic and kinetic studies to direct improvements in catalyst design 4) To use thermochemical data from objective 2 to generate predictive models for the properties of new complexes to apply to the development of catalysts for other reductive reactions, with an emphasis on carbon dioxide reduction to methanol Description: Wide-spread implementation of renewable but intermittent energy sources, such as solar, requires the development of efficient methods for energy storage. The high energy density of chemical bonds makes chemical fuels an ideal solution for energy storage. Hydrogen and reduced carbon compounds have been proposed as ideal candidates for chemical fuels. However, the generation of chemical fuels from electricity requires competent electrocatalysts. The proposed research focuses on developing electrocatalysts for the reduction of water to hydrogen, and carbon dioxide to formate. Both products can be used directly as an energy carrier in fuel cells, or as a reductant for more saturated chemical fuels. Hydrogen is the most common fuel used in current commercial fuel cells. Additionally, there is interest in formic acid as a liquid carrier for hydrogen because of its increased storage density and ease of dehydrogenation. Formate is also an intermediate in the sequential reduction of CO 2 to methanol, another potential chemical fuel with high energy storage density. Despite the utility of formate as a chemical fuel or precursor, there are very few examples of electrocatalysts for its production from CO 2 , and even fewer demonstrate high product selectivity. In heterogeneous catalysis, the Sabatier principle is used to generate volcano curves that describe optimal thermochemical properties for key intermediates that result in peak catalytic activity. In most cases, such as hydrogen production and oxidation, the most favorable metals (Pt, Re, Rh, and Ir) are rare and expensive. Molecular inorganic complexes provide an opportunity to use electronic and steric ligand effects to tune the critical thermodynamic parameters of abundant metals to values comparable to key surface intermediates on precious metals. This principle will be applied to the design of aqueous homogeneous catalysts for the reduction of H 2 O and CO 2 optimized to function at specific pH ranges. The critical intermediate in both of these reactions is a metal hydride. The strength of this bond, or hydricity (ΔGH - ) dictates the overall thermodynamics of the reduction of H + to H 2 and the sequential reduction of C1 substrates, such as CO 2 , CO, and H 2 CO (the latter to CH 3 OH). ΔGH - will be systematically measured for a series of first row metal complexes to form predictive models for metal and ligand electronic effects. Benefits and Outcomes: This approach addresses thermodynamic requirements essential for energy-efficient catalyst design and transition state barrier considerations for faster catalysis. The advancements made from this research will include the discovery of efficient, stable, and fast catalysts for hydrogen and formate generation from electricity. The ability to store energy efficiently is critical to the widespread use of renewable energy schemes. This provides a viable solution to produce high energy density fuels for stationary and mobile energy applications. The fundamental research conducted is also directly applicable to the design of catalysts for the generation of further reduced products such as methanol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Transforming the Process Industries through Electrification: Challenges and Opportunities

For the more than 70 years, the process industries have provided essential fuels and materials for global growth. Increased manufacturing production comes with substantial environmental impact. Reducing environmental impact through process electrification, carbon capture, and other emerging technologies would require large changes throughout the process industries. We review opportunities and challenges related to electrification, focusing on technical, operational, and economic considerations. Here, we conclude that electrification could completely eliminate scope 1 CO 2 emissions, increase product quality, and reduce the equipment size and cost. On the other hand, it calls for a significant expansion of power generation capacity, as well as new paradigms for process design and operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A Framework for Assessing Economic and Environmental Trade-offs of Internalized Emission Costs in ERCOT Grid Planning

The power grid is on the cusp of a massive transition driven by three major areas: 1) the growth in demand for electricity, 2) efforts to decarbonize the United States economy, and 3) a desire to mitigate social disparities from the impact of electricity generation on local populations. However, most studies of the electricity sector do not include equity impacts in their models. This study seeks to do so by developing a comprehensive and generalizable model tailored to the Electric Reliability Council of Texas (ERCOT) grid, designed to incorporate the equity impacts of electricity generation in a decarbonized and resilient framework. To integrate equity into our research, we incorporate environmental externalities into our capacity expansion model of ERCOT. Specifically, we factor in intermediate-level local marginal damages of precursor pollutants (NH3, NOx, primary PM2.5, SO2, and VOC) and global pollutant CO2 into the cost of generating electricity. We do this by taking into account county population, county ambient pollution concentration, and generator emission rates. Leveraging open-source modeling tools, such as PowerGenome, pyGRETA, and GenX we construct a county-level model to account for these costs. We integrate these marginal damages into the variable operations and maintenance costs of generators, for both existing and potential future builds. This study’s findings suggest that the value of a dynamic social cost of carbon (SSC) will cover criteria pollutant marginal damages within the ERCOT grid and solar and wind is expected to increase out to 2035. Key metrics evaluated within this research include fuel mix distribution across technologies, transmission and grid infrastructure costs, CO2 emissions, local pollutants marginal damages, and the variation in generation capacity built by the model. These results and framework can be used to support grid decisions that explicitly include distributional and procedural equity within a decarbonized and sustainable grid framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗