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DOE OSTI2022

Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. Here, to demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

Distributed Wind-Hybrid Microgrids with Autonomous Controls and Forecasting

Distributed wind-hybrid microgrids have the potential to provide key resilience and economic benefits to both the customers they serve and the utility grids they are connected to. Such microgrids will likely be a key part of the grid of the future, whether connected to large utility grids or linked together in multi-microgrid systems. Through the hybridization of distributed wind and solar photovoltaics, autonomous device-level and system-level controls, battery energy storage systems with smart inverters, and forecasting, these microgrids could maintain local stability and provide grid services - all with renewable power. In the literature, these elements have been considered individually. However, they have not been combined and demonstrated at a high fidelity, which is essential to prove the concept's operation before moving to hardware-in-the-loop and physical demonstrations. In this work, we develop a high-fidelity MATLAB-Simulink model of a real distributed wind-hybrid microgrid that includes all these elements. We demonstrate the microgrid maintaining stability and production in a variety of islanded, grid-connected, and transition scenarios. This includes riding through faults and grid transitions, handling resource variability, and providing grid services. The results demonstrate, at a high fidelity, how distributed wind-hybrid microgrids can operate in an economic and resilient fashion. Finally, we provide recommendations for future research to move advanced distributed wind-hybrid microgrids toward deployment.

ancillary services↗

DOE OSTI2023

Interfacial engineering via laser ablation for high-performing PEM water electrolysis

A rationalized interfacial design strategy was applied to tailor the porous transport layer (PTL)-catalyst layer (CL) contact and the PTL bulk-phase architecture. Particularly, at the PTL-CL interface, our results reveal that laser ablated sintered titanium power-based PTLs improve electrolyzer performance at both the H2NEW Consortium baseline catalyst loading of 0.4 mgIr cm -2 as well as at the ultra-low catalyst loading of 0.055 mgIr cm -2 . Under ultra-low catalyst loadings, the laser ablated PTL demonstrates maximum reduction of 230 mV compared to the commercial PTL at 4 A cm -2 , and reduces by 68 mV at 3.2 A cm -2 under H2NEW baseline loading. Laser ablation alters the titanium phase at the interface, so it forms more uniform structure like a microporous layer or a backing layer, leading to an increase in the surface area in contact with the catalyst layer while preventing the membrane from deforming into the PTL. Moreover, we reveal that bulk-phase architecture modification of the PTL by ablating patterned pores at the flow field-PTL interface improves mass transport without sacrificing contact at the CL-PTL interface. Overall, laser ablation of the PTL is an effective method to customize interfacial design to enhance proton exchange membrane electrolyzer performance.

08 HYDROGEN↗

DOE OSTI2023

Adaptive cold-load pickup considerations in 2-stage microgrid unit commitment for enhancing microgrid resilience

In an extended main grid outage spanning multiple days, load shedding serves as a critical mechanism for islanded microgrids to maintain essential power and energy reserves that are indispensable for fulfilling reliability and resiliency mandates. However, using load shedding for such purposes leads to increasing occurrence of cold load pickup (CLPU) events. Here, this study presents an innovative adaptive CLPU model that introduces a method for determining and incorporating parameters related to CLPU power and energy requirements into a two-stage microgrid unit commitment (MGUC) algorithm. In contrast to the traditional fixed-CLPU-curve approach, this model calculates CLPU duration, power, and energy demands by considering outage durations and ambient temperature variations within the MGUC process. By integrating the adaptive CLPU model into the MGUC problem formulation, it allows for the optimal allocation of energy resources throughout the entire scheduling horizon to fulfill the CLPU requirements when scheduling multiple CLPU events. The performance of the enhanced MGUC algorithm considering CLPU needs is assessed using actual load and photovoltaic (PV) data. Simulation results demonstrate significant improvements in dispatch optimality evaluated by the amount of load served, customer comfort, energy storage operation, and adherence to energy schedules. These enhancements collectively contribute to reliable and resilient microgrid operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Calibration of urban building energy model using smart meter data for district peak load prediction

Urban building energy modeling (UBEM) is a powerful approach to assessing baseline building energy performance and retrofits with new technologies across building stocks in cities. However, the accuracy of UBEM is often constrained by the limited availability of reliable data about building characteristics and operations, such as envelope efficiency levels, HVAC system performance, and end-use load patterns. Existing research has performed UBEM calibration using annual or monthly energy consumption data, which falls short when higher-resolution time series applications are needed, such as peak load prediction for utility operation planning. This study presents a new framework for calibrating building energy models at urban scale using smart meter data, targeting the accurate prediction of summer peak electricity loads to support robust grid planning. The framework first integrates various data sources to enhance baseline input assumptions for building models, and then calibrates the baseline models through a pattern-matching approach. A case study using CityBES and two years of AMI data from over 9000 residential customers in Portland, Oregon, demonstrated the workflow and its effectiveness. The calibrated models achieved a daily peak load mean absolute percentage error of 2.6 % during the heatwave in the calibration year, and 2.0 % in the validation year using another year of AMI data. Using the calibrated models, we analyzed the demand flexibility potential of the district building stock as an application of UBEM calibration. The findings affirm the appropriate use of UBEM for peak electric load forecasting and demand side management at the utility distribution system level.

AMI data↗

DOE OSTI2022

Printing noble metal alloy films with compositional gradient

Additive manufacturing has offered great promise in fabricating materials and devices with complex structures and unique properties. In addition to complex structures, additive manufacturing also allows for local changes to material compositions and/or structure, ideal for combinatorial material investigations. Besides, advantages over freedom of design, mass customization, waste minimization and the capability to fast prototyping, the on-demand control of compositional distributions in low dimensional materials remains challenging. In this work, we demonstrate a combinatorial aerosol jet printing technique to create noble metal films with gradient compositions. Nanoparticle sizes and ink chemistry are critical for aerosol jet printing and sintering process in order to achieve defect free thin films. Ruthenium (Ru) and platinum (Pt) nanoparticles with size <5 nm were synthesized and formulated into printable inks. Further, after thermal sintering at temperature as low as 400 °C, printed nanoparticles formed highly reflective films free from microstructural defect. Alloy films of gradient compositions were realized via in-situ mixing Ru and Pt ink streams and varying the mixing ratio during the printing process. This combinatorial printing method provides great potential to rapidly transform nanoparticle inks into new materials compositions and structures unobtainable via conventional methods for high-throughput materials studies.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Microstructure and hardness evolution induced by annealing of ion irradiated LiTaO 3

We report the evolution of the microstructure and mechanical properties of low-energy heavy-ion-irradiated LiTaO 3 crystals annealed at various temperatures was investigated by transmission electron microscopy (TEM) observations and nanoindentation tests to extend the applications of irradiated LiTaO 3 crystals. Before annealing, randomly distributed voids formed in an approximately 200 nm thick region at the end of the ion irradiation range, which was attributed to preferential absorption of interstitials by the amorphous-crystalline interface. After annealing, a new directional diffusion path of defects was identified, resulting in a void string. Furthermore, void string diffusion to the free surface was also found during the annealing temperature increase process, which offers a potential application in designing custom sized-thickness exfoliation at the nanoscale. Meanwhile, two competing processes were identified during the annealing process: epitaxial recrystallization from the amorphous-crystalline interface and grain nucleation process in the amorphous region, which formed phases different from the matrix crystal structure, resulting in an interaction between them. Furthermore, the hardness of irradiated samples was also found to be affected by the annealing process; in particular, a hardness decrease induced by the interstitial Fe ions in the recrystallized region was also confirmed, which can improve the processability of samples for further application.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Material erosion measurements and expected operational lifetime of a deployable photon sieve payload

Spacecraft operating in low-Earth orbit are subjected to a number of hazardous environmental constituents that can lead to decreased system performance and reduced operational lifetimes. Due to their thermal, optical, and mechanical properties, polymers are used extensively in space systems; however they are particularly susceptible to material erosion and degradation as a result of exposure to the LEO environment. The focus of this research is to examine the material erosion and mass loss experienced by a custom Kaptonlike polyimide due to exposure in a simulated low-earth orbit environment. The deployable membrane telescope design discussed in this research is named Peregrine and is the scientific payload for FalconSat-7, a 3U cubesat designed and developed at the United States Air Force Academy (USAFA) for the purpose of demonstrating the capability of deployable membrane telescope technology on a nanosatellite platform. In addition to the polymer samples, chrome, silver and gold specimens will be examined to measure the oxidation rate and act as a control specimen, respectively. A magnetically filtered atomic oxygen plasma source has previously been developed and characterized for the purpose of simulating the low-Earth orbit environment. The plasma source can be operated at a variety of discharge currents and gas flow rates, of which the plasma parameters downstream of the source are dependent. The characteristics of the generated plasma were examined as a function of these operating parameters to optimize the production of O + ions with energy relevant to LEO applications. The erosion yield of the Kapton-like polyimide was experimentally determined to be 2.84 x 10 -24 cm 3 per atom which is in close agreement when compared to the on-orbit measurement for reaction efficiency of Kapton HN. We report the experimentally determined reaction rate for the Kapton-like polyimide was used to estimate the operational lifetime of the photon sieve during the solar conditions expected beginning in May 2019. The effective lifetime is estimated between 126 and 153 days.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Design, construction, and test of the Gas Pixel Detectors for the IXPE mission

Due to be launched in late 2021, the Imaging X-ray Polarimetry Explorer (IXPE) is a NASA Small Explorer mission designed to perform polarization measurements in the 2–8 keV band, complemented with imaging, spectroscopy and timing capabilities. At the heart of the focal plane is a set of three polarization-sensitive Gas Pixel Detectors (GPD), each based on a custom ASIC acting as a charge-collecting anode. In this paper we shall review the design, manufacturing, and test of the IXPE focal-plane detectors, with particular emphasis on the connection between the science drivers, the performance metrics and the operational aspects. We shall present a thorough characterization of the GPDs in terms of effective noise, trigger efficiency, dead time, uniformity of response, and spectral and polarimetric performance. In addition, we shall discuss in detail a number of instrumental effects that are relevant for high-level science analysis—particularly as far as the response to unpolarized radiation and the stability in time are concerned.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

The value of adding black carbon to community monitoring of particulate matter

Low-cost particulate matter (PM) sensors are increasingly used by researchers, public health agencies, and the public to measure spatial and temporal variations in air pollution, which can inform strategies for community air pollution reduction. While low-cost PM sensors provide a valuable measure of harmful fine particulate matter (PM 2.5 ), a significant portion of ambient PM 2.5 is typically the secondary product of air pollution emitted by varied sources outside of community boundaries. In contrast, concentrations of black carbon (BC), a component of PM 2.5 , are directly emitted by a few specific sources, such as diesel engines within communities. Motivated by community organizations seeking to understand persistent sources of local pollution, this study deployed a suite of custom-built BC sensors alongside a network of low-cost PM sensors for four weeks in two seasons at 50 stationary locations in the adjacent cities of Richmond, North Richmond, and San Pablo, California, east of the San Francisco Bay. Concentrations of BC varied more than PM 2.5 both temporally and spatially. Monthly network-average BC was 3×higher in winter than late spring, while PM 2.5 was only 10% lower. In both seasons, average PM 2.5 concentrations at two-thirds of sites were within ±10% of the network average, whereas two-thirds of sites had BC levels outside of ±10% of the network-average concentration. The most and least polluted locations were more persistent across seasons for BC than PM 2.5 , and the temporal dynamics of BC at these sites were similar, signifying that they are impacted by the same emission sources. Together, these spatiotemporal trends show that BC is a better indicator of the proximity and activity of local pollution sources than PM 2.5 . Thus, including BC in addition to PM 2.5 in community monitoring networks can provide additional insights about local sources of air pollution.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Feedstock and Catalyst Impact on Bio-Oil Production and FCC Co-Processing to Fuels

NREL's thermochemical biomass conversion research is focused on ex-situ upgrading of biomass fast-pyrolysis (FP) vapors as an efficient route to completely biogenic pyrolysis-based fuel precursors, fuels, and value-added chemicals depending on catalyst and process conditions. A near term pathway being developed uses these liquids for co-processing with petroleum feedstocks to assess biogenic carbon incorporation in hydrocarbon fuel feedstocks for potential refinery use. In this work, the impact of feedstock and catalyst on catalytic fast pyrolysis oil (CFPO) composition was determined with the oils then assessed for biogenic fuel production via FCC (fluidized catalytic cracking) co-processing. Biomass vapors were generated via fast pyrolysis with destabilizing vapor components (char, inorganics, tar aerosols) removed by hot gas filtration to produce clean vapors more responsive to catalytic upgrading. A Davison Circulating Riser (DCR), a petroleum industry standard for fluidized catalytic cracking (FCC) catalyst evaluation, was coupled to a custom pyrolyzer system designed to produce consistent-composition pyrolysis vapors as feed to the DCR. Pyrolysis vapors, derived from pure hardwood and softwood, were upgraded using commercially available modified zeolite-based catalysts to produce CFPOs. These upgraded oils were analyzed via 31P and 13C NMR spectroscopy, GCxGC-TOF/MS, carbonyl and ultimate analysis (CHNO), and simulated distillation (SIMDIS) to assess both oil chemistry and distillation behavior as they relate to catalyst and feedstock type for producing fungible hydrocarbon product liquids. These exploratory vapor-phase-upgrading results demonstrated the feasibility of producing refinery-compatible hydrocarbon fuel intermediates entirely from biomass-derived fast-pyrolysis vapors using an industry-accepted DCR system for catalytic upgrading. The FCC co-processing results demonstrated the feasibility of using CFPOs with VGO feeds in FCC refinery operations to produce biogenic carbon containing fuels.

biogenic carbon↗

DOE OSTI2025

Near-infrared spectroscopy as a green analytical tool for sustainable biomass characterization for biofuels and bioproducts: An overview

Biomass, a widely used renewable energy source, requires characterization to optimize biofuel and bioproduct processes, customize feedstocks, and ensure economic and environmental sustainability. Conventional wet-chemistry methods for biomass analysis are slow, expensive, and require significant reagents and skilled personnel. In contrast, near-infrared (NIR) spectroscopy, a faster, cost-effective, and reagent-free green technology, enables non-destructive biomass analysis with minimal sample preparation. This study provides an overview of the fundamentals of NIR spectroscopy and explores its recent applications for analyzing various biomass properties important to the biofuel and bioproduct industry. The study also critically evaluates the challenges and opportunities of using NIR spectroscopy for biomass analysis. This review aims to guide future research for rapid and high throughput characterization of biomass in the biomass industry, supporting the United Nations’ sustainable development goal (SDG) 7: producing affordable and sustainable energy.

Biofuels↗

DOE OSTI2022

Advances and perspectives on mass transfer and enzymatic hydrolysis in the enzyme-mediated lignocellulosic biorefinery: A review

We report enzymatic hydrolysis is a critical process for the cellulase-mediated lignocellulosic biorefinery to produce sugar syrups that can be converted into a whole range of biofuels and biochemicals. Such a process operating at high-solid loadings (i.e., scarcely any free water or roughly ≥ 15% solids, w/w) is considered more economically feasible, as it can generate a high sugar concentration at low operation and capital costs. However, this approach remains restricted and incurs “high-solid effects”, ultimately causing the lower hydrolysis yields with increasing solid loadings. The lack of available water leads to a highly viscous system with impaired mixing that exhibits strong transfer resistance and reaction limitation imposed on enzyme action. Evidently, high-solid enzymatic hydrolysis involves multi-scale mass transfer and multi-phase enzyme reaction, and thus requires a synergistic perspective of transfer and biotransformation to assess the interactions among water, biomass components, and cellulase enzymes. Porous particle characteristics of biomass and its interface properties determine the water form and distribution state surrounding the particles, which are summarized in this review aiming to identify the water-driven multi-scale/multi-phase bioprocesses. Further aided by the cognition of rheological behavior of biomass slurry, solute transfer theories, and enzyme kinetics, the coupling effects of flow-transfer-reaction are revealed under high-solid conditions. Based on the above basic features, this review lucidly explains the causes of high-solid hydrolysis hindrances, highlights the mismatched issues between transfer and reaction, and more importantly, presents the advanced strategies for transfer and reaction enhancements from the viewpoint of process optimization, reactor design, as well as enzyme/auxiliary additive customization.

45 multi-scale, multi-phase, enzymatic reaction↗

DOE OSTI2024

Generative $β$-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the -hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial -hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Ten questions concerning energy flexibility in buildings

Demand side energy flexibility is increasingly being viewed as an essential enabler for the swift transition to a low-carbon energy system that displaces conventional fossil fuels with renewable energy sources while maintaining, if not improving, the operation of the energy system. Building energy flexibility may address several challenges facing energy systems and electricity consumers as society transitions to a low-carbon energy system characterized by distributed and intermittent energy resources. For example, by changing the timing and amount of building energy consumption through advanced building technologies, electricity demand and supply balance can be improved to enable greater integration of variable renewable energy. Although the benefits of utilizing energy flexibility from the built environment are generally recognized, solutions that reflect diversity in building stocks, customer behavior, and market rules and regulations need to be developed for successful implementation. In this paper, we pose and answer ten questions covering technological, social, commercial, and regulatory aspects to enable the utilization of energy flexibility of buildings in practice. In particular, we provide a critical overview of techniques and methods for quantifying and harnessing energy flexibility. We discuss the concepts of resilience and multi-carrier energy systems and their relation to energy flexibility. We argue the importance of balancing stakeholder engagement and technology deployment. Finally, we highlight the crucial roles of standardization, regulation, and policy in advancing the deployment of energy flexible buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

The moving target of visualization software for an increasingly complex world

Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. Furthermore, we summarize and discuss the results of a recent National Institute of Informatics Shonan seminar on these topics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

BASIN-3D: A brokering framework to integrate diverse environmental data

Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies.

Varadharajan, C↗

DOE OSTI2024

Implementation of an extensible property modeling framework in ESPEI with applications to molar volume and elastic stiffness models

Property models are becoming more widely adopted by commercial Calphad databases, but they are not nearly as common in non-commercial or traditional academic Calphad databases. A primary driver is that user-friendly Calphad modeling tools that support property models are not widely available. Here we present new property modeling capabilities that have been implemented in ESPEI (the Extensible, Self-optimizing Phase Equilibrium Infrastructure). These capabilities include both generating property model parameters from data and improvements to the algorithmic selection of the most appropriate model from a series of candidates. Additionally, two illustrative examples are given that use ESPEI to fit different property models. First, we generate molar volume model parameters for Group IV, V, and VI refractory BCC alloys based on the model by Lu et al. (2005). Second, we demonstrate the extensibility of ESPEI’s property modeling capabilities by implementing a custom PyCalphad model for BCC elastic stiffness parameters to generate and compare parameters to the ones assessed by Marker et al. (2018) using the same data. Property models generated by ESPEI can be used in PyCalphad or further optimized with uncertainty quantification using ESPEI.

36 MATERIALS SCIENCE↗