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DOE OSTI2024

Influence of crossover on capacity fade of symmetric redox flow cells

Volumetrically unbalanced compositionally symmetric cell cycling with potentiostatic (CV) or galvanostatic-with-potential-hold (CCCV) protocols is a rigorous technique for evaluating the calendar lifetime of reactants for redox flow batteries. Here, we evaluate the influence of reactant crossover through the membrane on symmetric cell cycling behavior. We tested symmetric cells of anthraquinone disulfonic acid (AQDS) with Nafion membranes of varied thickness and manufacture (NR211, NR212, N115, and N117, ranging 25–183 μm). Membranes were tested both as-received and pretreated with a common procedure of soaking in water at elevated temperature and then in dilute hydrogen peroxide. We found no significant difference in capacity fade rates of symmetric cells with any of the membranes as-received, indicating a negligible influence of crossover. However, we observed increased capacity fade with increased permeability through pretreated membranes. Supported by zero-dimensional modeling and operando UV-vis spectrophotometry, we propose a mechanism for net crossover in AQDS symmetric cells based on a higher time-averaged concentration of quinhydrone dimers in the non-capacity limiting side (NCLS) compared to the capacity limiting side (CLS), driving net crossover of AQDS reactants out of the CLS. Further, we illustrate other hypothetical scenarios of net crossover using the zero-dimensional model. Overall, many membrane–electrolyte systems used in symmetric cell studies have sufficiently low crossover flux as to avoid the influence of crossover on capacity fade, but under conditions of higher crossover flux, complex interactions of crossover and chemical reactions may result in diverse capacity fade trajectories, the mechanisms of which may be untangled with operando characterization and modeling.

25 ENERGY STORAGE↗

DOE OSTI2025

Computational investigation of the impact of metal–organic framework topology on hydrogen storage capacity

Metal–organic frameworks (MOFs) are promising, tunable materials for hydrogen storage. For application under cryogenic operating conditions, past work has run into a ceiling on performance due to a trade-off in the volumetric deliverable capacity (VDC) versus the gravimetric deliverable capacity (GDC). In this study, we computationally constructed and screened 105 230 MOF structures based on 529 nets to explore the effect of underlying topology on the hydrogen storage performance of the resulting materials. A machine learning model was developed based on simulated hydrogen uptake to facilitate screening of the entire dataset, and it successfully identified the top 10% of materials with a root-mean-square error of approximately 1 g L −1 as validated by subsequent grand canonical Monte Carlo simulations. We identified a promising structure based on the tsx topology that exhibits both VDC and GDC higher than the current benchmark material, MOF-5. Our data-driven analysis indicates that nets with higher net density yield MOFs with enhanced volumetric and gravimetric surface areas, thereby improving maximum VDC while shifting the capacity trade-off toward higher GDC.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Spin orbit torque switching of synthetic Co/Ir/Co trilayers with perpendicular anisotropy and tunable interlayer coupling

Spin orbit torque (SOT) has attracted much attention as an energy efficient electrical method to switch the magnetization in single magnetic layer with both in-plane and perpendicular anisotropy. Here, we report SOT switching of synthetic antiferromagnetic and ferrimagnetic Co/Ir/Co trilayers, where the net magnetization and the interlayer coupling strength are highly controllable. Here, a weak external field dependence of the SOT switching was observed due to the robust domain wall structure in the trilayer. The switching current density was observed to scale inversely with the net magnetization. For trilayers with the same net magnetization, switching current is independent of the layer order because switching in the thicker Co layer dominates. In both ferromagnetic and antiferromagetic regimes, the switching current reflects the interlayer coupling strength.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Novel technologies for emission reduction complement conservation agriculture to achieve negative emissions from row-crop production

Plants remove carbon dioxide from the atmosphere through photosynthesis. Because agriculture’s productivity is based on this process, a combination of technologies to reduce emissions and enhance soil carbon storage can allow this sector to achieve net negative emissions while maintaining high productivity. Unfortunately, current row-crop agricultural practice generates about 5% of greenhouse gas emissions in the United States and European Union. Here, to reduce these emissions, significant effort has been focused on changing farm management practices to maximize soil carbon. In contrast, the potential to reduce emissions has largely been neglected. Through a combination of innovations in digital agriculture, crop and microbial genetics, and electrification, we estimate that a 71% (1,744 kg CO 2 e/ha) reduction in greenhouse gas emissions from row crop agriculture is possible within the next 15 y. Importantly, emission reduction can lower the barrier to broad adoption by proceeding through multiple stages with meaningful improvements that gradually facilitate the transition to net negative practices. Emerging voluntary and regulatory ecosystems services markets will incentivize progress along this transition pathway and guide public and private investments toward technology development. In the difficult quest for net negative emissions, all tools, including emission reduction and soil carbon storage, must be developed to allow agriculture to maintain its critical societal function of provisioning society while, at the same time, generating environmental benefits.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Ripple-induced neoclassical toroidal viscous torque in Augmented-First Plasma operation phase in ITER

Abstract A systematic calculation is performed on the ripple-induced neoclassical toroidal viscous (NTV) torque for new ITER scenarios designed for the Augmented-First Plasma (A-FP) operation phase with the full tungsten wall, where the plasma-wall gap is varied in view of mitigating the impact of tungsten wall-plasma interactions. The torque calculation includes drift kinetic response of the plasma thermal and energetic particles to the n = 18 ( n is the toroidal harmonic number) ripple field. For the plasma scenario with ~45 cm plasma-wall gap at the outboard mid-plane and considering the corrected ripple level of 0.17% by the ferritic steel inserts, the computed net NTV torque acting on the plasma column is in the sub-Nm level. However, with decreasing the plasma-wall gap, the computed net NTV torque can reach a level comparable to that produced by the neutral-beam momentum injection in ITER. Ripple correction by ferritic inserts reduces the net torque by a factor of 3.3 for all the three A-FP scenarios considered. The n ω d = l ω b (with ω d and ω b being the toroidal precession and bounce frequencies of trapped particles, respectively, and l an integer number) type of resonance-enhancement of the NTV torque, due to thermal particles, is found to be weak in ITER despite high- n of 18. The same also holds for the ITER 10 MA steady state scenario from the D-T operation phase, where the aforementioned resonance associated with fusion-born alphas is also included. The ripple-induced NTV torque is well below that produced by the resonant magnetic perturbation applied for controlling the type-I edge-localized mode in ITER.

ITER↗

DOE OSTI2026

Tokamak Energy’s pre-concept design for a fusion power plant: an overview of ST-E1

Climate change and rapidly rising energy demand, driven in part by artificial intelligence and data-centre growth, create an urgent need for stable, low-carbon, and abundant power. Fusion is a promising long-term solution, yet its commercialisation faces a fundamental paradox in today’s investment environment: pilot plants are essential to de-risk physics, engineering, and operations, but their limited lifetime energy output and high upfront costs make them difficult to finance. This paper presents Tokamak Energy’s response: ST-E1, a pre-concept design for a low-aspect-ratio tokamak power plant engineered specifically to overcome this challenge. ST-E1 is designed from the outset for phased operation—pilot and commercial phases, with an upgrade phase in between—with emphasis on commercial viability, maintainability, nuclear engineering, modularity, and upgradability. A key design principle is the deliberate separation of long-lived assets, such as the magnet cage and vacuum vessel, from replaceable in-vessel systems. This provides an attractive and credible investment approach to generate operational data and de-risk key technologies while preserving most capital-intensive assets for later commercial phases. The architecture supports continuous optimisation toward high net electric power (targeting 800–1000 MW net electric), a normalised capital expenditure of $\$$ 12–14k/kW of net electric power, and high availability (targeting > 80%). A tokamak core with a 5 m major radius, aspect ratio of 2.3, and on-plasma axis toroidal field of 5.25 T was selected to meet these objectives. This paper summarises the ST-E1 design philosophy, principal features, and development methodology. It introduces a Focus Collection of 11 papers detailing the pre-concept design of the entire tokamak and corresponding plant.

ST-E1↗

DOE OSTI2022

Substantial carbon loss respired from a corn–soybean agroecosystem highlights the importance of careful management as we adapt to changing climate

Understanding agroecosystem carbon (C) cycle response to climate change and management is vital for maintaining their long-term C storage. We demonstrate this importance through an in-depth examination of a ten-year eddy covariance dataset from a corn–corn–soybean crop rotation grown in the Midwest United States. Ten-year average annual net ecosystem exchange (NEE) showed a net C sink of –0.39 Mg C ha –1 yr –1 . However, NEE in 2014 and 2015 from the corn ecosystem was 3.58 and 2.56 Mg C ha –1 yr –1 , respectively. Most C loss occurred during the growing season, when photosynthesis should dominate and C fluxes should reflect a net ecosystem gain. Partitioning NEE into gross primary productivity (GPP) and ecosystem respiration (ER) showed this C 'burp' was driven by higher ER, with a 51% (2014) and 57% (2015) increase from the ten-year average (15.84 Mg C ha –1 yr –1 ). GPP was also higher than average (16.24 Mg C ha –1 yr –1 ) by 25% (2014) and 37% (2015), but this was not enough to offset the C emitted from ER. This increased ER was likely driven by enhanced soil microbial respiration associated with ideal growing season climate, substrate availability, nutrient additions, and a potential legacy effect from drought.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Developing new pathways for energy and environmental decision-making in India: a review

Abstract India faces a dual challenge of economic development and responding to climate change. Although India’s per capita emissions are well below global average, the country is one of the world’s largest greenhouse gas emitters. Indian policymakers and stakeholders require high-quality data and research to assess low-emissions, sustainable development strategies. Peer-reviewed literature is a key source of this information and also a key venue for conversation amongst research leaders. This paper examines the recent peer-reviewed literature on India’s 2030 and 2050 pathways. We conducted a systematic literature review to identify key quantitative national modeling studies. From the 34 studies identified, we synthesized scenario data to draw common conclusions and identify critical research gaps. The main focus was on examining the coverage and the state of information available on low-carbon pathways. Overall, we find a few scenarios that are potentially consistent with a 2070 net-zero goal, but more limited assessment of pathways to reach net-zero emissions before this date. Mitigation pathways with greater ambition are required across all energy sectors to ensure a smooth transition to net-zero emissions by or before 2070. The scenarios confirm that reducing emissions to below 2 GtCO 2 yr −1 by mid-century would necessitate significant transformations of the Indian energy sector, such as, a decrease in unabated coal power capacity, transportation modal shift, and industrial process switching. The assessment also finds substantial differences in final energy estimates reported across studies, particularly in transportation. The lack of consistency in, and transparency about underlying drivers, assumptions, and even outputs across studies points to the critical need for the sorts of coordinated, multi-model studies that have proven exceptionally valuable for decision makers in other major emitting countries.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Uncertainty in land carbon budget simulated by terrestrial biosphere models: the role of atmospheric forcing

Global estimates of the land carbon sink are often based on simulations by terrestrial biosphere models (TBMs). The use of a large number of models that differ in their underlying hypotheses, structure and parameters is one way to assess the uncertainty in the historical land carbon sink. Here we show that the atmospheric forcing datasets used to drive these TBMs represent a significant source of uncertainty that is currently not systematically accounted for in land carbon cycle evaluations. We present results from three TBMs each forced with three different historical atmospheric forcing reconstructions over the period 1850–2015. We perform an analysis of variance to quantify the relative uncertainty in carbon fluxes arising from the models themselves, atmospheric forcing, and model-forcing interactions. We find that atmospheric forcing in this set of simulations plays a dominant role on uncertainties in global gross primary productivity (GPP) (75% of variability) and autotrophic respiration (90%), and a significant but reduced role on net primary productivity and heterotrophic respiration (30%). Atmospheric forcing is the dominant driver (52%) of variability for the net ecosystem exchange flux, defined as the difference between GPP and respiration (both autotrophic and heterotrophic respiration). In contrast, for wildfire-driven carbon emissions model uncertainties dominate and, as a result, model uncertainties dominate for net ecosystem productivity. At regional scales, the contribution of atmospheric forcing to uncertainty shows a very heterogeneous pattern and is smaller on average than at the global scale. We find that this difference in the relative importance of forcing uncertainty between global and regional scales is related to large differences in regional model flux estimates, which partially offset each other when integrated globally, while the flux differences driven by forcing are mainly consistent across the world and therefore add up to a larger fractional contribution to global uncertainty.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Empirical estimates of regional carbon budgets imply reduced global soil heterotrophic respiration

Resolving regional carbon budgets is critical for informing land-based mitigation policy. For nine regions covering nearly the whole globe, we collected inventory estimates of carbon-stock changes complemented by satellite estimates of biomass changes where inventory data are missing. The net land–atmospheric carbon exchange (NEE) was calculated by taking the sum of the carbon-stock change and lateral carbon fluxes from crop and wood trade, and riverine-carbon export to the ocean. Summing up NEE from all regions, we obtained a global ‘bottom-up’ NEE for net land anthropogenic CO2 uptake of –2.2 ± 0.6 PgC yr -1 consistent with the independent top-down NEE from the global atmospheric carbon budget during 2000–2009. This estimate is so far the most comprehensive global bottom-up carbon budget accounting, which set up an important milestone for global carbon-cycle studies. By decomposing NEE into component fluxes, we found that global soil heterotrophic respiration amounts to a source of CO 2 of 39 PgC yr -1 with an interquartile of 33–46 PgC yr -1 —a much smaller portion of net primary productivity than previously reported.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Nitrogen fixation and resorption efficiency differences among twelve upland and lowland switchgrass cultivars

In nitrogen (N)-limited terrestrial ecosystems, plants employ various strategies to acquire and conserve N, including translocation of N in perennial tissues and stimulation of N fixation in roots and soils. Switchgrass (Panicum virgatum L.) is a genotypically and phenotypically diverse perennial grass with two distinct ecotypes (lowland and upland) and numerous genotypes. It grows well in low-N soils, likely because of its ability to translocate N and to associate with N-fixing microbes, but little is known about variation in these traits among cultivars or even ecotypes. We measured N translocation, N fixation potential in roots and soils, soil net N mineralization, soil net nitrification, and biomass yields in 12 switchgrass cultivars grown in a replicated block experiment in southwestern Michigan, USA. Lowland cultivars had higher yields, rates of N translocation, soil net N mineralization, and N fixation potentials on washed, non-sterile roots, while upland cultivars exhibited higher N fixation potentials in root-free soil. N resorption efficiencies averaged 53±5 [SE] % for lowland versus 29±3% for upland cultivars. Additionally, there were significant among-cultivar differences for all response variables except mineralization and nitrification, with differences likely explained by cultivar-specific physiologies and microbial communities. Here, the ideal cultivar for biofuels is one that can maintain high yields with minimal fertilizer addition and there appear to be several cultivars that meet these criteria. In addition, results suggest substantial N cycle differences among cultivars that might be exploited by breeders to create new or improved high-yielding, N-conserving switchgrass lines.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Transport-based initial conditions for heavy-ion collisions at finite densities

Here, we employ the SMASH transport model to provide event-by-event initial conditions for the energy-momentum tensor and conserved charge currents in hydrodynamic simulations of relativistic heavy-ion collisions. We study the fluctuations and dynamical evolution of three conserved charge currents (net baryon, net electric charges, and net strangeness) with a four-dimensional lattice-QCD-based equation of state, NEOS-4D, in the hydrodynamic phase. Out-of-equilibrium corrections at the particlization are generalized to finite densities to ensure the conservation of energy, momentum, and the three types of charges. These theoretical developments are integrated within the X-SCAPE code as a unified framework for studying the nuclear matter properties in the Beam Energy Scan program.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Charge conservation and higher moments of charge fluctuations

Higher moments of distributions of net charge and baryon number in heavy-ion collisions have been proposed as signals of fundamental QCD phase transitions. In order to better understand background processes for these observables, models are presented which enable one to gauge the effects of local charge conservation, decays of resonances and clusters, Bose symmetrization, and volume fluctuations. Monte Carlo methods for generating samplings of particles consistent with local charge conservation are presented and are followed by a review of simple analytic models involving a single type of charge with a constant experimental efficiency. The main model consists of thermal emission superimposed onto a simple parametrization of collective flow, known as a blast wave, with emission being consistent with individual canonical ensembles. The spatial extent of local charge conservation is parameterized by the size and extent over which charge is conserved. Here, the sensitivity of third- and fourth-order moments, skewness and kurtosis, to these parameters, and to beam energy and baryon density is explored. Comparisons with STAR data show that a significant part of the observed non-Poissonian fluctuations in net-proton fluctuations are explained by charge and baryon-number conservation, but that measurements of the STAR collaboration for fluctuations of net electric charge significantly differ from expectations of the models presented here.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Power-to-Gas Systems for Active Load Management at EV Charging Sites with High DER Penetration

Intermittent renewable systems and increasing electric vehicle (EV) penetrations increase load intermittency—and will require expensive distribution system infrastructure upgrades. Power-to-gas (P2G) systems are highly controllable loads that can be modulated to regulate the net load behind the meter. In this work, we present an active load management approach using P2G systems to regulate the net load for an EV charging station with high penetrations of renewable generation. The system also validates the feasibility of the proposed approach using a 750-kW electrolyzer operated with the distribution system in a real-time power-hardware-in-the-loop test. Finally, we evaluate the impact of the size of the P2G system on its ability to regulate the net load.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗

DOE OSTI2024

Clustering Interval Load with Weather to Create Scenarios of Behind-the-Meter Solar Penetration

Forecasting load at the feeder level has become increasingly challenging with the penetration of behind-the-meter solar, as this self-generation is only visible to the utility as aggregated net-load. This work proposes a methodology for creation of scenarios of solar penetration at the feeder level for use by forecasters to test the robustness of their algorithm to progressively higher penetrations of solar. The algorithm draws on publicly available observations of weather condition (e.g., rainy/cloudy/fair) for use as proxies to sky clearness. These observations are used to mask and weight the interval deviations of similar native usage profiles from which average interval usage is calculated and subsequently added to interval net generation to reconstruct interval total generation. This approach improves the estimate of annual energy generation by 23%; where the net generation signal currently reflects 52% of total annual gener- ation, now 75% is captured. This methodology for creation of forecast testing scenarios is data driven and extensible to service territories which lack information on irradiance measurements and geocoordinates.

solar, load↗

DOE OSTI2023

Optimal Storage Response to Utility Tariff Structures and Potential Use of Capacity Charges

Energy storage is increasingly being deployed in behind-the-meter use cases, partially in response to falling lithium-ion prices and new utility tariff structures. Utilities are grappling with new tariff design in the presence of distributed energy resources (DER), trying to motivate, fairly price the contribution of, and, in some cases, discourage, certain operation of DER. There are opportunities for energy storage under emerging tariff structures, but utility net load management objectives for storage remain unclear. Diverse tariff structures motivate the use of energy storage to provide one or a combination of: energy arbitrage, energy shifting, solar self-consumption, and import shaving. This paper formulates a linear optimization to demonstrate the optimal storage tariff response, examining customer net load metrics under diverse utility tariff structures, such as time-of-use, net metering, feed-in tariffs, zero export tariffs, and demand charges. A key contribution of this paper is the introduction and examination of a capacity charge as mechanism that can both motivate reductions in both peak import, and exports, along with greater load levelling.

behind-the-meter↗

DOE OSTI2020

Optimal Energy Storage Schedules for Load Leveling and Ramp Rate Control in Distribution Systems

Continued integration of distributed energy resources (DERs) into the grid, such as solar PVs, at a large-scale, contributes into the famous Duck Curve. New DER management algorithms are therefore deemed necessary to alleviate rapid variations within net load profiles of distribution systems. Herein this paper proposes a process to determine the optimal energy storage schedules for leveling the distribution circuit feederhead net load. A series of sensitivity analyses shows how the proposed method can be used to determine the optimal energy storage schedules with different capacities, state of charge requirements, and net load ramp rate limitations.

25 ENERGY STORAGE↗

DOE OSTI2024

A Data-Driven Methodology for Contextual Unit Commitment Using Regression Residuals

Day after day, system operators are faced with the challenge of taking unit commitment (UC) decisions under uncertain net load conditions. The standard operating procedure for taking UC decisions begins by leveraging auxiliary data on covariates (such as the day of the week or latest weather information) to generate a point prediction for net load, which is used in solving a deterministic UC problem. Such an approach, however, is known to deliver a notoriously poor out-of-sample (OOS) performance, as it completely disregards the stochastic nature of net load. While stochastic programming models explicitly represent uncertainty, they mostly do so using a generic set of scenarios that neglect covariate observations, squandering useful auxiliary data that could be harnessed to glean insights into uncertainty. In this article, we discuss a contextual stochastic optimization approach to UC, which effectively exploits covariate observations while explicitly assessing uncertainty so as to boost the OOS performance of UC decisions. The key thrust of our approach is to leverage regression models, along with their empirical residuals, to set up and solve sample average approximation problems. Not only do we prove that our approach satisfies the requisite conditions for asymptotic optimality and consistency laid out in (Kannan et al., 2022), but we also assess its performance on several case studies conducted using real-world data collected in California ISO and New York ISO grids. In conclusion, results show that the proposed approach can significantly improve OOS performance compared to alternative methods proposed in the literature under varying dataset sizes.

Yurdakul, Ogun↗