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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Software-Defined Virtual Synchronous Condenser

Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids. However, the design of SCs usually depends on specific application requirements and may not be adaptive enough to the frequently-changing grid conditions caused by the transition from conventional to renewable power generation. This paper devises a software-defined virtual synchronous condenser (SDViSC) method to address the challenges. Our contributions are fourfold: 1) design of a virtual synchronous condenser (ViSC) to enable full converter wind turbines to provide built-in SC functionalities; 2) engineering SDViSCs to transfer hardware-based ViSC controllers into software services, where a Tustin transformation-based software-defined control algorithm guarantees accurate tracking of fast dynamics under limited communication bandwidth; 3) a software-defined networking-enhanced SDViSC communication scheme to allow enhanced communication reliability and reduced communication bandwidth occupation; and 4) Prototype of SDViSC on our real-time, cyber-in-the-loop digital twin of large-wind-farm in an RTDS environment. Furthermore, extensive test results validate the excellent performance of SDViSC to support reliable and resilient operations of wind farms under various physical and cyber conditions.

17 WIND ENERGY↗
DOE OSTI2021

The Distribution of U.S. Electric Utility Revenue Decoupling Rate Impacts from 2005 to 2017

Investments in energy efficiency and distributed generation reduce electric utility retail sales. Since electric utilities have historically collected a large portion of revenues from volumetric energy rates (¢/kWh), such reductions in sales can impact the utility’s ability to sufficiently recover non-production costs. Regulatory mechanisms that “decouple” utility revenues from sales were implemented to help make the utility indifferent to energy efficiency and distributed generation by ensuring the utility is able to collect an allowed level of revenue each year regardless of its sales. However, noticeable and consistent surcharges over time may create the perception of an incorrectly designed or implemented decoupling mechanism. To date, there are limited quantitative analyses of the rate adjustments due to decoupling mechanisms implemented among U.S. electric utilities (see Morgan, 2013). Given the recent rapid increase in distributed energy resource (DER) adoption in some states and utility service territories, consumers may be facing more prevalent and ongoing decoupling surcharges. This, in turn, may undermine stakeholder support for implementation of decoupling mechanisms and the associated utility support for energy efficiency and DERs. To determine the size of retail rate adjustments associated with decoupling mechanisms and whether they have a tendency towards bill surcharges or credits, we analyzed a large dataset of historical annual decoupling rate adjustments for 21 electric utilities in 11 states between 2005 and 2017. We found that decoupling mechanisms adjusted rates, both up and down, between rate cases, and the majority of those adjustments (54 percent) are small (within a range of -1 to 1 percent). However, we also found that 64 percent of the rate adjustment observations in our sample showed a positive rate adjustment. Importantly, once a surcharge is applied there is an 86 percent chance that there will be a surcharge in the next year as well. While our analysis did not seek to understand the root causes for such results, some possible factors include the accuracy of revenue requirement forecasts, emerging structural changes in customer use and production of energy, misaligned financial motivation, and other factors that influence sales (e.g., economic recession).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

The Transportation Energy and Mobility Pathway Options (TEMPO) Model: Overview and Validation of V1.0 [Slides]

This presentation documents the approaches and methods used in the Transportation Energy & Mobility Pathway Options™ (TEMPO) model to evaluate passenger and freight demand for transportation and mobility services, project vehicle ownership and technology adoption decisions, and determine transport mode choices to derive scenarios of future energy use and emissions. TEMPO is an all-inclusive transportation demand model that covers the entire United States, with an implicit spatial resolution and an hourly temporal resolution that allows for generating time-resolved energy use profiles to assess multisectoral integration aspects. Key features of the TEMPO model include the ability to perform endogenous out-of-sample forecasting to extrapolate recent emerging trends and analyze impacts of disruptive technological breakthroughs and behavioral changes. TEMPO employs an innovative representation of passenger mobility demand stemming from household-level decisions that determine vehicle adoption, ownership, and use based on sociodemographics (e.g., income, household composition), technology attributes (e.g., travel cost, time), geography (e.g., urban, suburban, rural) and population-specific multiday mobility and travel requirements. This representation enables a more forward-looking perspective on the use of new mobility options and the adoption of alternative fuel vehicles, as well as a more accurate representation of their energy usage profiles than previous modeling approaches. A comparison with the U.S. Energy Information Administration’s Annual Energy Outlook showcases the ability of TEMPO to accurately replicate widely accepted projections by representing the key elements of the entire transportation sector at the appropriate level of resolution. TEMPO is intended to generate future scenarios of technology adoption, energy use, and emissions in the transportation sector to compare alternatives, inform decision makers, and assess integration with energy infrastructure and supply systems at an appropriate spatiotemporal resolution.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Recyclable Design for Retaining High Solar Absorptivity of the Media in CSP

Efficient thermal energy storage is pivotal to lowering the levelized cost of electricity (LCOE) for Concentrating Solar Power (CSP) plants. In solid-particle systems, however, prolonged high-temperature service degrades particle solar absorptivity, eroding overall efficiency. This project demonstrates a hydrogen-assisted recovery process that reliably restores absorptivity to >90 %, offering a practical route to sustain long-term CSP performance. Bench-scale investigations mapped the reduction kinetics of optically faded particles across hydrogen concentrations, temperatures, and residence times. Coupling mass-spectrometric monitoring with machine-learning optimization minimized energy demand while maximizing absorptivity gain. The resulting process window—moderate hydrogen partial pressures, 15–30 min dwell times, and temperatures well below initial calcination levels—cuts energy consumption well below that of incumbent re-blackening methods. A prototype recovery reactor processed multiple 2 kg batches with repeatable outcomes, confirming scalability and operational robustness. Integrated techno-economic analysis indicates material and operating cost reductions exceeding 15 % relative to conventional particle replacement or chemical re-coating, translating directly into lower LCOE for next-generation CSP facilities. By uniting fundamental reaction-kinetics insight with pragmatic engineering, this work advances the solid-particle pathway, delivering a cost-effective, field-deployable solution to one of CSP’s key durability challenges and strengthening the commercial outlook for high-temperature renewable power.

14 SOLAR ENERGY↗
DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗
DOE OSTI2020

Online Energy-optimal Routing for Electric Vehicles with Combinatorial Multi-arm Semi-Bandit

We have seen a rapid growth in the adoption of electric vehicle in today’s commercial mobility service market, from carrying passengers to the delivery of goods. One cardinal issue concerning the operation of EVs is the optimal routing of EV fleets with limited battery capacity. In this study, we investigate the energy-optimal online routing problem for the fleet of EVs, which focuses on identifying real-time minimum electricity consumption paths (MECP) for multiple OD pairs with limited information. We develop a multi-OD combinatorial multi-arm semi-bandit model (MCMAB) that uses the fleet of EVs as sensors in the transportation network and promotes the utilization of common information shared by different OD pairs. We further enrich the model with the path elimination policy to obtain MECP of high confidence while significantly reducing the number of learning iterations and the number of explorations needed. We demonstrate the effectiveness of the MCMAB and the efficiency of the path elimination policy with comprehensive numerical experiments in Manhattan, NYC. The results show that the proposed online routing algorithms can achieve near-optimal MECPs efficiently, and the quality of the solutions is significantly better than using the shortest travel time paths as approximate MECPs.

Chen, Xiaowei↗
DOE OSTI2020

In Situ Oxidation Studies of High-Entropy Alloy Nanoparticles

Although high-entropy alloys (HEAs) have shown tremendous potential for elevated temperature, anticorrosion, and catalysis applications, little is known on how HEA materials behave under complex service environments. Herein, we studied the high-temperature oxidation behavior of Fe 0.28 Co 0.21 Ni 0.20 Cu 0.08 Pt 0.23 HEA nanoparticles (NPs) in an atmospheric pressure dry air environment by in situ gas-cell transmission electron microscopy. It is found that the oxidation of HEA NPs is governed by Kirkendall effects with logarithmic oxidation rates rather than parabolic as predicted by Wagner's theory. Further, the HEA NPs are found to oxidize at a significantly slower rate compared to monometallic NPs. The outward diffusion of transition metals and formation of disordered oxide layer are observed in real time and confirmed through analytical energy dispersive spectroscopy, and electron energy loss spectroscopy characterizations. Localized ordered lattices are identified in the oxide, suggesting the formation of Fe 2 O 3 , CoO, NiO, and CuO crystallites in an overall disordered matrix. Hybrid Monte Carlo and molecular dynamics simulations based on first-principles energies and forces support these findings and show that the oxidation drives surface segregation of Fe, Co, Ni, and Cu, while Pt stays in the core region. The present work offers key insights into how HEA NPs behave under high-temperature oxidizing environment and sheds light on future design of highly stable alloys under complex service conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optimization of a Lethal, Combat-Relevant Model of Sterile Inflammation in Mice for Drug Candidate Screening

ABSTRACT Introduction Extensive trauma, commonly seen in wounded military Service Members, often leads to a severe sterile inflammation termed systemic inflammatory response syndrome (SIRS), which can progress to multiple organ dysfunction syndrome (MODS) and death. MODS is a serious threat to wounded Service Members, historically causing 10% of all deaths in trauma admissions at a forward deployed combat hospital. The importance of this problem will be exacerbated in large-scale combat operations, in which evacuation will be delayed and care of complex injuries at lower echelons of care may be prolonged. The main goal of this study was to optimize an existing mouse model of lethal SIRS/MODS as a therapeutic screening platform for the evaluation of immunomodulatory drugs. Materials and Methods Male C57BL/6 mice were euthanized, and the bones and muscles were collected and blended into a paste termed tissue–bone matrix (TBX). The TBX at 12.5%–20% relative to body weight of each recipient mouse was implanted into subcutaneous pouches created on the dorsum of anesthetized animals. Mice were observed for clinical scores for up to 48 hours postimplantation and euthanized at the preset point of moribundity. To test effects of anesthetics on TBX-induced mortality, animals received isoflurane or ketamine/xylazine (K/X). In a separate set of studies, mice received TBX followed by intraperitoneal injection with 20 mg/kg or 40 mg/kg Eritoran or a placebo carrier. All Eritoran studies were performed in a blinded fashion. Results We observed that K/X anesthesia significantly increased the lethality of the implanted TBX in comparison to inhaled anesthetics. Although all the mice anesthetized with isoflurane and implanted with 12.5% TBX survived for 24 hours, 60% of mice anesthetized with K/X were moribund by 24 hours postimplantation. To mimic more closely the timing of lethal SIRS/MODS following polytrauma in human patients, we extended observation to 48 hours. We performed TBX dose–response studies and found that as low as 15%, 17.5%, and 20% TBX caused moribundity/mortality in 50%, 80%, and 100% mice, respectively, over a 48-hour time period. With 17.5% TBX, we tested if moribundity/mortality could be rescued by anti-inflammatory drug Eritoran, a toll-like receptor 4 antagonist. Neither 20 mg/kg nor 40 mg/kg doses of Eritoran were found to be effective in this model. Conclusions We optimized a TBX mouse model of SIRS/MODS for the purpose of evaluating novel therapeutic interventions to prevent trauma-related pathophysiologies in wounded Service Members. Negative effects of K/X on lethality of TBX should be further evaluated, particularly in the light of widespread use of ketamine in treatment of pain. By mimicking muscle crush, bone fracture, and necrosis, the TBX model has pleiotropic effects on physiology and immunology that make it uniquely valuable as a screening tool for the evaluation of novel therapeutics against trauma-induced SIRS/MODS.

General & Internal Medicine↗
DOE OSTI2021

Recycling of natural fiber composites: Challenges and opportunities

Natural fibers have been widely used for reinforcing polymers attributed to their sustainable nature, excellent stiffness to weight ratio, biodegradability, and low cost compared with synthetic fibers like carbon or glass fibers. Thermoplastic composites offer an advantage of recyclability after their service life, but challenges and opportunities remain in the recycling of natural fiber reinforced polymer composites (NFRPCs). This article summarized the effects of reprocessing/recycling on the material properties of NFRPCs. The material properties considered include mechanical performance, thermal properties, hygroscopic behavior, viscoelasticity, degradation, and durability. NFRPCs can generally be recycled approximately 4–6 times until their thermomechanical properties change. After recycling 7 times, the tensile strength of NFRPCs can decrease by 17%, and the tensile modulus can decrease by 28%. The mitigation approaches to overcome degradation of material properties of NFRPCs such as adding functional additives and virgin plastics are also discussed. The main challenges in these approaches such as degradation and incompatibility are discussed, and an effort is made to provide a rationale for reprocessing/recyclability assessment. Finally, future applications of NFRPCs such as additive manufacturing and automotive part use are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Online energy consumption forecast for battery electric buses using a learning-free algebraic method

Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data. It eliminates the time-consuming and computationally expensive offline training. Consequently, its prediction performance is not constrained by the quantity and quality of the training data. Moreover, the method can swiftly adapt to new situations not included in the previous driving cycles, which makes it especially suitable for emerging transport modes, e.g., on-demand transit services. In addition, its online execution only involves algebraic calculations, yielding superior calculation efficiency. Using real-world data, we comprehensively compare the performance of the proposed learning-free algebraic method with multiple representative machine-learning-based methods. Finally, the advantages and limitations of the proposed method are discussed in detail.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Landscape connectivity for the invisibles

Because of land use changes, a worldwide decrease in biodiversity is underway, mostly driven by habitat degradation and fragmentation. Increasing landscape connectivity (i.e. the degree to which the landscape facilitates movement between habitat patches) has been proposed as a key landscape-level strategy to counterbalance the negative effects of habitat fragmentation. A robust theoretical and methodological framework has been developed for the concept of connectivity, and an increasing body of empirical evidence supports the relevance of connectivity for biodiversity. However, the framework was built ignoring species that represent the dominant proportion of biodiversity on earth: microorganisms. The extent to which the existing conceptual and methodological frameworks on connectivity can be applied to microorganisms remain unknown. We reviewed existing evidence and analyzed methods to test the influence of connectivity on microorganisms. We included all types of microorganisms, from symbiotic to pathogenic and free-living microorganisms, across all ecosystems. We describe the effect of connectivity on microorganism populations and communities, and identify the limitations and large gaps in current knowledge. Microorganisms can differ from macroorganisms in their response to connectivity due to short (distance less than a meter) dispersal distance of some groups, longer time lag of microorganisms response (possibly accompanied by evolutionary processes) and host association. The latter relies on tight interactions and feedback effects that drive microbial-landscape relationships and lead to possible coadaptation processes. Incorporating the connectivity concept in microbial community assembly rules to preserve the diversity of microbial communities and the ecosystem services they provide could be a crucial step forward in the face of pressing global changes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Athena - Shuttle Bus Optimization and Event Driven Simulator [SWR 20-105]

The primary purpose of the software is to optimize shuttle routes to and from terminals, given some airport campus location like the rental car center. This code was developed as part of the DOE funded Athena project. One aspect of the Athena project was to investigate if the optimization of shuttle routes that move passengers to and from the five DFW terminals and the rental car center could result in lower energy consumption from the shuttle fleet while still maintaining an acceptable level of service to passengers. Hence, the optimization done by the code determines a set of shuttle routes, the type of shuttle on each route, and the number of shuttles servicing each route. The optimization aims to make these choices in a way that the energy consumption per hour by the fleet is minimized. Additionally, the software can fast simulate airport shuttle operations with collected data. The simulation inputs are passenger arrival rates, shuttle routes and frequencies, and simulation configurations. The outputs include: time dependent shuttle energy level, time dependent charging station usage, time dependent number of passengers on each bus, time dependent number of passengers at each bus stop, history of number of passengers left at each bus stop, route history of fleet shuttle buses and on-demand buses, and time dependent bus distance traveled.

Kelly, Kenneth↗
DOE OSTI2022

FLISR in the Presence of DERs ADMS Test Bed Use Case

Fault location, isolation, and restoration (FLISR) is one of the distribution automation applications that utilities are most interested in. It operates groups of switches on distribution feeders to improve the reliability of power delivery after localizing outages. FLISR is an essential function for enabling a self-healing grid, and it directly affects grid reliability and resilience. The presence of DERs brings both new opportunities and challenges for FLISR applications. For instance, some DERs can work as backup generation sources and help energize local networks so more switching options can be achieved with the coordination of FLISR operations; however, the existence of DERs also brings new challenges. The bidirectional power flow resulting from DERs might require dynamic changes to protective devices' fault current settings, and thus the FLISR application will need to dynamically adjust its operations in coordination with the dynamic protection settings. Also, some FLISR applications make their service restoration decisions and close tie-switches based on predefined rules; however, the presence of DERs and their impact on altering net loads on the local feeder network are not considered. For this use case, the project team is using the FLISR application of the Survalent ADMS operating on a real-time simulation of a feeder from Central Georgia EMC, a cooperative electric utility. The performance of the FLISR application is being evaluated for different locations and combinations of DERs, including grid-forming battery energy storage systems.

ADMS↗
DOE OSTI2022

Accelerated Materials Deployment in Advanced Nuclear Power Plants

The purpose of this report is to begin the development of a maximally efficient process for licensing and deploying new materials in Advanced Non-Light-Water Reactors (ANLWRs). Some new materials that are to be used in some new plants are seen as possibly introducing risks, because our understanding of those new materials’ behavior in the conditions generated by some novel plant designs is less complete than our understanding of the behavior of materials with long use histories in existing designs. In these cases, an approved code/standard or a code case to support the use of these materials in the novel design’s safety case may not exist for the regulator to utilize as part of the licensing determination. This circumstance creates the potential for an extremely long licensing process for new designs using new materials. The present strategy is to show how to manage these risks proactively, in such a way as to permit licensing decisions to be made in a timely manner, based on this risk management process. The present report outlines the gaps in the current codes to support deployment and use of novel materials and begins the development of the necessary risk management framework that is focused on the subject materials issues; it is based on risk-informed in-service surveillance practices, carried out in such a way as to compensate for current limitations in our state of knowledge. This development will enable licensing and deployment of the subject materials, conditional on the proactive surveillance process to be established. While this report is occasioned by limitations in our knowledge of certain materials issues that may arise in advanced designs, in-service surveillance is always done in order to compensate for a lack of knowledge: if we knew that components were not already failed and not trending toward failure, we would not perform surveillance, even in current-generation plants (except that prescriptive requirements would force us to do so). What is different about the surveillance program discussed here is that the issues are newer and the relevant experience base is less complete, so the surveillance presently contemplated may need to measure new things and/or measure them more often than has been traditional for surveillance coupons. The present report is devoted to the risk management framework and applies American Society of Mechanical Engineers Boiler and Pressure Vessel Code Section XI, Division [1] to establish the structure of a protocol for carrying out the necessary surveillance. These documents are generic: they do not tell us how often to surveille, or what to surveille, or what to measure, but rather how to determine those things, given certain technical inputs. The Regulatory Development R&D Program [2] is currently developing the companion supporting technical basis for the materials surveillance technology that, when completed and validated, can be used by owner/operator and NRC to implement a materials degradation management program for ANLWRs. This report also outlines salient points of discussion, positive potential outcomes, and potential concerns from industry and the USNRC. These aspects of the report intend to inform future work to develop a proposed technical process for adoption by the industry and endorsement by the USNRC to allow developers to propose a risk informed and conservative approach for the use of materials where operating experience/data and codes and standards may not exist for use of a novel material in an operating reactor environment. Additionally, such a technology could be leveraged to potentially reduce part of the upfront materials data requirements from ongoing long-term materials testing so that early action on license application could be undertaken by NRC, in parallel with the continuation of long-term data collection. This could accelerate the schedule for a first-of-a-kind ANLWR deployment or a nth-of-a-kind new materials insertion for established ANLWR designs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Nature-based climate solutions can help mitigate the radiative forcing that follows deforestation

Widespread expansion of agriculture and forestry has altered the surface of the Earth, the composition of the atmosphere, and as a result, the climate. Here we quantify the radiative forcing caused by the historical deforestation of an ecoregion in the U.S. Upper Midwest and the adoption of eight nature-based climate solutions. We combined regional forest inventory data with over three decades of remote sensing and in situ data from a replicated land use change experiment. Deforestation of the region caused net global warming (1626 ± 44 µW m -2 ), mainly from the 76 % reduction of ecosystem carbon stocks, but also from the 84 % reduction of the soil methane sink and the 115 % increase in soil nitrous oxide emissions. The associated albedo increase offset 24 % of this greenhouse gas induced warming. For the adoption of nature-based climate solutions, we found that conservation agriculture can provide -39 to -76 ± 31 µW m -2 of climate mitigation over a 100-year time period while short/medium length forestry rotations can provide more at -296 to -881 ± 44 µW m -2 and natural forest regeneration can provide the most at -1555 ± 44 µW m -2 . As the impacts of climate change on nature and society intensify, consideration should be given to the climate mitigation, habitat, and ecosystem services that nature-based climate solutions can provide.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2023

Conservation Voltage Reduction with Distributed Energy Resource Management System, Grid-Edge, and Legacy Devices

Distribution utilities use conservation voltage reduction (CVR) to obtain energy savings and lower peak demand by reducing bus voltages. Traditionally, the CVR is accomplished by controlling the legacy assets such as load tap changers, voltage regulators, and capacitor banks. The deployment of the advanced distribution management system (ADMS) and distributed energy resource management system (DERMS) enables the integration of distributed energy resources into the distribution networks and provide the grid services including CVR. This paper studies the coordinated operation of an ADMS and a DERMS in achieving CVR and voltage regulation. A commercial ADMS uses legacy devices and Edge-of-Network Grid Optimization (ENGO) devices to obtain energy savings through CVR. A prototype DERMS dispatches the photovoltaic smart inverters based on real-time optimal power low to ensure voltage regulation across the feeder. The results show that the coordinated operation of ADMS and DERMS is effective in achieving CVR and voltage regulation. Specifically, energy savings of up to 4.7% are observed in the real utility distribution system used in this study.

advanced distribution management system↗
DOE OSTI2022

Enabling DER Participation in Frequency Regulation Markets

Distributed energy resources (DERs) are playing an increasing role in ancillary services for the bulk grid, particularly in frequency regulation. In this article, we propose a framework for collections of DERs, combined to form microgrids and controlled by aggregators, to participate in frequency regulation markets. Our approach covers both the identification of bids for the market clearing stage and the mechanisms for the real-time allocation of the regulation signal. The proposed framework is hierarchical, consisting of a top layer and a bottom layer. The top layer consists of the aggregators communicating in a distributed fashion to optimally disaggregate the regulation signal requested by the system operator. The bottom layer consists of the DERs inside each microgrid whose power levels are adjusted so that the tie line power matches the output of the corresponding aggregator in the top layer. The coordination at the top layer requires the knowledge of cost functions, ramp rates, and capacity bounds of the aggregators. We develop meaningful abstractions for these quantities respecting the power flow constraints and taking into account the load uncertainties and propose a provably correct distributed algorithm for optimal disaggregation of regulation signals among the microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗