Results for “climate change mitigation” — Page 23

DOE OSTI2021

Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity

Abstract Earth is currently undergoing a global increase in atmospheric vapor pressure deficit (VPD), a trend which is expected to continue as climate warms. This phenomenon has been associated with productivity decreases in ecosystems and yield penalties in crops, with these losses attributed to photosynthetic limitations arising from decreased stomatal conductance. Such VPD increases, however, have occurred over decades, which raises the possibility that stomatal acclimation to VPD plays an important role in determining plant productivity under high VPD. Furthermore, evidence points to more far‐ranging and complex effects of elevated VPD on plant physiology, extending to the anatomical, biochemical, and developmental levels, which could vary substantially across species. Because these complex effects are typically not considered in modeling frameworks, we conducted a quantitative literature review documenting temperature‐independent VPD effects on 112 species and 59 traits and physiological variables, in order to develop an integrated and mechanistic physiological framework. We found that VPD increase reduced yield and primary productivity, an effect that was partially mediated by stomatal acclimation, and also linked with changes in leaf anatomy, nutrient, and hormonal status. The productivity decrease was also associated with negative effects on reproductive development, and changes in architecture and growth rates that could decrease the evaporative surface or minimize embolism risk. Cross‐species quantitative relationships were found between levels of VPD increase and trait responses, and we found differences across plant groups, indicating that future VPD impacts will depend on community assembly and crop functional diversity. Our analysis confirms predictions arising from the hydraulic corollary to Darcy's law, outlines a systemic physiological framework of plant responses to rising VPD, and provides recommendations for future research to better understand and mitigate VPD‐mediated climate change effects on ecosystems and agro‐systems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

The short-term comprehensive impact of the phase-out of global coal combustion on air pollution and climate change

With the continuous intensification of global warming, the reduction and ultimate phase-out of coal combustion is an inevitable trend in the future global energy transformation. This study comprehensively analyzed the impact of phasing out coal combustion on global emissions and concentrations of air pollutants, radiative fluxes, meteorology and climate using Community Earth System Model 2 (CESM2). The results indicate that after the global phase-out of coal combustion, there is a marked decrease in the concentrations of sulfur dioxide (SO 2 ), nitrogen oxides (NO x ) and fine particulate matter (PM 2.5 ), with some regions experiencing a reduction of exceeding 50%. There is no significant change in global ozone (O 3 ) concentration. There are decreasing AOD and positive radiative fluxes globally in the short term, though the cloud contributes minor negative radiative fluxes. The global air temperature may increase by approximately (0.02 ± 0.15) °C on average with regional and seasonal variations, and the precipitation may potentially increase by approximately (2.7 ± 40.6) mm yr −1 globally and over 20% in equatorial regions in the short term. But combined with the decreasing trend of cloud water content in the Northern Hemisphere, it indicates a potential increase in the extremity of precipitation events. In conclusion, this study provides references for global control of air pollution, mitigation strategies of climate change, and transformation of energy structures under the objective of “carbon neutrality”, such as focusing on the negative climate impacts of exacerbating regional warming and increasing extreme precipitation resulting from the rapid reduction of aerosols in the short term.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2026

Kent Island Boundary Layer Field Campaign Report

Understanding the mechanisms governing the urban atmospheric environment is critical for informing urban populations about the impacts of climate change and associated mitigation and adaptation measures. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) was conducted to study the interactions among the earth’s surface, the atmospheric boundary layer (ABL), aerosols, atmospheric composition, clouds, radiation, and precipitation at each experimental site, and to examine how the spatial gradients across the region interact to create the climate conditions in Baltimore. In this activity we performed weekly soundings, alternating between day and night from Kent Island, to get low-frequency, long-term observations of ABL/lower tropospheric thermodynamic structure. However, during the CoURAGE intensive operational period (IOP), we performed high-frequency sampling (2x/day), at times coinciding with CoURAGE IOP soundings at the main site. The short-term, high-frequency sounding information that was launched at 12Z and 18Z was to complement the longer-term data set.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Pervasive alterations to snow-dominated ecosystem functions under climate change

Climate change projections consistently demonstrate that warming temperatures and dwindling seasonal snowpack will elicit cascading effects on ecosystem function and water resource availability. Despite this consensus, little is known about potential changes in the variability of ecohydrological conditions, which is also required to inform climate change adaptation and mitigation strategies. Considering potential changes in ecohydrological variability is critical to evaluating the emergence of trends, assessing the likelihood of extreme events such as floods and droughts, and identifying when tipping points may be reached that fundamentally alter ecohydrological function. Using a single-model Large Ensemble with sophisticated terrestrial ecosystem representation, we characterize projected changes in the mean state and variability of ecohydrological processes in historically snow-dominated regions of the Northern Hemisphere. Widespread snowpack reductions, earlier snowmelt timing, longer growing seasons, drier soils, and increased fire risk are projected for this century under a high-emissions scenario. In addition to these changes in the mean state, increased variability in winter snowmelt will increase growing-season water deficits and increase the stochasticity of runoff. Thus, with warming, declining snowpack loses its dependable buffering capacity so that runoff quantity and timing more closely reflect the episodic characteristics of precipitation. This results in a declining predictability of annual runoff from maximum snow water equivalent, which has critical implications for ecosystem stress and water resource management. So our results suggest that there is a strong likelihood of pervasive alterations to ecohydrological function that may be expected with climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Design and Assessment of a Novel Approach for Ecosystem Warming Experiments in High‐Energy Tidal Wetlands

Abstract Coastal salt marshes have an important role in climate change adaptation and mitigation. Direct and indirect responses to warming are expected to vary along the marsh elevation gradient, making ecosystem responses to warming at this marine‐terrestrial ecotone uncertain. The Marsh Ecosystem Response to Increased Temperatures (MERIT) experiment was established in 2018 on the North Sea coast of Germany. Experimental plots are evenly distributed over three elevational marsh zones (pioneer, low marsh, and high marsh) and include three temperature treatments (ambient, +1.5°C, +3.0°C). MERIT's novel design combines active warming (horizontal surface warming cables and vertical soil warming pins) with passive, partially covered domes. For performance assessment, temperature deltas between ambient and warmed plots were calculated and evaluated at seasonal, daily, and diurnal timescales. We used Linear Mixed Models with Residual Maximum Likelihood for evaluating warming treatment effects and constraining environmental factors. MERIT was effective at ecosystem warming in this high‐energy environment both above‐ and belowground. Mixed models show that warming treatment dominates temperature differences belowground and at the soil surface, along with factors such as wind speed, flooding duration, and solar radiation. Aboveground warming was lower than belowground warming, but the dome design minimized issues seen in other open‐top chamber experiments. The combination of passive aboveground warming with feedback‐controlled active surface and belowground heating provides a setup for understanding warming effects on tidal ecosystems without altering the natural impacts of wind, radiation, and tidal inundations at high‐energy coastlines. Our design creates opportunities to expand future warming experiments to remote locations and technically challenging environments.

Environmental Sciences & Ecology↗

DOE OSTI2024

Projected income data under different shared socioeconomic pathways for Washington state

Abstract High-resolution income projections under different Shared Socioeconomic Pathways (SSPs) are essential for the climate change research communities to devise climate change adaptation and mitigation strategies. To generate income projections for Washington state, we obtain state-level GDP per capita projections and convert them into projected annual household income. The resulting state-level income projections are subsequently downscaled to the census block-level based on the Longitudinal Origin-Destination Employment Statistics (LODES) dataset. For accuracy assessment, we downscale historical income data from state- level to block- and block group-level and compare the downscaled results against the actual income data from LODES. County-level accuracy assessment is also conducted based on American Community Survey. The results demonstrate a good agreement (Average R 2 of 0.67, 0.8, and 0.99 for block-, block group-, and county-level, respectively) between the downscaled income data and the reference data, thereby validating the methodology employed. Our approach is applicable to other states for income projections, which can be utilized by a broader audience, including those involved in demographic analysis, economic research, and urban planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Correlative Surface Imaging Reveals Chemical Signatures for Bacterial Hotspots on Plant Roots

The rhizosphere is arguably the most complex microbial habitat on earth, comprising an integrated network of plant roots, soil and a highly diverse microbial community (the rhizosphere microbiome)1. Understanding, predicting and controlling plant-microbe interactions in the rhizosphere will allow us to harness the plant microbiome as a means to increase or restore plant ecosystem productivity, improve plant responses to a wide range of environmental perturbations, and mitigate effects of climate change by designing ecosystems for long-term soil carbon storage1. To this end, it is imperative to develop new molecular approaches with high spatial resolution to capture interactions at the plant-microbe, microbe-microbe, and plant-plant interfaces. In this work, we designed an imaging sample holder that allows integrated surface imaging tools to map the same locations of a plant root-microbe interface with submicron lateral resolutions, providing novel in vivo analysis of root-microbe interactions. Specifically, confocal fluorescence microscopy, time-of-flight secondary ion mass spectrometry (ToF-SIMS), X-ray photoelectron spectroscopy (XPS), and scanning electron microscope (SEM) were used for the first time for correlative imaging of the Brachypodium distachyon root and its interaction with Pseudomonas SW25, a typical plant growth-promoting soil bacterium. Imaging data suggest that the root surface is inhomogeneous and that the interaction between Pseudomonas and Brachypodium roots was confined to only few spots along the sampled root segments and that the bacterial attachment spots were enriched in Na- and S-related and high-mass organic species. We conclude that attachment of the Pseudomonas cells to the root surface is outcompeted by strong root-soil mineral interactions but facilitated by formation of extracellular polymeric substances (EPS).

Plant root, Plant growth-promoting bacteria, soil,↗

DOE OSTI2021

CO2 Electrolyzer to Produce Formic Acid Using Flue Gas at Industry Relevant Current Density

The electrochemical conversion of carbon dioxide (CO2) into valuable fuels and feedstocks has gained great attention recently due to the global urgency to mitigate the rapid climate change. A wide variety of chemicals can be obtained through the electrochemical CO2 reduction reaction (CO2RR). Among all the chemicals generated through CO2RR, formic acid has attracted great interest because of its wide applications in silage preservation in agriculture, as an antibacterial agent in animal food additives, as a chemical additive in leather and tanning processing, as a chemical component in textile dyeing and finishing, as a reagent in pharmaceutical and chemical synthesis, and as deicing agents and a component in drilling fluids. Recently, the use of formic acid as a feedstock for biorefineries has also attracted increasing interest.

Yang, Hongzhou↗

DOE OSTI2024

SimCCS 3.0 User GUIDE

Carbon Capture and Storage (CCS) is an important technology aimed at reducing CO 2 emissions and contributing to the mitigation strategies of climate change. The process involves three main steps: CO 2 capture, transport, and storage. Specifically, CO 2 is captured from industrial facilities, such as coal-fired power plants and natural gas processing plants. It is then transported through infrastructure networks, such as pipelines, rails, and ships, and injected into underground geological formations, such as depleted oil and gas reservoirs or deep saline aquifers, for permanent storage. The entire CCS value chain encompasses multiple components, including CO 2 sources, transport networks, and storage sites. The proper design of CCS transport infrastructure is an essential step for the deployment of CCS technology. However, the design is not a trivial process, as it presents many challenges and requires consideration of numerous economic, social, and environmental factors.

42 ENGINEERING↗

DOE OSTI2021

Impacts of Biofuel Blending on MCCI Ignition Delay with Review of Methods for Defining Cycle-by-Cycle Ignition Points from Noisy Cylinder Pressure Data

Conventional diesel combustion, also known as Mixing-Controlled Compression Ignition (MCCI), is expected to be the primary power source for medium- and heavy-duty vehicles for decades to come. Displacing petroleum-based ultra-low-sulfur diesel (ULSD) as much as possible with low-net-carbon biofuels will become necessary to help mitigate effects on climate change. Neat biofuels may have difficulty meeting current diesel fuel standards but blends of 30% biofuel in ULSD show potential as ‘drop-in’ fuels. These blends must not make significant changes to the combustion phasing of the MCCI process if they are to be used interchangeably with neat ULSD. An important aspect of MCCI phasing is the ignition delay (ID), i.e. the time between the start of fuel injection and the initial premixed autoignition that initiates the MCCI process. Bench experiments can evaluate the expected ignition delay of a fuel via cetane number (CN) or alternative methods such as the indicated cetane number (ICN); however, neither CN nor ICN correlate perfectly with the ignition delay measured in actual engine experiments. Furthermore, there is no standardized methodology on how to quantify MCCI ignition delay from engine cylinder pressure measurements, creating difficulties in cross-study comparison. In this study, several engine ignition delay calculation methods are evaluated for robustness in deriving ignition delay on both a cycle-averaged and cycle-to-cycle basis. Eight biofuel blends with varying ICN, oxygen concentration and other fuel properties were used to study the different methods. This yields a thorough analysis of how certain biofuel blends affect ignition delay and the entire MCCI process, as well as a thorough evaluation of the differences between the ID calculation methods. Many of these methods are equally valid, but the choice of method has a significant impact on the resulting ID, which must be carefully considered when evaluating results across multiple studies.

47 OTHER INSTRUMENTATION↗

NASA NTRSDate not supplied

An Analytic Collaborative Framework for the Earth System Observatory

NASA's Earth System Observatory groundbreaking observations will provide critical measurements to address societal relevant problems in climate change, natural hazard mitigation, fighting forest fires, and improving real-time agricultural processes. Central to the ESO vision is the notion of Open-Source Science (OSS), a collaborative culture enabled by technology that promotes the open sharing of data, information, and knowledge aiming to facilitate and accelerate scientific understanding, and the agile development of applications for the benefit of society. The larger vision of an Earth System Digital Twin (ESDT) calls for integrated Earth science frameworks that mirror the Earth by a proxy digital construct that includes km-scale resolution Earth system models and data assimilation systems along with an integrated set of analytic tools to enable the next generation of science discoveries and evidence-based decision making. The goal of this project is to develop an Analytic Collaborative Framework for ESO missions, based on realistic, science-based observing system simulations and the Program of Record (PoR). Tying it all together is a cloud-based cyberinfrastructure that will enable each uniquely designed satellite in the Earth System Observatory to work in tandem to create a 3D, holistic view of Earth. In this presentation, we lay the technological groundwork for enabling such a vision. Our approach consists of the 3 main interconnected building blocks: 1. Cloud-optimized representative datasets for ESO missions and the PoR to serve as basis for developing and prototyping an Analytic Collaborative Framework. 2. An Algorithm Workbench for enabling experimentation and exploration of synergistic algorithms not only for instruments within a mission, but also including the PoR and other ESO missions. 3. A series of concrete Open-Source Science demonstrations including use cases that span science discovery and end-user applications with direct societal impact. While our ultimate goal is to include all of the main missions comprising the Earth System Observatory, in our initial 2 years we will focus on AOS and SBG, two missions for which specific synergisms have been identified in a recent workshop. In this presentation we will describe our approach and discuss some illustrative examples of our framework.

Arlindo da SIlva↗

NASA NTRSDate not supplied

An Analytic Collaborative Framework for the Earth System Observatory

NASA's Earth System Observatory groundbreaking observations will provide critical measurements to address societal relevant problems in climate change, natural hazard mitigation, fighting forest fires, and improving real-time agricultural processes. Central to the ESO vision is the notion of Open-Source Science (OSS), a collaborative culture enabled by technology that promotes the open sharing of data, information, and knowledge aiming to facilitate and accelerate scientific understanding, and the agile development of applications for the benefit of society. The larger vision of an Earth System Digital Twin (ESDT) calls for integrated Earth science frameworks that mirror the Earth by a proxy digital construct that includes km-scale resolution Earth system models and data assimilation systems along with an integrated set of analytic tools to enable the next generation of science discoveries and evidence-based decision making. The goal of this project is to develop an Analytic Collaborative Framework for ESO missions, based on realistic, science-based observing system simulations and the Program of Record (PoR). Tying it all together is a cloud-based cyberinfrastructure that will enable each uniquely designed satellite in the Earth System Observatory to work in tandem to create a 3D, holistic view of Earth. In this presentation, we lay the technological groundwork for enabling such a vision. Our approach consists of the 3 main interconnected building blocks: 1. Cloud-optimized representative datasets for ESO missions and the PoR to serve as basis for developing and prototyping an Analytic Collaborative Framework. 2. An Algorithm Workbench for enabling experimentation and exploration of synergistic algorithms not only for instruments within a mission, but also including the PoR and other ESO missions. 3. A series of concrete Open-Source Science demonstrations including use cases that span science discovery and end-user applications with direct societal impact. While our ultimate goal is to include all of the main missions comprising the Earth System Observatory, in our initial 2 years we will focus on AOS and SBG, two missions for which specific synergisms have been identified in a recent workshop. In this presentation we will describe our approach and discuss some illustrative examples of our framework.

Arlindo da SIlva↗

NASA NTRS2023

Observational Constraints Reduce Model Spread but Not Uncertainty in Global Wetland Methane Emission Estimates

The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Kuang-Yu Chang↗

DOE OSTI2021

Impacts of Biofuel Blending on MCCI Ignition Delay with Review of Methods for Defining Cycle-by-Cycle Ignition Points from Noisy Cylinder Pressure Data: Preprint

Conventional diesel combustion, also known as Mixing-Controlled Compression Ignition (MCCI), is expected to be the primary power source for medium- and heavy-duty vehicles for decades to come. Displacing petroleum-based ultra-low-sulfur diesel (ULSD) as much as possible with low-net-carbon biofuels will become necessary to help mitigate effects on climate change. Neat biofuels may have difficulty meeting current diesel fuel standards but blends of 30% biofuel in ULSD show potential as ‘drop-in’ fuels. These blends must not make significant changes to the combustion phasing of the MCCI process if they are to be used interchangeably with neat ULSD. An important aspect of MCCI phasing is the ignition delay (ID), i.e. the time between the start of fuel injection and the initial premixed autoignition that initiates the MCCI process. Bench experiments can evaluate the expected ignition delay of a fuel via cetane number (CN) or alternative methods such as the indicated cetane number (ICN); however, neither CN nor ICN correlate perfectly with the ignition delay measured in actual engine experiments. Furthermore, there is no standardized methodology on how to quantify MCCI ignition delay from engine cylinder pressure measurements, creating difficulties in cross-study comparison. In this study, several engine ignition delay calculation methods are evaluated for robustness in deriving ignition delay on both a cycle-averaged and cycle-to-cycle basis. Eight biofuel blends with varying ICN, oxygen concentration and other fuel properties were used to study the different methods. This yields a thorough analysis of how certain biofuel blends affect ignition delay and the entire MCCI process, as well as a thorough evaluation of the differences between the ID calculation methods. Many of these methods are equally valid, but the choice of method has a significant impact on the resulting ID, which must be carefully considered when evaluating results across multiple studies.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Source apportionment of VOCs, IVOCs and SVOCs by positive matrix factorization in suburban Livermore, California

Abstract. Gas- and particle-phase molecular markers provide highly specific information about the sources and atmospheric processes that contribute to air pollution. In urban areas, major sources of pollution are changing as regulation selectively mitigates some pollution sources and climate change impacts the surrounding environment. In this study, a comprehensive thermal desorption aerosol gas chromatograph (cTAG) was used to measure volatile, intermediate-volatility and semivolatile molecular markers every other hour over a 10 d period from 11 to 21 April 2018 in suburban Livermore, California. Source apportionment via positive matrix factorization (PMF) was performed to identify major sources of pollution. The PMF analysis identified 13 components, including emissions from gasoline, consumer products, biomass burning, secondary oxidation, aged regional transport and several factors associated with single compounds or specific events with unique compositions. The gasoline factor had a distinct morning peak in concentration but lacked a corresponding evening peak, suggesting commute-related traffic emissions are dominated by cold starts in residential areas. More monoterpene and monoterpenoid mass was assigned to consumer product emissions than biogenic sources, underscoring the increasing importance of volatile chemical products to urban emissions. Daytime isoprene concentrations were controlled by biogenic sunlight- and temperature-dependent processes, mediated by strong midday mixing, but gasoline was found to be the dominant and likely only source of isoprene at night. Biomass burning markers indicated residential wood burning activity remained an important pollution source even in the springtime. This study demonstrates that specific high-time-resolution molecular marker measurements across a wide range of volatility enable more comprehensive pollution source profiles than a narrower volatility range would allow.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Quantification and Mitigation of Long-Term Impacts of Urbanization and Climate Change in the Tropical Coastal City of San Juan, Puerto Rico

Urbanization, along with other cases of land cover and land use changes, has significant climate impacts in tropical regions with the added complexity of occurring within the context of global warming. The individual and combined effects of these two factors on the surface energy balance of a tropical city are investigated by use of an integrated atmospheric modeling approach, taking the San Juan Metropolitan Area (SJMA), Puerto Rico as the test case. To achieve this goal, an ensemble of climate and weather simulations is performed, with the climate scenarios combining urban development and sprawl with regional climate change over the past 50 years, and the short-term simulations designed to test the sensitivity to different urban vegetation configurations as mitigating alternatives. As indicator of change, we use the thermal response number (TRN), which is a measure of the sensible heating to the thermal storage of a surface or region, and the Bowen ratio, which is defined as the ratio of sensible to latent heat fluxes. The TRN of the area occupied by the SJMA has decreased as a consequence of replacing the low land coastal plain vegetation with man made materials, indicating that it takes less energy to raise the surface temperature of the urban area, whereas the TRN of forested regions has remained virtually unchanged. The global warming signal also has effects on the thermal response of the SJMA, where dryer current conditions generate lower TRN values. Differences due to global warming are more evident in the Bowen ratio pattern, mostly associated with the drier present conditions observed and its effects on sensible and latent heat fluxes. In terms of testing different mitigation strategies, the short-term simulations show that the urban area is more efficient in partitioning surface energy balance terms when green roofs are specified, as opposed to including vegetation inside the urban core.

Comarazamy, Daniel↗

NASA NTRS2013

Influences of Regional Climate Change on Air Quality Across the Continental U.S. Projected from Downscaling IPCC AR5 Simulations

Projecting climate change scenarios to local scales is important for understanding, mitigating, and adapting to the effects of climate change on society and the environment. Many of the global climate models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture regional-scale changes in temperatures and precipitation. We use a regional climate model (RCM) to dynamically downscale the GCM's large-scale signal to investigate the changes in regional and local extremes of temperature and precipitation that may result from a changing climate. In this paper, we show preliminary results from downscaling the NASA/GISS ModelE IPCC AR5 Representative Concentration Pathway (RCP) 6.0 scenario. We use the Weather Research and Forecasting (WRF) model as the RCM to downscale decadal time slices (1995-2005 and 2025-2035) and illustrate potential changes in regional climate for the continental U.S. that are projected by ModelE and WRF under RCP6.0. The regional climate change scenario is further processed using the Community Multiscale Air Quality modeling system to explore influences of regional climate change on air quality.

Air quality control↗

DOE OSTI2023

Machine learning for modern power distribution systems: Progress and perspectives

The application of machine learning (ML) to power and energy systems (PES) is being researched at an astounding rate, resulting in a significant number of recent additions to the literature. As the infrastructure of electric power systems evolves, so does interest in deploying ML techniques to PES. However, despite growing interest, the limited number of reported real-world applications suggests that the gap between research and practice is yet to be fully bridged. To help highlight areas where this gap could be narrowed, this article discusses the challenges and opportunities in developing and adapting ML techniques for modern electric power systems, with a particular focus on power distribution systems. These systems play a crucial role in transforming the electric power sector and accommodating emerging distributed technologies to mitigate the impacts of climate change and accelerate the transition to a sustainable energy future. The objective of this article is not to provide an exhaustive overview of the state-of-the-art in the literature, but rather to make the topic accessible to readers with an engineering or computer science background and an interest in the field of ML for PES, thereby encouraging cross-disciplinary research in this rapidly developing field. To this end, the article discusses the ways in which ML can contribute to addressing the evolving operational challenges facing power distribution systems and identifies relevant application areas that exemplify the potential for ML to make near-term contributions. At the same time, key considerations for the practical implementation of ML in power distribution systems are discussed, along with suggestions for several potential future directions.

Marković, Marija (ORCID:0000000247839837)↗