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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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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Microbial Community Structure–Function Relationships in Yaquina Bay Estuary Reveal Spatially Distinct Carbon and Nitrogen Cycling Capacities

Linking microbial community structure to ecological processes requires understanding of the functional roles among individual populations and the factors that influence their distributions. These structure–function relationships are particularly difficult to disentangle in estuaries, due to highly variable physico-chemical conditions. Yet, examining microbe-mediated turnover of resources in these “bioreactor” ecosystems is critical for understanding estuarine ecology. In this study, a combined metagenomics and metaproteomics approach was used to show that the unequal distribution of microbial populations across the Yaquina Bay estuary led to a habitat-specific taxonomic and functional structure and a clear spatial distribution in microbe-mediated capacities for cycling of carbon and nitrogen. For example, size-fractionation revealed that communities inhabiting suspended particulate material encoded more diverse types of metabolisms (e.g., fermentation and denitrification) than those with a planktonic lifestyle, suggesting that the metabolic reactions can differ between size fractions of the same parcel of an estuarine water column. Similarly, communities inhabiting oligotrophic conditions in the lower estuary were enriched in genes involved in central carbon metabolism (e.g., TCA cycle), while communities in the upper estuary were enriched in genes typical of copiotrophic populations (e.g., cell growth, cell division). Integrating gene and protein data revealed that abundant populations of Flavobacteriales and Rhodobacterales encoded similar genomic functions, yet differed significantly in protein expression, dedicating a large proportion of their respective proteomes to rapid growth and division versus metabolic versatility and resource acquisition. This suggested potentially distinct life-strategies between these two co-occurring lineages and was concomitant with differing patterns of positive evolutionary selection on their encoded genes. Microbial communities and their functions across Yaquina Bay appear to be structured by population-level habitat preferences, resulting in spatially distinct elemental cycling, while within each community, forces such as competitive exclusion and evolutionary selection influence species life-strategies and may help maintain microbial diversity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Examining Uncertainty in Demand Response Baseline Models and Variability in Automated Response to Dynamic Pricing

Controlling electric loads to deliver power system services presents a number of interesting challenges. For example, changes in electricity consumption of Commercial and Industrial (C&I) facilities are usually estimated using counterfactual baseline models, and model uncertainty makes it difficult to precisely quantify control responsiveness. Moreover, C&I facilities exhibit variability in their response. This paper seeks to understand baseline model error and demand-side variability in responses to open-loop control signals (i.e. dynamic prices). Using a regression-based baseline model, we define several Demand Response (DR) parameters, which characterize changes in electricity use on DR days, and then present a method for computing the error associated with DR parameter estimates. In addition to analyzing the magnitude of DR parameter error, we develop a metric to determine how much observed DR parameter variability is attributable to real event-to-event variability versus simply baseline model error. Using data from 38 C&I facilities that participated in an automated DR program in California, we find that DR parameter errors are large. For most facilities, observed DR parameter variability is likely explained by baseline model error, not real DR parameter variability; however, a number of facilities exhibit real DR parameter variability. In some cases, the aggregate population of C&I facilities exhibits real DR parameter variability, resulting in implications for the system operator with respect to both resource planning and system stability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Computational Risk Analysis of Propane Releases in Maintenance Facilities

Liquefied petroleum gas (LPG) is a viable, cleaner alternative to traditional diesel fuel used in busses and other heavy-duty vehicles and could play a role in helping the US meet its lower emission goals. While the LPG industry has focused efforts on developing vehicles and fueling infrastructure, we must also establish safe parameters for maintenance facilities which are servicing LPG fueled vehicles. Current safety standards aid in the design of maintenance facilities, but additional quantitative analysis is needed to prove safeguards are adequate and suggest improvements where needed. In this report we aim to quantify the amount of flammable mass associated with propane releases from vehicle mounted fuel vessels within enclosed garages. Furthermore, we seek to qualify harm mitigation with variable ventilations and facility layout. To accomplish this we leverage validated computational resources at Sandia National Laboratories to simulate various release scenarios representative of real world vehicles and maintenance facilities. Flow solvers are used to predict the dynamics of fuel systems as well as the evolution of propane during release events. From our simulated results we observe that both inflow and outflow ventilation locations play a critical role in reducing flammable cloud size and potential overpressure values during a possible combustion event.

03 NATURAL GAS↗
DOE OSTI2018

A Data-Driven Approach to Dynamic Var Compensator Placement for Local Voltage Stabilization on Feeders with High PV Penetration

Distribution feeders with a high penetration of distributed photovoltaic (PV) systems can experience voltage variability caused by the changing load flow injections corresponding to the availability of the solar resource. The rapid, uncontrolled voltage range fluctuations can affect the system. Tap-changing voltage regulators, switched capacitors, and smart inverter functions have been used to stabilize voltage. However, in some cases, when these options are insufficient or unavailable, dedicated reactive power control devices such as dynamic var compensators (DVCs) can be used for local voltage stabilization. This paper introduces a statistical framework to characterize voltage variability and stabilization, proposes a data-driven approach to DVC placement, and presents the results of a planning study on a real feeder using real data.

Reiman, Andrew P.↗
DOE OSTI2026

Queue wait time prediction in high performance computing (HPC) systems

High Performance Computing (HPC) systems are critical enablers for groundbreaking scientific research across various domains. Efficient resource allocation, facilitated by job scheduling, is paramount for maximizing the utilization of HPC systems. However, the variability in wait times for queued jobs poses challenges for users, necessitating accurate job wait time estimation. This paper explores the influence of job characteristics, including job size (the number of nodes requested and walltime), the queue to which the job is submitted and other resource requirements, on job wait times in leadership-class HPC systems. Focusing on the Theta Cray XC40 and Polaris machines at Argonne National Laboratory, the study evaluates the performance of different supervised learning algorithms in predicting job wait times. It also evaluates the impact of data preprocessing, including outlier detection, Principal Component Analysis (PCA), and feature selection, on the performance of wait time prediction models. The findings reveal insights into the relationship between job characteristics and wait times, offering a foundation for optimizing resource allocation and enhancing user experience. The methodologies and tools developed in this study are adaptable to other leadership-class HPC systems, providing a valuable contribution to the broader HPC community aiming to improve job scheduling efficiency and user satisfaction.

Okafor, Nwamaka↗
DOE OSTI2024

Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production

Abstract. The mid-Atlantic will experience rapid wind plant development due to its promising wind resource located near large population centers. Wind turbines and wind plants create wakes, or regions of reduced wind speed, that may negatively affect downwind turbines and plants. We evaluate wake variability and annual energy production with the first yearlong modeling assessment using the Weather Research and Forecasting model, deploying 12 MW turbines across the domain at a density of 3.14 MW km−2, matching the planned density of 3 MW km−2. Using a series of simulations with no wind plants, one wind plant, and complete build-out of lease areas, we calculate wake effects and distinguish the effect of wakes generated internally within one plant from those generated externally between plants. We also provide a first step towards uncertainty quantification by testing the amount of added turbulence kinetic energy (TKE) by 0 % and 100 %. We provide a sensitivity analysis by additionally comparing 25 % and 50 % for a short case study period. The strongest wakes, propagating 55 km, occur in summertime stable stratification, just when New England's grid demand peaks in summer. The seasonal variability of wakes in this offshore region is much stronger than the diurnal variability of wakes. Overall, yearlong simulated wake impacts reduce power output by a range between 38.2 % and 34.1 % (for 0 %–100 % added TKE). Internal wakes cause greater yearlong power losses, from 29.2 % to 25.7 %, compared to external wakes, from 14.7 % to 13.4 %. The overall impact is different from the linear sum of internal wakes and external wakes due to non-linear processes. Additional simulations quantify wake uncertainty by modifying the added amount of turbulent kinetic energy from wind turbines, introducing power output variability of 3.8 %. Finally, we compare annual energy production to New England grid demand and find that the lease areas can supply 58.8 % to 61.2 % of annual load. We note that the results of this assessment are not intended to make nor are they suitable to make commercial judgments about specific wind projects.

17 WIND ENERGY↗
DOE OSTI2016

Enhancing Power System Operational Flexibility With Flexible Ramping Products: A Review

With the increased variability and uncertainty of net load induced from high penetrations of renewable energy resources and more flexible interchange schedules, power systems are facing great operational challenges in maintaining balance. Among these, the scarcity of ramp capability is an important culprit of power balance violations and high scarcity prices. To address this issue, market-based flexible ramping products (FRPs) have been proposed in the industry to improve the availability of ramp capacity. This paper presents an in-depth review of the modeling and implementation of FRPs. The major motivation is that although FRPs are widely discussed in the literature, it is still unclear to many how they can be incorporated into a co-optimization framework that includes energy and ancillary services. The concept and a definition of power system operational flexibility as well as the needs for FRPs are introduced. The industrial practices of implementing FRPs under different market structures are presented. Market operation issues and future research topics are also discussed. In conclusion, this paper can provide researchers and power engineers with further insights into the state of the art, technical barriers, and potential directions for FRPs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

An Examination of the Hydropower Licensing and Federal Authorization Process

Site-permitting and regulation are necessary to ensure hydropower projects (both original and relicensed) comply with statutory requirements and address multiple stakeholder priorities that consider a range of factors, including water quality, species protection, cultural resource impacts, and recreation. However, the time involved in acquiring a license for an individual hydropower project can be highly variable by project, leading to increased project costs, financial risks, and uncertainties. In part, this variability is the result of a regulatory structure that has evolved over time to include multiple approvals and compliance requirements administered by the Federal Energy Regulatory Commission (FERC), U.S. Army Corp of Engineers (USACE), federal land management agencies, federal and state resource agencies, and Indian Tribes. Ultimately, the time, benefits, costs, and risks to developers associated with hydropower regulatory processes and/or the preparation (e.g., studies) required for regulatory agency review are not well documented or synthesized in the public domain, which may increase uncertainty and variability within the process. This report addresses these needs by presenting results of a comprehensive examination of hydropower licensing including quantitative and qualitative analyses of timelines, causal factors, and their combined effect on risk and costs to developers.

13 HYDRO ENERGY↗
DOE OSTI2022

Battery Energy Storage Scenario Analyses Using the Lithium-Ion Battery Resource Assessment (LIBRA) Model

Meeting aggressive carbon emission goals will entail widespread deployment of renewable sources of electricity. Because these sources are variable, there is a need to develop scalable energy storage technologies. The U.S. Department of Energy is supporting efforts to increase U.S. manufacturing and recycling capabilities for LIBs and to decrease costs of stationary storage batteries. Many factors influence the domestic manufacturing and cost of stationary storage batteries, including availability of critical raw materials (lithium, cobalt, and nickel), competition from various demand sectors (consumer electronics, vehicles, and battery energy storage), resource recovery (recycling), government policies, and learning in the industry, among other factors. Understanding how these factors interact and identifying synergies and bottlenecks is important for developing effective strategies for the LIB stationary energy storage system. We developed the Lithium-Ion Battery Resource Analysis (LIBRA) model as a tool to help stakeholders better understand the following types of questions: What are the roles of R&D, industrial learning, and scaling of demand in lowering the cost of battery energy storage system production? How do the intersections between the EV and stationary storage sectors affect the battery supply chain? For various stationary storage and EV penetration scenarios, what volumes of critical materials might be required and what role can resource recovery play? What does expected demand for both EVs and stationary storage portend for mineral resources and overall mineral scarcity? The LIBRA model is developed using a System dynamics (SD) modeling approach to represent interactions across the segments of the battery materials supply chain. System dynamics models can capture the complex interactions and feedback between the various system components that influence supply and demand. The LIBRA model is comprised of several interacting modules that represent specific portions of the LIB supply chain. The model tracks the buildout of the domestic LIB industry over time (2020 - 2050) and in the context of competing demands for raw materials, recycling, and markets for LIBs. The LIBRA model represents major systemic feedback loops and delays across the supply chain. This report provides a complete documentation for the LIBRA model, including model assumptions, data, scenario analysis results, and sensitivity analysis of the model's input space.

25 ENERGY STORAGE↗
DOE OSTI2026

Dynamical Downscaling of Earth System Model Data for Energy System Analysis

Assessing energy resources (e.g., solar, wind, and hydro) under future scenarios requires datasets with sufficient spatial and temporal detail to capture variability and extreme events. While global-scale Earth System Model (ESM) projections are widely used, their coarse resolution limits direct application to regional energy system analyses. Dynamical downscaling offers a robust approach to generate physically consistent, fine-scale datasets that better represent local atmospheric processes impacting energy resources. In this work, we present a two-stage approach for producing high-resolution historical and future projections over the contiguous United States (CONUS). First, we optimize the Weather Research and Forecasting (WRF) model configuration for energy-relevant variables - solar irradiance, wind speed, and precipitation - by conducting ERA5-driven simulations at 8-km and 28-km resolution. Multiple physics schemes and model configurations within the WRF are evaluated against observational datasets including the National Solar Radiation Database (NSRDB), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the Stage IV multi-radar/multi-sensor precipitation product for the CONUS domain. Using the best-performing configuration, we dynamically downscale MPI-ESM1-2-HR simulations for 2000-2060 under SSP2-4.5 and SSP5-8.5 scenarios at 4-km spatial and hourly temporal resolution. This presentation will provide a comprehensive analysis of the results from multiple numerical experiments and high-resolution ESM projections. In addition, we will discuss potential applications of our high-resolution datasets within the energy sector and outline future research avenues dedicated to evaluating how extreme weather events influence system performance and resilience.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Evaluating Demand Response Impacts on Capacity Credit of Renewable Distributed Generation in Smart Distribution Systems

Environmental concerns and depletion of traditional energy lead to the booming development of renewable distributed generation (RDG) in the past decade. However, due to intermittent nature of renewable energy sources, to what extent RDG could provide capacity to power systems becomes a critical issue to the utility company when implementing long-term system strategic (generation expansion) planning. On the other hand, in a smart-grid frame, the popularization of different varieties of demand-side resources enables the system to operate at more flexible modes ever before. The potential variability in load demand not only introduces additional dynamics and uncertainties to the system, but could also affect the reliability benefits of RDG. Therefore, in practice, to effectively estimate the reliability value of RDG in power systems, the potential interaction between generation- and demand-side must be properly captured. In this paper, a study to assess the capacity credit (CC) of RDG in a context of distributed generation system is performed with consideration of the impacts of demand response (DR). A compound reliability model for DR is presented, which considers the uncertainties involved in both instant response and follow-on load recovery processes. On this basis, an assessment framework for the CC of RDG based on sequential Monte Carlo simulation is developed by which the inter-temporal characteristics of DR resources can be fully captured. The numerical study is implemented based on the IEEE-38 bus test case. The calculation results demonstrate that the CC of RDG would depend on a variety of factors, including penetration level, responsiveness of load demand and the correlations between RDG and DR availability. Also, it is shown that accounting for the underlying effect of DR is of absolute importance, otherwise the CC of RDG might be estimated erroneously in real practices.

capacity credit↗
DOE OSTI2025

Role of Renewables in Grid Resilience

This chapter focuses on revealing the impact of renewable energies, especially distributed energy resources, on modern power system resilience. It is widely recognized that the increasing adoption of renewable energy is bringing more uncertainty and variability, posing new challenges to grid resilience. However, both supply and demand are becoming more active in grid resilience management. The communities themselves would benefit from the utilization of local resources and flexible management to gain resilience during the extreme events.

grid resilience↗
DOE OSTI2012

Integration of Variable Generation and Cost-Causation (Fact Sheet)

Variable renewable energy generation sources, such as wind and solar energy, provide benefits such as reduced environmental impact, zero fuel consumption, and low and stable costs. Advances in both technologies can reduce capital costs and provide significant control capabilities. However, their variability and uncertainty - which change with weather conditions, time of day, and season - cancause an increase in power system operating costs compared to a fully controllable power plant. Although a number of studies have assessed integration costs, calculating them correctly is challenging because it is difficult to accurately develop a baseline scenario without variable generation that properly accounts for the energy value. It is also difficult to appropriately allocate costs giventhe complex, nonlinear interactions between resources and loads.

14 SOLAR ENERGY↗
DOE OSTI2010

Marine Corps Base Camp Lejeune Shorebird foraging data

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of development pressures in surrounding areas, impairments due to other anthropogenic disturbances, and increasing requirements for compliance with environmental regulations. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and to optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a minimum 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the first six years of the program (DCERP1) are presented here.The overarching objectives of DCERP are to: (1) understand the effects of military training activities, infrastructure development, and other coastal military installations; (2) develop models, tools, and indicators to evaluate ecosystem health; and (3) recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of development pressures in surrounding areas, impairments due to other anthropogenic disturbances, and increasing requirements for compliance with environmental regulations. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and to optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a minimum 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the first six years of the program (DCERP1) are presented here.The overarching objectives of DCERP are to: (1) understand the effects of military training activities, infrastructure development, and other coastal military installations; (2) develop models, tools, and indicators to evaluate ecosystem health; and (3) recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package contains a raw dataset for paired logistic regression examining habitat and prey variables affecting foraging site selection vs a non-used site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Phase II Nevada Water Resources Data, Modeling, and Visualization (DMV) Center (Final Report)

Water is unquestionably a critical resource throughout the United States. In the semi-arid west -- an area stressed by increase in human population and sprawl of the built environment -- water is the most important limiting resource. Crucially, science must understand factors that affect availability and distribution of water. To sustain growing consumptive demand, science needs to translate understanding into reliable and robust predictions of availability under weather conditions that could be average but might be extreme. These predictions are needed to support current and long-term planning. Similar to the role of weather forecast and climate prediction, water prediction over short and long temporal scales can contribute to resource strategy, governmental policy and municipal infrastructure decisions, which are arguably tied to the natural variability and unnatural change to climate. Change in seasonal and annual temperature, precipitation, snowmelt, and runoff affect the distribution of water over large temporal and spatial scales, which impact the risk of flooding and the groundwater recharge. Anthropogenic influences and impacts increase the complexity and urgency of the challenge. The goal of this project has been to develop a decision support framework of data acquisition, digital modeling, and 3D visualization. This integrated framework consists of tools for compiling, discovering and projecting our understanding of processes that control the availability and distribution of water. The framework is intended to support the analysis of the complex interactions between processes that affect water supply, from controlled availability to either scarcity or deluge. The developed framework enables DRI to promote excellence in water resource management, particularly within the Lake Tahoe basin. In principle, this framework could be replicated for other watersheds throughout the United States. Phase II of this project builds upon the research conducted during Phase I, in which the hydrologic framework was investigated and the development initiated. Phase II concentrates on practical implementation of the earlier work but emphasizes applications to the hydrology of the Lake Tahoe basin. Phase 1 efforts have been refined and extended by creating a toolset for geographic information systems (GIS) that is usable for disparate types of geospatial and geo-referenced data. The toolset is intended to serve multiple users for a variety of applications. The web portal for internet access to hydrologic and remotely sensed product data, prototyped in Phase I, has been significantly enhanced. The portal provides high performance access to LANDSAT-derived data using techniques developed during the course of the project. The portal is interactive, and supports the geo-referenced display of hydrologic information derived from remotely sensed data, such as various vegetative indices used to calculate water consumption. The platform can serve both internal and external constituencies using inter-operating infrastructure that spans both sides of the DRI firewall. The platform is intended grow its supported data assets and to serve as a template for replication to other geographic areas. An unanticipated development during the project was the use of ArcGIS software on a new computer system, called the IBM PureSytems, and the parallel use of the systems for faster, more efficient image processing. Additional data, independent of the portal, was collected within the Sagehen basin and provides detailed information regarding the processes that control hydrologic responses within mountain watersheds. The newly collected data include elevation, evapotranspiration, energy balance and remotely sensed snow-pack data. A Lake Tahoe basin hydrologic model has been developed, in part to help predict the hydrologic impacts of climate change. The model couples both the surface and subsurface hydrology, with the two components having been independently calibrated. Results from the coupled simulations involving both surface water and groundwater processes show that it is possible to fairly accurately simulate lake effects and water budget variables over a wide range of dry and wet cycles in the historical record. The Lake Tahoe basin is representative of the hydrology, topography and climate throughout the Sierra Nevada Range, and the entire model development is prototypical of the efforts required to replicate the decision support framework to other locales. The Lake Tahoe model in particular, could allow water managers to evaluate more accurately components of the water budget (ET, runoff, groundwater, etc) and to answer important questions regarding water resources in northern Nevada. This report discusses the geographic scale and the hydrologic complexity of the calibrated model developed as part of this project, as well as simulation results for historical and future climate projects To enable human-driven data exploration and discovery, de novo software for a globalized rendering module that extends the capability of our evolving custom visualization engine from Phase I (called SMEngine) has been developed. The new rendering component, called Horizon, supports terrain rendering capable of displaying and interrogating both remotely sensed and modeled data. The development of Horizon necessitated adaptation of the visualization engine to allow extensible integration of components such as the global rendering module and support for associated features. The resulting software is general in its GIS capability, but a specific Lake Tahoe visualization application suitable for immersive decision support in the DRIVE6 virtual reality facility has been developed. During the development, various features to enhance the value of the visualization experience were explored, including the use of hyperspectral image overlays. An over-arching goal of the visualization aspect of the project has been to develop and demonstrate the CAVE (CAVE Automatic Virtual Environment) as a practical tool for hydrologic research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Single-cell measurement of plasmid copy number and promoter activity

Abstract Accurate measurements of promoter activities are crucial for predictably building genetic systems. Here we report a method to simultaneously count plasmid DNA, RNA transcripts, and protein expression in single living bacteria. From these data, the activity of a promoter in units of RNAP/s can be inferred. This work facilitates the reporting of promoters in absolute units, the variability in their activity across a population, and their quantitative toll on cellular resources, all of which provide critical insights for cellular engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Harmonic Modeling, Data Generation and Analysis of Power Electronics-Interfaced Residential Loads

The share of electronics-based residential load is expected to rise as devices such as variable frequency drives (VFDs), electric vehicle chargers, and inverter-based distributed energy resources (DERs), e.g., photovoltaic (PV) systems become more common. These loads may introduce significant harmonics into power networks that need to be closely studied in order to perform accurate load modeling and forecasting. However, it can be difficult to obtain harmonic-rich voltage and current data - necessary for identifying accurate load models - for residential electrical loads. Recognizing this need, we identify and model a set of electronics-based end-use loads and DERs in an electromagnetic transients program (EMTP) tool for a residence with a single- phase split-phase supply. Further, a procedure is developed to model harmonic interactions between end-use loads connected to the same non-ideal supply voltage in a residential setting. Finally, a harmonic-rich dataset produced via the proposed procedure is utilized to identify frequency coupling matrix (FCM) based load model. Numerical results demonstrate the accuracy of the model, and explore model identifiability with limited data points.

harmonics, power quality, load modeling↗
DOE OSTI2016

A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015–2030

This report describes a comprehensive effort undertaken by the National Renewable Energy Laboratory (NREL) to understand the cost of offshore wind energy for markets in the United States. The study models the cost impacts of a range of offshore wind locational cost variables for more than 7,000 potential coastal sites in U.S. offshore wind resource areas. It also assesses the impact of more than 50 technology innovations on potential future costs for both fixed-bottom and floating wind systems. Comparing these costs to an initial site-specific assessment of local avoided generating costs, the analysis provides a framework for estimating the economic potential for offshore wind. The analysis is intended to inform a broad set of stakeholders and enable an assessment of offshore wind as part of energy development and energy portfolio planning. It provides information that federal and state agencies and planning commissions could use to inform initial strategic decisions about offshore wind developments in the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗