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At least 721 records · Page 40

wtDigiTwin (Wind Turbine Digital Twin) [SWR-21-15]

This wind turbine digital twin software (wtDigiTwin) provides a digital twin solution for wind turbine applications. The focus of wtDigiTwin is to estimate loads, motions and environmental conditions for an operating wind turbine. The program uses supervisory control and data acquisition (SCADA) measurements as inputs, together with a wind turbine model. The wind industry is currently challenged by the high cost of operation and maintenance. These costs could be mitigated if component failures are predicted, but such predictions are difficult unless the turbines are equipped with expensive measuring devices. The alternative is to use a digital twin such as wtDigiTwin to estimate the necessary signals. wtDigiTwin can perform online prediction of signals that are otherwise not measured, using a limited set of reliable measurements and a physics-based model. The predicted signals can be used in applications that have direct cost benefits: 1) real-time estimation of the fatigue consumption of key components of the wind turbine; 2) root cause analyses and failure detections ; 3) lifetime reassessments ; 4) improvements to follow-on designs. The current version provides examples to estimate wind speed, thrust, torque, tower-top position, and tower loads on an onshore wind turbine using the following measurements tower top acceleration, generator torque, pitch, and rotational speed. The model combines a linear state-space model, a wind speed estimator, and a Kalman filter algorithm that integrates measurements with the state model to perform state estimations. The state space model is obtained either using OpenFAST linearizations, or using the yams package provided with the software.

Branlard, Emmanuel↗

PV O&M Cost Model [SWR-21-14]

This tool presents a method for calculating costs associated with the operation and maintenance (O&M) of photovoltaic (PV) systems. The tool compiles details regarding the cost and frequency of multiple O&M services to estimate annual O&M costs ($/year) for each year of an analysis period, the net present value ($) of life cycle costs accumulated over the analysis period, and the reserve account amount ($) that might be required to fund unexpected repairs. The reserve account includes parts inventory and is important for providing a source of funds to make repairs quickly and avoid lost production. This method allows a detailed selection of services to perform based on system size, market served (e.g., residential, commercial, or utility), type and configuration of system components (e.g., micro-, string, or central inverter), and site and environmental conditions (e.g., pollen, bird populations) which is an improvement over simple per unit valuations of O&M costs ($/kW/year). This model also distinguishes costs that vary from year to year and increase at different rates over time as modeled by heuristic failure distributions (e.g., Weibull or Lognormal distribution) based on actuarial data for many of the services.

Walker, Andy↗

Science Applications of Phased Array Radars

Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near instantaneous sampling of the atmosphere with flexible beam forming, multi-functionality, low operational and maintenance costs, and without mechanical inertia limitations. These PAR features are transformative compared to those offered by our current reflector-based meteorological radars. The integration of PARs into meteorological research has the potential to revolutionize the way we observe the atmosphere. The rate of adoption of PARs in research will depend on many factors including i) the need to continue educating the scientific community on the full technical capabilities and trade-offs of PARs through an engaging dialogue with the science and engineering communities and ii) the need to communicate the breadth of scientific bottlenecks that PARs can overcome in atmospheric measurements and the new research avenues that are now possible using PARs in concert with other measurement systems. The former is the subject of a companion article that focuses on PAR technology while the latter is the objective here.

54 ENVIRONMENTAL SCIENCES↗

USE OF COGNITIVE WORK ANALYSIS IN DEVELOPING A NUCLEAR POWER PLANT NEW STATE VISION

The United States nuclear industry is in need of identifying and implementing a new strategy that will lower operating and maintenance costs while maintaining safety for existing plants. The industry must also have a clear and strategic vision of their transformative new state that focuses on ways in which technologies can be integrated to maximize the benefits of technology and people. While there are ongoing efforts in this area, this work discusses how the use of cognitive work analysis may further support these current efforts. This paper provides an overview of cognitive work analysis, as well as the state of current new state development efforts. The use of cognitive work analysis, as a tool to enhance existing practices, is presented in ensuring that the development of a new state vision is systematic and complete.

99 GENERAL AND MISCELLANEOUS↗

Comparative Thermal Performance of Downdraft and Updraft Forced Convection in a Representative 3x3 PWR Fuel Assembly

A plethora of theoretical, numerical, and experimental investigations have relied on updraft forced convection of operating fuel assemblies for light water-cooled fuel assemblies. The inertial scales of turbulence in updraft flow through PWR fuel assemblies are primarily influenced by upstream mixing patterns within the lower plenum and consequent acceleration of flow through the lower core and support plates into the bottom nozzles. In secondary heat transfer applications such as steam generation, recuperation and economization, downdraft flow is utilized to retrieve maximal sensible heat from the primary coolant. Sub-channel volumes aligned vertically could also benefit from force of gravity in developing turbulent flows. A large reduction in pumping power, associated cost of operation and maintenance, and improved balance of plant is suggested to be possible through downdraft forced convection in PWR-type reactors. This article explores the impact of downdraft forced convection within the well-studied 3x3 sub-channel within a range of 20 hydraulic diameters across a representative intermediate flow mixing grid. A range of inflow Reynolds number from 10,000 to 60,000 is simulated over a range of equivalent heat loads of 20% to 100% spanning 20 hydraulic diameters. The fuel rods are represented in true geometric detail, and a constant-value heat profile is assumed for the investigation. At each inflow condition, the turbulence intensity, peak velocity, local heat transfer coefficient, peak wall heat flux, and peak wall temperature are collected across the sub-channel and compared with an updraft configuration evaluated at the same conditions.

Rao, Vivek↗

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Closed Loop Geothermal Working Group: GeoCLUSTER App, Subsurface Simulation Results, and Publications

To better understand the heat production, electricity generation performance, and economic viability of closed-loop geothermal systems in hot-dry rock, the Closed-Loop Geothermal Working Group -- a consortium of several national labs and academic institutions has tabulated time-dependent numerical solutions and levelized cost results of two popular closed-loop heat exchanger designs (u-tube and co-axial). The heat exchanger designs were evaluated for two working fluids (water and supercritical CO2) while varying seven continuous independent parameters of interest (mass flow rate, vertical depth, horizontal extent, borehole diameter, formation gradient, formation conductivity, and injection temperature). The corresponding numerical solutions (approximately 1.2 million per heat exchanger design) are stored as multi-dimensional HDF5 datasets and can be queried at off-grid points using multi-dimensional linear interpolation. A Python script was developed to query this database and estimate time-dependent electricity generation using an organic Rankine cycle (for water) or direct turbine expansion cycle (for CO2) and perform a cost assessment. This document aims to give an overview of the HDF5 database file and highlights how to read, visualize, and query quantities of interest (e.g., levelized cost of electricity, levelized cost of heat) using the accompanying Python scripts. Details regarding the capital, operation, and maintenance and levelized cost calculation using the techno-economic analysis script are provided. This data submission will contain results from the Closed Loop Geothermal Working Group study that are within the public domain, including publications, simulation results, databases, and computer codes. GeoCLUSTER is a Python-based web application created using Dash, an open-source framework built on top of Flask that streamlines the building of data dashboards. GeoCLUSTER provides users with a collection of interactive methods for streamlining the exploration and visualization of an HDF5 dataset. The GeoCluster app and database are contained in the compressed file geocluster_vx.zip, where the "x" refers to the version number. For example, geocluster_v1.zip is Version 1 of the app. This zip file also contains installation instructions. **To use the GeoCLUSTER app in the cloud, click the link to "GeoCLUSTER on AWS" in the Resources section below. To use the GeoCLUSTER app locally, download the geocluster_vx.zip to your computer and uncompress this file. When uncompressed this file comprises two directories and the geocluster_installation.pdf file. The geo-data app contains the HDF5 database in condensed format, and the GeoCLUSTER directory contains the GeoCLUSTER app in the subdirectory dash_app, as app.py. The geocluster_installation.pdf file provides instructions on installing Python, the needed Python modules, and then executing the app.

15 GEOTHERMAL ENERGY↗

Techno-Economic Simulation Results Using dGeo for EGS-Based District Heating in the Northeastern United States

This dataset presents the results of techno-economic simulations performed using the Distributed Geothermal Market Demand Model (dGeo) to evaluate the feasibility of Enhanced Geothermal Systems (EGS)-based district heating in the Northeastern United States. Developed by the National Renewable Energy Laboratory (NREL), dGeo is a geospatially resolved, bottom-up modeling framework designed to explore the deployment potential of geothermal distributed energy resources. The dataset, created as part of the Cornell EGS Ground-Truthing Project, provides census tract-level data that includes inputs and outputs such as thermal demand, road length, energy prices, geothermal system sizing, annual energy contributions from geothermal and natural gas peaking boilers, system capital costs (CAPEX), operation and maintenance costs (OPEX), and the levelized cost of heat (LCOH). Key simulation parameters include geothermal gradients, measured well depths, production temperatures, and district heating piping lengths based on S1400 neighborhood road lengths. The simulations assume a target bottom hole temperature of 80C and the development of new district heating networks in each census tract.

15 GEOTHERMAL ENERGY↗

TEAMER: MADWEC Techno-Economic Analysis

The objective of this project was for the facility to conduct a techno-economic assessment (TEA) of the Maximal Asymmetric Drag Wave Energy Converter (MADWEC), developed by the University of Massachusetts Dartmouth (UMass Dartmouth). MADWEC is used for powering remote monitoring and Autonomous Underwater Vehicle (AUV) charging systems compared to other existing power supply options. The assessment estimates capital expenditures (CapEx), operational expenditures (OpEx), and power performance for 18 scenarios with the purpose of identifying key cost drivers, comparing total system cost, and comparing the power performance of the power supply options in terms of required installed capacity and estimated theoretical annual energy performance. The 18 assessed scenarios include two end-uses: 1) AUV charging and 2) offshore remote monitoring); three power sources: 1) MADWEC), 2) photovoltaic (PV) solar buoy, 3) and traditional battery swapping); and three locations; 1) nearshore, 2) far-offshore, and 3) high-latitude). In addition, other project goals included developing high level installation, operation, and maintenance plans for each scenario. The techno-economic model, created in Microsoft Excel, estimates CapEx, OpEx, and the power performance of each power supply source. The model has a dynamic format that allows custom inputs to accommodate future changes to the systems being assessed. This is a TEA for the MADWEC project, TEAMER RFTS 7 (request for technical support) program.

16 TIDAL AND WAVE POWER↗

Energy Storage Cost and Performance Database

In support of the U.S. Department of Energy’s Energy Storage Grand Challenge, Pacific Northwest National Laboratory is applying its rich history of battery research and development to provide the U.S. Department of Energy (DOE) and industry with a guide to current energy storage costs and performance metrics for various technologies. Cost and performance metrics for individual technologies track the following to provide an overall cost of ownership for each technology: cost to procure, install, and connect an energy storage system; associated operational and maintenance costs; and end-of-life costs. These metrics are intended to support DOE and industry stakeholders in making sound decisions about future R&D directions and priorities that move the United States closer to its goal of energy independence. The technologies currently being evaluated are lithium-ion [lithium iron phosphate (LFP) and nickel manganese cobalt (NMC)] batteries, vanadium redox flow batteries, lead-acid batteries, zinc-based batteries, hydrogen energy storage, pumped storage hydropower, gravitational energy storage, compressed air energy storage, and thermal energy storage.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Developing a low-cost renewable supply of hydrogen with high-temperature electrochemistry

Abstract Producing inexpensive hydrogen using electricity to split water or to extract hydrogen from hydrocarbon compounds is a two-sided coin: one side is obtaining and exploiting low-cost, emissions-free energy sources while the opposite side is establishing low-cost robust, durable, and efficient materials for the conversion processes. This article explores the materials needed for water splitting electrolysis, electrochemical abstraction of hydrogen from light alkanes, and looping thermal-chemical reaction processes that typically form and then dissociate an acid compound in a two- or three-step process. The focus is on the R&D needs of materials that are used for high-temperature electrochemistry, focusing on solid-oxide, ion-conducting cell materials sets (SOECs). To exploit the availability of low-cost electricity, these materials must stand up to cyclic operations. They also must be durable for years of service to reduce operating and maintenance costs, and they must achieve high conversion efficiencies to avoid large, energy-intensive recycle loops. Graphical abstract

Boardman, Richard D.↗

pvOps: a Python package for empirical analysis of photovoltaic field data

The purpose of pvOps is to support empirical evaluations of data collected in the field related to the operations and maintenance (O&M) of photovoltaic (PV) power plants. pvOps presently contains modules that address the diversity of field data, including text-based maintenance logs, current-voltage (IV) curves, and timeseries of production information. The package functions leverage machine learning, visualization, and other techniques to enable cleaning, processing, and fusion of these datasets. These capabilities are intended to facilitate easier evaluation of field patterns and extraction of relevant insights to support reliability-related decision-making for PV sites. The open-source code, examples, and instructions for installing the package through PyPI can be accessed through the GitHub repository.

14 SOLAR ENERGY↗

U.S. Effort Support to Examinations at Fukushima - Meeting Notes with Updated Information Requests (FY2020)

Much is still not known about the end-state of core materials in each unit that was operating on March 11, 2011 at the Fukushima Daiichi Nuclear Power Station (Daiichi). Information obtained from Daiichi is required to inform Decontamination and Decommissioning (D&D) activities, improving the ability of the Tokyo Electric Power Company Holdings, Incorporated (TEPCO Holdings) to characterize potential hazards and to ensure the safety of workers involved with cleanup activities. This document, which has been updated to include Fiscal Year 2020 (FY2020) information, summarizes results from a U.S. effort to review Daiichi information and extract insights to enhance the safety of existing and future nuclear power plant designs. This U.S. effort, which was initiated in 2014 by the Department of Energy Office of Nuclear Energy (DOE-NE), is completed by a group of experts in reactor safety and plant operations that identify examination needs and evaluate recent Daiichi examination data to address these needs. Since its inception, comprehensive annual reports were issued that document significant safety insights being obtained in areas of special emphasis: system and component performance, radionuclide surveys and sampling, debris end-state location, combustible gas effects, and plant operations and maintenance. In addition to reducing uncertainties related to severe accident modeling progression, these insights have and continue to be used to update guidance for severe accident prevention, mitigation, and emergency planning. Reduced uncertainties in modeling the events at Daiichi improve the realism of reactor safety evaluations that inform future D&D activities. For FY2020, it was decided that the program would gain more benefit from a more concise report that emphasizes new information and insights that affect prior findings and recommendations from the U.S. experts participating in this effort. A key aspect of prior U.S. efforts, the updated list of information requests, is included in this letter report to ensure that they are transmitted to organizations within Japan. In addition, findings and associated recommendations are provided regarding information presented by TEPCO Holdings, Nuclear Damage Compensation and Decommissioning Facilitation Corporation (NDF), and the Japan Atomic Energy Agency (JAEA). This letter report also continues to emphasize how information obtained from the affected reactors at Daiichi has been and will continue to be used to update severe accident management strategies, improve maintenance activities (especially in areas of radiation protection) and reduce uncertainties in systems analysis code models. In addition, recommendations are included that would expand the use of this information to provide insights regarding maintenance, radiation protection, design, and siting activities of existing and new reactors.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Abstract for CRADA among the National Energy Technology Laboratory, National Technology & Engineering Solutions of Sandia, The Regents of the University of California, and Tri-State Generation and Transmission Association, Inc. (AGMT-0855)

The objective of this CRADA is to facilitate cooperation among Tri-State Generation and Transmission Association, Inc. and collaborators of the Institute for the Design of Advanced Energy Systems (IDAES), including the National Energy Technology Laboratory, Sandia National Laboratories, and Lawrence Berkeley National Laboratory. The goal is to develop steady-state and dynamic optimization models of power plants. Tri-State will provide data related to the design and operation of power plants, which will be used to validate the IDAES team’s models and tools. These models will provide insight into potential improvements which could improve heat rate, reduce operations and maintenance costs, and/or improve reliability of the existing fleet of coal plants in the United States.

20 FOSSIL-FUELED POWER PLANTS↗

Performance of Photovoltaic Systems Recorded by Open Solar Performance and Reliability Clearinghouse (oSPARC)

This article analyzes data from 2,200 photovoltaic systems collected through the Open Solar Performance and Reliability Clearinghouse (oSPARC, 2018) to draw conclusions about the measured performance of the systems, how it compares to expectations, and how wide the variation is. The systems range in size from 2 kW to 1,200 kW, with an average size of 229 kW, and were activated in oSPARC between March 13, 2007 and Dec. 16, 2016. Data regarding co-incident insolation and ambient temperature are provided through the oSPARC platform for the population of systems and summarized here. Results indicate that - after correcting for measured insolation, derating for temperature, and correcting for balance-of-system efficiency and degradation as a result of system age - the performance ratio of these 2,200 systems averages 91.7%. Adjusting for known parameters such as age and efficiency, the ratio would ideally be 100%. Thus, the 'underperformance' of this system sample is measured at 9.3% and suggests that as much as 9.3% could be recovered if the systems were caused to perform as expected through optimal operation and maintenance. Efforts to correlate the performance ratio with environmental conditions resulted in very low coefficients of regression, but the method already normalizes for actual site insolation and temperature, which are responsible for much of the site-specific variation. An exemption is air-quality nonattainment areas, where the measured performance of plants averages 86.9% and is consistently lower than the average of 95.9% in areas that are in compliance with air quality standards, likely because of particulate matter such as diesel soot exacerbating the soiling rate.

14 SOLAR ENERGY↗

Benchmarking Utility-Scale PV Operational Expenses and Project Lifetimes: Results from a Survey of U.S. Solar Industry Professionals

This paper draws on a survey of solar industry professionals and other sources to clarify trends in the expected useful life and operational expenditure (OpEx) of utility-scale photovoltaic (PV) plants in the United States. Solar project developers, sponsors, long-term owners, and consultants have increased project-life assumptions over time, from an average of ~21.5 years in 2007 to ~32.5 years in 2019. Current assumptions range from 25 years to more than 35 years depending on the organization; 17 out of 19 organizations surveyed or reviewed use 30 years or more. Levelized, lifetime OpEx estimates have declined from an average of ~$\$$35/kW DC -yr for projects built in 2007 to an average of ~$\$$17/kW DC -yr in 2019. Across 13 sources, the range in average lifetime OpEx for projects built in 2019 is broad, from $\$$13 to $\$$25/kW DC -yr. Operations and maintenance (O&M) costs—one component of OpEx—have declined precipitously in recent years, to $\$$5-8/kW DC -yr in many cases. Property taxes and land lease costs are highly variable across sites, but on average are—together—of similar magnitude. Other OpEx line items include security, insurance, and asset management. Given 2007-2009 values for not only project life and OpEx but also other drivers of the levelized cost of energy (LCOE, excluding the investment tax credit), the LCOE for utility-scale PV projects built from 2007 through 2009 averaged $\$$305 /MWh. Using 2019 values for all parameters yields an average LCOE of $\$$51 /MWh. The decline in LCOE from $\$$305 /MWh to $\$$51 /MWh was predominantly caused by reductions in up-front expenditures (and, to a much lesser extent, by changes in capacity factors, financing costs, and tax rates), but 9% ($\$$22 /MWh) of the overall decline is due to improvements in project life and OpEx. Project life extensions and OpEx reductions have had similarly sized impacts on LCOE over this period, at $\$$11 /MWh each. Had project life and OpEx not improved over the last decade, LCOE in 2019 would have instead been $\$$73 /MWh—43% higher. Given the limited quantity and comparability of previously available data on these cost drivers, the data and trends presented here may inform assumptions used by electric system planners, modelers, and analysts. The results may also provide useful benchmarks to the solar industry, helping developers and assets owners compare their expectations for project life and OpEx with those of their peers.

14 SOLAR ENERGY↗

Metal Alloy and RHEA Additive Manufacturing for Nuclear Energy and Aerospace Applications

An open-literature search was conducted to consider the current status of the additive manufacturing (AM) industry with respect to metal alloys and refractory high entropy alloys (RHEAs). Key areas of interest include methodologies and applications that are suitable for the nuclear and aerospace industries, as well as other industrial applications. We investigated various promising 3D metal technologies, with emphasis on cost, operation, throughput, maintenance, and output volume size. In addition, technical issues and the current status of the metal printing market are summarized. The project scope also included the manufacturing of open-literature RHEA test coupons at Sandia's laser engineered net shape (LENS) machine.

36 MATERIALS SCIENCE↗

Addressing Soiling: From Interface Chemistry to Practicality

Natural soiling has reduced the energy output of photovoltaic (PV) systems since the technology was first used. Projecting even a small average annual soiling loss translates to billions of dollars in annual lost revenue worldwide. Production losses due to soiling can be very high in some locations, substantially increasing the levelized cost of electricity (LCOE) due to lost power production, increased operating and maintenance costs, and/or increased finance cost due to the uncertainty. Furthermore, although soiling has been discussed in the literature for more than 70 years, solutions to many problems are still needed. The National Renewable Energy Laboratory (NREL) is working with the PV industry to develop the tools/knowledge so that the effects of soiling can be predicted for different environmental conditions and cost-effective mitigation can be implemented. For this project, NREL performed a number of research and development tasks/subtasks in the following general areas to 1) predict PV module soiling losses based on environmental factors at a PV installation and from its energy production data, 2) quantitatively measure the adhesion forces to understand the physics enabling soiling, and 3) develop related standards on PV module coatings and artificial soiling. At the inception of the project, the PV industry considered these efforts to be the most important and immediate soiling issues that we could address to have the highest impact on LCOE. These focused efforts have led to outstanding accomplishments that have been communicated and very well received by the community.

14 SOLAR ENERGY↗