Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “RESOURCE ASSESSMENT”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Probabilistic Approach to Quantifying the Contribution of Variable Generation and Transmission to System Reliability: Preprint

The increasing electrical load served by variable generation (VG), such as wind and solar energy, in the United States and many other countries has stimulated an interesting line of research to better quantify the capacity value of these resources. Methods applied traditionally to thermal units based on their average outage rates do not apply to VG because of their uncertain and non-dispatchablenature. The North American Electric Reliability Corporation's Integration of Variable Generation Task Force recently released a report that highlighted the need to develop and benchmark underlying loss-of-load expectation and related metrics that reasonably and fairly calculate the contribution to planning reserves, or capacity value, of solar and wind power. As the fraction of generation comingfrom VG becomes more significant, their estimated capacity value will have a larger impact on system planning. In this paper, we provide a method to include VG in traditional probabilistic-based adequacy methods. This method has been implemented in the Renewable Energy Probabilistic Resource Assessment tool developed at the National Renewable Energy Laboratory. Through an example based on the U.S. Western Interconnection, this method is applied to assess the effect that transmission can have on resource adequacy. We also analyze the interactions between available transmission and capacity value for VG.

14 SOLAR ENERGY↗
DOE OSTI2018

Assessing variability of wind speed: comparison and validation of 27 methodologies

Because wind resources vary from year to year, the intermonthly and interannual variability (IAV) of wind speed is a key component of the overall uncertainty in the wind resource assessment process, thereby creating challenges for wind farm operators and owners. We present a critical assessment of several common approaches for calculating variability by applying each of the methods to the same 37-year monthly wind-speed and energy-production time series to highlight the differences between these methods. We then assess the accuracy of the variability calculations by correlating the wind-speed variability estimates to the variabilities of actual wind farm energy production. We recommend the robust coefficient of variation (RCoV) for systematically estimating variability, and we underscore its advantages as well as the importance of using a statistically robust and resistant method. Using normalized spread metrics, including RCoV, high variability of monthly mean wind speeds at a location effectively denotes strong fluctuations of monthly total energy generation, and vice versa. Meanwhile, the wind-speed IAVs computed with annual-mean data fail to adequately represent energy-production IAVs of wind farms. Finally, we find that estimates of energy-generation variability require 10±3 years of monthly mean wind-speed records to achieve a 90 % statistical confidence. This paper also provides guidance on the spatial distribution of wind-speed RCoV.

17 WIND ENERGY↗
DOE OSTI2023

Tidal Resource Gaps Analysis Technical Report

The tidal resource gaps project was created to address a growing body of evidence that models underpredict tidal current speeds compared to measurements at a number of the top-ranking tidal energy sites. In response, this project compared opportunistic tidal power density measurements from 16 tidal energy hot spot sites with estimates from resource assessment data to identify discrepancies. To improve the accuracy of resource estimates from model data, updated data from eight improved model simulations were obtained. Model improvements included grid refinement, domain coupling, and the use of unstructured or nested grids. New resource estimates were calculated from the updated models, and these data were used to update the tidal hot spots-a list of promising tidal energy sites around the United States.

16 TIDAL AND WAVE POWER↗
DOE OSTI2019

The Critical Role of International Standards and Certification for the Marine Energy Industry

In 2007 the International Electrotechnical Commission (IEC) initiated Technical Committee (TC) IEC/TC 114 to develop standards for marine energy converters including wave, tidal and other water current converters. Following the inaugural plenary meeting in 2008, the first IEC/TC 114 Technical Specification (TS), Terminology, was published in 2011 and significant work on the development of other TSs is ongoing. Initial conformity assessment activities began in 2011 and the Marine Energy Operational Management Committee (ME-OMC) of the IEC Renewable Energy (IECRE) System was established in 2014. An update on the status of international, consensus-based, marine energy standards and certification deliverables will be provided, including the development of Renewable Energy Test Reports (RETRs) under the IECRE. Currently, IEC/TC 114 has fourteen participating member (P-Member) countries and thirteen observer member (O-Member) countries. Nine Project Teams (PTs), two Ad-hoc Groups (AHGs), and seven Maintenance Teams (MTs) are engaged in writing new TSs, assessing information on TS applications and preparing the published TSs for release of 2nd Editions. To date, IEC/TC 114 has published ten TSs covering subjects including wave and tidal energy converter performance assessment and wave and tidal resource assessment. Additional TSs are expected to be published in 2019 covering river and ocean thermal energy converters and resources, among others. TSs developed by IEC/TC 114 and applied by the industry are essential to reduce risk and increase confidence in marine energy systems. These TSs serve as the fundamental underpinnings of international, 3rd party conformity assessment systems, such as the IECRE. The IECRE is currently comprised of fifteen Member Bodies and operates three Sectors: Marine Energy (ME), Solar Photovoltaic (PV) and Wind Energy (WE). The IECRE ME sector has reached a critical milestone in the development of 3rd party conformity assessment deliverables for the marine energy industry. With the approval of IECRE 03 Ed. 2 and OD 300-200 Ed. 1, the ME Sector established a mechanism for the acceptance of Renewable Energy Test Laboratories (RETLs) with a scope in the IEC TS 62600-200 (Power Performance Assessment of Electricity Producing Tidal Energy Converters). A three-year transition period for test laboratory self- assessment based on ISO/IEC 17025 accreditation has been implemented to encourage RETL participation in the ME Sector. A detailed review of IEC/TC 114 and ME-OMC activities is provided and the critical connection between the development and implementation of international standards, and the associated conformity assessment rules and procedures, and the continued growth and commercial success of the marine energy industry is highlighted and examined. Emphasis is given to the role standards and certification play in risk reduction, improved quality and reliability, and increased confidence for the entire range of industry stakeholders.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

The Baseline Performance Reference for Irradiance in PV System Applications

This report proposes the definition of a new baseline performance reference (BPR). The definition goes beyond existing standards pertaining to photovoltaic (PV) reference cells and devices to define the response under all possible operating conditions in the field. Field evaluations using BPR devices will be more sensitive to performance anomalies than pyranometers because they track PV system power output more closely. At the same time, they will be able to detect a broader range of performance anomalies than traditional matched reference devices, which might have matching defects. The BPR definition also opens the door to new practices in resource assessment and yield prediction. Solar resource data can be collected or modeled and validated directly as BPR irradiance, and PV system simulations based on BPR irradiance need fewer assumptions and less processing to obtain the effective irradiance on modules. As a result, lower uncertainty in yield assessments can be expected.

14 SOLAR ENERGY↗
DOE OSTI2018

CO 2 -SCREEN tool: Application to the oriskany sandstone to estimate prospective CO 2 storage resource

Here, the ability to accurately predict the CO 2 storage resource in saline formations is important to make high-level, energy-related government policy and business decisions. CO 2 -SCREEN (Storage prospeCtive Resource Estimation Excel aNalysis) is a tool developed by the United States Department of Energy – National Energy Technology Laboratory (US-DOE-NETL) to screen saline formations for prospective CO 2 storage resources. CO 2 -SCREEN uses DOE methods and equations to serve as a consistent mechanism for calculating prospective CO 2 storage resources. CO 2 -SCREEN is comprised of two files: an Excel file used for inputs and outputs and a GoldSim Player file used to run Monte Carlo simulations. CO 2 -SCREEN requires input of physical geologic parameters (i.e. thickness, porosity) as well as efficiency factor ranges (i.e. net-to-gross thickness) to calculate a mass storage estimate. An application of CO 2 -SCREEN is demonstrated here using well log data from the Oriskany Sandstone portion in Pennsylvania. The Oriskany Sandstone is divided into 20 km x 20 km grid cells in which 151 cells contain well log data. CO 2 -SCREEN calculates prospective CO 2 storage resource for each grid cell based on the well log data and uses lithology and depositional environment information for efficiency factor ranges. The Oriskany Sandstone CO 2 storage resource estimate for Pennsylvania, calculated by CO 2 -SCREEN, ranges from 0.07 to 1.28 gigatons (Gt) with a P 50 value of 0.32 Gt. This resource assessment analysis is done to demonstrate the use of CO 2 -SCREEN and results are comparable to previous studies which encourages the application of CO 2 -SCREEN to other saline formations and warrants exploring the expansion of this tool to assess the CO 2 storage resource in other formations such as shale and depleted oil and gas reservoirs.

03 NATURAL GAS↗
DOE OSTI2021

Wave resource characterization at regional and nearshore scales for the U.S. Alaska coast based on a 32-year high-resolution hindcast

A wave resource characterization was performed for the southern coast of Alaska based on a 32-year hindcast covering the period from 1979 to 2010. The characterization closely followed International Electrotechnical Commission (IEC) Technical Specifications. An unstructured-grid Simulating WAves Nearshore (SWAN) model, which had an approximate spatial resolution of 300 m within 30 km from the nearest shoreline, was developed. Additionally, extensive model validation and error characterization was performed. The model was found to perform well with an average absolute percent error of 8.6% in significant wave height, averaged over 18 buoys. Statistics for the six IEC wave resource parameters were calculated and aggregated at 20 km from shore to quantify the incident wave power and its variability at a regional scale. The southern coast of the Aleutian Archipelago was found to have the most available wave energy in the region. A nearshore resource assessment was performed by evaluating resource hotspots located 1 km from shore. More than 900 nearshore stations had an Optimum Hotspot Identifier value of 5 (kW/m) at diverse water depths, thereby positioning Alaska as a promising location for wave energy development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Facilitation of Community Energy Plans (DOE-AVCP Final Report)

The purpose of the Cooperative Agreement was based on the mission the two agencies have in common. The Association of Village Council Presidents (AVCP) has been a critical player in accomplishing what Alaskan Native communities need since 1964 with various programs including energy assistance. The AVCP/DOE Partnership enabled AVCP to assist 10 of 56 remote Alaska Native villages in the development of a community-led Community Energy Plan. These plans have empowered the 10 Tribes to address their own energy development needs. The community energy plans that AVCP assisted the communities with identified the community’s energy vision, goals, and a high level project timeline of each goal. The plans also include the technical potential, resource assessment, grant and technical assistance resources. The AVCP/DOE Partnership also enabled AVCP to provide tribal leaders and staff from the 56 Federally-Recognized Tribes with information about the policies and programs of the Department, support regional workshops and forums, and provide directed technical assistance for initial energy project support.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2011

Macroalgae Analysis: A National GIS-based Analysis of Macroalgae Production Potential (Summary Report and Project Plan)

The overall project objective is to conduct a strategic analysis to assess the state of macroalgae as a feedstock for biofuels production. The objective in FY11 is to develop a multi-year systematic national assessment to evaluate the U.S. potential for macroalgae production using a GIS-based assessment tool and biophysical growth model developed as part of these activities. The initial model development for both resource assessment and constraints was completed and applied to the demonstration areas. The model for macroalgal growth was extended to the EEZ off the East and West Coasts of the United States, and a plan to merge the findings for an initial composite assessment was developed. In parallel, an assessment of land-based, port, and offshore infrastructure needs based on published and grey literature was conducted. Major information gaps and challenges encountered during this analysis were identified. Also conducted was an analysis of the type of local, state, and federal requirements that pertain to permitting land-based facilities and nearshore/offshore culture operations

09 BIOMASS FUELS↗
DOE OSTI2021

Wind Plant Performance Prediction Benchmark Phase 1 (Technical Report)

Financial risk resulting from the uncertainty associated with developing, owning, and operating wind power plants remains a barrier to reducing the levelized cost of energy (LCOE). On average, modern wind power plants in the U.S. underperform their expected annual energy output by 3.5-4.5% , with many underperforming by over 10%. To compensate for this uncertainty, investors require a larger return on investment (ROI) and apply "knock-down" factors that mask much of the underlying sources of uncertainty. Wind energy projects thus have reduced access to low-cost capital. Furthermore, operating wind plants often take a simple approach to estimating operations & maintenance (O&M) costs (e.g. straight-line estimates based on similar plants), which can eat into profits. To overcome these issues, the wind industry must improve the models they use for estimating wind plant performance and operations. An industry consortium (IC) requested that the National Renewable Energy Laboratory (NREL) lead a Department of Energy (DOE) working group to benchmark the accuracy of wind power plant energy predictions against real operational data. The IC was also motivated by DOE and NREL's potential to characterize systematic energy underperformance, identify sources of uncertainty, and explore root causes. The Wind Plant Performance Prediction (WP3) project was created out of this request, and this report represents the successful completion of Phase 1 of the WP3 project. During the project, wind plant owners provided both pre-construction and operational data to NREL. The pre-construction data was provided to wind resource assessment (WRA) consultants so they could conduct energy yield assessments (EYA). NREL took all of the completed EYAs, along with the operational data, and conducted an operational assessment to benchmark the EYA results against actual operational data. Given the large amounts of sensitive data required for this effort, as well as historical opposition to sharing data within industry, successful completion of Phase 1 represents an unprecedented milestone for industry data sharing. To improve the accuracy and confidence of pre-construction EYAs, wind plant owners and investors need better, more certain, energy yield predictions. The WP3 Benchmark Project is an industry-driven response to this reality. For the first time, industry has taken the important step of working together at scale, sharing valuable operational data with DOE and NREL in order to investigate the sources of bias and uncertainty in these energy estimates. This IC provides wind plant preconstruction and operational data to NREL in an organized and documented fashion and provides guidance and feedback as needed. The IC also provides introspection of the design of experiment, key metrics of success, data challenges, analysis best practices, and quality of results.

17 WIND ENERGY↗
DOE OSTI2018

Evaluating the Methodologies of Assessing Long-Term Variability of Wind Speed

Wind resources vary from year to year, and hence the long-term variability of wind speed is a key component of the overall uncertainty in the wind resource assessment process. Because wind-turbine power generation is highly sensitive to wind speed, the variability of wind resources has important implications for renewable power production. As many approaches of calculating the spread of distribution exist, we aim to identify the methods most appropriate for all types of wind-speed distributions. We review and compare various methods, such as the Coefficient of Variation (CoV) and the Robust Coefficient of Variation (RCoV). Because the length of data available also affects the resultant spread, we determine the minimum length of wind-speed records to quantify variability accurately. We use the net monthly energy production of 607 wind farms in the United States from the Energy Information Administration and the wind speeds of the MERRA-2 reanalysis dataset from NASA. After filtering the energy data via linear regression, we assess the accuracy of the variability calculations by comparing the correlations between the wind-speed variability estimates and the variabilities of wind-farm power productions. Results from statistically robust and resistant methods yield higher correlations between wind-speed and energy-production variabilities, as robust methods do not assume Gaussian wind-speed distributions and resistant methods are insensitive to wind speed extremes. We recommend RCoV to systematically estimate long-term variability, with 10 years of monthly mean data to achieve 90% statistical confidence. We will also provide generalized guidance on how to estimate long-term variability including case studies where we identify regions with high wind speeds and low wind-speed RCoVs, such as the Great Plains and the Upper Midwest in central United States.

17 WIND ENERGY↗
DOE OSTI2012

U.S. Department of Energy Workshop Report: Solar Resources and Forecasting

This report summarizes the technical presentations, outlines the core research recommendations, and augments the information of the Solar Resources and Forecasting Workshop held June 20-22, 2011, in Golden, Colorado. The workshop brought together notable specialists in atmospheric science, solar resource assessment, solar energy conversion, and various stakeholders from industry and academia toreview recent developments and provide input for planning future research in solar resource characterization, including measurement, modeling, and forecasting.

14 SOLAR ENERGY↗