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Wind resource characterization and assessment was identified through a 2014 DOE Request for Information as one of the top four Distributed Wind Portfolio R&D focus areas. This presentation provides an overview of NREL's distributed wind resource assessment project work: the background and motivations for the work; an assessment of the current state of the industry; and challenges, barriers, and high-payoff opportunities.
Numerical weather prediction (NWP) models have been widely used for wind resource assessment. Model runs with higher spatial resolution are generally more accurate, yet extremely computational expensive. An alternative approach is to use data generated by a low resolution NWP model, in conjunction with statistical methods. In order to analyze the accuracy and computational efficiency of different types of NWP-based wind resource assessment methods, this paper performs a comparison of three deterministic and probabilistic NWP-based wind resource assessment methodologies: (i) a coarse resolution (0.5 degrees x 0.67 degrees) global reanalysis data set, the Modern-Era Retrospective Analysis for Research and Applications (MERRA); (ii) an analog ensemble methodology based on the MERRA, which provides both deterministic and probabilistic predictions; and (iii) a fine resolution (2-km) NWP data set, the Wind Integration National Dataset (WIND) Toolkit, based on the Weather Research and Forecasting model. Results show that: (i) as expected, the analog ensemble and WIND Toolkit perform significantly better than MERRA confirming their ability to downscale coarse estimates; (ii) the analog ensemble provides the best estimate of the multi-year wind distribution at seven of the nine sites, while the WIND Toolkit is the best at one site; (iii) the WIND Toolkit is more accurate in estimating the distribution of hourly wind speed differences, which characterizes the wind variability, at five of the available sites, with the analog ensemble being best at the remaining four locations; and (iv) the analog ensemble computational cost is negligible, whereas the WIND Toolkit requires large computational resources. Future efforts could focus on the combination of the analog ensemble with intermediate resolution (e.g., 10-15 km) NWP estimates, to considerably reduce the computational burden, while providing accurate deterministic estimates and reliable probabilistic assessments.
Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems has shown similar advantages for more recent assessments. While a full 12 months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with 1 or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the US. On average, correction with even 1 month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes and relative errors within 0.22 m s −1 and 4 percentage points of the median bias magnitudes and relative errors achieved using the standard 12 months of data for correction. However, in cases when the shortest observational periods (1 to 2 months) used for correction are not well correlated with the overlapping ERA5 reference, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of correlation when using 6 months or less of observational data for correction. Translating the analysis to wind energy, median relative errors in the capacity factor are on average within 10 % using 1 month of training. If the observation period used for correction is not well correlated with the reference data, however, misrepresentation of the observed capacity factor can be substantial. The risk associated with poor correlation between the observed and reference datasets decreases with increasing training period length. In the worst-correlation scenarios, the median capacity factor relative errors from using 1, 3, and 6 months are within 47 %, 26 %, and 16 %, respectively.
The US offshore wind industry is maturing with several projects in various stages of development. These projects require site wind and environmental data before and during operation. Conventional techniques such as fixed-bottom meteorological towers present economical and permitting challenges for the US. Floating Light Detection and Ranging (LiDAR) buoys offer significant advantages including reduced costs, less permitting, and reusability. This paper presents the validation of the first floating LiDAR buoy in Northeast US waters. The buoy, named DeepCLiDAR, includes a LiDAR, ecological monitoring sensors, and metocean sensors. A three-phase LiDAR validation plan was executed, and its results are presented. The objective of the validation plan was to verify the accuracy of measurements made by the LiDAR buoy in wave environments against an unmoving reference wind measurement. Due to a lack of reference met masts, the use of a LiDAR on land as a baseline reference was implemented for validation. Comparison to a reference LiDAR instead of a traditional meteorological tower was a unique approach required in the Northeast US waters due to the absence of a reference fixed-bottom meteorological tower in the region at the time of this study. The testing included a comparison of wind speed measurements made by the buoy deployed 15 km offshore from the mainland and a land-based reference LiDAR located on a nearby island. This paper presents the methodology and results of this program, which indicate favorable agreement. This was the first such validation program in the Northeast USA which is now seeing rapid development of offshore wind.
This fact sheet describes wind resource assessments and mapping conducted at the NWTC.
Alternate Energy Solutions, Inc. (“AESWR”) was engaged by the Town of Brookston (“Brookston”) to assemble, erect and maintain one 60 m XHD meteorological tower manufactured by NRG Systems, Inc.; for monitoring, recording and evaluating collected wind data. It is the opinion of AESWR staff that study results support the development of a wind turbine project at the Bol Family Farm provided: a) additional land is leased for the project; b) project construction costs are controlled; and c) a prudent power purchase agreement is negotiated with a power take-off entity. We believe that a project having an aggregate nameplate rating sized from 6.0 MW to 20 MW would be appropriate for this location. We recommend 100-125 acres of land per installed MW be used as a general rule for acquiring wind energy land lease agreements, total land lease holdings to be acquired would then approach 750 acres to 2,500 acres.
This presentation describes the current standards for conducting a national-scale wind resource assessment for power generation, along with the risk/benefit considerations to be considered when beginning a wind resource assessment. The presentation describes changes in turbine technology and viable wind deployment due to more modern turbine technology and taller towers and shows how the Philippines national wind resource assessment evolved over time to reflect changes that arise from updated technologies and taller towers.
The U.S. Environmental Protection Agency (EPA) launched the RE-Powering America's Land initiative to encourage development of renewable energy (RE) on potentially contaminated land and mine sites. EPA is collaborating with the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) to evaluate RE options at Naval Station (NAVSTA) Newport in Newport, Rhode Island where multiple contaminated areas pose a threat to human health and the environment. Designated a superfund site on the National Priorities List in 1989, the base is committed to working toward reducing the its dependency on fossil fuels, decreasing its carbon footprint, and implementing RE projects where feasible. The Naval Facilities Engineering Service Center (NFESC) partnered with NREL in February 2009 to investigate the potential for wind energy generation at a number of Naval and Marine bases on the East Coast. NAVSTA Newport was one of several bases chosen for a detailed, site-specific wind resource investigation. NAVSTA Newport, in conjunction with NREL and NFESC, has been actively engaged in assessing the wind resource through several ongoing efforts. This report focuses on the wind resource assessment, the estimated energy production of wind turbines, and a survey of potential wind turbine options based upon the site-specific wind resource.
This paper proposes a methodology to include the wind power forecasting ability, or 'forecastability,' of a site as a design criterion in wind resource assessment and wind power plant design stages. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology is adopted to maximize the capacity factor of a wind power plant. The 1-hour-ahead persistence wind power forecasting method is used to characterize the forecastability of a potential wind power plant, thereby partially quantifying the integration cost. A trade-off between the maximum capacity factor and the forecastability is investigated.
In support of the U.S. Department of Energy (DOE) Wind and Water Power Technologies Office (WWPTO) goals, researchers from DOE's National Renewable Energy Laboratory (NREL), National Wind Technology Center (NWTC) are investigating the Distributed Wind Resource Assessment (DWRA) process, which includes pre-construction energy estimation as well as turbine site suitability assessment. DWRA can have a direct impact on the Wind Program goals of maximizing stakeholder confidence in turbine performance and safety as well as reducing the levelized cost of energy (LCOE). One of the major components of the LCOE equation is annual energy production. DWRA improvements can maximize the annual energy production, thereby lowering the overall LCOE and improving stakeholder confidence in the distributed wind technology sector by providing more accurate predictions of power production. Over the long term, one of the most significant benefits of a more defined DWRA process could be new turbine designs, tuned to site-specific characteristics that will help the distributed wind industry follow a similar trajectory to the low-wind-speed designs in the utility-scale industry sector. By understanding the wind resource better, the industry could install larger rotors, capture more energy, and as a result, increase deployment while lowering the LCOE. a direct impact on the Wind Program goals of maximizing stakeholder confidence in turbine performance and safety as well as reducing the levelized cost of energy (LCOE). One of the major components of the LCOE equation is annual energy production. DWRA improvements can maximize the annual energy production, thereby lowering the overall LCOE and improving stakeholder confidence in the distributed wind technology sector by providing more accurate predictions of power production. Over the long term, one of the most significant benefits of a more defined DWRA process could be new turbine designs, tuned to site-specific characteristics that will help the distributed wind industry follow a similar trajectory to the low-wind-speed designs in the utility-scale industry sector. By understanding the wind resource better, the industry could install larger rotors, capture more energy, and as a result, increase deployment while lowering the LCOE.
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Offshore wind resource assessments for the conterminous U.S. and Hawai’i have been developed before, but Alaska’s offshore wind resource has never been rigorously assessed. Alaska, with its vast coastline, presents ample potential territory in which to build offshore wind farms, but significant challenges have thus far limited Alaska’s deployment of utility-scale wind energy capacity to a modest 62 MW (or approximately 2.7% of the state’s electric generation) as of this writing, all in land-based wind farms. This study provides an assessment of Alaska’s offshore wind resource, the first such assessment for Alaska, using a 14-year, high-resolution simulation from a numerical weather prediction and regional climate model. This is the longest-known high-resolution model data set to be used in a wind resource assessment. Widespread areas with relatively shallow ocean depth and high long-term average 100-m wind speeds and estimated net capacity factors over 50% were found, including a small area near Alaska’s population centers and the largest transmission grid that, if even partially developed, could provide the bulk of the state’s energy needs. The regional climate simulations were validated against available radiosonde and surface wind observations to provide the confidence of the model-based assessment. The model-simulated wind speed was found to be skillful and with near-zero average bias (−0.4–0.2 m s−1) when averaged over the domain. Small sample sizes made regional validation noisy, however.
This report presents a state-of-the-art wind resource data set produced by the National Renewable Energy Laboratory (NREL) for the California Pacific Outer Continental Shelf (OCS). This data set replaces NREL's Wind Integration National Dataset (WIND) Toolkit for the California OCS, which was produced and released publicly in 2013 and is currently the principal data set used by stakeholders for wind resource assessment in the continental United States.
The focus of the distributed wind resource assessment (DWRA) performance framework is to first clarify the key parameters that define the wind resource for any distributed wind turbine project and then describe the loss and uncertainty factors associated with long-term performance of wind turbine projects. Next it will identify and define the key parameters required to analyze the operational performance of a project. This framework could then be the foundation for understanding the predictability of the power and revenue from a project. Improving the predictability and reliability of wind power generation and operations will reduce costs and potentially establish a framework to attract new capital to the distributed wind industry sector. This report is the first part of a research effort to investigate the impact that improved resource assessment and energy estimation for distributed wind can have on consumer confidence, cost of capital, cost of ownership and operations, perception of financial risk, and levelized cost of energy.
Under this Agreement, NREL will work with the Participant to characterize wind resource assessment measurement systems needed for the design, construction, and integration of wind energy conversion systems to produce electricity for utility grid applications. This work includes, but is not limited to, research and development of hardware and software systems needed to advance wind energy resource assessment technology at speed and scale for use by electric utilities and wind power system integrators.
NANA Regional Corporation (NRC) of northwest Alaska is located in an area with abundant wind energy resources. In 2007, NRC was awarded grant DE-FG36-07GO17076 by the US Department of Energy's Tribal Energy Program for funding a Wind Resource Assessment Project (WRAP) for the NANA region. The NANA region, including Kotzebue Electric Association (KEA) and Alaska Village Electric Cooperative (AVEC) have been national leaders at developing, designing, building, and operating wind-diesel hybrid systems in Kotzebue (starting in 1996) and Selawik (2002). Promising sites for the development of new wind energy projects in the region have been identified by the WRAP, including Buckland, Deering, and the Kivalina/Red Dog Mine Port Area. Ambler, Shungnak, Kobuk, Kiana, Noorvik & Noatak were determined to have poor wind resources at sites in or very near each community. However, all five of these communities may have better wind resources atop hills or at sites with slightly higher elevations several miles away.
India is one of the largest wind energy markets in the world. In 1986 Gujarat was the first Indian state to install a wind power project. In February 2013, the installed wind capacity in Gujarat was 3,093 MW. Due to the uncertainty around existing wind energy assessments in India, this analysis uses the Weather Research and Forecasting (WRF) model to simulate the wind at current hub heights for one year to provide more precise estimates of wind resources in Gujarat. The WRF model allows for accurate simulations of winds near the surface and at heights important for wind energy purposes. While previous resource assessments published wind power density, we focus on average wind speeds, which can be converted to wind power densities by the user with methods of their choice. The wind resource estimates in this study show regions with average annual wind speeds of more than 8 m/s.
The U.S. Environmental Protection Agency (EPA) launched the RE-Powering America's Land initiative to encourage development of renewable energy on potentially contaminated land and mine sites. EPA collaborated with the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) and the Mille Lacs Band of Chippewa Indians to evaluate the wind resource and examine the feasibility of a wind project at a contaminated site located on the Mille Lacs Indian Reservation in Minnesota. The wind monitoring effort involved the installation of a 60-m met tower and the collection of 18 months of wind data at multiple heights above the ground. This report focuses on the wind resource assessment, the estimated energy production of wind turbines, and an assessment of the economic feasibility of a potential wind project sited this site.