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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 OSTI2017

WIND Toolkit Offshore Summary Dataset

This dataset contains summary statistics for offshore wind resources for the continental United States derived from the Wind Integration National Datatset (WIND) Toolkit. These data are available in two formats: GDB - Compressed geodatabases containing statistical summaries aligned with lease blocks (aliquots) stored in a GIS format. These data are partitioned into Pacific, Atlantic, and Gulf resource regions. HDF5 - Statistical summaries of all points in the offshore Pacific, Atlantic, and Gulf offshore regions. These data are located on the original WIND Toolkit grid and have not been reassigned or downsampled to lease blocks. These data were developed under contract by NREL for the Bureau of Oceanic Energy Management (BOEM).

17 WIND ENERGY↗

DOE OSTI2014

Economic and Technical Feasibility Study of Utility-Scale Wind Generation for the New York Buffalo River and South Buffalo Brownfield Opportunity Areas

Through the RE-Powering America's Land initiative, the economic and technical feasibility of utilizing contaminated lands in the Buffalo, New York, area for utility-scale wind development is explored. The study found that there is available land, electrical infrastructure, wind resource, and local interest to support a commercial wind project; however, economies of scale and local electrical markets may need further investigation before significant investment is made into developing a wind project at the Buffalo Reuse Authority site.

17 WIND ENERGY↗

DOE OSTI2023

Storage Effectiveness in Enabling Variable Generation and Avoiding Fossil Emissions

In this paper, we investigate the relationship between storage and variable generation (VG) and how they can be used to replace fossil-fired technologies and their associated emissions. Unlike fossil generation, VG relies on variable resources (wind, solar, etc.), and the timing of these resources does not always match system needs, creating challenges both in terms of when energy is delivered and the reliability of that delivery. These challenges are typically addressed in one of two ways: paring VG with storage to provide firmed, time-shiftable energy or by building additional VG throughout an interconnection so that adequate amounts of VG are always available. The choice of how much to build of each (storage and VG) is typically an economic one. In this work, we focus on the former case - investigating the role of storage in bringing renewables onto the grid.

13 HYDRO ENERGY↗

DOE OSTI2021

New methods to improve the vertical extrapolation of near-surface offshore wind speeds

Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in US Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air–sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10 m wind speeds from spatially resolved satellite-based wind atlases.

17 WIND ENERGY↗

NASA NTRS1982

Wind turbine siting: A summary of the state of the art

The process of siting large wind turbines may be divided into two broad steps: site selection, and site evaluation. Site selection is the process of locating windy sites where wind energy development shows promise of economic viability. Site evaluation is the process of determining in detail for a given site the economic potential of the site. The state of the art in the first aspect of siting, site selection is emphasized. Several techniques for assessing the wind resource were explored or developed in the Federal Wind Energy Program. Local topography and meteorology will determine which of the techniques should be used in locating potential sites. None of the techniques can do the job alone, none are foolproof, and all require considerable knowledge and experience to apply correctly. Therefore, efficient siting requires a strategy which is founded on broad based application of several techniques without relying solely on one narrow field of expertise.

Hiester, T. R.↗

DOE OSTI2012

Impacts from Deployment Barriers on the United States Wind Power Industry: Overview & Preliminary Findings (Presentation)

Regardless of cost and performance some wind projects are unable to proceed to commissioning as a result of deployment barriers. Principal deployment barriers in the industry today include: wildlife, public acceptance, access to transmission, and radar. To date, methods for understanding these non-technical barriers have failed to accurately characterize the costs imposed by deployment barriersand the degree of impact to the industry. Analytical challenges include limited data and modeling capabilities. Changes in policy and regulation, among other factors, also add complexity to analysis of impacts from deployment barriers. This presentation details preliminary results from new NREL analysis focused on quantifying the impact of deployment barriers on the wind resource of the UnitedStates, the installed cost of wind projects, and the total electric power system cost of a 20% wind energy future. In terms of impacts to wind project costs and developable land, preliminary findings suggest that deployment barriers are secondary to market drivers such as demand. Nevertheless, impacts to wind project costs are on the order of $100/kW and a substantial share of the potentiallydevelopable windy land in the United States is indeed affected by deployment barriers.

17 WIND ENERGY↗

DOE OSTI2023

Development of a Metocean Reference Site near the Massachusetts and Rhode Island Wind Energy Areas

This project developed the first long-term U.S.-based offshore MetOcean Reference Site (MORS-1) by capitalizing on a unique combination of one of the few existing publicly available offshore wind energy metocean observational campaigns in the United States and the only existing research-grade offshore fixed tower. Data collected at MORS-1 has facilitated improved wind resource assessments, improved short-term power production estimates, and reduced costs for sensor validation and calibration efforts, which translate into reduced overall wind energy project risk and cost for developers. Now operational, MORS-1 serves the needs of both industry and researchers using a nonprofit, joint industry-academic partnership model. Led by the Woods Hole Oceanographic Institution, the MORS-1 development effort focused on creating both a recognized organizational structure that will ensure support of the MORS-1 by the wider wind energy industry and research community, and a highly validated data collection and sensor validation facility that will serve as the premier location for cost- and uncertainty-reducing resource characterization and research efforts.

17 WIND ENERGY↗

DOE OSTI2012

20% Wind by 2030: Overcoming the Challenges (Final Technical Report)

The funds allocated through the Wind Powering America (WPA) grant were utilized by the State of Montana to support broad outreach activities communicating the benefits and opportunities of increased wind energy and transmission development. The challenges to increased wind development were also clearly communicated with the understanding that a clearer comprehension of the challenges would be beneficial in overcoming the obstacles to further development. The ultimate purpose of these activities was to foster the increased development of Montana's rich wind resources through increased public acceptance and wider dissemination of technical resources.

17 WIND ENERGY↗

DOE OSTI2012

Washoe Wisk'e'em Project

The Washoe Tribe Wiskem Project (Project) was a Congressionally Directed Project identified for funding in the Energy and Water Development and Related Agencies Appropriations Act, 2010. The Project focused on installing up to four small vertical wind turbines at designated locations on Tribal lands to offset energy costs for the Tribe. The Washoe Tribe will use and analyze data collected from the wind turbines to better understand the wind resource.

17 WIND ENERGY↗

DOE OSTI2021

Extreme Wind and Waves in U.S. East Coast Offshore Wind Energy Lease Areas

The Outer Continental Shelf along the U.S. east coast exhibits abundant wind resources and is now a geographic focus for offshore wind deployments. This analysis derives and presents expected extreme wind and wave conditions for the sixteen lease areas that are currently being developed. Using the homogeneous ERA5 reanalysis dataset it is shown that the fifty-year return period wind speed (U50) at 100 m a.s.l. in the lease areas ranges from 29.2 to 39.7 ms -1 . After applying corrections to account for spectral smoothing and averaging period, the associated pseudo-point U50 estimates are 34 to 46 ms -1 . The derived uncertainty in U50 estimates due to different distributional fitting is smaller than the uncertainty associated with under-sampling of the interannual variability in annual maximum wind speeds. It is shown that, in the northern lease areas, annual maximum wind speeds are generally associated with intense extratropical cyclones rather than cyclones of tropical origin. Extreme wave statistics are also presented and indicate that the 50-year return period maximum wave height may substantially exceed 15 m. From this analysis, there is evidence that annual maximum wind speeds and waves frequently derive from the same cyclone source and often occur within a 6 h time interval.

16 TIDAL AND WAVE POWER↗

DOE OSTI2013

Land-Based Wind Potential Changes in the Southeastern United States (Presentation)

Recent advancements in utility-scale wind turbine technology and pricing have vastly increased the potential land area where turbines can be deployed in the United States. This presentation quantifies the new developable land potential (e.g., capacity curves), visually identifies new areas for possible development (e.g., new wind resource maps), and begins to address deployment barriers to wind in new areas for modern and future turbine technology.

17 WIND ENERGY↗

DOE OSTI2014

New National Wind Potential Estimates for Modern and Near-Future Turbine Technologies (Poster)

17 WIND ENERGY↗

DOE OSTI2014

Wind Technologies & Evolving Opportunities (Presentation)

This presentation covers opportunities for wind technology; wind energy market trends; an overview of the National Wind Technology Center near Boulder, Colorado; wind energy price and cost trends; wind turbine technology improvements; and wind resource characterization improvements.

17 WIND ENERGY↗

DOE OSTI2018

Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development

Optimal wind farm locations require a strong and reliable wind resource and access to transmission lines. As onshore and offshore wind energy grows, preferred locations become saturated with numerous wind farms. An upwind wind farm generates 'wake effects' (decreases in downwind wind speeds) that undermine a downwind wind farm's power generation and revenues. Here we use a diverse set of analysis tools from the atmospheric science, economic and legal communities to assess costs and consequences of these wake effects, focusing on a West Texas case study. We show that although wake effects vary with atmospheric conditions, they are discernible in monthly power production. In stably stratified atmospheric conditions, wakes can extend 50+ km downwind, resulting in economic losses of several million dollars over six years for our case study. However, our investigation of the legal literature shows no legal guidance for protecting existing wind farms from such significant impacts.

17 WIND ENERGY↗

DOE OSTI2022

The sensitivity of the Fitch wind farm parameterization to a three-dimensional planetary boundary layer scheme

Abstract. Wind plant wake impacts can be estimated with a number of simulation methodologies, each with its own fidelity and sensitivity to model inputs. In turbine-free mesoscale simulations, hub-height wind speeds often significantly vary with the choice of a planetary boundary layer (PBL) scheme. However, the sensitivity of wind plant wakes to a PBL scheme has not been explored because, as of the Weather Research and Forecasting model v4.3.3, wake parameterizations were only compatible with one PBL scheme. We couple the Fitch wind farm parameterization with the new NCAR 3DPBL scheme and compare the resulting wakes to those simulated with a widely used PBL scheme. We simulate a wind plant in pseudo-steady states under idealized stable, neutral, and unstable conditions with matching hub-height wind speeds using two PBL schemes: MYNN and the NCAR 3DPBL. For these idealized scenarios, average hub-height wind speed losses within the plant differ between PBL schemes by between −0.20 and 0.22 m s−1, and correspondingly, capacity factors range between 39.5 %–53.8 %. These simulations suggest that PBL schemes represent a meaningful source of modeled wind resource uncertainty; therefore, we recommend incorporating PBL variability into future wind plant planning sensitivity studies as well as wind forecasting studies.

17 WIND ENERGY↗

DOE OSTI2015

Wind Measurements from Arc Scans with Doppler Wind Lidar

When defining optimal scanning geometries for scanning lidars for wind energy applications, we found that it is still an active field of research. Our paper evaluates uncertainties associated with arc scan geometries and presents recommendations regarding optimal configurations in the atmospheric boundary layer. The analysis is based on arc scan data from a Doppler wind lidar with one elevation angle and seven azimuth angles spanning 30° and focuses on an estimation of 10-min mean wind speed and direction. When flow is horizontally uniform, this approach can provide accurate wind measurements required for wind resource assessments in part because of its high resampling rate. Retrieved wind velocities at a single range gate exhibit good correlation to data from a sonic anemometer on a nearby meteorological tower, and vertical profiles of horizontal wind speed, though derived from range gates located on a conical surface, match those measured by mast-mounted cup anemometers. Uncertainties in the retrieved wind velocity are related to high turbulent wind fluctuation and an inhomogeneous horizontal wind field. Moreover, the radial velocity variance is found to be a robust measure of the uncertainty of the retrieved wind speed because of its relationship to turbulence properties. It is further shown that the standard error of wind speed estimates can be minimized by increasing the azimuthal range beyond 30° and using five to seven azimuth angles.

17 WIND ENERGY↗

DOE OSTI2019

Oregon Offshore Wind Site Feasibility and Cost Study

The work performed in this Oregon study assessed the present and future costs of floating offshore wind technology deployment in the state of Oregon at commercial scale. It is widely recognized that floating offshore wind energy technology will be needed if significant offshore wind deployment in the Pacific region is to occur. The study was performed by the National Renewable Energy Laboratory (NREL) and its subcontractors and funded by the Bureau of Ocean Energy Management (BOEM). It was based, in part, on assumptions and analysis from an NREL report titled 'A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015-2030' (Beiter et al. 2016), which supported the 'National Offshore Wind Strategy' (Gilman et al. 2016). The strategy builds on the previous DOE Wind Vision Study Scenario of 86 gigawatts (GW) of offshore wind deployed by 2050 in the United States. Under the Wind Vision scenario, 20% (17.2 GW installed capacity) of the nation's total offshore wind in 2050 comes from the Pacific coastal states (DOE 2015), which will require the development of floating wind technologies. Although most offshore development to date has been in depths of 50 meters (m) or less, 97% of Oregon's offshore wind resource is in water depths greater than 60 m, where floating wind is assumed to be needed. Floating offshore wind technology is still in its nascent stage of development but is advancing toward commercialization in both Europe and Asia. In this report, we use available floating prototype costs, pilot-scale costs, and commercial-scale costs from fixed-bottom offshore wind projects to model and analyze the cost of floating wind for five hypothetical wind sites in Oregon using specific geographical and utility grid information.

17 WIND ENERGY↗