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Kilcher, Levi

Publications and source records attributed to Kilcher, Levi.

At least 37 records · Page 2

2017 Western Passage Tidal Energy Resource Characterization Measurements

These data are from tidal resource characterization measurements collected between April and July 2017 in Western Passage near Eastport, Maine, USA. The dataset contains the following four sub-datasets, each of which is described in more detail in the README.pdf. 1. A bottom-mounted Teledyne RDI Workhorse 600 kHz acoustic Doppler current profiler (ADCP) was deployed at 44.92015 N, 66.98915 W in ~50 m of water from 3 April to 18 July (106 days). Data were recorded in 6-minute increments in the ENU (East, magnetic North, Up) coordinate system with bin-mapping enabled. 2. A bottom-mounted Nortek Signature 500 kHz ADCP was deployed at 44.92192 N, 66.98913 W in ~50 m of water from 4 April to 18 July (105 days). Data were sampled and recorded at 2 Hz and recorded in the ENU (East, magnetic North, Up) coordinate system. 3. Between those stations along a cross-channel transect, a Stable Tidal Turbulence Mooring (STTM) positioned ~10 m above the seabed was deployed for one week during a spring tide. The STTM was outfitted with two Nortek Vector acoustic Doppler velocimeters equipped with inertial motion units (ADVs), a bottom-tracking downward-looking Teledyne RDI Workhorse 600 kHz ADCP to provide motion-corrected flow and turbulence characteristics at high temporal resolution, and an upward-looking Teledyne RDI Sentinel V20 ADCP. The STTM was deployed at 44.92098 N, 66.98922 W from 24-31 May. 4. A vessel-mounted Teledyne RDI Workhorse 300 kHz ADCP collected current data along three transects over two days, 4-5 April. The data processing used DOLfYN version 0.11.2. All hdf5 files (i.e., files ending in `.h5`) contained here can be opened using that version of DOLfYN (e.g., `dat = dolfyn.load('')`). All distances are in meters (e.g., depth, range, MLLW, hab, eta, z_), and all velocities in m/s. See the DOLfYN documentation https://lkilcher.github.io/dolfyn/), and/or the Nortek and Teledyne RDI documentation for additional details. Additional details on the dataset can be found in the README.pdf, including: - Format details of each data file. - How to regenerate the data-processing (using the files in the `wp2017_processing.zip` archive).

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Turbulence Measurements from Compliant Moorings. Part I: Motion Characterization

Abstract High-fidelity measurements of turbulence in the ocean have long been challenging to collect, in particular in the middle of the water column. In response, a measurement technique has been developed to deploy an acoustic Doppler velocimeter (ADV) to midwater locations on a compliant mooring. A variety of instrumentation platforms have been deployed as part of this work with a range of dynamic motion characteristics. The platforms discussed herein include the streamlined StableMoor buoy (SMB), the Tidal Turbulence Mooring (TTM) system based on a conventional 0.9-m spherical buoy, and a 100-lb sounding weight suspended from the stern of a research vessel. The ADV head motion is computed from inertial motion sensors integrated into an ADV, and the spectra of these signals are investigated to quantify the motion of each platform. The SMB with a single ADV head mounted on the nose provided the most stable platform for the measurement of tidal turbulence in the inertial subrange for flow speeds exceeding 1.0 m s−1. The modification of the SMB with a transverse wing configuration for multiple ADVs showed a similar frequency response to the nose configuration in the horizontal plane but with large contamination in the vertical direction as a result of platform roll. While the ADV motion on the TTM was significant in the horizontal directions, the vertical motion of this configuration was the most stable of all configurations tested. The sounding weight measurements showed the greatest motion at the ADV head but are likely to be influenced by both prop-wash and vessel motion.

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An Assessment of the Economic Potential of Offshore Wind in the United States from 2015 to 2030

Output data from an NREL report entitled "An Assessment of the Economic Potential of Offshore Wind in the United States from 2015 to 2030" (NREL/TP-6A20-67675), which analyzes the spatial variation of levelized cost of energy (LCOE) and levelized avoided cost of energy (LACE) to understand the economic potential of fixed-bottom and floating offshore wind technologies across more than 7,000 U.S. coastal sites between 2015 and 2030.

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An Assessment of the Economic Potential of Offshore Wind in the United States from 2015 to 2030

This study describes an assessment of the spatial variation of levelized cost of energy (LCOE) and levelized avoided cost of energy to understand the economic viability of fixed-bottom and floating offshore wind technologies across major U.S. coastal areas between 2015 and 2030. In particular, this study offers insights into the available offshore wind resource by region at different levels of LCOE and an assessment of the economically viable resource capacity in the United States.

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Marine Hydrokinetic Energy Site Identification and Ranking Methodology Part I: Wave Energy

Marine hydrokinetic energy is a promising and growing piece of the renewable energy sector that offers high predictability and additional energy sources for a diversified energy economy. This report investigates the market opportunities for wave energy along the U.S. coastlines. It is part one of a two-part investigation into the United State's two largest marine hydrokinetic resources (wave and tidal). Wave energy technology is still an emerging form of renewable energy for which large-scale, grid-connected project costs are currently poorly defined. Ideally, device designers would like to know the resource conditions at economical project sites so they can optimize device designs. On the other hand, project developers need detailed device cost data to identify sites where projects are economical. That is, device design and siting are, to some extent, a coupled problem. This work describes a methodology for identifying likely deployment locations based on a set of criteria that wave energy experts in industry, academia, and national laboratories agree are likely to be important factors for all technology types. This work groups the data for the six criteria into 'locales' that are defined as the smaller of either the local transmission grid or a state boundary. The former applies to U.S. islands (e.g., Hawaii, American Samoa) and rural villages (e.g., in Alaska); the latter applies to states in the contiguous United States. These data are then scored from 0 to 10 according to scoring functions that were developed with input from wave energy industry and academic experts. The scores are aggregated using a simple product method that includes a weighting factor for each criterion. This work presents two weighting scenarios: a long-term scenario that does not include energy price (weighted zero) and a near term scenario that includes energy price. The aggregated scores are then used to produce ranked lists of likely deployment locales. In both scenarios, Hawaii and the Pacific Northwest (northern California, Oregon, and Washington) rank at the top of the lists. Hawaii ranks highest in the near-term scenario because it has high energy costs. In the long-term scenario, Oregon ranks highest because it has a large market and an energetic resource. Several East Coast states and Puerto Rico are also identified as potential wave energy deployment sites if technological innovations make it possible to efficiently generate electricity from the modest resource there. There are also several small-market sites in Alaska and U.S. Pacific Islands that rank particularly well in the near-term analysis due to their high energy prices. These locations may represent opportunities to demonstrate economical wave energy conversion as a stepping-stone to larger markets. Several factors that will affect wave project costs and siting have not been considered here -- including permitting constraints, conflicting use, seasonal resource variability, extreme event likelihood, and distance to ports -- because consistent data are unavailable or technology-independent scoring could not be identified. As the industry continues to mature and converge around a subset of device archetypes with well-defined costs, more precise investigations of project siting that include these factors will be possible. For now, these results provide a high-level guide pointing to the regions where markets and resource will one day support commercial wave energy projects.

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Marine Hydrokinetic Energy Site Identification and Ranking Methodology Part II: Tidal Energy

Marine hydrokinetic energy is a promising and growing piece of the renewable energy sector that offers high predictability and additional energy sources for a diversified energy economy. This report investigates the market opportunities for tidal energy along the U.S. coastlines. It is part one of a two-part investigation into the United States' two largest marine hydrokinetic resources (wave and tidal). Tidal energy technology is still an emerging form of renewable energy for which large-scale grid-connected project costs are currently poorly defined. Ideally, device designers would like to know the resource conditions at economical project sites so they can optimize device designs. On the other hand, project developers need detailed device cost data to identify sites where projects are economical. That is, device design and siting are, to some extent, a coupled problem. This work describes a methodology for identifying likely deployment locations based on a set of criteria that tidal energy experts in industry, academia, and national laboratories agree are likely to be important factors for all technology types. Several factors that will affect tidal project costs and siting have not been considered here -- including permitting constraints, conflicting use, seasonal resource variability, extreme event likelihood, and distance to ports -- because consistent data are unavailable or technology-independent scoring could not be identified. As the industry continues to mature and converge around a subset of device archetypes with well-defined costs, more precise investigations of project siting that include these factors will be possible. For now, these results provide a high-level guide pointing to the regions where markets and resource will one day support commercial tidal energy projects.

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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↗

Early Market Opportunity MHK Energy Site Identification - Wave and Tidal Resources

This data was compiled for the 'Early Market Opportunity Hot Spot Identification' project. The data and scripts included were used in the 'MHK Energy Site Identification and Ranking Methodology' Reports (see resources below). The Python scripts will generate a set of results--based on the Excel data files--some of which were described in the reports. The scripts depend on the 'score_site' package, and the score site package depends on a number of standard Python libraries (see the score_site install instructions).

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Advanced Energy Harvesting Control Schemes for Marine Renewable Energy Devices

The Advanced Energy Harvesting Control Schemes for Marine Renewable Energy Devices (Project) investigated, analyzed and modeled advanced turbine control schemes with the objective of increasing the energy harvested by hydrokinetic turbines in turbulent flow. Ocean Renewable Power Company (ORPC) implemented and validated a feedforward controller to increase power capture; and applied and tested the controls on ORPC’s RivGen® Power Systems in Igiugig, Alaska. Assessments of performance improvements were made for the RivGen® in the Igiugig environment and for ORPC’s TidGen® Power System in a reference tidal environment. Annualized Energy Production (AEP) and Levelized Cost of Energy (LCOE) improvements associated with implementation of the recommended control methodology were made for the TidGen® Power System in the DOE reference tidal environment. System Performance Advancement (SPA) goals were selected for the project. SPA targets were to improve Power to Weight Ratio (PWR) and system Availability, with the intention of reducing Levelized Cost of Electricity (LCOE). This project focused primarily reducing in PWR. Reductions in PWR of 25.5% were achieved. Reductions of 20.3% in LCOE were achieved. This project evaluated four types of controllers which were tested in simulation, emulation, a laboratory flume, and the field. The adaptive Kω2 controller performs similarly to the non-adaptive version of the same controller and may be useful in tidal channels where the mean velocity is continually evolving. Trends in simulation were largely verified through experiments, which also provided the opportunity to test assumptions about turbine responsiveness and control resilience to varying scales of turbulence. Laboratory experiments provided an essential stepping stone between simulation and implementation on a field-scale turbine. Experiments also demonstrated that using “energy loss” as a metric to differentiate between well-designed controllers operating at an optimal tip-speed ratio set-point is difficult, which anticipated the outcome from field experiments. The clear message is that the feedforward Kω2 controller out-performs the feedback controllers in almost all aspects and modes of evaluation. The controllers proved a substantial improvement over the baseline performance of the TidGen® turbine, in terms of energy capture. The effects of noise-contaminated angular velocity signals were investigated and validated by simulation as an explanation for the performance limitations observed for TidGen® turbine operations in Eastport, Maine. Measurements of loads performed as part of the laboratory testing indicate that there are limited differences in average axial thrust force between control architectures. This suggests that none of the control strategies are likely to substantially affect loads on the turbine support structure. Velocity measurements during the ORPC RivGen® turbine deployment at Igiugig, Alaska, in 2014 were used to assess the variability of the river flow. Results suggest that the river flow is approximately steady, in the mean sense, at any particular location in the river, with random turbulent fluctuations that are around 10% of the mean flow. The mean flow in the center channel of the river is 2.5 m/s, with reductions near the riverbanks and in the shallows. Spectral analysis and lagged correlation results indicate that temporal fluctuations at a given point are dominated by large scale fluctuations, such that measurements at the turbine location are just as useful for inflow control implementation as upstream measurements. At this site, and likely at many other river sites, flow is generally steady at a given location, but flow varies dramatically between locations, particularly laterally across the river. The primary result is that a lateral change in position of a few meters results in changes to flow speed that far exceed the turbulence fluctuations at any given location. The turbulence is dominated by long time scales. Following final system tests, the RivGen® device was submerged and each evaluated controller was tested across a range of gain/set point values and filter configurations for a minimum of 5 minutes, with longer runs attempted for well-performing cases. In addition to testing controllers during the 2015 deployment season, LGL Alaska Research Associates Inc. (LGL) performed a fish monitoring study in compliance with Alaska Department of Fish and Game fisheries habitat permit for the project. During the season, LGL reviewed 10% of 179 one-hour blocks of footage (6 minutes on the hour) and documented a total of over 1200 fish in the vicinity of the RivGen® device, including over 800 salmon smolt and over 350 adult salmon. No evidence of adverse effects including passage delay by upstream migrating salmon was noted. This is an important result for future deployments and has a direct impact on commercial system designs.

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Measuring Turbulence from Moored Acoustic Doppler Velocimeters. A Manual to Quantifying Inflow at Tidal Energy Sites

This work details a methodology for measuring hub height inflow turbulence using moored acoustic Doppler velocimiters (ADVs). This approach is motivated by the shortcomings of alternatives. For example, remote velocity measurements (i.e., from acoustic Doppler profilers) lack sufficient precision for device simulation, and rigid tower-mounted measurements are very expensive and technically challenging in the tidal environment. Moorings offer a low-cost, site-adaptable and robust deployment platform, and ADVs provide the necessary precision to accurately quantify turbulence.

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Admiralty Inlet Advanced Turbulence Measurements: May 2015

This data is from measurements at Admiralty Head, in Admiralty Inlet (Puget Sound) in May of 2015. The measurements were made using Inertial Motion Unit (IMU) equipped ADVs mounted on a 'StableMoor' (Manufacturer: DeepWater Buoyancy) buoy and a Tidal Turbulence Mooring (TTM). These platforms position ADV heads above the seafloor to make mid-depth turbulence measurements. The inertial measurements from the IMU allows for removal of mooring motion in post processing. The mooring and buoy motion has been removed from the stream-wise and vertical velocity signals (u, w). The lateral (v) velocity has some 'persistent motion contamination' due to mooring sway. The TTM was deployed with one ADV, it's position was: 48 09.145', -122 41.209' The StableMoor was deployed twice, the first time it was deployed in 'wing-mode' with two ADVs ('Port' and 'Star') at: 48 09.166', -122 41.173' The second StableMoor deployment was in 'Nose' mode with one ADV at: 48 09.166', -122 41.174' Units ----- - Velocity data (_u, urot, uacc) is in m/s. - Acceleration (Accel) data is in m/s^2. - Angular rate (AngRt) data is in rad/s. - The components of all vectors are in 'ENU' orientation. That is, the first index is True East, the second is True North, and the third is Up (vertical). - All other quantities are in the units defined in the Nortek Manual. Motion correction and rotation into the ENU earth reference frame was performed using the Python-based open source DOLfYN library (http://lkilcher.github.io/dolfyn/). Details on motion correction can be found there. Additional details on TTM measurements at this site can be found in the included Marine Energy Technology Symposium paper.

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MULTI-SCALE COHERENT TURBULENCE AT TIDAL ENERGY SITES

Turbulence is known to affect the performance and survivability of tidal turbines, yet characterization of turbulence in the field remains limited. Here, we refine and demonstrate a new approach to turbulence measurements, in which an array of multiple Acoustic Doppler Velocimeters (ADV) is suspended above the seabed at the hub height of a tidal turbine. These measurements provide information on the intensity, structure, and coherence of turbulence across the scale of a turbine rotor (< 10 m). Deployment of multiple moorings expands the analysis to array scales (> 10 m). Motion correction of the moored ADV data is essential to this approach and is verified using the turbulent kinetic energy spectra. Additional measurements include a bottommounted 5-beam Acoustic Doppler Current Profiler, from which scales can be assessed using the velocities a separation distances along a given beam. These methods are demonstrated with data collected at the site of the Snohomish PUD pilot project in Admiralty Inlet, Puget Sound, WA (USA). Coherent motion is found to be largely isotropic, such that coherence is high only at scales less than the advective length scale or the water depth, whichever is less.

ADV↗

Admiralty Inlet Advanced Turbulence Measurements: June 2014

This data is from measurements at Admiralty Head, in Admiralty Inlet (Puget Sound) in June of 2014. The measurements were made using Inertial Motion Unit (IMU) equipped ADVs mounted on Tidal Turbulence Mooring's (TTMs). The TTM positions the ADV head above the seafloor to make mid-depth turbulence measurements. The inertial measurements from the IMU allows for removal of mooring motion in post processing. The mooring motion has been removed from the stream-wise and vertical velocity signals (u, w). The lateral (v) velocity has some 'persistent motion contamination' due to mooring sway. Each ttm was deployed with two ADVs. The 'top' ADV head was positioned 0.5m above the 'bottom' ADV head. The TTMs were placed in 58m of water. The position of the TTMs were: ttm01 : (48.1525, -122.6867) ttm01b : (48.15256666, -122.68678333) ttm02b : (48.152783333, -122.686316666) Deployments TTM01b and TTM02b occurred simultaneously and were spaced approximately 50m apart in the cross-stream direction. Units ----- - Velocity data (_u, urot, uacc) is in m/s. - Acceleration (Accel) data is in m/s^2. - Angular rate (AngRt) data is in rad/s. - The components of all vectors are in 'ENU' orientation. That is, the first index is True East, the second is True North, and the third is Up (vertical). - All other quantities are in the units defined in the Nortek Manual. Motion correction and rotation into the ENU earth reference frame was performed using the Python-based open source DOLfYN library (http://lkilcher.github.io/dolfyn/). Details on motion correction can be found there. Additional details on TTM measurements at this site can be found in the included Marine Energy Technology Symposium paper.

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