Wind Energy Areas Research Articles

Spatial planning offshore wind energy farms in California for mediating fisheries and wildlife conservation impacts

Achieving a blue economy will require reconciling the value of emerging ocean uses with their impacts on the seascape and sectors with historical access to marine resources and areas. To meet this challenge, we developed an analytical framework for conducting marine spatial planning through tradeoff analysis, and applied it to prospective offshore wind energy development in the ∼974 km2 Morro Bay, California, USA Wind Energy Area (WEA). We generated spatial data layers estimating MW power production and impacts on fisheries value and marine wildlife conservation (seabird and cetacean populations) from wind farm development. We then quantified each sector's response to plans of development across the WEA and inside three leases recently acquired by the energy industry for prospective development. Finally, we integrated the sector response data into an analytical framework for mitigating sector tradeoffs with novel spatial planning solutions (maps of wind farm size, location, and configuration) that optimally maximize value to the emergent energy sector (MW power) while minimizing impacts to historical (fisheries and wildlife) sectors. We found that western sites in the WEA had the highest potential power production concurrent with the lowest impact on the historical sectors, revealing the eastern lease to be less efficient at optimally balancing the sector's objectives relative to the development of the central or western leases or the optimal spatial plans identified in the tradeoff analysis. Within a lease, tradeoff analysis found spatial planning able to generate out-sized savings in fisheries value with only modest losses in MW power – for example, by avoiding development in just 5% of the eastern lease to preserve nearly half its fisheries value and still generate 95% its total power potential. Small-scale development opportunities (e.g., a pilot project) with significant power potential and no fisheries impact were also identified, in this case by placing turbines in an area in the western lease with no fisheries value and high power production potential. These plans would also have a relatively low impact on the wildlife conservation sectors, due to decreases in vulnerability levels of both seabird and cetacean populations to turbines going from east to west across the WEA. Our results can inform site evaluation and permitting processes for wind energy development in the Morro Bay WEA. We also expect the tradeoff analysis framework we developed to provide a simple and actionable analytical tool for supporting marine spatial planning of offshore wind energy and other emerging blue economy activities from a balanced perspective that values emerging uses of marine resources alongside existing socio-economic and conservation interests.

Autonomous thermal tracking reveals spatiotemporal patterns of seabird activity relevant to interactions with floating offshore wind facilities

Planning is underway for placement of infrastructure needed to begin offshore wind (OSW) energy generation along the West Coast of the United States and elsewhere in the Pacific Ocean. In contrast to the primarily nearshore windfarms currently in the North Atlantic, the seabird communities inhabiting Pacific Wind Energy Areas (WEAs) include significant populations of species that fly by dynamic soaring, a behavior dependent on wind and in which flight height increases steeply with wind speed. Therefore, a more precise and detailed assessment of their 3D airspace use is needed to better understand the potential collision risks that OSW turbines may present to these seabirds. Toward this end, a novel technology called the ThermalTracker-3D (TT3D), which uses thermal imaging and stereo vision, was developed to render high-resolution (on average within ±5 m) flight tracks and related behavior of seabirds. The technology was developed and deployed on a wind-profiling LiDAR buoy in the Humboldt WEA, located 34 to 57 km off California’s coast. During the at-sea deployment between 24 May and 13 August 2021, the TT3D successfully tracked birds moving between 10 and 500 m from the device, around the clock, and in all weather conditions; a total of 1407 detections and their corresponding 3D flight trajectories were recorded. Mean altitudes of detections ranged 6-295 m above sea level (asl). Considering the degree of overlap with anticipated rotor swept zones (RSZ), which extend 25-260 m asl, 79% of detected birds (per m3 of airspace) moved below the RSZ, 21% moved at heights overlapping the RSZ, and another 0.04% occurred at heights exceeding the RSZ. The high-resolution tracks provided valuable insight into seabird space use, especially at heights that make them vulnerable to collision during various environmental conditions (e.g., darkness, strong winds). Observations made by the TT3D will be useful in filling critical knowledge gaps related to estimating collision and avoidance between seabirds and OSW facilities in the Pacific and elsewhere. Future research will focus on enhancing the TT3D’s identification capabilities to the lowest taxon through validation studies and artificial intelligence, further contributing to seabird conservation efforts associated with OSW.

Ecological indicators to monitor offshore wind interactions with fisheries resources

Abstract Offshore wind development (OWD) will generate much needed renewable energy, but it will also introduce several stressors to the marine ecosystem. Therefore, there is a need to develop information-rich monitoring programs to assess ecological impacts and inform solutions to mitigate adverse effects. This paper evaluates potential indicators of OWD impacts on fisheries resources that could be considered for monitoring programs, including indices of aggregate biomass, sensitive species, fish size, and trophic dynamics. Short-term (year-to-year) variability and the direction and strength of long-term trends were explored at both the scale of the US Southern New England wind energy area (WEA) and at the scale of the Southern New England region. The majority of candidate OWD indicators exhibited substantial temporal variability at either the WEA scale, the region scale, or both, highlighting the importance of addressing temporal variability in the design and duration of monitoring programs. Recommendations are provided to advance informative monitoring approaches both in the USA and elsewhere where such approaches are urgently needed. Among these is a recommendation for a minimum of 3–5 years of baseline data collection and continued monitoring for the lifetime of the wind project. This will enable an understanding of the temporal structure inherent to the time series of ecological indicators measured so that OWD impacts can be disentangled from those caused by other ecosystem pressures.

Acoustic presence and demographics of sperm whales (Physeter macrocephalus) off southern New England and near a US offshore wind energy area

Abstract Construction in the southern New England wind energy area (WEA), a large-scale offshore wind farm on the east coast of the United States, started in June 2023. Baseline data was collected from 2020 to 2022, with six passive acoustic recorders (SoundTraps) deployed at shallow (<60 m) sites in the vicinity of Nantucket Shoals and Cox’s Ledge. Data were analysed for sperm whale presence, and demographic composition was assessed using interclick intervals. Presence varied by site, season, and year. Sperm whales were detected year-round but the majority (78%) of days with acoustic occurrences were between May and August. Three demographic classes (putative social groups, adult males, and midsize animals) were detected across multiple seasons, with social groups detected most frequently. Sound propagation tests were conducted at two sites and predicted detection ranges within 20–40 km indicate that sperm whales were likely in proximity to the WEA. These results provide a baseline that will be used to assess ongoing sperm whale presence, especially that of social groups which may be more sensitive to disturbance. This study highlights why sperm whales, classed as endangered in US waters, should be considered in mitigation plans and permitting efforts for offshore wind energy.

An Eulerian perspective on habitat models of striped bass occurrence in an offshore wind development area

Abstract Offshore wind energy development, including along the US Atlantic coast, frequently occurs within important multispecies migration corridors; however, assessing the regional factors influencing the local Eulerian occurrence of these species poses a significant challenge. We used generalized models incorporating lagged variables and hierarchical formulations to account for temporal dependencies and hierarchical structure that occur outside the narrower frame of a sampled project area. Acoustically tagged striped bass, the most frequently detected species regionally, were sampled using a gridded acoustic telemetry array in the Maryland Wind Energy Area of the US Mid-Atlantic Bight. The daily occurrence of striped bass was better explained by broad-scale sea surface temperature warming patterns than by local concurrent environmental conditions, demonstrating the importance of drivers that occur across the wider spatial scales of migration. Weekly residency patterns were similar between tagging origin groups, suggesting that Chesapeake Bay, Hudson River, Delaware Bay, and other Northwest Atlantic populations migrate synchronously through the Southern Mid-Atlantic Bight and are similarly influenced by sea surface temperature. Our study demonstrates that adapting an Eulerian approach to include lagged variables can improve regional assessments of fish on the move until richer Lagrangian insights become possible through future coordination of telemetry arrays throughout the Mid-Atlantic flyway.

AIRU-WRF: A physics-guided spatio-temporal wind forecasting model and its application to the U.S. Mid Atlantic offshore wind energy areas

The reliable integration of wind energy into modern-day electricity systems heavily relies on accurate short-term wind forecasts. We propose a spatio-temporal model called AIRU-WRF (short for the AI-powered Rutgers University Weather Research &Forecasting), which combines numerical weather predictions (NWPs) with local observations in order to make wind speed forecasts that are short-term (minutes to hours ahead), and of high resolution, both spatially (site-specific) and temporally (minute-level). In contrast to purely data-driven methods, we undertake a “physics-guided” machine learning (ML) approach which captures salient physical features of the local wind field without the need to explicitly solve for those physics, including: (i) modeling wind field advection and diffusion via physically meaningful kernel functions, (ii) integrating exogenous predictors that are both meteorologically relevant and statistically significant; and (iii) linking the multi-type NWP biases to their driving mesoscale weather conditions. Tested on real-world data from the U.S. Mid Atlantic where several offshore wind projects are in-development, AIRU-WRF achieves notable improvements, in terms of both wind speed and power, relative to various forecasting benchmarks including physics-based, hybrid, statistical, and deep learning methods.

Investigation of the importance of criteria in potential wind farm sites via machine learning algorithms

Wind energy has received greater attention than other energy resources due to its superior economics, low greenhouse gas emissions, and limitless wind resources. As a result, wind energy capacity has significantly increased, and the selection of the best locations for wind farms is an issue that has received extensive research. A significant step toward environmentally responsible land use planning is the site suitability assessment for the placement of wind farms. This study was conducted to determine the best locations for wind farms and to prioritize different locations and alternatives in the West of Turkey by using Maximum Entropy (MaxEnt) and Logistic Regression (LR) Methods based on Geographic Information Systems (GIS). Eight criteria were selected for creating the suitability map: air density, power density, wind speed, capacity factor, elevation, slope, aspect, and land use. Both methods were effective at choosing locations for wind farms because all the results were statistically significant in the consistency tests. MaxEnt calculated the potential wind energy fields with high accuracy and reliability with 0.915 AUC and LR multiple R square values of 0.883. Compared to the current installed power values, the MaxEnt analysis results were more consistent with the recent status. İzmir has been calculated as the province with the highest potential for wind energy area of 663 km2 by MaxEnt and 620.4 km2 by LR.

Year-long buoy-based observations of the air–sea transition zone off the US west coast

Abstract. Two buoys equipped with Doppler lidars owned by the US Department of Energy (DOE) were deployed off the coast of California in autumn of 2020 by Pacific Northwest National Laboratory. The buoys collected data for an entire annual cycle at two offshore locations proposed for offshore wind development by the Bureau of Ocean Energy Management. One of the buoys was deployed approximately 50 km off the coast near Morro Bay in central California in 1100 m of water. The second buoy was deployed approximately 40 km off Humboldt County in northern California in 625 m of water. The buoys provided the first-ever continuous measurements of the air–sea transition zone off the coast of California. The atmospheric and oceanographic characteristics of the area and estimates of annual energy production at both the Morro Bay and Humboldt wind energy areas show that both locations have a high wind energy yield and are prime locations for future floating offshore wind turbines. This article provides a description and comprehensive analysis of the data collected by the buoys, and a final post-processed dataset is uploaded to a data archive maintained by the DOE. Additional analysis was conducted to show the value of the data collected by the DOE buoys. All post-processed data from this study are available on the Wind Data Hub website: https://a2e.energy.gov/data# (last access: 14 September 2023). Near-surface, wave, current, and cloud datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783807 (Krishnamurthy and Sheridan, 2023b) and https://doi.org/10.21947/1959715 (Krishnamurthy and Sheridan, 2023a), respectively. Lidar datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783809 (Krishnamurthy and Sheridan, 2023d) and https://doi.org/10.21947/1959721 (Krishnamurthy and Sheridan, 2023c), respectively.

Acoustic ecology of harbour porpoise (Phocoena phocoena) between two U.S. offshore wind energy areas

Abstract Offshore wind energy is set to develop rapidly in waters off the east coast of the United States. There is considerable overlap between areas proposed for offshore wind development and harbour porpoise habitats in the Gulf of Maine (GOM) and Southern New England waters. Baseline data on the occurrence and foraging activity of porpoises was collected using 10 echolocation-click detectors (F-PODs) from 2020 to 2022. Porpoises were present year-round in the GOM with peak detections in the summer and fall. In line with previous reported distribution patterns, porpoise occurrence in Southern New England was high in fall, winter and spring, but porpoises were largely absent in the summer. One site in the GOM, Mount Desert Rock (MDR), was an anomaly as porpoise detections here were highest in the winter. On average, foraging was identified in 29% of all porpoise detections, with the most foraging occurring at MDR (53%). Results from generalized additive models suggest that time of year, hour of day, lunar illumination, and temperature are significant contributors to porpoise presence and/or foraging effort. European studies show that harbour porpoises exhibit behavioural changes, disruption of foraging and displacement due to wind energy development. Therefore, early identification of areas of importance, mitigating impacts, and monitoring changes is essential for the protection of this species in US waters.

Establishing baselines for predicting change in ambient sound metrics, marine mammal, and vessel occurrence within a US offshore wind energy area

Abstract Evaluating potential impacts on marine animals or increased sound levels resulting from offshore wind energy construction requires the establishment of baseline data records from which to draw inference. This study provides 2 years of baseline data on cetacean species’ presence, vessel activity, and ambient sound levels in the southern New England wind energy area. With eight species/families present in the area for at least 9 months of the year, this area represents an important habitat for cetaceans. Most species showed seasonality, with peak daily presence in winter (harbour porpoise, North Atlantic right, fin, and humpback whales), summer (sperm whales), spring (sei whales), or spring and fall/autumn (minke whales). Delphinids were continuously present and blue whales present only in January. The endangered North Atlantic right whales were present year round with high presence in October through April. Daily vessel presence showed an increase from summer through fall/autumn. On average, ambient sound levels were lowest in summer and increased late 2021 through 2022 with most temporal variability occurring across lower frequencies. The area showed a complex soundscape with several species sharing time–frequency space as well as overlap of vessel noise with the communication range of all baleen whale species.

Experimental Optimization Environment for Developing an Intracycle Pitch Control in Cross Flow Turbines

Cross-flow tidal turbines also known as vertical-axis turbines have not reached the efficiency as it is usual found for horizontal-axis turbines. This can be mainly accounted to the intensive development effort dedicated to the latter in the past 30 years in the area of wind energy. Less attention has been given to cross-flow turbines as they present mechanical cyclic loads that detriment their durability. Nevertheless, there is great potential for their employment in tidal energy exploitation as they are intrinsically independent from the flow direction and have a higher power output per area in farm installation. For this reason the research for cross-flow turbines have regain impulse.
 Among various approaches to improve their hydrodynamic characteristics found in the literature, the intracycle pitch control is one of the most promising one. It consists in actively pitching each blade as function of the azimuth angle.
 The present paper will introduce the overall experimental environment and methods for developing an optimal intracycle control for cross-flow turbines in tidal power generation. This is the main component of project OPTIDE at Otto-von-Guericke-University Magdeburg, Germany in cooperation with the LEGI at University Grenoble-Alpes, France and the University of Applied Sciences Magdeburg-Stendal.
 The experimental system consists of a turbine flume model equipped with a speed controlled generator. The three bladed turbine model has independent controlled pitch actuators embedded in each blade and a force sensing system in one of the blade holding arms. Local high-dynamic systems allow to control the speed of the generator and the pitch position as a function of the rotational angle of the runner i.e. pitch trajectory.
 A superimposed governing system which automates the experiment has been implemented. This allows to perform an optimization process with the experiment in the loop. Evolutionary algorithms are employed to solve the two objectives of the optimization: (1) Maximizing the efficiency by (2) minimizing the alternating structural loads on the runner. These kind of problems are usually solved with numerical simulations using finite element method (FEM) and computational fluid dynamics (CFD). However, this commonly comes with very high computational efforts and uncertainty. For this reason in our approach simulations are replaced by an experimental optimization technique.
 The optimization cycle implemented in the superimposed governing system chooses a set of individuals (pitch trajectories) for each generation on base of the genetic algorithm. Each pitch trajectory is performed sequentially while power output and loads are measured. This allows to evaluate each individual in about one minute. The best individuals are then the base for the offspring of the new generation. Cross-over from best performing parents and subsequent mutation ensure both, convergence and a thorough exploration of the entire design space.
 It is expected that this method will fast and reliably find the optimal pitch trajectories under various conditions. Selected cases will be analysed in detail by means of particle-image velocimetry (PIV) with synchronized force and torque measurements to research the underlying hydrodynamic mechanisms and the effects of the flow control performed by the pitching motion.

Protected species considerations for ocean planning: A case study for offshore wind energy development in the U.S. Gulf of Mexico

AbstractObjectiveOcean planning provides opportunities for managers to evaluate tradeoffs among environmental, social, economic, cultural, and management considerations in the development of place‐based activities. Early integration of mobile protected species considerations into ocean planning reduces the likelihood of future resource conflict. Transparency and problem solving with potential conflicts in mind during the early planning stages can help to minimize contention and increase efficiency in permitting and may also minimize litigation challenges during project design and implementation. Starting with a large area, such as the Bureau of Ocean Energy Management's (BOEM) initial 12.1‐million‐ha call area in federal waters of the U.S. Gulf of Mexico, provided substantial geographic scope for identifying suitable areas for eventual offshore wind lease sales that also aim to minimize conflict across multiple resources and uses.MethodsTo support ocean planning for this large‐scale activity, a generalized scoring system for protected species status and trends that facilitates relative comparison between species was developed. Spatial data for species listed under the U.S. Endangered Species Act or the Marine Mammal Protection Act were assembled. Species layers were scored based on species status and trend. The cumulative vulnerability for 23 species groups whose distributions overlap suitable areas proposed for eventual lease sales, termed wind energy areas (WEAs) by BOEM, was calculated.ResultIntegrating this combined protected species data layer into the broader Gulf of Mexico WEA ocean planning process helped to reduce potential protected species conflicts by 70%.ConclusionThis generalized approach is directly applicable to other WEAs under consideration within the United States and is transferable to a variety of ocean spatial planning applications.

Forage Fish Species Prefer Habitat within Designated Offshore Wind Energy Areas in the U.S. Northeast Shelf Ecosystem

AbstractAs the world develops sources of renewable energy, there is an intensifying interest in offshore wind energy production. The Northeast U.S. Continental Shelf (NES) ecosystem has favorable wind dynamics, with active development of wind energy. In this study, we present species distribution models that consider both occupancy and biomass responses for a broad spectrum of fish and macroinvertebrate taxa (n = 177). Building upon prior analyses, habitat was differentiated into overall and core habitats based on statistical distributions of habitat scores. Overall habitat was used to show each species' regional distribution based on fishery‐independent survey captures between 1976 and 2019, whereas core habitat represented where the focus of the species' abundance was located as a subset of overall habitat. Wind energy developments may modify the water column in ways that impact lower‐trophic‐level productivity; therefore, added attention was given to the response of forage species. Over 20% of species showed preferential use of putative and potential wind development areas, including a disproportionate number of forage taxa. Principal usage varied by season, with forage species like Atlantic Menhaden Brevoortia tyrannus and Atlantic Mackerel Scomber scombrus preferentially using the lease areas in spring and Round Herring Etrumeus teres and longfin inshore squid Doryteuthis pealeii using lease areas in autumn. For species with relatively low usage of the lease areas, there was a tendency for the usage related to overall habitat to be lower than usage for core habitat; in contrast, for species with high usage of the lease areas, that usage was higher for overall habitat than for core habitat. The area of habitat tended to have positive trends across species, with these positive trends being disproportionately higher among forage taxa. These results frame the importance of wind lease areas for species in the NES, particularly forage taxa that fulfill many important ecological functions.

Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis

Given the urgency of mitigating the effects of global warming and the depletion of fossil energy sources, renewable sources of energy, such as wind power, are the focus of the future. However, due to the rapid growth of this technology, concerns about the security and reliability of wind turbines are increasing, especially because of associated hazards and financial costs. Hence, health monitoring and fault identification for wind turbine blades have become an important focus of research. Thus, the objective of this study was to generate data on the current scenario of the techniques used to identify failures and defects in wind turbines and their components. Through the results found, companies can find ways to make decisions and identify potential new technologies. In this way, a technology prospection was conducted that focused on patents to investigate the use of vibration analysis, thermography, and machine learning. A total of 635 patent documents were found, and the evolution in the number of patents over the years has demonstrated the current interest in developing new technologies in this research area. China, the world’s leading country in the area of wind energy, was the country with the highest number of filings, followed by the United States. In the patent documents analyzed, it was possible to identify that those innovative technologies for predicting and detecting failures are a topic of interest for the world’s largest economies. Additionally, it was clear from the results that the application of artificial intelligence to traditional techniques is a current trend and will continue in the future. Technological prospection studies can foster the development of new methods and devices, providing economic and environmental gains for the wind energy industry.

Spatial segregation between phenotypes of the diablotin black-capped petrel Pterodroma hasitata during the non-breeding period

Despite growing support for ecosystem-based approaches, conservation is mostly implemented at the species level. However, genetic differentiation exists within this taxonomic level, putting genetically distinct populations at risk of local extinction. In the diablotin black-capped petrel Pterodroma hasitata, an endangered gadfly petrel endemic to the Caribbean, 2 phenotypes have been described: a smaller dark form and a heavier light form, which are genetically distinct. To assess possible differences in the marine distributions of phenotypes, in May 2019, we captured 5 adult black-capped petrels of each phenotype at sea in the western North Atlantic and equipped them with satellite transmitters. We used generalized linear mixed models to test the importance of phenotype on geographic distribution. Using kernel density estimations, we located use areas, quantified spatial overlap between forms, and assessed form-specific exposure to marine threats. Petrels were tracked for 11 to 255 d (mean ± SD: 102.1 ± 74.2 d). During the non-breeding period, all individuals ranged from 28.4 to 43.0° latitude. Phenotypes had significantly distinct non-breeding distributions, independent of time of year. The dark form used waters of the Carolinian marine ecoregion, and the light form used pelagic waters of the Virginian ecoregion, to the north. The dark form was more exposed to marine threats than the light form, in particular to mercury, microplastics, and marine traffic. The light form overlapped with proposed wind energy areas off the central US coast. These differences in exposure suggest possible differences in vulnerability, which can have repercussions on the viability of this imperiled species.

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.

AIS-based characterization of navigation conflicts along the US Atlantic Coast prior to development of wind energy

This study characterizes navigation conflicts in a region with a large traffic volume along the United States (US) Atlantic Coast, using an empirical approach that analyzes Automated Identification System (AIS) data for a full year (2010). The region includes areas proposed for wind energy development, and this characterization of vessel conflicts could be useful as a baseline to evaluating the effect of offshore wind areas on navigation conflicts and vessel collisions. There have been no collisions reported within the study area. This study analyzes the one year of AIS data to obtain vessel tracks and makes simultaneous pairwise comparisons to determine the number and rate of vessel encounters and near-misses (navigation conflicts). For vessel pairs in an encounter (3 nm distance; crossing, head-on, overtaking), a ‘blind vessel’ assumption was used to calculate the number of potential near misses and potential collisions. Graphical analysis of the frequency of vessels involved in potential near misses produced vessel response curves that provided the time of peak frequencies prior to the time of closest approach for each encounter type. These peak times were up to 600 s prior to the time of closest approach. The analyses of potential near misses and collision used the data during the period between the time of peak frequency and the time of closest approach to filter the data used in the calculation of the probability of potential collisions. The period included collsion avoidance maneuvering. Statistical analyses are made to estimate the point values and uncertainty of near miss rates and the probability of potential collisions for each type of encounter from which bounding estimates of the collision rates are made. These bounding collision rates are found to be comparable with estimates from previous studies. Causation probabilities were also estimated and compared with previous studies. The response curves may also have utility in the simulation of navigation conflicts. The use of surrogate data to evaluate the effect of wind energy areas is considered, though their usefulness depends on comparability of traffic density and the level and type of traffic control systems implemented.

On the Clock: Gulf of Mexico Enters the Offshore Wind Development Race

_ Offshore wind farms could in the next decade find a home in the Gulf of Mexico (GOM), more than 8 decades after the first oil flowed from its first offshore field. The Bureau of Ocean Energy Management in July identified the first two draft wind energy areas (WEAs)—one off the coast of Louisiana, the other off Texas—that could harness the Gulf’s steady breezes. Offshore wind is a key piece in President Biden’s strategy of addressing climate change by transitioning the US onto renewable energy. His administration in March 2021 set a target of deploying 30 GW of offshore wind capacity by 2030, “enough to power 10 million homes with clean energy, support 77,000 jobs, and spur $12 billion per year in private investment in offshore wind projects”, according to the announcement. This is the equivalent to more than 2% of the US utility-scale electricity generating capacity and about 25% of the total US wind electricity generating capacity, according to a Congressional Research Service report. In 2020 the country’s total generating capacity was about 1,212 GW, with about 119 GW of that produced from wind energy resources—virtually all onshore. Currently, the US has only two commercial offshore wind farms bringing the US total of offshore wind capacity to 42 MW. The five-turbine Block Island Wind Farm off the Rhode Island coast has a capacity of 30 MW, and the two-turbine Coastal Virginia Offshore Wind Project has a current capacity of 12 MW. New Energy, Historic Basin Interest in the Gulf’s offshore wind potential has increased in recent months as development efforts off the country’s East and West Coasts continue to advance and the Biden administration has pledged hundreds of millions of dollars in federal funding. One of the Gulf’s WEAs is located about 24 nautical miles (nm) off the coast of Galveston, Texas. The area under review totals 546,645 acres and has the potential to power 2.3 million homes with clean wind energy. The other WEA is located about 56 nm off the coast of Lake Charles, Louisiana, totaling 188,023 acres and has the potential to power 799,000 homes. This stretch of the Gulf Coast is historically significant as the location of the first free-standing platform to produce oil from the Gulf’s Creole field, installed about 1.5 miles offshore Cameron, Louisiana, in 1938. The GOM oil and gas industry has grown from humble beginnings to become one of the country’s most important energy resource and infrastructure regions. It is its energy infrastructure—its ports, its vessels, and its skilled workforce—that offshore wind developers find attractive. While the Gulf’s winds are not as strong as Atlantic and Pacific winds—about 7.5 to 8.5 m/s as compared to 8.0 to 10 m/s—this proximity to the oil and gas supply chain that can be leveraged and a milder climate that decreases operating costs and increases turbine access are considered its strongest advantages, according to the National Renewable Energy Laboratory (NREL).

A Linear Theory of Wind Farm Efficiency and Interaction

Abstract We investigate the role of gravity waves (GW), farm shape, and wind direction on the efficiency and interaction of wind farms using a two-layer linearized dynamical model with Rayleigh friction. Five integrated diagnostic quantities are used: total wind deficit, the first moment of vorticity, turbine work, disturbance kinetic energy, and vertical energy flux. The work done on the atmosphere by turbine drag is balanced by dissipation of disturbance kinetic energy. A new definition of wind farm efficiency is proposed based on “turbine work.” While GWs do not change the total wind deficit or the vorticity pattern, they alter the spatial pattern of wind deficit and typically make a wind farm less efficient. GWs slow the winds upwind and reduce the wake influence on nearby downstream wind farms. GWs also propagate part of the disturbance energy upward into the upper atmosphere. We applied these ideas to the proposed 45 km × 15 km wind energy areas off the coast of New England. The proximity of these farms allows GWs to play a significant role in farm interaction, especially in winter with northwesterly winds. The governing equations are solved directly and by using fast Fourier transforms (FFT). The computational speed of the linear FFT model suggests its future use in optimizing the design and day-by-day operation of these and other wind farms. Significance Statement When a wind farm is generating electricity, the drag of the wind turbines slows the regional winds. As wind farms grow larger and more closely spaced, this wind reduction will limit the efficiency of wind farms and their economic return. In this paper we analyze an idealized mathematical model of the atmospheric response to wind farm drag including nonlocal gravity wave effects. We propose a new definition of farm efficiency based on the atmospheric disturbance that a farm creates. We also propose a fast Fourier transform (FFT) method for carrying out these estimates.

Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain?

Abstract. Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) Model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at a site in Oklahoma, United States, and in a United States Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (∼-1ms-1) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a limited negative bias (∼-0.5ms-1) offshore and a slight positive bias (∼+0.5ms-1) at the land-based site. On the other hand, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED's higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle. At the land-based site, this is partially caused by wind plant wake effects not being accurately captured by WTK-LED.