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Wind energy is widely deployed and will likely grow in service of reducing the world's dependency on fossil fuels. The first generation of wind turbines are now coming to the end of their service lives, and there are limited options for the reuse or recycling of the composite materials they are made of. Current literature has verified that there is no existing recycling pathway (i.e., mechanical, chemical, thermal methods of recovery, etc.) for end-of-life materials in wind blades that can meet cost parity with landfilling in the US. However, to the authors' knowledge there is no study to date that uncovers the cost structures associated with repurposing wind turbine blades in the US. Repurposing could offer a cost-competitive advantage through displacement of higher-value products, rather than materials or chemical constituents alone. This study implements life cycle assessment (LCA) and life cycle cost analysis (LCC) to assess the environmental and financial implications at each stage of repurposing wind turbine blades as the primary load-carrying elements for high-voltage transmission line structures in the United States. This case study contribution to knowledge is based on the successful management of construction waste by analyzing an application for repurposing construction demolition waste. Specifically, this study presents an environmental and financial analysis of repurposing wind turbine blades as transmission line poles. Under this case study, our results show that BladePoles have lower greenhouse gas emissions than steel poles, and we anticipate BladePoles will be less costly than steel poles. Overall emissions are most sensitive to combustion emissions, driven primarily by transportation distance and hours of required crane operations during the installation process. Compared to other evaluated recycling methods, repurposing wind blades as BladePoles has the least overall global warming potential. As renewable energy production grows, managing infrastructure at its end-of-life is increasingly relevant—for example, wind turbine blades. This case study presents a financial and environmental analysis of repurposing decommissioned wind turbine blades as transmission poles, called BladePoles. This paper presents the cost and associated greenhouse gas emissions at each stage of the process. The case study also compares this reuse application to typical steel pole deployment, finding that for the same 60-year life span and 161 kV, 230 kV, and 345 kV transmission line poles, the BladePole cost is lower than the steel pole. Greenhouse gas emissions are most sensitive to transportation distance from the wind farm to the transmission project and the time of crane use for installation are key parameters in this case study and reducing them directly reduces the total greenhouse emissions overall. [ABSTRACT FROM AUTHOR]

The applicability of the ERA5 reanalysis for estimating wind and solar energy generation over the contiguous United States is evaluated using wind speed and irradiance variables from multiple observational data sets. After converting ERA5 and observed meteorological variables into wind power and solar power, comparisons demonstrate that significant errors in the ERA5 reanalysis exist that limit its direct applicability for a wind and solar energy analysis. Overall, ERA5-derived solar power is biased high, while ERA5-derived wind power is biased low. During winter, the ERA5-derived solar power is biased high by 23% on average, while on an annual basis, the ERA5-derived wind power is biased low by 20%. ERA5-derived solar power errors are found to have consistent characteristics across the contiguous United States. Errors for the shortest duration and most extreme solar negative anomaly events are relatively small in the ERA5 when completely overcast conditions occur in both the ERA5 and observations. However, longer-duration anomaly events on weekly to monthly timescales, which include partially cloudy days or a mix of cloudy and sunny days, have significant ERA5 errors. At 10 days duration, the ERA5-derived average solar power produced during the largest negative anomaly events is 62% greater than observed. The ERA5 wind speed and derived wind power negative biases are largely consistent across the central and northwestern U.S., and offshore, while the northeastern U.S. has an overall small net bias. For the ERA5-derived most extreme negative anomaly wind power events, at some sites at 10 days duration, the ERA5-derived wind power produced can be less than half of that observed. Corrections to ERA5 are derived using a quantile–quantile method for solar power and linear regression of wind speed for wind power. These methods are shown to avoid potential over-inflation of the reanalysis variability resulting from differences between point measurements and the temporally and spatially smoother reanalysis values. The corrections greatly reduce the ERA5 errors, including those for extreme events associated with wind and solar energy droughts, which will be most challenging for electric grid operation. [ABSTRACT FROM AUTHOR]

Renewable power generation is the key to decarbonizing the electricity system. Wind power is the fastest-growing renewable source of electricity in the United States. However, expanding wind capacity often faces local opposition, partly due to a perceived visual disamenity from large wind turbines. Here, we provide a US-wide assessment of the externality costs of wind power generation through the visibility impact on property values. To this end, we create a database on wind turbine visibility, combining information on the site and height of each utility-scale turbine having fed power into the U.S. grid, with a high-resolution elevation map to account for the underlying topography of the landscape. Building on hedonic valuation theory, we statistically estimate the impact of wind turbine visibility on home values, informed by data from the majority of home sales in the United States since 1997. We find that on average, wind turbine visibility negatively affects home values in an economically and statistically significant way in close proximity (<5 miles/8 km). However, the effect diminishes over time and in distance and is indistinguishable from zero for larger distances and toward the end of our sample. [ABSTRACT FROM AUTHOR]

State-level renewable portfolio standards (RPSs) aim to encourage renewable energy and discourage greenhouse gas (GHG) emissions from the electric power sector in the United States. Do they work? Some prominent government agencies and advocacy groups assert that U.S. renewables growth has been largely due to RPSs. That seems unlikely, given that in most regions, renewables exceed RPS requirements. But it is not an easy question to answer, thanks to interstate trading and the possibility that states with abundant renewable resources might set the most ambitious RPS goals. We combine the best features of four recent academic studies, using ordinary least-squares and instrumental variables approaches. In some specifications, RPSs do appear to reduce the use of natural gas to generate electricity and decrease GHG emissions, while boosting the use of wind and solar power. But the effects are small--consistent with the academic findings and in contrast to the public claims and policy goals. [ABSTRACT FROM AUTHOR]

Obtaining reliable and high-quality wind speed probability forecast results is of great significance to wind energy utilization and power system management. In this paper, multi-network deep ensemble method, which combines the intelligent optimization algorithm and deep ensemble method, is proposed to deal with the probabilistic prediction problems. This method can effectively integrate a variety of different deep learning neural networks and provide reliable uncertainty estimates for prediction. Furthermore, spatiotemporal multi-network deep ensemble model, which employs multi-network deep ensemble method, is proposed to deal with the probabilistic spatiotemporal wind speed forecasting problems. In the model, three advanced convolutional recurrent neural networks are integrated to capture spatiotemporal information from the underlying meteorological variables. Intelligent optimization algorithm is used to assign weights to each network in the ensemble. In addition, an uncertainty quantification method, which quantify the uncertainty by adjusting the network structure and optimize the uncertainty by utilizing the truncated negative log-likelihood scoring rule, is introduced to provide reliable probability forecasts. The proposed model is applied to a real-world case in the United States. The test results demonstrate that spatiotemporal multi-network deep ensemble model can not only provide high-precision point prediction results, but also provide suitable interval predictions and reliable probability prediction results. Moreover, the impact of input features on model prediction results is also evaluated. [ABSTRACT FROM AUTHOR]

The Cross-Calibrated Multi-Platform (CCMP) wind analysis is a satellite-based blended wind product produced using a two-dimensional variational method. The current version available publicly is Version 2 (CCMP2.0), which includes buoy winds in addition to satellite winds. Version 3 of the product (CCMP3.0) is being produced with several improvements in analysis algorithms, without including buoy winds. Here, we compare CCMP3.0 with a special version of CCMP2.0 that did not include buoy winds, so both versions are independent of buoy measurements. We evaluate them using wind data from buoys around the coasts of the United States and discuss the implications for the wind power industry and offshore wind farms. CCMP2.0 uses ERA-Interim 10 m winds as the background to fill observational gaps. CCMP3.0 uses ERA5 10 m neutral winds as the background. Because ERA5 winds are biased towards lower values at higher wind conditions, CCMP3.0 corrected this bias by matching ERA5 wind speeds with satellite scatterometer wind speeds using a histogram matching method. Our evaluation indicates that CCMP3.0 has better agreement with the independent buoy winds, primarily for higher winds (>10 m/s). This is reflected by the higher correlation and lower root-mean-squared differences of CCMP3.0 versus buoy winds, especially for higher wind conditions. For the U.S. coastal region (within 200 km), the mean wind speed of CCMP3.0 is enhanced by 1–2%, and the wind speed standard deviation is enhanced by around 3–5%. These changes in wind speed and its standard deviation from CCMP2.0 to CCMP3.0 cause an 8–12% increase in wind power density. The wind power density along the U.S. coastal region is also correlated with various climate indices depending on locations, providing a useful approach for predicting wind power on subseasonal to interannual timescales. [ABSTRACT FROM AUTHOR]

The larger problem, obviously, is that the US Congress has been unable for years to pass major environmental legislation, much less greenhouse gas reduction legislation. In early 2021, with Joe Biden recently having been inaugurated as president of the United States, I wrote an editorial asking whether the US could be a dependable long-term partner when it comes to addressing climate change.[1] In the editorial I posed a fundamental question: '[C]an the world's climate change leaders depend on the US to re-engage as a long-term good faith partner rather than being subject to the impulses of an American president who does not believe in climate change?. [Extracted from the article]

As wind energy rapidly expands worldwide, information to minimize impacts of this development on biodiversity is urgently needed. Here we demonstrate how data collected by weather radar networks can inform placement and operation of wind facilities to reduce collisions and minimize habitat‐related impacts on nocturnally migrating birds. We found over a third of nocturnal migrants flew through altitudes within the rotor‐swept zone surrounding the North American Great Lakes, a continentally important migration corridor. Migrating birds concentrated in terrestrial stopover habitats within 20‐km from shorelines, a distance well beyond the current guidelines for construction of new land‐based facilities, and their distributions varied seasonally and at local and regional scales, creating predictable opportunities to minimize impacts from wind energy development and operation. Networked radar data are available across the United States and other countries and broad application of this approach could provide information critical to bird‐friendly expansion of this globally important energy source. [ABSTRACT FROM AUTHOR]

Using the Regional Energy Deployment System (ReEDS) model, we estimate the deadweight loss imposed by county-level wind power development restrictions in the form of increased electricity costs due to suboptimal siting. This is accomplished by optimizing the power system of the United States' Midcontinent Independent System Operator (MISO) from 2020 to 2050. We perform the optimization with and without land-use constraints arising from simulated potential local ordinances restricting wind power development, and under multiple scenarios reflecting different renewable portfolio standards (RPS). We find that local restrictions on wind power increase the total system cost by 0.15%–0.3% and the wholesale electricity price by 1.8%–2.7%, depending on the RPS scenario. Changes in the generation and installed capacity mixes are more substantial and depend on both the level of county restrictions on wind power, and RPS requirements, thus indicating an interaction between RPS requirements and local wind power restrictions. We also find that plausible restrictions on wind development do not pose major barriers to meeting renewable energy targets in a cost-effective manner. • Potential local wind power restrictions increase the total system cost by 0.15%–0.3%. • Restrictions increase wholesale price by 1.8%–2.7%. • Restrictions affect generation mixed and installed capacity more than price and cost. • Wind and solar energy substitution relationship is clearer with higher RPS goals. [ABSTRACT FROM AUTHOR]

Policymakers are constantly faced with the complex task of managing novel challenges. At times, these challenges result from new technologies: Consider fights over allocating air rights for drones or decisions about how to share scarce vaccines in a pandemic. Other times the resources are old, but the challenges are new, such as how to fairly allocate water in times of unprecedented drought or previously undesirable rare earth minerals that are in demand for modern manufacturing and energy production. Often, instead of carefully tailoring a regime to the new resource, decisionmakers simply rely on mechanisms they are familiar with. When jurisdictions borrow from each other, scholars call this a "legal transplant"--as when one state copies another state's innovations or when the federal government learns from the "laboratories of democracy." This Article unveils a new dimension of legal transplants: transplants across subject areas. By transplants across subject areas, this article refers to instances when a jurisdiction looks for doctrines in other legal areas, often within its own legal system, when regulating a new resource or addressing a new challenge. This Article makes three key contributions. First, it identifies a new type of transplant--between subject matters within a jurisdiction. Second, it analyzes the reasons for internal, cross-subject legal transplants and the criteria for selecting which subject areas to copy from. Third, the Article brings the legal transplants literature to bear, specifically, on natural resource law. It explores two cases, groundwater and wind energy, where policymakers and courts have borrowed from other resource schemes, often ignoring the scientific and social differences between these natural resources. Other areas of law, such as the incorporation of contract doctrines in landlord-tenant relations, are also described to show the explanatory power of the natural transplant framework. This conceptual framework is then applied to new mineral developments in space and the deep sea. Cross-subject transplants may be more prevalent than previously appreciated, and understanding them will pave the way to analyze the regulation of new developments in natural resources, infrastructure, and beyond. [ABSTRACT FROM AUTHOR]

The possibility of substantial offshore wind energy development in the United States (U.S.) is rapidly advancing. Several offshore wind projects have been proposed proximate to federally-recognized tribal territory. Historically marginalized, indigenous communities have for centuries experienced injustices during the expansion of the U.S. energy system. Few studies have systematically examined the responses of indigenous communities to offshore wind, and little is known about the ways that indigenous concerns are leveraged by non-members to advance a position advocating for or against offshore development. In this study, we examine the discourses that surround indigenous communities through legally mandated decision-making processes for two proposed offshore wind projects in the northeast U.S. We show that narratives surrounding indigenous stakeholders in the offshore wind scoping process can be thematically identified as: 1. religious, cultural, and spiritual value , 2. land and identity , and 3. process and procedures. However, the concerns and perspectives of indigenous communities are mostly brought forth by non-group members, and were found to be leveraged or diminished by non-indigenous individuals pushing anti- or pro-offshore wind sentiment. This reveals the finding that indigenous concerns are being co-opted or sidelined through formal and legal decision-making processes in the U.S. The results indicate that the formal consultation process failed to meet standards of energy justice by inadvertently giving outsize voice to lesser impacted communities. Therefore, our study cautions that energy justice is not achieved solely through "inclusive" processes and decision-makers should be diligent in considering the multi-faceted aspects of justice. [ABSTRACT FROM AUTHOR]

The present paper reviews the green hydrogen uptake policies in the United States and the European Union. While the EU has a rather broad public support strategy that envisages the entire hydrogen value chain, the U.S. policies are more focused on hydrogen research hubs and subsidies having electric vehicle applications. From the experience of the solar and wind power rollout of the past three decades, we know that the subsidization schemes of the end product play an important role in the transition to renewables. These kinds of subsidization schemes are still lacking for clean hydrogen both in the EU and the U.S. This paper provides a brief overview of selected subsidization schemes from other renewables markets, which are then applied to define an exemplary subsidization path to empower large-scale growth in a market for green hydrogen. Here is proposed a long-term subsidization scheme – deemed as an adequate strategy for the expansion of clean hydrogen in an electricity mix. The suggested exemplary subsidization path recommends shifting from feed-in tariffs to direct marketing with market premiums once a certain threshold is reached, while both can be flanked by the issuance of a Guarantee of Origin for green hydrogen so that there is one market for hydrogen and another market for the certificates. • -A long-term subsidization scheme for the rollout of green hydrogen in the electricity mixes is proposed. • -An exemplary subsidization path to empower large-scale growth in a market for green hydrogen is drafted. • -U.S. and EU are used as case studies to capture lessons for subsidization of green hydrogen economy uptake. • -The U.S. has more centralized green hydrogen rollout policies, whereas the EU has a wider policy toolkit. • -Shifting from FIT to direct marketing with market premiums is key once a certain pre-defined threshold is reached. [ABSTRACT FROM AUTHOR]

Strong growth in lithium-ion battery (LIB) demand requires a robust understanding of both costs and environmental impacts across the value-chain. Recent announcements of LIB manufacturers to venture into cathode active material (CAM) synthesis and recycling expands the process segments under their influence. However, little research has yet provided combined costs and environmental impact assessments across several segments of the LIB value-chain. To address this gap, we provide a combined cost assessment and life cycle assessment (LCA), covering CAM synthesis, cell manufacturing and hydrometallurgy recycling. 1 kWh cell capacity (NMC 811 -C) is chosen as functional unit. Results for cell manufacturing in the United States show total cell costs of $94.5 kWh−1, a global warming potential (GWP) of 64.5 kgCO 2 eq kWh−1, and combined environmental impacts (normalizing and weighing 16 impact categories) of 4.0 × 10−12 kWh−1. Material use contributes 69% to costs and 93% to combined environmental impacts. Energy demand, meanwhile, accounts for 35% of GWP. Initially, hydrometallurgy recycling adds 5 to 10% to total costs, GWP, and environmental impacts. Including recycling credits, as recycled material substitutes new virgin material, shows benefits for recycling. Combined environmental impacts benefit most from recycling (−75%), followed by costs (−44%) and GWP (−37%). Further, we present a comprehensive dashboard which reveals how different scenarios, such as, using wind power instead of grid electricity, influence costs, GWP, and environmental impacts across process segments. Switching to low-carbon energy, for example, reduces GWP more than recycling would. Also, our dashboard shows that recycling or low scrap are more suitable options if reduction of costs or combined environmental impacts is the objective. [Display omitted] • CAM synthesis accounts for >45% of costs, CO2eq and combined environmental impacts. • Recycling costs of < $9 kWh-1 are small compared to manufacturing costs of $95 kWh−1. • Recycling reduces normalized & weighted environmental impact of cells by 75%. • Benefit of recycling on CO2eq emissions is comparably small. • Low scrap improves costs and environmental impacts more than low-carbon energy. [ABSTRACT FROM AUTHOR]

Wind gusts are common to everyday life and affect a wide range of interests including wind energy, structural design, forestry, and fire danger. Strong gusts are a common environmental hazard that can damage buildings, bridges, aircraft, and trains, and interrupt electric power distribution, air traffic, waterways transport, and port operations. Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. A project at the University of Wisconsin–Milwaukee is addressing the need for improved peak gust forecasts with the development of the meteorologically stratified gust factor (MSGF) model. The MSGF model combines gust factors (the ratio of peak wind gust to average wind speed) with wind speed and direction forecasts to predict hourly peak wind gusts. The MSGF method thus represents a simple, viable option for the operational prediction of peak wind gusts. Here we describe the results of a project designed to provide the site-specific gust factors necessary for operational use of the MSGF model at a large number of locations across the United States. Gust web diagrams depicting the wind speed– and wind direction–stratified gust factors, as well as peak gust climatologies, are presented for all sites analyzed. [ABSTRACT FROM AUTHOR]

Offshore wind energy is an attractive alternative in pursuing the nation's clean energy goals due to the significant demand for electricity in the coastal areas of the United States. Locating sites further offshore in deeper water can provide stronger, more consistent wind power resources and can mitigate aesthetic concerns. This motivates a need for improvements in the floating offshore wind turbine (FOWT) technology. As foundation costs comprise a significant fraction of the total cost for offshore wind power development, reducing the cost of the mooring system can play a significant role in making floating offshore wind economically competitive. Previous studies led to the development of a novel, efficient multiline ring anchor (MRA) system that can provide significant capital cost savings. Preliminary research shows that the MRA has a clear advantage under lateral loading by attaching wing plates to the cylindrical core of the anchor. In this study, two-dimensional finite-element (2D FE) analyses were performed to understand how wing plates affect the MRA performance under horizontal loading and provide reliable estimates of the ultimate load capacity. The results show the collapse mechanisms and bearing factors can be affected by width, the total number of wing plates, and load angles. This study also presents plastic limit analysis (PLA), based on the upper bound solution, to validate the 2D FE results by comparison and to confirm whether the postulated collapse mechanism was correct. The results obtained in the current study indicated that PLA can be a benchmark solution to evaluate the ultimate load capacity of the MRA with a satisfactory agreement with the FE-computed values. [ABSTRACT FROM AUTHOR]

Objective: This article investigates how different types of policies (morality based and highly technical) inform early‐stage policy learning and diffusion processes. Methods: Using process tracing, interviews, archival research, and cross‐case analysis, this article describes and compares the emergence of medical marijuana and offshore wind energy policies in U.S. states. Using iterative data collection and analysis, the study identifies which factors proffered by the literature affect the policy learning and diffusion process. Results: Despite representing different types of policies, there were significant similarities across both cases: State actors drew lessons from Europe; policy lessons from Europe were weaponized by national political interests in an effort to stymy state‐level diffusion; and, as policies diffused to other states, organized opposition curtailed. Conclusion: This study demonstrates how the stage of diffusion can be as determinative of the policy learning context as the type of policy, and highlights how the diffusion and typology literatures can inform each other. [ABSTRACT FROM AUTHOR]

Guiding wind energy sector growth through suitability analysis is a growing priority. We present in this work a logistic regression model that predicts suitable sites for state-level and nationwide wind energy development in the United States. The model's aggregation of publicly available data to 20 different grid cell resolutions, along with four predictor configurations, allows end-users to investigate commercial wind farm site suitability for their region, project size, and predictors of interest. Model performance maximizes at higher grid cell resolutions and when using a complete and/or refined predictor set. Validation of the model's performance against existing commercial wind farm locations demonstrates its ability to consistently diagnose over 75% of grid cell states correctly. As such, high suitability grid cells that currently lack wind farms could represent candidate locations for wind farm construction. This model and its aggregated datasets can be applied in other suitability analysis contexts, particularly solar energy development. • We apply Logistic Regression to United States Wind Farm Site Suitability. • We aggregate predictor datasets to 20 nationwide hexagonal grid cell sizes. • Existing commercial wind farm locations allow for validation of model performance. • False positive grid cells can suggest future sites for wind energy development. • This model can perform suitability analyses of other renewable energy types. [ABSTRACT FROM AUTHOR]

The United States has only recently begun investing in commercial-scale offshore wind energy (OWE). Although the United States is slow to progress, it is uniquely positioned to build on the existing knowledge that coastal European countries have applied for their own energy transitions. In this study, we analyze how federal and regional plans for expanding the OWE sector in the United States brought to the surface decade-long tensions related to multi-scale governance mismatches, jurisdictional conflicts, and unclear pathways for implementing national industrial policies. Drawing upon the European experience with OWE, we employ a dynamic multi-level perspective framework enriched by socio-ecological elements to examine the United States energy transition through its most promising technology. From our framework we identify six categories of OWE developments characterized by both unique and shared elements between the United States and European coastal countries. These elements are: (1) role of local communities, (2) governance structures, (3) multi-scale government interactions, (4) regional socioeconomic structures, (5) socio-ecological impacts, and (6) relationships with existing industries. Drawing upon our analysis, we identify and conceptually map four research areas in need of further development for the United States and the research community— (1) knowledge, (2) potential, (3) adaptation, and (4) learning. These insights provide critical information to ensure that the United States expansion into offshore energy generation is characterized by elements of justice, equity, and inclusive regional economic development. • Drawing from the European OWE experience, the U.S. is poised to rapidly expand their own OWE sector. • International comparison of North Atlantic countries and their pathways to OWE development. • The multi-level perspective provides a holistic evaluation of the success and failure of OWE across four North Atlantic countries. [ABSTRACT FROM AUTHOR]

The interaction of wave and currents with the seafloor and seabed can result in the erosion, liquefaction and failure of near surface sediments. The recent exposure of power cables for an offshore wind project in the United States has highlighted the need for more robust assessments of seafloor and seabed instability. Pipelines and cables can then become exposed or experience unacceptable and damaging deformations. This paper examines these processes. Experimental results have identified two different types of liquefaction. The first type results from the cumulative rise in pore pressures caused by repeated cyclic loading from waves. This type of liquefaction is most likely to occur in freshly deposited sediments that remain undrained or partially drained during wave loading events. The second type of liquefaction occurs from the combination of low initial stresses in the near seafloor sediments and the changing effective stresses within the seabed from wave action. This liquefaction will be more temporary and will depend on the time during each wave cycle when principal stresses are near zero or slightly negative. In addition to the experimental results two types of advanced numerical approaches are presented. These approaches greatly enhance the ability to make accurate predictions of seafloor and seabed instability. The first approach uses finite elements to examine stress changes within the seabed. Validation of the approach is presented in the paper by comparison with a previous analytical solution. A Computational Fluid Dynamics approach was used to determine the fluid velocities from waves and currents within the bottom boundary layer which can then be used to estimate the bottom seafloor shear stresses. These shear stresses can cause erosion with the potential to expose pipelines and cables. In addition to erosional and liquefiable zones, the paper also identifies a zone where the soil may yield and produce unacceptable deformations for pipelines and cables. The main recommendation from this study is that scour and erosion need to be evaluated by considering both fluid and soil mechanics aspects of the problem. • Seabed instability from waves can cause exposure of buried pipes/cables. • Experimental results have identified two different types of liquefaction. • Ability to make accurate predictions of seafloor and seabed instability. [ABSTRACT FROM AUTHOR]

Renewable energy resources, particularly offshore wind energy, and their role in combating global climate change have gained significant interest in recent years. Considering the potential life cycle impacts of such systems is essential to support effective policy and decision making. This study used life cycle assessment to compare the environmental sustainability of an offshore wind farm case study employing a novel foundation design to one employing a conventional foundation design. Literature has not yet examined the life cycle environmental sustainability of large-scale United States based facilities using a detailed data inventory from a real case study. The life cycle environmental single score of the novel design was 18% lower than that of the conventional design, highlighting that alternative foundation materials like concrete can help reduce the overall life cycle environmental impact of a large scale offshore wind farm. In general, the proposed novel design is favorable from a life cycle perspective compared to the conventional design, particularly in the impact categories of ecotoxicity, eutrophication, carcinogenics, noncarcinogenics, and respiratory effects. The energy payback time was found to be less than one year for both designs. Additional benefits of the novel design concept include lower costs, easier installation, domestic job opportunities, decreased reliance on foreign supply chains, ability to expand the design to deeper and disaster prone waters globally, and increased support for current political goals. Scenario sensitivity analyses showed that the environmental impact of wind energy systems may decrease with domestic sourcing of foundation materials and foundation reuse after decommissioning. [Display omitted] • Concrete foundations are novel designs for offshore wind energy systems. • Life cycle environmental impacts of a large scale United States based case study of a novel offshore wind energy design were evaluated. • The novel offshore wind foundation design has a lower environmental single score compared to the conventional design. • Energy payback times of the conventional and novel offshore wind farms are less than one year. • Social and political benefits (e.g., material costs, installation, domestic jobs) with novel foundation design. [ABSTRACT FROM AUTHOR]

A just transition to renewable energy requires accounting for the effects of offshore wind projects (OWPs) on the fishing industry. Research on the interaction of OWPs with coastal communities and fisheries in the United Kingdom and the United States offers insights into minimizing conflict and enhancing constructive engagement between fishers and wind energy developers. Recent innovations include earlier and more meaningful inclusion of fisheries representatives in planning and decisionmaking, involving fisheries liaisons in the process, and conducting more cumulative studies and taking collaborative approaches to considering the effects of OWP on fishing. [ABSTRACT FROM AUTHOR]

The energy system in the United States has a significant opportunity to meet society's needs for reliable, affordable, and clean energy by utilizing offshore wind. Development is moving quickly in the Atlantic Ocean, with the first large-scale utility project approval anticipated in December 2020. Offshore wind will have positive and negative impacts on existing ocean resources and ocean users, yet there is no precedent in the United States for evaluating effects on fisheries and fishers from utility-scale development. US regulations stipulate that wind developers conduct extensive surveys and impact monitoring for the approximate 30-year life of each project. This requirement creates an unprecedented and exciting opportunity for regional-scale, multi-decade, and cooperative monitoring and research that can inform decision-making for US regulators managing wind development and fisheries. This paper offers a vision for creating collaborative science partnerships designed to help build trust and transparency among two important US industries--fishing and offshore wind. [ABSTRACT FROM AUTHOR]

Providing peaking capacity could be a significant U.S. market for energy storage. Of particular focus are batteries with 4-h duration due to rules in several regions along with these batteries' potential to achieve life-cycle cost parity with combustion turbines compared to longer-duration batteries. However, whether 4-h energy storage can provide peak capacity depends largely on the shape of electricity demand. Under historical grid conditions, beyond about 28 GW nationally the ability of 4-h batteries to provide peak capacity begins to fall. We find that the addition of renewable generation can significantly increase storage's potential by changing the shape of net demand patterns; for example, beyond about 10% penetration of solar photovoltaics, the national practical potential for 4-h storage to provide peak capacity doubles. The impact of wind generation is less clear and likely requires more detailed study considering the exchange of wind power across multiple regions. • Storage can reduce the net demand for electricity and replace conventional peaking capacity. • The practical potential for 4-h storage as peaking capacity in the U.S. is about 28 GW. • This potential increases greatly with increased deployment of solar photovoltaics. • This potential is not significant impacted by wind potential. [ABSTRACT FROM AUTHOR]

Much literature on federalism and multilevel governance argues that federalist institutional arrangements promote renewable energy policies. However, the U.S. case supports a different view that federalism has ambivalent effects. Policy innovation has occurred at the state level and to some extent has led to policy adoption by other states and the federal government, but the extent is limited by the veto power of fossil fuel interests that are rooted in many state governments and in Congress, buttressed by increasing Republican Party hostility to environmental and climate policy. This argument is supported by a detailed analysis of five periods of federal and state renewable energy policy-making, from the Carter to the Trump administrations. The negative effects of federalism on national renewable energy policy in the United States, in contrast to the West European cases in this special issue, are mainly due to the interaction of its federalist institutions with party polarization and a strong domestic fossil fuel industry. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting (WRF) Model has been extensively used for wind energy applications, and current releases include a scheme that can be applied to examine the effects of wind turbine arrays on the atmospheric flow and electricity generation from wind turbines. Herein we present a high-resolution simulation using two different wind farm parameterizations: 1) the "Fitch" parameterization that is included in WRF releases and 2) the recently developed Explicit Wake Parameterization (EWP) scheme. We compare the schemes using a single yearlong simulation for a domain centered on the highest density of current turbine deployments in the contiguous United States (Iowa). Pairwise analyses are applied to diagnose the downstream wake effects and impact of wind turbine arrays on near-surface climate conditions. On average, use of the EWP scheme results in small-magnitude wake effects within wind farm arrays and faster recovery of full WT array wakes. This in turn leads to smaller impacts on near-surface climate variables and reduced array–array interactions, which at a systemwide scale lead to summertime capacity factors (i.e., the electrical power produced relative to nameplate installed capacity) that are 2%–3% higher than those from the more commonly applied Fitch parameterization. It is currently not possible to make recommendations with regard to which wind farm parameterization exhibits higher fidelity or to draw inferences with regard to whether the relative performance may vary with prevailing climate conditions and/or wind turbine deployment configuration. However, the sensitivities documented herein to the wind farm parameterization are of sufficient magnitude to potentially influence wind turbine array siting decisions. Thus, our research findings imply high value in undertaking combined long-term high-fidelity observational studies in support of model validation and verification. [ABSTRACT FROM AUTHOR]

Wind power forecast uncertainty exposes wind farms to volatile real-time electricity prices and increases wind power integration costs. Wind power forecast uncertainty could address these challenges and facilitate the process of siting suitable wind farm locations. In this study, the Analog Ensemble (AnEn) is employed to generate probabilistic wind speed forecasts at 80-m height using past forecast and analysis fields from the Global Forecast System (GFS). The AnEn predictions are used as proxy measurements for how difficult it is to estimate wind speed at different locations in the contiguous United States. The results show significant spatial variations in the wind speed error over the domain. This measure of uncertainty is paramount when determining the most suitable locations for large wind farms. We observed that locations with higher average wind speed are associated with larger degrees of forecast uncertainty which increases the difficulty to predict wind speed at these locations. Our analysis showed high correlation between forecast uncertainty and wind power output volatility which indicates higher risk of operating in real time electricity markets for wind farms located in areas with higher wind speeds. Further, a simple risk analysis using Sharpe ratio was performed to evaluate the riskiness of wind farms in the U.S. • Introducing a measure to quantify the degree of difficulty to predict wind power. • Standard deviation of power is an appropriate measure for riskiness of wind farms. • Higher wind power forecast uncertainty in areas with higher average wind speed. • Higher wind speed could justify the higher risk associated with wind power forecast. [ABSTRACT FROM AUTHOR]

Acoustic signals are often critical elements of mating displays, and lekking male greater prairie‐chickens (Tympanuchus cupido pinnatus) use their boom vocalization for this purpose. We quantified the acoustic characteristics of the boom chorus created by multiple male greater prairie‐chickens vocalizing simultaneously at leks in Brown County, NE, USA, in 2013 and 2014. We used these data to evaluate (a) the role of the boom chorus in prairie‐chicken breeding dynamics and (b) the impact of a wind energy facility on the acoustic signal of the boom chorus. We sampled the chorus using audio recorders placed in transects extending from leks; the chorus exhibited an average peak frequency of 297 ± 13 Hz. The mean chorus signal‐to‐noise ratio declined from 15.7 dB (50 m) to 2.6 dB (800 m), and wind speed and direction, topography, and relative humidity caused variation in signal‐to‐noise ratio at a given distance and location. Chorus recordings from leks within 1,000 m of a wind turbine had lower signal‐to‐noise ratio (βturbine = −5.659, SE = 1.289) than leks farther from turbines. The chorus signal‐to‐noise ratio increased slightly with more males present on the lek (~0.1 dB for each additional male; βmales = 0.177; SE = 0.037) and considerably more as more females visited the lek (~1.4 dB for each additional female; βfemales = 2.498, SE = 0.235; βfemales2 = −0.309, SE = 0.039). Our results provide support for the signal enhancement hypothesis that proposes the boom chorus is influenced notably by male–male competition for females on the lek, rather than functioning solely to advertise the presence of the lek to recruit females. Our results also suggest the choruses emanating from small leks have the greatest potential to be masked by anthropogenic (wind turbine) noise, which may affect the breeding success of male and female prairie‐chickens. [ABSTRACT FROM AUTHOR]

As the deployment of wind and solar energy increases in the USA, energy storage (ES) will play an important role in future electric power grids to help manage the variability from high penetration levels of renewable generation, ES can provide promising power/energy demand to coordinate with renewables generation, as a virtual power plant (VPP). There is an industry need for the capability in power system studies to model ternary pumped storage hydropower (T-PSH), a pumped storage technology that offers increased system benefits. This study presents a comprehensive vendor-neutral dynamic model of T-PSH in GE's commercial software positive sequence load flow. A new governor model is developed with detailed gate valve modelling and a shared-penstock function. Specifically, this model is designed to simulate the seamless transition among three operation modes: generation mode, pumping mode, and hydraulic short-circuit mode. The developed T-PSH model has been tested and validated on a 10-bus test system. A comparison study of T-PSH to conventional pumped storage hydropower has also been conducted on the Western interconnection system. [ABSTRACT FROM AUTHOR]

• Operational wind turbines contribute to the soundscape of the surrounding environment. • Low frequency turbine noise may interfere with wildlife vocalizations and behavior. • Wind speed and direction, time of day, and distance to turbine describe the area of soundscape impact. • Soundscape management should focus on turbine noise impacting wildlife of conservation concern. The development of wind energy facilities in grasslands has increased in recent years. We assessed sound levels near a 36-turbine wind energy facility in a contiguous grassland in Brown County, Nebraska, USA, using a grid sampling design. Greater prairie-chickens (Tympanuchus cupido pinnatus), a bird species of special conservation concern, occupy the landscape near the wind facility. We evaluated the variation in wind turbine noise in the landscape at 296 Hz, the average peak frequency of the critically important courtship ('boom') vocalization of male prairie-chickens. The levels of ambient sounds were affected by ordinal day (β = −0.0752, SE = 0.0073) and wind speed (β = 1.1679, SE = 0.0419). Sound levels (dB) at the wind facility were affected by distance to the nearest turbine (β = 0.6917, SE = 0.0111). Sound levels increased during the breeding season (ordinal day: β = 0.0166, SE = 0.0028), during the day (time of day: β = 0.0035, SE = 0.0015), and at higher wind speeds (β = 1.1929, SE = 0.0261). Sound levels were higher downwind from the facility (wind direction: β = 0.9914, SE = 0.1691) and at sites that were within the viewshed of turbines (topography: β = 0.3819, SE = 0.0824). At 100 m from the turbines, under average temporal, landscape, and wind conditions, wind turbine noise at 296 Hz was 25 dB above the ambient sound level. At 500 m from the turbines, wind turbine noise at 296 Hz was 16 dB above the ambient sound level. Our results demonstrate the variation of wind turbine noise in the soundscape and suggest a protocol for evaluating effects of wind turbine sound on sound-sensitive wildlife. [ABSTRACT FROM AUTHOR]

Strategic conservation efforts for cryptic species, especially bats, are hindered by limited understanding of distribution and population trends. Integrating long‐term encounter surveys with multi‐season occupancy models provides a solution whereby inferences about changing occupancy probabilities and latent changes in abundance can be supported. When harnessed to a Bayesian inferential paradigm, this modeling framework offers flexibility for conservation programs that need to update prior model‐based understanding about at‐risk species with new data. This scenario is exemplified by a bat monitoring program in the Pacific Northwestern United States in which results from 8 years of surveys from 2003 to 2010 require updating with new data from 2016 to 2018. The new data were collected after the arrival of bat white‐nose syndrome and expansion of wind power generation, stressors expected to cause population declines in at least two vulnerable species, little brown bat (Myotis lucifugus) and the hoary bat (Lasiurus cinereus). We used multi‐season occupancy models with empirically informed prior distributions drawn from previous occupancy results (2003–2010) to assess evidence of contemporary decline in these two species. Empirically informed priors provided the bridge across the two monitoring periods and increased precision of parameter posterior distributions, but did not alter inferences relative to use of vague priors. We found evidence of region‐wide summertime decline for the hoary bat (λ^ = 0.86 ± 0.10) since 2010, but no evidence of decline for the little brown bat (λ^ = 1.1 ± 0.10). White‐nose syndrome was documented in the region in 2016 and may not yet have caused regional impact to the little brown bat. However, our discovery of hoary bat decline is consistent with the hypothesis that the longer duration and greater geographic extent of the wind energy stressor (collision and barotrauma) have impacted the species. These hypotheses can be evaluated and updated over time within our framework of pre–post impact monitoring and modeling. Our approach provides the foundation for a strategic evidence‐based conservation system and contributes to a growing preponderance of evidence from multiple lines of inquiry that bat species are declining. [ABSTRACT FROM AUTHOR]

In order to contribute to conservation planning efforts for golden eagles (Aquila chrysaetos) in the western U.S., we developed nest site models using >6,500 nest site locations throughout a >3,483,000 km2 area of the western U.S. We developed models for twelve discrete modeling regions, and estimated relative density of nest sites for each region. Cross-validation showed that, in general, models accurately estimated relative nest site densities within regions and sub-regions. Areas estimated to have the highest densities of breeding golden eagles had from 132–2,660 times greater densities compared to the lowest density areas. Observed nest site densities were very similar to those reported from published studies. Large extents of each modeling region consisted of low predicted nest site density, while a small percentage of each modeling region contained disproportionately high nest site density. For example, we estimated that areas with relative nest density values <0.3 represented from 62.8–97.8% ( = 82.5%) of each modeling area, and those areas contained from 14.7–30.0% ( = 22.1%) of the nest sites. In contrast, areas with relative nest density values >0.5 represented from 1.0–12.8% ( = 6.3%) of modeling areas, and those areas contained from 47.7–66.9% ( = 57.3%) of the nest sites. Our findings have direct application to: 1) large-scale conservation planning efforts, 2) risk analyses for land-use proposals such as recreational trails or wind power development, and 3) identifying mitigation areas to offset the impacts of human disturbance. [ABSTRACT FROM AUTHOR]

Machine learning is frequently applied in the wind energy industry to build statistical models of wind farm power production using atmospheric data as input. In the field of wind power forecasting, in particular, there has been substantial research into finding the best-performing learning algorithms that improve model predictions. Overlooked in the literature, however, is the influence of atmospheric turbulence and stability measurements in improving model predictions. It has been well-established through observations and physical models that these effects can have considerable influence on wind farm power production; yet consideration of these effects in statistical models is almost entirely absent from the literature. In this work, we examine the impact of atmospheric turbulence and stability inputs on statistical model predictions of wind farm power output. Hourly observations from a wind farm in the Pacific Northwest United States located in very complex terrain are used. Five common learning algorithms and nine atmospheric variables are considered, five of which represent some measure of turbulence or stability. We find a considerable improvement in hourly power predictions when some measure of turbulence or stability is included in the model. In particular, turbulent kinetic energy was found to be the most important variable apart from wind speed and more important than wind direction, pressure, and temperature. By contrast, the choice of learning algorithm is shown to be relatively less important in improving predictions. Based on this work, we recommend that turbulence and stability variables become standard inputs into statistical models of wind farm power production. • Turbulence and stability are almost never considered in wind plant ML models. • All turbulence and stability variable are statistically significant. • Turbulent kinetic energy is the most significant variable after wind speed. • Choice of variables is more important than choice of algorithm. [ABSTRACT FROM AUTHOR]

• Operational expenditures for wind plants have declined ∼50% over last two decades. • Learning rates show 9% reduction for each doubling of global wind capacity. • Continued improvements in OpEx may contribute >10% of total future cost reductions. This paper draws on a survey of wind industry professionals to clarify trends in the operational expenditures (OpEx) of U.S. land-based wind power plants. We find that average all-in lifetime OpEx has declined from approximately $80/kW-yr (∼$35/MWh) for projects built in the late 1990s to a level approaching $40/kW-yr (∼$11/MWh) for projects built in 2018. Turbine operations and maintenance costs represent the single largest component of overall OpEx and the primary source of cost reductions. We observe wide ranges of OpEx over time; respondents cite a range in average expected costs for recently commissioned projects from $33/kW-yr to $59/kW-yr. We also use historical OpEx learning rates, showing a 9% OpEx reduction for each doubling of global wind capacity, to project a further $5–$8/kW-yr reduction from 2018 to 2040. These findings suggest that continued OpEx reductions may contribute 10% or more of the expected reductions in land-based wind's levelized cost of energy. Moreover, these estimates understate the importance of OpEx owing to the multiplicative effects through which operational advancements influence not only operations and maintenance costs but also component reliability, performance, and plant-level availability—thereby also affecting levelized costs by enhancing annual energy production and plant lifetimes. Given the limited quantity and comparability of previously available OpEx data, the trends reported here may inform OpEx assumptions used by electric planners, analysts, and modelers. The results may also provide useful benchmarks to the wind industry, helping developers and asset owners compare their expectations with historical experience and other industry projections. [ABSTRACT FROM AUTHOR]

Abstract The main aim of this study was to analyse scientific and energy production, funding, collaboration among countries and most cited papers on the renewable energies topic through bibliometric and social network study of articles included in the Web of Science database. 12,167 papers were recovered from 2007 to 2016. Wind power is the energy from which a greater number of articles have been published (n = 3,930), followed by solar energy (n = 2,570) and ocean energy (n = 1,565). The United States leads the world production of articles (n = 2,320), followed by China (n = 1,629), the United Kingdom (n = 1007), Germany (n = 730) and Spain (n = 729). China is the leading country in financed works (80%), followed by South Korea (77%), Spain (61%) and the United States (59%). An important level of international collaboration has been identified, notably popping up the triangle made by United States, China and the European Union. Highlights • Scientific study based in the analysis of the energy production, funding and collaboration among countries. • Wind power is the energy from which a greater number of articles have been published. • The United States leads the world production of articles. • China is the leading country in financed works. • The triangle made by United States, China and the European Union have the most important level of collaboration. [ABSTRACT FROM AUTHOR]

Renewable energy has gained popularity as an alternative to fossil fuels, which regularly emit large amounts of Greenhouse Gases and consume/withdraw large amounts of water, but renewable energy market penetration is still limited while fossil fuels are still the U.S.'s dominant power source. This is due to resistance in the market, or in this case, the failure of renewable energy policies to achieve long-term environmental sustainability due to neglected external factors (economic, societal, etc.). No available literature analyzes potential sources and/or effects of this policy resistance, so this research investigates the underlying mechanisms in the renewable energy generation market by utilizing a system dynamics model. A two-alternative Generalized Bass Model was developed to simulate the renewable energy market (specifically with respect to solar PV and wind energy), including the environmental, societal, and economic concerns associated with each of the alternatives evaluated in this study, so as to identify and address possible causes of policy resistance and its subsequent effects on environmental impacts (esp. GHG emissions and water withdrawal rates). Based on this model, three separate policy areas (solar PV investments, wind power investments, and the elimination of fossil fuel subsidies) and various combinations thereof were proposed and tested within the context of the model. Based on the results of this study, it is highly recommended to invest as generously as possible into multiple renewable energy industries, reduce fossil fuel subsidies (in turn freeing up funding for renewable energy investments), and seek further advancement in renewable energy technologies (e.g. enhancing the useable lifetimes of wind turbines). A balanced policy have potential to increase the share of renewable's up to roughly 40% in the U.S. by 2050, as well as 17% and 32% GHG and water withdrawal reduction potential by 2050. • A system dynamics model of the U.S. RE market is developed. • Short lifetimes and decreasing demand as impacts decrease both hinder the RE industry. • Multi-industry policies and developments are needed to meet 2050 RE market goals. • Impacts can be reduced by up to 32%, but uncertainties should still be addressed. • Further development of GHG and WW reduction strategies is also recommended. [ABSTRACT FROM AUTHOR]

In those countries where wind plays a major role in the energy mix (EU, China and USA) actions have been carried out to develop offshore wind energy, albeit to varying degrees. These actions range from studying offshore wind to the development of laws and planning related to the construction of wind farms. Europe currently leads the way in offshore wind energy (with 84% of global installations), having achieved technical and commercial maturity, including the first floating wind farm to generate electricity, together with an emerging zero-subsidy culture. The Chinese wind industry has seen rapid development since 2005, however, well established laws, the use of a one-stop-shop system in the licencing process, and the establishment of higher feed-in tariffs (FITs), could all boost the Chinese offshore wind industry further. The possible future role of the USA in the offshore wind industry is now in the hands of its decision makers. A more streamlined licencing process, together with a long-term vision enshrined within stable economic incentives, could help to boost the offshore wind industry in the USA. • Analysis of offshore wind resources and regulations is key to the wind energy growth. • European offshore wind industry has reached technological and commercial maturity. • The EU, China, and the USA have all taken steps to develop offshore wind energy. • The role of the USA in future offshore wind energy depends on its decision makers. • China has entered a new and decisive phase in the offshore wind energy development. [ABSTRACT FROM AUTHOR]

Abstract Wind speed and power forecast is an essential component to ensure grid stability and reliability. The traditional forecasting methods fail to address the non-linearity in the wind speed time-series, thus paving way for machine intelligent algorithms. This paper discusses a hybrid machine intelligent wind forecasting model utilizing different variants of Support Vector Regression (SVR) built on wavelet transform. Various performance indices are evaluated to identify the possible best one among four different machine learning regressors for wind forecasting application. Apart from standard ε-SVR and LS-SVR, two new regression models, namely, ε-Twin Support vector regression (ε-TSVR) and Twin Support vector regression (TSVR) are used to forecast short-term wind speed, and are compared with Persistence model for four wind farm sites. The effect of larger dataset on forecasting performance is evaluated for two wind farm sites from USA and India. Further, wind power ramp events are investigated at different hub heights and the forecasting performance of different variants of SVR is compared for five wind farm sites. Highlights • Machine intelligent SVR variants are discussed for short-term wind forecasting. • Overfitting and underfitting scenarios of variants of SVR are assessed. • Forecasting performance of SVR variants is assessed during wind power ramp event. • Wind power ramp events are analyzed at different hub heights for 5 wind farm sites. [ABSTRACT FROM AUTHOR]

This paper proposes a low-complexity pseudo-inverse Legendre neural network (PILNNR) with radial basis function (RBF) units in the hidden layer for accurate wind power prediction on a short-term basis varying from 10- to 60-min interval. The random input weights between the expanded input layer using Legendre polynomials and the RBF units in the hidden layer are optimized with a metaheuristic firefly (FF) algorithm for error minimization and improvement of the learning speed. For comparison, two other forecasting models, namely pseudo-inverse RBF (PIRBFNN-FF) neural network and PILNNR [with tanh functions in the hidden layer (PILNNT-FF)] with input-to-hidden layer weights being optimized by FF algorithm, are also presented in this paper. Also the weights between the hidden layer and the output neuron of these neural models are obtained by Moore–Penrose pseudo-inverse algorithm. Further to improve the stability of the weight learning procedure, the L2-norm-regularized least squares (ridge regression) technique is used. A superior predictive ability test is performed on the three proposed wind power forecasting models using bootstrapping procedure in order to identify the best model. Several case studies using wind power data of the wind farms in the states of Wyoming and California in USA and Sotavento wind farm in Spain are presented in this paper. [ABSTRACT FROM AUTHOR]

The rapid pace of wind‐energy development has increased stakeholder concerns regarding the potential effects on wildlife. Locations targeted for wind‐energy development frequently overlap prairie grouse and greater sage‐grouse (Centrocercus urophasianus) habitats. Research suggests that anthropogenic developments may have negative effects on these species. There is, however, no information published regarding the effect of wind‐energy development on Columbian sharp‐tailed grouse (Tympanuchus phasianellus columbianus), a subspecies that has twice been petitioned for Endangered Species Act protection. To address this need, from 2014 to 2015 we studied Columbian sharp‐tailed grouse nesting ecology across restored grasslands in eastern Idaho, USA, where a 215‐turbine wind‐energy complex had been developed. We monitored 147 nests from 135 females captured at leks 0.1–13.8 km from wind turbines. We used an information‐theoretic approach to evaluate the influence of wind‐energy infrastructure and habitat characteristics on nest‐site selection and daily nest survival. We did not detect any influence of wind‐energy infrastructure on nest‐site selection or nest survival. Nest‐site selection and daily nest survival were influenced by vegetation structure and composition measured at 2 spatial scales. Females selected nest sites with more restored grassland containing >30% forb cover within the nesting core‐use area (i.e., 60 ha around the nest) and exhibited a functional response to the availability of that land cover type. Daily nest survival was best predicted by visual obstruction at the nest site and the amount of restored grassland containing >30% forb cover within the nesting core‐use area. We recommend wildlife managers continue to implement management practices that will provide bunchgrass‐dominated grasslands with >30% forb cover in restored grasslands (e.g., Conservation Reserve Program fields) within Columbian sharp‐tailed grouse range. © The Wildlife Society, 2019 Columbian sharp‐tailed grouse nest‐site selection and nest survival was not influenced by wind‐energy development in restored grasslands in eastern Idaho, USA. Vegetation features at nest sites and within the core area used by nesting females influenced nesting ecology and future management to improve nest survival should promote bunchgrass dominated grasslands with > 30% forb canopy cover. [ABSTRACT FROM AUTHOR]

With the growth of global energy demand, the proportion of the total installed wind capacity continues to increase. Wind speed forecasting is essential to enhance the utilization of wind energy. However, it is not easy to make accurate wind speed forecasting since wind speed time series data has nonlinearity, fluctuation, and intermittence. These features of wind speed time series data make classical models unable to obtain expected forecasting results. Many researchers proposed wind speed forecasting models to overcome the shortage. Nevertheless, some wind speed prediction methods are limited because they cannot mine all implicit features of wind speed data. This paper proposes a novel wind speed forecasting model that can capture all implicit information of wind speed data and obtain accuracy prediction results. To be specific, ensemble empirical mode decomposition (EEMD) is firstly applied to remove the noise in the original wind speed data. Secondly, we build a novel deep learning model based on the attention mechanism and convolutional neural network (CNN). The proposed forecasting model can focus the important part of wind speed data and reduce the high computational complexity of CNN. Finally, a full connect neural network layer is employed to obtain wind speed forecasting results. In order to validate the performance of the proposed model, we take the wind speed data of the M2 tower at 20-meter height of the National Wind Power Technology Center of the United States as an example. The experimental results demonstrate that the forecasting errors of the proposed model are smaller than other comparative models. And the Diebold-Mariano test confirms that the proposed model exhibits a significant difference in performance compared with the comparison models. [ABSTRACT FROM AUTHOR]

Highlights • We analyze the economic value of a U.S windfarm using long-term LCOE information. • Long-term LCOE of wind energy is obtained from two decades of history. • Monte Carlo and Risk-Neutral valuation is used to valuate a reference windfarm. • Managerial flexibilities and generation cost uncertainty make windfarms more valuable. • Real Option accounts for the flexibility of decision-making with stochastic costs. Abstract Renewable energies are a natural replacement for conventional or fossil fuel energy generation. One of these sources, wind energy, has exhibited significant cost reductions during the last decades. Even though this historical cost reduction trend is well known in the industry and in the academia, information on this cost trend and volatility has been scarcely incorporated in previous works, thus a natural concern arises when assessing their influence on the economic valuation of wind farms. Moreover, wind projects are modular in size and have a short time-to-market, providing a valuable managerial flexibility to defer investment under non-attractive market conditions, such as high development cost scenarios. Traditional evaluation tools often consider those scenarios, wrongly assuming that wind-developers would move forward with the project under such conditions. Accordingly, this paper analyzes the effect of incorporating both generation cost history and investment flexibility in the valuation of wind farms in the United States using Real Options methodology. Taking two decades of cost data and a reference wind farm, this "uncertainty-flexibility" effect is incorporated and valued, finding that the value of the wind project is 14.2% larger than the one neglecting such effect – where cost stochasticity and managerial flexibility are ignored. Therefore, traditional tools for project valuation, such as discounted cash flow and scenario analysis can no longer solely be used, as they do not properly account for the cost reduction trend, uncertainty, and managerial flexibility that a wind farm could face. [ABSTRACT FROM AUTHOR]

Offshore wind farms are a crucial component for the improvement of renewable energy in the United States. The Bureau of Ocean Energy Management (BOEM) designated ~170 km2 of shelf area for wind energy development off the coast of Maryland, USA. In order to understand potential environmental impacts of wind turbine installation on the benthic ecosystem within the designated area, we conducted a study to visually characterize bottom habitats and epibenthic communities in the Mid-Atlantic Outer Continental Shelf blocks of the Maryland wind energy area. Seven 5 km long transects were sampled using a towed camera sled with a downward-facing digital camera that captured images at 5 frames·s-1s. Additional small-mesh beam trawling was also conducted at selected locations complementary for species identification. Image data were analyzed using two image selection methods, random and systematic (i.e. video frames were selected at various intervals). For both methods, estimates of community diversity (Hill’s N2) stabilized with sample sizes ranging from 316 to 398 frames. Our results allowed us to define distinct epibenthic communities and bottom habitats that are associated with offshore wind energy sites and to develop a sampling technique for digital images that can be applied to other research programs. [ABSTRACT FROM AUTHOR]

This paper discusses the growth of wind power generation in the United States. As wind power has spread, states have used different methods for valuing wind farms for property taxation. Generation of electricity by wind takes two forms: in utility-grade generation, large turbines, usually in farms, are designed to produce electricity for the grid, and in distributed generation, smaller units are not connected to the grid but are designed to generate power for a business, manufacturing complex, agriculture, or local networks. Only utility-grade generation is considered here. The valuation methods used in the principal wind states are described. Also, a more detailed analysis of alternative methods of valuation is presented. Finally, specific issues for assessors to consider in valuing wind farms are discussed. [ABSTRACT FROM AUTHOR]

Highlights • Mid-to-long term wind and solar joint generation prediction model established. • Copula for extracting nonlinear and non-Gaussian distributed key weather factors. • Long short term memory network to construct the prediction model with fewer data. • The proposed method is able to improve prediction accuracy validated by real cases. Abstract The accurate estimation of mid-to-long term wind and photovoltaic power generation is important to the power grid's plan improvement, dispatching optimization, management development, and consumption enhancement. These constitute key factors for the realization of power mutual assistance and complementary dispatch of power generation in the broad area of renewable energy. However, owing to the large time scale of mid-to-long term prediction, the low accuracy of weather prediction, the limited data samples of historical power generation, and the significant difference between power generation prediction and short-term power prediction, short-term power prediction technology cannot be directly copied. Thus, the industry has not established yet an effective approach for mid-to-long term wind and photovoltaic power generation predictions. To solve these problems, this study proposed a method for the mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network to achieve an effective extraction of the key meteorological factors that affect power generation owing to nonlinear effects and tendencies, and to deeply exploit the long-term dependencies and tendencies from the limited available data samples. Therefore, the proposed approach is suitable for mid-to-long term wind and photovoltaic power generation prediction using limited data samples. Firstly, the non-linear effects and tendency correlation measurements of the copula function were used to extract the key meteorological factors that influence wind and photovoltaic power generation. Secondly, independent wind/photovoltaic prediction models were established based on long short term memory network using the best input condition obtained by comparing these models to the persistence model. Additionally, the independent wind/photovoltaic models were further compared to support vector machine model with the optimal input condition. Thirdly, the joint prediction models of wind and photovoltaic power generation based on long short term memory network were established using different inputs. The persistence model and the support vector machine model were used as benchmarks to compare the elicited performances. Finally, the validity and applicability of the proposed approach were extensively evaluated using actual data from wind farms and photovoltaic power stations in China and the United States. The independent and joint power generation prediction results demonstrated that the proposed approach outperforms both the persistence model and the support vector machine model, and can have widespread applicability in limited data sample cases. [ABSTRACT FROM AUTHOR]

Highlights • Future residential demand with heat pumps are estimated in four cities. • Local wind and solar generation mixes are compared with and without storage. • Cost comparisons of distributed technology options including storage are studied. Abstract To reduce greenhouse gas emissions, the electricity sector is going through two main transitions. First, the electric grid is integrating variable renewable generation, such as wind and solar. Second, demands are changing as heating systems are shifting from gas-based to high efficiency electric heat pumps. This paper provides a comparative analysis of future energy scenarios with distributed technology options including (1) wind and solar generation; (2) heat pumps for heating and cooling; and (3) battery and thermal storage in representative residential blocks in four cities, including New York City, New York; Minneapolis, Minnesota; Tallahassee, Florida; and Fort Collins, Colorado. These cities are located in three climate regions with different weather patterns which result in different demand profiles and different local renewable resources. Future energy demand scenarios with 100% penetration of air source or ground source heat pumps for heating and cooling are estimated for the four residential city blocks. Under a future scenario with all electric demand with air source heat pumps and high renewable energy penetration, this study finds that (1) the optimal wind and solar generation mix varies with location and amount of storage and (2) battery storage is more cost effective than thermal storage, ground source heat pumps, and overbuilt renewable generation. [ABSTRACT FROM AUTHOR]

Abstract The Midcontinent Independent System Operator (MISO) is the second largest organized competitive wholesale electricity market in the U.S. Wholesale electricity prices in MISO have decreased over the past several years, as both natural gas prices have fallen, and large amounts of wind generation capacity have been added. Using an empirical econometric approach studying historical hourly real-time electricity prices, load, natural gas prices, and other MISO market data, a series of econometric models are estimated using historical data from 2008 to 2016, to identify the impact of increased wind generation on wholesale electricity prices in MISO. This is the first such empiric study for MISO, the largest wholesale electricity market by geography in the world. Furthermore, this is the first empiric study estimating impacts of wind generation on electricity price that utilizes a comprehensive set of predictor variables to control for many sources of potential bias. The results show a statistically significant impact of wind generation on real-time hourly MISO system prices, ranging from an estimated decrease of $0.14 to $0.34 per mega-watt hour, for each 100 mega-watt hours of additional wind generation. It is observed that the estimated marginal impacts of additional wind generation have declined over time. This is likely attributable to the long-term shifts in the supply and demand dynamics experienced in the MISO energy market, as well as to changes made to the MISO market structure to better accommodate variable generation. Highlights • Empiric study measuring effects of wind generation on electricity price in MISO. • 100 MWh increase in wind leads to $0.14 - $0.34 decrease in wholesale price. • Decreasing marginal price effects over time. [ABSTRACT FROM AUTHOR]

Abstract The focus of this paper is to analyse the determinants of wind energy development in the United States and how procedural and regulatory frameworks influence the deployment of wind power facilities. The empirical analysis uses statistical regression models integrating geospatial, macroeconomic and socio-environmental control variables. Using wind penetration as well as wind capacity additions as dependent variable permits a more differentiated analysis of both absolute and relative growth factors. This enables a precise assessment of state-to-state variations in permitting, zoning and siting procedures that wind developers have to clear before being authorised to start construction. Quantifying the number of state-level financial support measures and various permitting and regulatory process stages allowed for a more comprehensive assessment of administrative barriers to wind energy development than prior research studies. The results indicate a partial reversal of previous findings that showed that a high quantity of state-level regulations negatively affects wind capacity additions. Exogenous factors such as the ratio of in-state federal lands, population density, and especially wind energy potential, as well as federal statutes and incentives remain the main drivers of wind capacity additions and overall wind energy penetration. Contrasting prior literature, the influence of localised financial incentives or regulatory approval procedures appears to be minor; therefore streamlining national policies and incentives at the federal level might prove more effective than promoting wind development at the state level. We point out that future research should also examine the role of quality of state-level regulations in addition to quantity. Highlights • Most up-to-date United States state-level study using recent data sets from 2010 to 2016. • Number of financial incentives & regulatory measures are weak growth indicators. • Overall wind potential is the strongest driver of wind development. • Novel dependent variable for wind penetration enables relative growth analysis. • Quantity of environmental regulations did not influence energy growth. [ABSTRACT FROM AUTHOR]

The paper considers opportunities to reduce emissions of CO 2 through increases in commitments to wind in a representative US power market. A model is applied to simulate market operations for different wind levels focusing on implications of the reduction in clearing prices arising due to increasing inputs of zero marginal cost power from wind, a dilemma referred to as the missing money problem. The resulting decrease in income poses problems for existing thermal and nuclear generating systems, at the same time making investments in wind uneconomic in the absence offsetting policy interventions. Two options are considered to subsidize cost: an investment credit (IC) or a subsidy on production (PC). The dilemma could be addressed also with a carbon tax targeted to increase income. It is assumed that the cost associated with the IC and PC options should be borne by the consumer, offsetting benefits from lower wholesale prices. It is assumed further that income from the carbon tax should be rebated to the consumer offsetting related increases in clearing prices. IC and PC options offer opportunities to reduce emissions at low or even negative net costs to the consumer. Higher costs are associated with the option of a carbon tax. [ABSTRACT FROM AUTHOR]

China and the United States are both major countries in the world in the production of and trade in wind energy products, and they are also important trading partners. Therefore, it is of great practical significance to analyze and evaluate the Sino-US bilateral wind power complementarity for re-examining their respective comparative advantages and re-understanding the benefits that trade liberalization brings to both countries, in an attempt to strengthen the trade relations between China and the United States and enhance trade complementarities in wind energy products under the global circumstances of fierce competition. In this paper, based on the trade data from the UN Comtrade database in 2007-2016, a quantitative analysis was conducted on the complementarity of Sino-US wind energy products, by adopting the quantitative research methods of trade combination degree index (TCD), export similarity index (SI) and trade complementarity index (TCI). The results shows that: i) the total trade volume of wind energy products between China and the United States has increased rapidly, and the trade surplus between China and the United States has been expanding year by year; ii) the overall trade balance of wind energy products trade between the two countries is generally stable, and the dependence of China's wind energy products on the United States is much higher than that of the U.S wind energy products on China; iii) the Sino-US wind energy products between China and the United States are highly competitive in the international market; iv) Sino-US wind energy products have different degrees of complementarity in bilateral trade. Finally, this paper proposed policy implications based on the findings of this study. [ABSTRACT FROM AUTHOR]