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DOE OSTI2014

2013 Wind Technologies Market Report

This annual report provides a detailed overview of developments and trends in the U.S. wind power market, with a particular focus on 2013. This 2013 edition updates data presented in previous editions while highlighting key trends and important new developments. The report includes an overview of key installation-related trends; trends in wind power capacity growth; how that growth compares to other countries and generation sources; the amount and percentage of wind energy in individual states; the status of offshore wind power development and the quantity of proposed wind power capacity in various interconnection queues in the United States.

17 WIND ENERGY↗
DOE OSTI2022

Chapter 9.12 - Weather, Climatic and Ecological Impacts of Onshore Wind Farms

Wind power is an environmentally sustainable technology that is likely to be part of the solution to the climate change, air pollution, and energy security problems. Despite many positive benefits, the rapid development of wind power has raised concerns about some potential adverse environmental impacts. While converting wind?s kinetic energy into electricity, wind turbines (WTs) modify properties of the atmospheric boundary layer (ABL) including the vertical profiles and surface-atmosphere exchanges of energy, momentum, mass, moisture, and trace gases. Given the current installed capacity and the projected installation worldwide, wind farms (WFs) are likely becoming a major driver of manmade land use change on Earth. Hence, understanding WT-atmosphere-surface interactions and assessing potential environmental impacts of WFs are of significant scientific, societal and economic importance. Here we review our progress in assessing potential impacts of onshore wind power on weather, climate and vegetation activity. A consensus is emerging based on observations and modeling studies that WFs cause a local to regional warming effect, particularly at nighttime, while the impacts on precipitation, wind patterns, crop yields and vegetation activity are uncertain. The warming effect results simply from vertical heat redistribution within the ABL due to turbine-enhanced vertical turbulent mixing in the wakes. At the global scale, with a substantial installation of WFs, mesoscale and climate models predicted large regional changes but small global impacts on temperature, while the impacts on precipitation, clouds, wind patterns and large-scale circulation have large uncertainties and are region specific and scale dependent. Despite increasing number of research efforts, our assessment of potential WF impacts is still very limited. Although the WF impacts are mostly local and limited to the near-surface ABL, this is the layer where we live and plants grow. Hence, more studies are needed to improve our understanding of WT-atmosphere-surface interactions and our capability to model and project the weather, climatic and ecological impacts of large WFs.

atmospheric boundary layer↗
DOE OSTI2016

NREL and DONG Energy Collaboration for Grid Simulator Controls and Testing: Cooperative Research and Development Final Report, CRADA Number CRD-13-527

The National Renewable Energy Laboratory (NREL) and DONG Energy are interested in collaborating for the development of control algorithms, modeling, and grid simulator testing of wind turbine generator systems involving NWTC's advanced Controllable Grid Interface (CGI). NREL and DONG Energy will work together to develop control algorithms, models, test methods, and protocols involving NREL's CGI, as well as appropriate data acquisition systems for grid simulation testing. The CRADA also includes work on joint publication of results achieved from modeling and testing efforts. Further, DONG Energy will send staff to NREL on a long-term basis for collaborative work including modeling and testing. NREL will send staff to DONG Energy on a short-term basis to visit wind power sites and participate in meetings relevant to this collaborative effort. DOE has provided NREL with over 10 years of support in developing custom facilities and capabilities to enable testing of full-scale integrated wind turbine drivetrain systems in accordance with the needs of the US wind industry. NREL currently operates a 2.5MW dynamometer and is in the processes of commissioning a 5MW dynamometer and a grid simulator (referred to as a 'Controllable Grid Interface' or CGI). DONG Energy is the market leader in offshore wind power development, with currently over 1 GW of on- and offshore wind power in operation, and 1.3 GW under construction. DONG Energy has on-going R&D projects involving high voltage DC (HVDC) transmission.

17 WIND ENERGY↗
DOE OSTI2017

2016 Wind Technologies Market Report

The U.S. Department of Energy (DOE)’s Wind Technologies Market Report provides an annual overview of trends in the U.S. wind power market. You can find the report, a presentation, and a data file on the Files tab, below. Additionally, several data visualizations are available in the Data Visualizations tab. Highlights of this year’s report include: -Wind power additions continued at a rapid clip in 2016: $13 billion was invested in new wind power plants in 2016. In 2016, wind energy contributed 5.6% of the nation’s electricity supply, more than 10% of total electricity generation in fourteen states, and 29% to 37% in three of those states—Iowa, South Dakota, and Kansas. -Bigger turbines are enhancing wind project performance: Increased blade lengths, in particular, have dramatically increased wind project capacity factors, one measure of project performance. For example, the average 2016 capacity factor among projects built in 2014 and 2015 was 42.6%, compared to an average of 32.1% among projects built from 2004 to 2011 and 25.4% among projects built from 1998 to 2001. -Low wind turbine pricing continues to push down installed project costs: Wind turbine prices have fallen from their highs in 2008, to $800–$1,100/kW. Overall, the average installed cost of wind projects in 2016 was $1,590/kW, down $780/kW from the peak in 2009 and 2010. -Wind energy prices remain low: After topping out at nearly 7¢/kWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped to around 2¢/kWh—though this nationwide average is dominated by projects that hail from the lowest-priced Interior region of the country (such as Texas, Iowa, Oklahoma). These prices, which are possible in part due to federal tax support, compare favorably to the projected future fuel costs of gas-fired generation. -The supply chain continued to adjust to swings in domestic demand for wind equipment: Wind sector employment reached a new high of more than 101,000 full-time workers at the end of 2016. For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>90%), towers (65-80%), and blades and hubs (50-70%), but is much lower (<20%) for most components internal to the turbine. -Continued strong growth in wind capacity is anticipated in the near term: With federal tax incentives still available, though declining, various forecasts for the domestic market show expected wind power capacity additions averaging more than 9,000 MW/year from 2017 to 2020.

17 WIND ENERGY↗
DOE OSTI2018

Advancing the Growth of the U.S. Wind Industry: Federal Incentives, Funding, and Partnership Opportunities

Wind power is a burgeoning power source in the U.S. electricity portfolio, supplying more than 6% of U.S. electricity generation. The U.S. Department of Energy’s (DOE’s) Wind Energy Technologies Office (WETO) focuses on enabling industry growth and U.S. competitiveness by supporting early-stage research on technologies that enhance energy affordability, reliability, and resilience and strengthen U.S. energy security, economic growth, and environmental quality. Outlined below are the primary federal incentives for developing and investing in wind power, resources for funding wind power, and opportunities to partner with DOE and other federal agencies on efforts to move the U.S. wind industry forward.

developments↗
DOE OSTI2019

Optimizing wind farm siting to reduce power system impacts of wind variability

We introduce and solve two variants of a biobjective optimization model to reduce the negative impact of wind variability on the power system by strategically locating wind farms. The first model variant considers average changes in wind power over time; the second captures extreme fluctuations in wind power. A complementary set of wind sites is selected with the aim of minimizing both residual demand and the variability in residual demand. Because exact optimization is computationally intensive, we develop two heuristics—forward and backward greedy algorithms—to find approximate solutions. The results are compared with the exact optimization results for a well-selected subset of the data as well as to the results from selecting sites based on average wind alone. The two models are solved using demand data and potential wind sites for the Southwest Power Pool. Though both objectives can be improved by adding more sites, for a fixed number of sites, minimizing residual demand and variability in residual demand are competing objectives. Here, we find an approximate efficient frontier to compare trade-offs between the two objectives. We also vary the parameter in the heuristic that controls how the two objectives are prioritized. For the case study, the backward greedy algorithm is more effective at reducing the wind power variability than the forward greedy algorithm. Furthermore, using the backward algorithm for the full dataset is more effective than solving the exact optimization on a subset of the data when the results are evaluated using the full dataset.

17 WIND ENERGY↗
DOE OSTI2016

Energy Storage Opportunities and Capabilities in a Type 3 Wind Turbine Generator

Wind power plants and other renewable power plants with power electronic interfaces are capable of delivering frequency response (both governor and/or inertial response) to the grid by a control action; thus, the reduction of available online inertia as conventional power plants are retired can be compensated by designing renewable power plant controls to include frequency response. The source of energy to be delivered as inertial response is determined by the type of generation and control strategy chosen. The cost of energy storage is expected to drop over time, and global research activities on energy storage are very active, funded both by the private industry and governments. Different industry sectors (e.g., transportation, energy) are the major drivers of the recent storage research and development. This work investigates the opportunities and capabilities of deploying energy storage in renewable power plants. In particular, we focus on wind power plants with doubly-fed induction generators, or Type 3 wind turbine generator (WTGs). We find that the total output power of a system with Type 3 WTGs with energy storage can deliver a power boost during inertial response that is up to 45% higher than one without energy storage without affecting the torque limit, thus enabling an effective delivery of ancillary services to the grid.

17 WIND ENERGY↗
DOE OSTI2016

Assessing the Future of Distributed Wind: Opportunities for Behind-the-Meter Projects

Wind power is one of the fastest growing sources of new electricity generation in the United States. Cumulative installed capacity was more than 74,000 megawatts (MW) at year-end 2015 and wind power supplied 4.7% of total 2015 U.S. electricity generation. Despite the growth of the wind power industry, the distributed wind market has remained limited. Cumulative installations of distributed wind through 2015 totaled 934 MW. This first-of-a-kind exploratory analysis characterizes the future opportunity for behind-the-meter distributed wind, serving primarily rural or suburban homes, farms, and manufacturing facilities. This work focuses only on the grid-connected, behind-the-meter subset of the broader distributed wind market. We estimate this segment to be approximately half of the 934 MW of total installed distributed wind capacity at year-end 2015. Potential from other distributed wind market segments including systems installed in front of the meter (e.g., community wind) and in remote, off-grid locations is not assessed in this analysis and therefore, would be additive to results presented here. These other distributed wind market segments are not considered in this initial effort because of their relatively unique economic and market attributes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-correlated Mechanical Input Power

State and parameter estimation of power transmission networks is important for monitoring power grid operating conditions and analyzing transient stability. Wind power generation depends on fluctuating input power levels, which are correlated in time and contribute to uncertainty in turbine dynamical models. The ensemble Kalman filter (EnKF), a standard state estimation technique, uses a deterministic forecast and does not explicitly model time-correlated noise in parameters such as mechanical input power. However, this uncertainty affects the probability of fault-induced transient instability and increased prediction bias. Here a novel approach is to model input power noise with time-correlated stochastic fluctuations, and integrate them with the network dynamics during the forecast. While the EnKF has been used to calibrate constant parameters in turbine dynamical models, the calibration of a statistical model for a time-correlated parameter has not been investigated. In this study, twin experiments on a standard transmission network test case are used to validate our time-correlated noise model framework for state estimation of unsteady operating conditions and transient stability analysis, and a methodology is proposed for the inference of the mechanical input power time-correlation length parameter using time-series data from PMUs monitoring power dynamics at generator buses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Chapter 14 - Reliability of Wind Turbines

The global wind energy industry has grown at a fast pace during the past half-decade. Advancements from design and manufacturing to operation and maintenance have led to reduced capital and maintenance costs, which make wind power an indispensable source for a comprehensive solution to global electricity needs. Once wind turbines are installed, the opportunity to lower wind power costs is mainly through improved operation and maintenance practices. Modern wind turbines are equipped with tens or hundreds of measurement channels and are generating an abundance of data, with lot of efforts being put into data analysis by both the research community and the industry. One type of analysis is through the exploration of reliability engineering methods based on readily available data or maintenance records collected at typical wind power plants. If adopted and conducted appropriately, these analyses can quickly save operation and maintenance costs in a potentially impactful manner. The wind industry has adopted this discipline more broadly in recent years. This chapter discusses wind turbine reliability by highlighting the methodology of reliability engineering life data analysis. It first briefly discusses the fundamentals of wind turbine reliability and the current industry status. Then, the reliability engineering method for life analysis, including data collection, model development, and forecasting, is presented in detail and illustrated through two case studies. The chapter concludes with some remarks on potential opportunities to improve wind turbine reliability. An owner and operator's perspective is taken and mechanical components are used to exemplify the potential benefits of reliability engineering analysis to improve wind turbine reliability and availability.

database↗
DOE OSTI2024

Land-Based Wind Market Report: 2024 Edition

Wind power additions in the United States totaled 6.5 gigawatts (GW) of capacity in 2023. 1 Wind power growth has historically been supported by the industry’s primary federal incentive—the production tax credit (PTC)—as well as numerous state-level policies. Long-term improvements in the cost and performance of wind power technologies have also been key drivers for wind additions, yielding low-priced wind energy for utility, corporate, and other power purchasers. Nonetheless, 2023 was a slow year in terms of new wind deployment, the lowest since 2014. Elevated interest rates played a role in slowing deployment, as did interconnection and siting challenges.

17 WIND ENERGY↗
DOE OSTI2021

Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets

This dataset contains turbine- and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in Steady State (FLORIS) model, version 2.3.0. The 252,500 cases include 500 unique wind plants generated randomly by a specialized Plant Layout Generator (PLayGen) that samples randomized realizations of wind plant layouts from one of four canonical configurations: (i) cluster, (ii) single string, (iii) multiple string, (iv) parallel string. Other wind plant layout parameters were also randomly sampled, including the number of turbines (25-200) and the mean turbine spacing (3D-10D, where D denotes the turbine rotor diameter). For each layout, 500 different sets of atmospheric conditions were randomly sampled. These include wind speed in 0-25 m/s, wind direction in 0 deg.-360 deg., and turbulence intensity chosen from low (6%), medium (8%), and high (10%). For each atmospheric inflow scenario, the individual turbine yaw angles were randomly sampled from a one-sided truncated Gaussian on the interval 0 deg.-30 deg. oriented relative to wind inflow direction. This random data is supplemented with a collection of yaw-optimized samples where FLORIS was used to determine turbine yaw angles that maximize power production for the entire plant. To generate this data, a subset of cases were selected (50 atmospheric conditions from 50 layouts each for a total of additional 2,500 cases) for which FLORIS was re-run with wake steering control optimization. The IEA onshore reference turbine, which has a 130 m rotor diameter, a 110 m hub height, and a rated power capacity of 3.4 MW was used as the turbine for all simulations. The simulations were performed using NREL's Eagle high performance computing system in February 2021 as part of the Spatial Analysis for Wind Technology Development project funded by the U.S. Department of Energy Wind Energy Technologies Office. The data was collected, reformatted, and preprocessed for this OEDI submission in May 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub repository under explore_wind_plant_data_h5.ipynb.

AI↗
DOE OSTI2015

Media analysis of the representations of fusion and other future energy technologies

Media representations of energy have a relevant impact on public opinion and public support for investment in new energy sources. Fusion energy is one among several emerging energy technologies that requires a strong public investment on its research and development. This paper aims to characterise and compare the media representations of fusion and other emerging energy technologies in Portugal and in Spain. The emerging energy technologies selected for analysis are wave and tidal power, hydrogen, deep sea offshore wind power, energy applications of nanotechnology, bio-fuels from microalgae and IV generation nuclear fission. This work covered the news published in a selection of newspapers in Portugal and Spain between January 2007 and June 2013. (authors)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Wind Energy Modeling and Simulation, Volume 2: Turbine and System

In order to optimise the yield of wind power from existing and future wind plants, the entire breadth of the system of a plant, from the wind field to the turbine components, needs to be modelled in the design process. The modelling and simulation approaches used in each subsystem as well as the system-wide solution methods to optimize across subsystem boundaries are described in this reference. Chapters are written by technical experts in each field, describing the current state of the art in modelling and simulation for wind plant design. This comprehensive, two-volume research reference will provide long-lasting insight into the methods that will need to be developed for the technology to advance into its next generation. Volume 2 covers turbine level aerodynamics, aeroelasticity, rotors drivetrains and electrical systems, wind turbine control, offshore foundations, system optimization, and grid modelling.

17 WIND ENERGY↗
DOE OSTI2013

Ramping Performance Analysis of the Kahuku Wind-Energy Battery Storage System

High penetrations of wind power on the electrical grid can introduce technical challenges caused by resource variability. Such variability can have undesirable effects on the frequency, voltage, and transient stability of the grid. Energy storage devices can be an effective tool in reducing variability impacts on the power grid in the form of power smoothing and ramp control. Integrating anenergy storage system with a wind power plant can help smooth the variable power produced from wind. This paper explores the fast-response, megawatt-scale, wind-energy battery storage systems that were recently deployed throughout the Hawaiian islands to support wind and solar projects.

17 WIND ENERGY↗
DOE OSTI2015

2014 Wind Technologies Market Report

Wind power capacity additions in the United States rebounded in 2014, and continued growth through 2016 is anticipated. Recent and projected near-term growth is supported by the industry’s primary federal incentive—the production tax credit (PTC)—which is available for projects that began construction by the end of 2014. Wind additions are also being driven by recent improvements in the cost and performance of wind power technologies, which have resulted in the lowest power sales prices ever seen in the U.S. wind sector. Growing corporate demand for wind energy and state-level policies play important roles as well. Expectations for continued technological advancements and cost reductions may further boost future growth. At the same time, the prospects for growth beyond 2016 are uncertain. The PTC has expired, and its renewal remains in question. Continued low natural gas prices, modest electricity demand growth, and limited near-term demand from state renewables portfolio standards (RPS) have also put a damper on growth expectations. These trends, in combination with increasingly global supply chains, have limited the growth of domestic manufacturing of wind equipment. What they mean for wind power additions through the end of the decade and beyond will be dictated in part by future natural gas prices, fossil plant retirements, and policy decisions.

17 WIND ENERGY↗
DOE OSTI2023

The transmission ramifications of social and environmental siting considerations on wind energy deployment

Increasing the capacity of wind power is critical to achieving climate goals, however its continued deployment faces environmental and social siting challenges. For example, the United States government is increasingly emphasizing the importance of a just energy transition by considering the social impacts of energy and environmental justice (EEJ). In this study, we investigate the impact of considering available EEJ metrics and environmental impacts into siting wind power and transmission by applying SimWIND PRO . SimWIND PRO is an infrastructure optimization tool that can site wind energy technologies and transmission by concurrently considering wind resource potential, transmission costs, EEJ, and environmental impacts. We demonstrate the impacts of considering EEJ and environmental factors in the context of Midcontinent Independent System Operator’s (MISO) western region, which includes some of the best wind energy potential in the United States. We show that prioritizing EEJ and environmental considerations in wind deployment can result in exponentially more transmission deployment for the same amount of wind power delivered, and results in selecting different wind farm sites. Our results also show that, depending on how it is considered, it is possible that constraining sites based on EEJ and environmental factors can reduce the available capacity of wind energy enough that energy transition capacity targets cannot be met.

17 WIND ENERGY↗