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DOE OSTI2016

2015 Wind Technologies Market Report

Annual wind power capacity additions in the United States surged in 2015 and are projected to continue at a rapid clip in the coming five years. Recent and projected near-term growth is supported by the industry’s primary federal incentive—the production tax credit (PTC)—having been extended for several years (though with a phase-down schedule, described further on pages 68-69), as well as a myriad of state-level policies. Wind additions are also being driven by improvements in the cost and performance of wind power technologies, yielding low power sales prices for utility, corporate, and other purchasers. At the same time, the prospects for growth beyond the current PTC cycle remain uncertain: growth could be blunted by declining federal tax support, expectations for low natural gas prices, and modest electricity demand growth. This annual report—now in its tenth year—provides a detailed overview of developments and trends in the U.S. wind power market, with a particular focus on 2015. The report begins with an overview of key installation-related trends: trends in U.S. 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. Next, the report covers an array of wind power industry trends: developments in turbine manufacturer market share; manufacturing and supply-chain developments; wind turbine and component imports into and exports from the United States; project financing developments; and trends among wind power project owners and power purchasers. The report then turns to a summary of wind turbine technology trends: turbine size, hub height, rotor diameter, specific power, and IEC Class. After that, the report discusses wind power performance, cost, and pricing trends. In so doing, it describes trends in project performance, wind turbine transaction prices, installed project costs, and operations and maintenance (O&M) expenses. It also reviews the prices paid for wind power in the United States and how those prices compare to short-term wholesale electricity prices and forecasts of future natural gas prices. Next, the report examines policy and market factors impacting the domestic wind power market, including federal and state policy drivers as well as transmission and grid integration issues. The report concludes with a preview of possible near-term market developments. This edition of the annual report updates data presented in previous editions while highlighting key trends and important new developments from 2015. The report concentrates on larger, utility-scale wind turbines, defined here as individual turbines that exceed 100 kW in size.

17 WIND ENERGY↗
DOE OSTI2023

Exploring the Impact of Near-Term Innovations on the Technical Potential of Land-Based Wind Energy

Land based wind may play a critical role in reaching emissions reductions goals and high renewable contribution scenarios with capacity expansion modeling results estimating over 1 terawatt of land-based wind by 2035 to reach 100% clean electricity. Deployment of land-based wind at this magnitude may require significant investments in transmission infrastructure and will require significant land area for new wind and transmission. Cost reductions via technological advancements in wind turbine design, construction, and maintenance will have a major role in enabling the scale of deployment required. Since 1998, the levelized cost of wind energy has fallen by over 60% due to improvements in capacity factors, advancements in turbine controls, and cost reductions in installation, operation, and maintenance. These cost reduction pathways, generally referred to as wind technology "innovations", have enabled significant increases in the capacity and electric generation share of wind power in the United States. Nevertheless, achievements of past wind innovations have not led to widespread wind deployment outside of high wind speed geographies. This study evaluates the potential of near-term innovations to expand the geographic range of economically viable land-based wind power production in the United States. Many challenges to future deployment of wind power can be associated with increasing concentration in high-wind areas. As more wind power is deployed in these same areas, it is likely that residential and regulatory resistance to further deployment will increase, access to transmission will diminish, and options for distant companies and governments with renewable energy goals will remain limited. Therefore, this analysis aims to emphasize the potential for innovations to enable land-based wind in regions with limited wind deployment and with lower wind resource and better access to transmission.

17 WIND ENERGY↗
DOE OSTI2013

Renewable Energy for Rural Economic Development

When Renewable Energy for Rural Economic Development (RERED) began in 2005, Utah had no commercial wind power projects in operation. Today, the state hosts two commercial wind power plants, the Spanish Fork Wind Project and the Milford Wind Corridor Project, totaling 324 megawatts (MW) of wind capacity. Another project in San Juan County is expected to break ground very soon, and two others, also in San Juan County, are in the approval process. RERED has played a direct role in advancing wind power (and other renewable energy and clean technology innovations) in Utah through its education outreach and research/publication initiatives. RERED has also witnessed and studied some of the persistent barriers facing wind power development in communities across Utah and the West, and its research expanded to examine the diffusion of other energy efficiency and clean technology innovations. RERED leaves a legacy of publications, government reports, and documentary films and educational videos (archived at www.cleantech.usu.edu) to provide important insights for entrepreneurs, policymakers, students, and citizens about the road ahead for transitioning society onto a cleaner, more sustainable future.

17 WIND ENERGY↗
DOE OSTI2016

Electric Grid Expansion Planning with High Levels of Variable Generation

Renewables are taking a large proportion of generation capacity in U.S. power grids. As their randomness has increasing influence on power system operation, it is necessary to consider their impact on system expansion planning. To this end, this project studies the generation and transmission expansion co-optimization problem of the US Eastern Interconnection (EI) power grid with a high wind power penetration rate. In this project, the generation and transmission expansion problem for the EI system is modeled as a mixed-integer programming (MIP) problem. This study analyzed a time series creation method to capture the diversity of load and wind power across balancing regions in the EI system. The obtained time series can be easily introduced into the MIP co-optimization problem and then solved robustly through available MIP solvers. Simulation results show that the proposed time series generation method and the expansion co-optimization model and can improve the expansion result significantly after considering the diversity of wind and load across EI regions. The improved expansion plan that combines generation and transmission will aid system planners and policy makers to maximize the social welfare. This study shows that modelling load and wind variations and diversities across balancing regions will produce significantly different expansion result compared with former studies. For example, if wind is modeled in more details (by increasing the number of wind output levels) so that more wind blocks are considered in expansion planning, transmission expansion will be larger and the expansion timing will be earlier. Regarding generation expansion, more wind scenarios will slightly reduce wind generation expansion in the EI system and increase the expansion of other generation such as gas. Also, adopting detailed wind scenarios will reveal that it may be uneconomic to expand transmission networks for transmitting a large amount of wind power through a long distance in the EI system. Incorporating more details of renewables in expansion planning will inevitably increase the computational burden. Therefore, high performance computing (HPC) techniques are urgently needed for power system operation and planning optimization. As a scoping study task, this project tested some preliminary parallel computation techniques such as breaking down the simulation task into several sub-tasks based on chronology splitting or sample splitting, and then assigning these sub-tasks to different cores. Testing results show significant time reduction when a simulation task is split into several sub-tasks for parallel execution.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Capacity Optimization of a Centralized Charging Station in Joint Operation with a Wind Farm

In the context of large-scale wind power integration and rapid development of electric vehicles (EVs), a joint operation pattern was proposed to use a centralized charging station (CCS) to address high uncertainties incurred by wind power integration. Additionally, this would directly remove the significant indeterminacy of wind power. Because the CCS is adjacent to a wind power gathering station, it could work jointly with wind farms to operate in power system as an independent enterprise. By combining the actual operational characteristics of the wind farm and the CCS, a multidimensional operating index evaluation system was created for the joint system. Based on the wind farm’s known capacity, a multi-target capacity planning model was established for CCS to maximize the probability of realizing diverse indices of the system based on dependent-chance goal programming. In this model, planning and dispatching are combined to improve the feasibility of results in the operational stage. This approach takes the effects of randomness into account for wind power and battery swapping. The model was solved through combining Monte Carlo simulation and a genetic algorithm (GA) based on segmented encoding. Finally, as shown in the simulation results, the proposed model could comprehensively include factors such as initial investment, wind power price, battery life, etc., to optimize CCS capacity and to ultimately improve the operating indices of the joint system.

17 WIND ENERGY↗
DOE OSTI2019

2018 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. Highlights of this year’s report include: ~Wind power capacity additions continued at a robust pace in 2018: $11 billion was invested in new wind power plants in 2018. In 2018, wind energy contributed 6.5% of the nation’s electricity supply, more than 10% of total electricity generation in fourteen states, and more than 30% in three of those states—Kansas, Iowa, and Oklahoma. 232 gigawatts of wind are seeking transmission interconnection, 2% of which represents proposed wind plants paired with storage. ~Bigger turbines are enhancing wind project performance: Increased blade lengths have dramatically increased wind project capacity factors, one measure of project performance, and taller towers are on the horizon. The average 2018 capacity factor among projects built from 2014 through 2017 was 42%, compared to an average of 31% among projects built from 2004 to 2011 and 24% among projects built from 1998 to 2001. Low wind turbine pricing continues to push down installed project costs: Wind turbine prices have fallen to $700–$900/kW. The average installed cost of wind projects in 2018 was $1,470/kW, down 40 percent since the peak in 2009 and 2010. ~Wind energy prices are at historical lows: After topping out at 7¢/kWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped to below 2¢/kWh—though this nationwide average is dominated by projects that hail from the lowest-priced Interior region of the country. These prices, which are possible in part due to federal tax support, compare favorably to the projected future fuel costs of gas-fired generation. Solar PPA prices have declined precipitously, pressuring wind’s competitive position. ~The grid-system value of wind has declined over the last decade, but rebounded over the last two years. The value of wind in wholesale power markets is impacted by the location of wind plants, their hourly output profiles, and how those characteristics correlate with real-time electricity prices and capacity markets. The market value of wind in 2018 was the lowest in the Southwest Power Pool and Texas (average of $17/MWh and $18/MWh, respectively) whereas the highest-value market was New England (at $41/MWh). Wind energy prices are generally competitive with these value estimates. ~The domestic supply chain for wind equipment is diverse: For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>90%), towers (75-90%), and blades and hubs (50-70%), but is much lower (<20%) for most components internal to the nacelle. Wind sector employment reached a new high of more than 114,000 full-time workers at the end of 2018. ~Continued strong growth in wind capacity is anticipated in the near term: With federal tax incentives still available, various forecasts for the domestic market show expected wind power capacity additions of 9,000-12,000 MW in 2019 and 11,000-15,000 in 2020, with market contraction anticipated beginning in 2021 as those tax incentives are phased out.

17 WIND ENERGY↗
DOE OSTI2018

2017 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. Highlights of this year’s report include: -Wind power capacity additions continued at a rapid pace in 2017: $11 billion was invested in new wind power plants in 2017. In 2017, wind energy contributed 6.3% of the nation’s electricity supply, more than 10% of total electricity generation in fourteen states, and more than 30% in four of those states—Iowa, Kansas, Oklahoma, and South Dakota. -Bigger turbines are enhancing wind project performance: Increased blade lengths have dramatically increased wind project capacity factors, one measure of project performance, and taller towers appear to be on the horizon. The average 2017 capacity factor among projects built from 2014 through 2016 was 42%, compared to an average of 31.5% among projects built from 2004 to 2011 and 23.5% 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 $750–$950/kW. Overall, the average installed cost of wind projects in 2017 was $1,610/kW, down $795/kW from the peak in 2009 and 2010. -Wind energy prices remain low: After topping out at 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 Oklahoma, Nebraska, Kansas). 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 domestic supply chain for wind equipment is diverse: Wind sector employment reached a new high of more than 105,000 full-time workers at the end of 2017. For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>90%), towers (70-90%), 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, various forecasts for the domestic market show expected wind power capacity additions of 8,000 to 11,000 MW/year from 2018 to 2020, with market contraction anticipated beginning in 2021 as those tax incentives are phased out.

17 WIND ENERGY↗
DOE OSTI2014

Wind Resource Assessment of Gujarat (India)

India is one of the largest wind energy markets in the world. In 1986 Gujarat was the first Indian state to install a wind power project. In February 2013, the installed wind capacity in Gujarat was 3,093 MW. Due to the uncertainty around existing wind energy assessments in India, this analysis uses the Weather Research and Forecasting (WRF) model to simulate the wind at current hub heights for one year to provide more precise estimates of wind resources in Gujarat. The WRF model allows for accurate simulations of winds near the surface and at heights important for wind energy purposes. While previous resource assessments published wind power density, we focus on average wind speeds, which can be converted to wind power densities by the user with methods of their choice. The wind resource estimates in this study show regions with average annual wind speeds of more than 8 m/s.

17 WIND ENERGY↗
DOE OSTI2023

On the Low Risk of SSR in Type III Wind Turbines Operating in Grid-Forming Control: Preprint

We have shown in a previous work that the risk of sub-synchronous resonance (SSR) between a wind power plant with Type III wind turbines and series-compensated transmission lines is low when the wind turbines in the plant are operated in grid-forming mode, instead of the standard grid-following mode. The fundamental mechanism behind the improved damping characteristics is explained in this paper by modeling the positive and negative sequence impedances of Type III wind turbines for GFM operation mode. It is discovered that the GFM control naturally acts against the negative resistance of Type III wind turbines that results from an interaction between the proportional gain of the rotor-side converter current controller and negative slip. The developed sequence impedance models and improved damping behavior are verified using PSCAD simulations of a 2.5-MW Type III GFM wind turbine. The modeling predictions are also supported by experimentally measuring the sequence impedance response of a 2.5-MW Type III wind turbine during operation in GFL and GFM modes.

grid-following turbine↗
NASA NTRS1990

Solar power

This paper describes categories of solar technologies and identifies those that are economic. It compares the private costs of power from solar, wind, nuclear, coal, oil, and gas generators. In the southern United States, the private costs of building and generating electricity from new solar and wind power plants are less than the private cost of electricity from a new nuclear power plant. Solar power is more valuable than nuclear power since all solar power is available during peak and midpeak periods. Half of the power from nuclear generators is off-peak power and therefore is less valuable. Reliability is important in determining the value of wind and nuclear power. Damage from air pollution, when factored into the cost of power from fossil fuels, alters the cost comparison in favor of solar and wind power. Some policies are more effective at encouraging alternative energy technologies that pollute less and improve national security.

Walton, A. L.↗
NASA NTRS1985

Development of large wind energy power generation system

The background and development of an experimental 100 kW wind-energy generation system are described, and the results of current field tests are presented. The experimental wind turbine is a two-bladed down-wind horizontal axis propeller type with a 29.4 m diameter rotor and a tower 28 m in height. The plant was completed in March, 1983, and has been undergoing trouble-free tests since then. The present program calls for field tests during two years from fiscal 1983 to 1984. The development of technologies relating to the linkage and operation of wind-energy power generation system networks is planned along with the acquisition of basic data for the development of a large-scale wind energy power generation system.

Source record↗
DOE OSTI2023

Quantification of storage required for preserving frequency security in wind‐integrated systems

Abstract The penetration of wind power generation into the power grid has been accelerated in recent times due to the aggressive emission targets set by governments and other regulatory authorities. Although wind power has the advantage of being environment‐friendly, wind as a resource is intermittent in nature. In addition, wind power contributes little inertia to the system as most wind turbines are connected to the grid via power electronic converters. These negative aspects of wind power pose serious challenges to the frequency security of power systems as penetration increases. In this work, an approach is proposed where an energy storage system (ESS) is used to mitigate frequency security issues of wind‐integrated systems. ESSs are well equipped to supply virtual inertia to the grid due to their fast‐acting nature, thus replenishing some of the energy storage capability of displaced inertial generation. In this work, a probabilistic approach is proposed to estimate the amount of inertia required by a system to ensure frequency security. Reduction in total system inertia due to the displacement of conventional synchronous generation by wind power generation is considered in this approach, while also taking into account the loss of inertia due to forced outages of conventional units. Monte Carlo simulation is employed for implementing the probabilistic estimation of system inertia. An ESS is then sized appropriately, using the system swing equation, to compensate for the lost inertia. The uncertainty associated with wind energy is modeled into the framework using an autoregressive moving average technique. Effects of increasing the system peak load and changing the wind profile on the expected system inertia are studied to illustrate various factors that might affect system frequency security. The proposed method is validated using the IEEE 39‐bus test system.

17 WIND ENERGY↗
DOE OSTI2017

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. Our paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. Furthermore, a set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Developing high-resolution spatial data of migration corridors for avian species of concern in regions of high potential wind development

The future of the US economy, our national security, and our environmental quality all depend on decreasing our reliance on foreign oil and on fossil fuels. An essential component of decreasing this reliance is the development of alternative energy sources. Wind power is among the most important alternative energy sources currently available, and the mid-Atlantic region is a primary focus for wind power development. In addition to being important to the development of wind power, the mid-Atlantic region holds a special responsibility for the conservation of the eastern North America's golden eagles (Aquila chrysaetos). This small population breeds in northeastern Canada, winters in the southern Appalachians, and nearly all of these birds pass through the mid-Atlantic region twice each year. Movement of these birds is not random and, particularly during spring and autumn, migrating golden eagles concentrate in a narrow 30-50 mile wide corridor in central Pennsylvania. Thus, because the fate of these rare birds may depend on responsible management of the habitat they use it is critical to use research to identify ways to mitigate prospective impacts on this and similar raptor species. The goal of this project was to develop high-resolution spatial risk maps showing migration corridors of and habitat use by eastern golden eagles in regions of high potential for wind development. To accomplish this, we first expanded existing models of raptor migration for the eastern USA to identify broad-scale migration patterns. We then used data from novel high-resolution tracking devices to discover routes of passage and detailed flight behavior of individual golden eagles throughout the eastern USA. Finally, we integrated these data and models to predict population-level migration patterns and individual eagle flight behavior on migration. We then used this information to build spatially explicit, probabilistic maps showing relative risk to birds from wind development. This project has numerous benefits to people and to wildlife, primarily because it will provide a framework for safer and less controversial development of wind power. Because golden eagles are an important "umbrella" for other raptors, this project benefits a suite of species that may be impacted by wind turbines. Finally this work is a recognized priority for central Appalachian states and it is explicitly called for in, and meets the goals of, numerous state wildlife conservation plans. The final product we created, a region-wide map of relative risk to eagles of development of wind power, has allowed us to make specific recommendations regarding siting and operation of and mitigation at wind facilities. This approach also serves as a model for other projects to protect eagles in other places and to conserve suites of species beyond raptors.

17 WIND ENERGY↗
DOE OSTI2013

Advancements in Wind Integration Study Data Modeling: The Wind Integration National Dataset (WIND) Toolkit; Preprint

Regional wind integration studies in the United States require detailed wind power output data at many locations to perform simulations of how the power system will operate under high-penetration scenarios. The wind data sets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as be time synchronized with available load profiles. The Wind Integration National Dataset (WIND) Toolkit described in this paper fulfills these requirements. A wind resource dataset, wind power production time series, and simulated forecasts from a numerical weather prediction model run on a nationwide 2-km grid at 5-min resolution will be made publicly available for more than 110,000 onshore and offshore wind power production sites.

17 WIND ENERGY↗
DOE OSTI2023

Land-Based Wind Market Report: 2023 Edition

Wind power additions in the United States totaled 8.5 gigawatts (GW) in 2022. Wind power growth has historically been supported by the industry’s primary federal incentive—the production tax credit (PTC)—as well as myriad state-level policies. Long-term improvements in the cost and performance of wind power technologies have also been key drivers for wind additions. Nonetheless, 2022 was a relatively slow year in terms of new wind power deployment—the lowest since 2018—due in part to ongoing supply chain pressures, higher interest rates, and interconnection and siting challenges, but also the reduction in the value of the PTC that was in place up until the passage of the Inflation Reduction Act (IRA) in August 2022.

17 WIND ENERGY↗
DOE OSTI2019

Chapter 9: Cost of Wind Energy Modeling

This chapter focuses on considerations made when modeling cost of energy to be used in any of the abovementioned applications. The level of fidelity required to model each of the four, primary cost of energy elements depends on the characteristics of the wind power plant under consideration. Some 'top-down' models utilize empirical data to reflect currently installed wind power plants, while physics -based, or 'bottom up,' models may be implemented to estimate aspects of future wind-power-plant technology. Regardless of the ultimate application of cost of energy modeling, it is important to carefully assess the wind-power-plant characteristics to determine the type of model and the level of fidelity required to represent each of the four cost of energy elements.

17 WIND ENERGY↗