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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Impacts of turbine and plant upsizing on the levelized cost of energy for offshore wind

Turbine and plant upsizing are major trends in offshore wind deployment, although the quantitative impact on project costs has not been well-characterized. The uncertain value of continued wind turbine and project growth limits the ability of the supply chain to prepare for future technology trends, leading to challenges in the realization of larger projects. This analysis explores the levelized cost of energy impacts of turbine ratings between 6 and 20 MW and plant capacities between 250 and 2,500 MW for fixed-bottom offshore wind using techno-economic cost models for foundation, electrical, installation, and operation and maintenance costs, along with annual energy production. We consider a nominal set of technology assumptions for all scenarios to isolate economies of size and scale without additional benefits from decreasing turbine capital costs, quantity discounts for larger projects, or optimized technology solutions. These results indicate that using a 20-MW wind turbine in a 2,500-MW power plant array can reduce the levelized cost of energy by over 23% relative to the global average turbine and plant size installed in 2019; primarily because of reductions in the balance-of-system and operation and maintenance costs. We also identify improved installation vessels, optimized export systems, and novel operation and maintenance strategies as additional cost reduction opportunities. These results suggest that upsizing represents a significant cost reduction opportunity for offshore wind energy and will continue to be a main factor in shaping the future of the sector.

17 WIND ENERGY↗
DOE OSTI2021

Spatial impacts of technological innovations on the levelized cost of energy for offshore wind power plants in the United States

Recent studies predict significant decreases in the future levelized cost of energy (LCOE) of offshore wind energy, much of which is attributed to anticipated cost reductions from technological innovation. This study evaluates the spatial variability of LCOE caused by technology-induced decreases in a range of capital, operational, and financial cost categories. A spatial cost model of fixed-bottom and floating offshore wind plants is used to model the impact across thousands of potential United States sites. A specified change in an individual turbine subsystem cost produces a range of LCOE outcomes due to the varying geospatial characteristics of the considered sites and the nonlinear, interactive dependency on these input parameters; for example, a 10.8% improvement in net capacity factor can reduce LCOE by between 6% and 20% at different sites. This work expands upon the existing offshore wind literature, which typically evaluates cost sensitivities at a single site and does not consider the spatial variance in LCOE. The results suggest that the impact of technological innovations can be considerable and should be considered on a spatial as well as temporal basis when prioritizing technology innovation research or funding decisions to advance offshore wind technologies in the United States.

17 WIND ENERGY↗
DOE OSTI2021

Scaling trends for balance-of-system costs at land-based wind power plants: Opportunities for innovations in foundation and erection

Wind power plant sizes, hub heights, and turbine ratings have increased since 2008 to optimize the cost and performance of wind power; however, the limits of these economies of scale remain unclear. Here, we explore how the costs incurred to install turbines at a wind power plant—the balance-of-system (BOS) costs—scale with turbine rating, hub height, and plant size. We also investigate how these changes in BOS costs influence the levelized cost of energy (LCOE). We show that increasing the plant size from 150 to 400 MW could reduce the BOS costs by 21%. We also show that if the foundation costs decreased by 50%, building a wind power plant with 5-MW turbines (having rotor diameters of 166 m and hub heights of 120 m) could decrease the LCOE by 5%. These results could help inform future BOS cost-reduction opportunities and thereby reduce future capital costs for land-based wind power.

17 WIND ENERGY↗
DOE OSTI2021

The Costs and Feasibility of Floating Offshore Wind Energy in the O'ahu Region

The State of Hawai'i has set a target to achieve a 100% Renewable Portfolio Standard (RPS) by 2045, and is well suited to become the first state to achieve this goal due to its relatively small load, high electricity prices, heavy reliance on imported fossil fuels, and favorable conditions for wind and solar. The Bureau of Ocean Energy Management contracted NREL to conduct a cost and feasibility study to provide information to decision makers on Hawai'i about the viability of floating offshore wind to be a part of the 100% RPS. We used NREL's Offshore Regional Cost Analyzer (ORCA) spatial cost model to evaluate the Levelized Cost of Energy (LCOE) in the region surrounding O'ahu as this is the island with the highest energy demand. The ORCA results showed that LCOE could range from around $\$$83 MWh to $\$$194 MWh for commercial operation dates in 2019 but has the potential to decrease to $\$$48 MWh - $\$$109 MWh by 2032 due to maturing global supply chains, increasing turbine rating, and new technological innovations. These costs are expected to be competitive with global floating wind costs in the early 2030s. The strong wind resource, proximity to infrastructure on land, and benign metocean conditions can potentially compensate for the logistical complexities of installing projects in Hawai'i far from mainland supply chains if sufficient investments are made to develop ports, grid infrastructure, and workforce on O'ahu to support the construction and operation of offshore wind projects. In addition to the cost results, this report also discusses the likely technologies that would comprise floating wind projects near O'ahu, the existing infrastructure available to projects, unique conditions facing offshore wind in the region such as exposure to hurricanes and limits on allowable export cable capacity, newly developed wind resource data sets for the region, and local stakeholder perspectives on offshore wind.

17 WIND ENERGY↗
DOE OSTI2020

Exploring Renewable Energy Opportunities in Select Southeast Asian Countries: A Geospatial Analysis of the Levelized Cost of Energy of Utility-Scale Wind and Solar Photovoltaics

The costs of renewable energy-based electricity generation have fallen precipitously in recent years to levels that are increasingly competitive with traditional generation such as fossil fuel-based generation. As these costs become increasingly competitive, private developers, policymakers, and energy system planners are searching for opportunities to harness high-quality renewable energy resources. Developing economies are setting ambitious targets and exploring how cost-effective, grid-connected renewable energy options can help power economic growth and meet growing electricity demands. This includes the member states of the Association of Southeast Asian Nations (ASEAN) that are determined to reach a target of 23% of renewable energy in the region's total primary energy supply by 2025. A critical gap to identifying opportunities and scaling up renewable energy is the lack of quality data and analyses to support decisions on the investment and deployment of renewables - including wind and solar photovoltaics (PV). This work supports decision making by providing high-quality data and spatial analysis of the cost of utility-scale wind and solar PV generation in select countries of Southeast Asia - specifically, the ASEAN member states. Generation costs are expressed as the levelized cost of energy (LCOE) - a commonly used metric that represents the net present value of the unit cost of electricity during the lifetime of a particular electricity generation technology. This is the first spatial estimate of LCOE for these technologies within the ASEAN member states - providing insights into the roles that renewable energy resource quality and other factors may play in generation costs.

14 SOLAR ENERGY↗
DOE OSTI2020

ORBIT: Offshore Renewables Balance-of-System and Installation Tool

This report describes the Offshore Renewables Balance-of-system Installation Tool ("ORBIT"), a new model developed by the National Renewable Energy Laboratory (NREL) to evaluate the balance-of-system (BOS) costs associated with offshore wind projects. In the context of wind energy projects BOS costs encompass all expenses required to construct a project other than the capital expenditures (CapEx) of the turbines and towers, including the procurement costs for all other components (such as substructures, cables, and electrical infrastructure), offshore and land-based construction costs, port costs, site surveying fees, permitting fees, and leasing fees are all categorized as BOS costs. BOS costs significantly contribute to the levelized cost of energy (LCOE) for offshore wind, typically comprising over 50\% of the CapEx for a fixed-bottom offshore wind project and 60\% for a floating project. In addition, technology solutions and installation methods vary drastically between projects as they are impacted by factors such as vessel availability, geographic considerations and site geotechnical conditions. These effects require a model with appropriate fidelity to understand how these costs scale as turbine rating increases and the offshore wind supply chain, particularly offshore construction vessels, is expanded. For offshore wind, cost savings attributed to increased turbine rating are primarily realized through BOS procurement and project installation as fewer substructures and less cable are required. BOS costs represent both a modeling challenge, as well as an opportunity, for project developers to optimize solutions to reduce costs. It is critical to understand how these costs are affected by novel technologies, innovative installation processes, and operational constraints in order identify meaningful cost reductions for offshore wind energy.

17 WIND ENERGY↗
DOE OSTI2021

Gaining Insights in Loading Events for Wind Turbine Drivetrain Prognostics

Wind energy is one of the largest sources of renewable energy in the world. To further reduce the operations and maintenance (O&M) costs of wind farms, it is essential to be able to accurately pinpoint the root causes of different failure modes of interest. An example of such a failure mode that is not yet fully understood is white etching cracks (WEC). This can cause the bearing lifetime to be reduced to 5–10% of its design value. Multiple hypotheses are available in literature concerning its cause. To be able to validate or disprove these hypotheses, it is essential to have historic high-frequency measurement data (e.g., load and vibration levels) available. In time, this will allow linking to the history of the turbine operating data with failure data. This paper discusses the dynamic loading on the turbine during certain events (e.g., emergency stops, run-ups, and during normal operating conditions). By combining the number of specific events that each turbine has seen with the severity of each event, it becomes possible to assess which turbines are most likely to show signs of damage.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
NASA NTRS1990

Laser Atmospheric Wind Sounder (LAWS) phase 1. Volume 3: Project cost estimates

The laser atmospheric wind sounder (LAWS) cost modeling activities were initiated in phase 1 to establish the ground rules and cost model that would apply to both phase 1 and phase 2 cost analyses. The primary emphasis in phase 1 was development of a cost model for a LAWS instrument for the Japanese Polar Orbiting Platform (JPOP). However, the Space Station application was also addressed in this model, and elements were included, where necessary, to account for Space Station unique items. The cost model presented in the following sections defines the framework for all LAWS cost modeling. The model is consistent with currently available detail, and can be extended to account for greater detail as the project definition progresses.

Source record↗
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 OSTI2019

NREL's Balance-of-System Cost Model for Land-Based Wind

NREL's Land-based Balance of System Systems Engineering (LandBOSSE) model is a tool for modeling the balance of system (BOS) costs of land-based wind plants. BOS costs currently account for approximately 30% of the capital expenditures needed to install a land-based wind plant and include all costs associated with installing a wind plant, such as permitting, labor, material, and equipment costs associated with site preparation, foundation construction, electrical infrastructure, and tower installation. NREL developed LandBOSSE after identifying a need for a hybrid of process-based and empirically derived model that can provide flexibility for assessing wind plant BOS costs at a system level. NREL's prior BOS models have relied on empirical fits of legacy industry data, which limit their predictive ability. LandBOSSE, however, was designed to help users explore tradeoffs between innovative design scenarios while balancing the level of detail and speed required for model execution. The model was developed using a hybrid of process-based and empirically derived methods to create a modular model design that will allow for updates as wind energy technology evolves. The goal of LandBOSSE is to allow researchers, analysts, wind power developers, government agencies, and the public to explore how BOS may vary for different wind plant designs.

17 WIND ENERGY↗
DOE OSTI2021

Technology Innovation Pathways for Distributed Wind Balance-of-System Cost Reduction

This exploratory analysis characterizes the balance-of-system (BOS) cost reduction opportunity for small, commercial, medium, and large distributed wind systems. To do this, we used the National Renewable Energy Laboratory’s (NREL’s) Land-based Balance of System Systems Engineering (LandBOSSE) model (Eberle et al. 2019). This model calculates the capital expenditures (CapEx) associated with installation and the system components (e.g., foundation and electrical infrastructure) other than the rotor nacelle assembly and tower. Then, building on the cost reduction potential assessment, we provide a more qualitative evaluation of prospective technology innovation concepts that may enable realization of those cost reductions for distributed wind systems.

17 WIND ENERGY↗
DOE OSTI2021

2020 System Optimization Awardee: Pecos Wind Power

With support from the Competitiveness Improvement Project's System Optimization Award, Pecos Wind Power will help lower the cost of distributed wind technology and expand deployment. To do so, the company will optimize their PW85 distributed wind turbine to achieve a production levelized cost of energy of $0.099 per kilowatt hour, which is a 48% cost reduction in distributed wind energy costs. This reduction is a critical step toward Pecos Wind Power's ultimate target of $0.089 per kilowatt-hour. This fact sheet provides an overview of Pecos Wind Power's project, how the company will achieve the goals of the award, and how the project fits within the overall Competitiveness Improvement Project.

CIP↗
NASA NTRS1982

Multiple and variable speed electrical generator systems for large wind turbines

A cost effective method to achieve increased wind turbine generator energy conversion and other operational benefits through variable speed operation is presented. Earlier studies of multiple and variable speed generators in wind turbines were extended for evaluation in the context of a specific large sized conceptual design. System design and simulation have defined the costs and performance benefits which can be expected from both two speed and variable speed configurations.

Andersen, T. S.↗
DOE OSTI2019

Improving estimates of transmission capital costs for utility-scale wind and solar projects to inform renewable energy policy

Estimating the overall costs of transmission needed to integrate variable renewable energy (VRE) onto the grid is challenging. An improved understanding of these transmission costs would support electricity system planning as VRE penetrations increase. This paper brackets VRE transmission capital costs using multiple approaches based on interconnection studies, actual transmission projects, capacity-expansion simulation models, and aggregated U.S. VRE-related transmission expenditures. Each approach possesses advantages and drawbacks, and combining the approaches lends confidence to the results. The resulting range of average levelized VRE transmission costs is 1– 10 dollars/MWh, which is generally lower than earlier estimates in the literature. These transmission capital costs can increase the direct plant-level levelized cost of VRE by 3%–33%, based on levelized costs of energy of 29– 56 dollars/MWh for utility-scale wind and 36–46 dollars/MWh for utility-scale solar. As VRE deployment continues to expand, policy makers can use this information to (1) assess the benefits of transmission avoidance and deferral when comparing distributed energy resources versus utility-scale projects, (2) evaluate the potential costs of large-scale public transmission investments, and (3) better analyze system-level costs of utility-scale VRE technologies. Future research can expand on the framework presented here by providing a review of operation and maintenance costs for transmission systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Control Co-Design of Wind Turbines

Wind energy is recognized worldwide as cost-effective and environmentally friendly, and it is among the fastest-growing sources of electrical energy. To further decrease the cost of wind energy, wind turbines are being designed at ever-larger scales. To expand the deployment of wind energy, wind turbines are also being designed on floating platforms for placement in deep-water locations offshore. Both larger-scale and floating wind turbines pose challenges because of their greater structural loads and deflections. Complex, large-scale systems such as modern wind turbines increasingly require a control co-design approach, whereby the system design and control design are performed in a more integrated fashion. This article reviews recent developments in control co-design of wind turbines. We provide an overview of wind turbine design objectives and constraints, issues in the design of key wind turbine components, modeling of the wind turbine and environment, and controller coupling issues. Wind turbine control functions and the integration of control design in co-design are detailed with a focus on co-design compatible control approaches.

17 WIND ENERGY↗
DOE OSTI2020

Wind Energy Technologies Office Multi-Year Program Plan Fiscal Years 2021—2025

WETO's Multi-Year Program Plan outlines the Office’s research priorities and plans through the year 2025. With the objectives of reducing the cost of wind energy, enabling the integration of substantial amounts of wind energy into the evolving national energy system, and creating siting and environmental solutions to reduce environmental impacts, the Plan provides insight into DOE’s wind energy research priorities, and will guide planning and execution of future R&D activities. It is organized in six sections: offshore wind, land-based wind, distributed wind, systems integration, modeling and analysis, and crosscutting initiatives.

17 WIND ENERGY↗
DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗
NASA NTRS1981

An algorithm for minimum-cost set-point ordering in a cryogenic wind tunnel

An algorithm for minimum cost ordering of set points in a cryogenic wind tunnel is developed. The procedure generates a matrix of dynamic state transition costs, which is evaluated by means of a single-volume lumped model of the cryogenic wind tunnel and the use of some idealized minimum-costs, which is evaluated by means of a single-volume lumped model of the cryogenic wind tunnel and the use of some idealized minimum-cost state-transition control strategies. A branch and bound algorithm is employed to determine the least costly sequence of state transitions from the transition-cost matrix. Some numerical results based on data for the National Transonic Facility are presented which show a strong preference for state transitions that consume to coolant. Results also show that the choice of the terminal set point in an open odering can produce a wide variation in total cost.

Tripp, J. S.↗