Engineering topics

Tinnesand, Heidi

Publications and source records attributed to Tinnesand, Heidi.

At least 19 records

DOE OSTI2024

WTK-LED: The WIND Toolkit Long-Term Ensemble Dataset

To satisfy a wide group of stakeholders across various wind energy disciplines, including but not limited to stakeholders in the distributed and utility scale wind industry, the new emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, and researchers in academia, and to close some of the gaps that current public datasets have, we aimed at developing an updated version of the meteorological WIND Toolkit, named WIND Toolkit Long-term Ensemble Dataset (WTK-LED), which is a meteorological dataset providing time series every 5 min and 2 km, including model uncertainty of wind speed at every modeling grid point so that users are provided with a range of possible wind speeds every 2 km. The data were produced using the Weather Research and Forecasting Model (WRF). The vertical grid used in WTK-LED includes many vertical layers in the atmospheric boundary layer to provide information of atmospheric quantities across the rotor layer of utility scale and distributed wind turbines. The WTK-LED includes: 1) Numerical simulations covering the continental United States, Alaska, and Hawaii, with high-resolution data being available for 3 years (2018-2020). 2) Climate simulations from Argonne National Laboratories covering the North American continent, including Alaska, Canada, and most of Mexico and the Caribbean Islands. These simulations complement the new WTK-LED to offer a 4-km dataset covering 20 years, from 2001-2020. 3) Specific long-term,high-resolution offshore simulations have been conducted separately for the US coasts, Hawaii, and the Great Lakes, leading to the 2023 National Offshore Wind data set. This report focuses on a description of the land-based WTK-LED for CONUS, Hawaii, and Alaska, for the 3-year 2-km/5-min dataset and the 20-year 4-km/hourly dataset, as well as the uncertainty quantification method. We also provide limited validation results. Based on our results to date, we suggest use cases and applications for each dataset of the WTK-LED.

17 WIND ENERGY↗

DOE OSTI2024

Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US

Current wind resources within the United States (US) indicate a potential to profitably install nearly 1,400 gigawatts of distributed wind (DW) capacity. This amount is equivalent to over half of the United States’ current energy demand from electricity, making it enough to power millions of homes and businesses and replace countless fossil fuel-based generating plants. Despite the potential growth of DW in the US, deployments are presently hindered by a lack of confidence in resource estimation methods. One potential challenge is that smaller-scale turbines, with hub heights of 40 meters or less, are disproportionately impacted by obstacles such as buildings and vegetation. These obstacles may produce complex wake effects, best modeled with high-fidelity complex fluid dynamics (CFD) models that are too computationally expensive to use for routine siting and resource assessment. Thus, installers today make use of heuristics and simple equations to approximate the impact of obstacles while also leveraging long-term resource data from commercial or publicly available atmospheric models. This study evaluates these historical and commonly used methods alongside new lower-order obstacle models produced from CFD simulations and measurement-based bias correction. The preliminary results from this study show the importance of taking care in the choice and application of mesoscale atmospheric models and the significant value of bias correction using measurements from nearby meteorological towers. Detailed obstacle modeling provides only modest additional gains in performance and, in some cases, can add error, especially at sites where turbines have already been located to avoid obvious impact from upwind obstacles. These findings reinforce the importance of collecting in situ measurements and suggest that obstacle models may be better applied in practice to automated or computer-aided siting, rather than in economic wind resource assessments.

17 WIND ENERGY↗

DOE OSTI2024

Inventory of Clean Energy Education and Workforce Programs in Connecticut's I-91 Corridor

This document reports on the findings of an inventory of the educational and workforce development resources in a four-county area on the I-91 corridor in Connecticut. The overarching goal of this exercise is to help the local workforce leaders in Bridgeport, Connecticut to optimize their training programs to support the county's clean energy transition. The inventory generally finds that Fairfield County, where Bridgeport is located, lacks adequate education and training programs compared to other surrounding counties with similar populations. More than half of Fairfield County's programs occur through high school or career technical education programs, suggesting that there are minimal opportunities for workers who are not currently high school students. Much of the existing focus within Fairfield County is on general construction or plumbing and electrical skills. There is room to expand by offering more programs focusing on energy efficiency and renewable energy skills. The document outlines some potential next steps and questions for consideration for the local workforce leaders in Bridgeport. Bridgeport is a city located within Fairfield County in southwestern Connecticut. It is the largest city in Connecticut with an estimated population of 150,000. Through the Department of Energy-funded Communities LEAP program, NREL conducted an analysis of existing education and workforce development (EWD) programs located in Fairfield County that align with (or could support) the worker pipeline for occupations related to building energy efficiency (EE), renewable energy (RE), and clean energy manufacturing. The analysis also looked at EWD offerings in other counties in Connecticut as a point of comparison.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Building the Case for Hybrid Energy Developments: Webinar Recording Day 2

INL, PNNL, and NREL, with support from our academic and industry partners, hosted a 2-day webinar to learn more about the benefits of hybrid energy systems, considerations for designing the right system for communities, and practical tools that can help with the design and development process. In Day 2 of this webinar, the focus was on tools that can be used to help inform design decisions for hybrid systems, including resource analysis, siting, resilience, valuation, and resource optimization. Day 2 also highlighted funding opportunities for hybrid systems.

14 SOLAR ENERGY↗

DOE OSTI2023

Building the Case for Hybrid Energy Developments: Webinar Recording Day 2

14 SOLAR ENERGY↗

DOE OSTI2022

U.S. Offshore Wind Workforce Assessment

As the U.S. offshore wind market prepares for rapid growth, our understanding of specific workforce requirements and pathways to meet those requirements is not keeping pace. Without this deeper understanding, there is a risk that the workforce will not develop as efficiently or robustly as is needed to fully realize the benefits of offshore wind. The "U.S. Offshore Wind Workforce Assessment" provides a more detailed assessment of the workforce demand, supply, and pathways to support key stakeholders including industry, state and local governments, educational organizations, and unions in their efforts to attract, educate, train, and retain a domestic workforce to support this burgeoning industry.

17 WIND ENERGY↗

DOE OSTI2022

Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands

Abstract. Growth in adoption of distributed wind turbines for energy generation is significantly impacted by challenges associated with siting and accurate estimation of the wind resource. Small turbines, at hub heights of 40 m or less, are greatly impacted by terrestrial obstacles such as built structures and vegetation that can cause complex wake effects. While some progress in high-fidelity complex fluid dynamics (CFD) models has increased the potential accuracy for modelling the impacts of obstacles on turbulent wind flow, these models are too computationally expensive for practical siting and resource assessment applications. To understand the efficacy of available models in situ, this study evaluates classic and commonly used methods alongside new state-of-the-art lower-order models derived from CFD simulations and machine learning approaches. This evaluation is conducted using a subset of an extensive original dataset of measurements from more than 300 operational wind turbines in the northern Netherlands. The results show that data-driven methods (e.g. machine learning and statistical modelling) are most effective at predicting production at real sites with an average error in annual energy production of 2.5 %. When sufficient data may not be available de novo to support these data-driven approaches, models derived from high-fidelity simulations show promise and reliably outperform classic methods. On average these models have 6.3 %–11.5 % error compared with 26 % for classic methods and 27 % baseline error for reanalysis data without obstacle correction. While more performant on average, these methods are also sensitive to the quality of obstacle descriptions and reanalysis inputs.

17 WIND ENERGY↗

DOE OSTI2022

Validation of wind resource and energy production simulations for small wind turbines in the United States

Abstract. Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, MyWindTurbine.com, and Global Wind Atlas 3 (GWA3), which are seen to overestimate actual annual capacity factors by 2.5, 4.2, 11.5, and 7.3 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produces more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.

17 WIND ENERGY↗

DOE OSTI2021

Distributed Wind Research Program Workshop Report

The U.S. Department of Energy’s (DOE’s) Wind Energy Technologies Office (WETO) Distributed Wind Research Program seeks to enable wind technology as an affordable, accessible, and compatible distributed energy resource (DER) option for individuals, businesses, and communities. WETO’s Distributed Wind Research Program includes national laboratory projects and partnerships with industry and academia. WETO organized the Distributed Wind Research Program Workshop to bring together laboratory and industry project stakeholders to achieve the following objectives: Educate - Create a shared high-level understanding for the breadth of WETO’s distributed wind research Collaborate - Facilitate coordination and collaboration between funding recipients to leverage resources Innovate - Understand future research challenges and opportunities. This workshop convened over 80 participants from various organizations. Workshop participants were asked for their individual feedback based on their own expertise and experience.

17 WIND ENERGY↗

DOE OSTI2021

Distributed Wind Research Program Workshop Report

The U.S. Department of Energy’s (DOE’s) Wind Energy Technologies Office (WETO) Distributed Wind Research Program includes national laboratory projects and partnerships with industry and academia. WETO organized the Distributed Wind Research Program Workshop to facilitate collaboration between laboratory and industry project stakeholders. This report summarizes key workshop findings, current technology research and development collaboration and coordination opportunities, potential modes of collaboration and coordination, and future technology research and development opportunities.

17 WIND ENERGY↗

DOE OSTI2021

Bias Characterization, Vertical Interpolation, and Horizontal Interpolation for Distributed Wind Siting Using Mesoscale Wind Resource Estimates

Much like their counterparts in utility-scale wind energy, developers of industrial, small-scale and distributed wind turbine deployments need to understand and accurately characterize the wind resource to properly assess the power generation and financial ramifications during siting and planning. National Renewable Energy Laboratory’s WIND (Wind Integration National Dataset) Toolkit (WTK) provides a best-in-class wind resource dataset generated using the Weather Research and Forecasting (WRF) model. This dataset includes parameters such as the wind speed, wind direction, and temperature at various heights, plus atmospheric stability near the surface. This data is available at 2-km spatial resolution and five-minute temporal resolution across 7 years, from 2007 to 2013 through a publicly accessible API interface. The Tools Assessing Performance (TAP) project seeks to extend this dataset to allow long term resource estimates and leverage it to better equip distributed wind equipment manufacturers, owner-operators, and installation professionals with better tools for practical siting applications. In this report, we present the results from our investigation within the TAP project focused on characterization of bias in WTK-based wind speed estimates and evaluation of vertical and horizontal interpolation techniques. We discuss the tradeoffs between different techniques and their combinations, as well as describe the lower bounds we determine for the studied validation errors. While the specific estimates we present are specific to WTK and the validation dataset we have chosen for this investigation (NREL's Wind Resource Meteorological Database), the overall analysis and the studied techniques are general enough to be applied to a broader set of wind datasets, both simulation-based and observational.

17 WIND ENERGY↗

DOE OSTI2020

An Evaluation of Advanced Tools for Distributed Wind Turbine Performance Estimation

We evaluate various classes of distributed wind turbine performance tools across two sites in the United States. The class of tools ranges from the simple mass conservation model to the coupled Reynolds-averaged Navier-Stokes model, all initiated by the WIND Toolkit data set. The resource estimation at the site is evaluated against measured data at the mast location. Taking a sample 100-kW wind turbine and constant losses, we evaluate Openwind, Continuum, and WindNinja tools and document annual energy production (AEP) and time-series statistics associated with the performance estimation of the wind turbine. Using a methodology that is consistent and unbiased across the three options currently available in the industry, we elaborate results at the two sample locations and discuss the probable sources of discrepancy in the AEP estimates. Two main sources of the discrepancy come from the input WIND Toolkit data and the spatial modeling techniques of the tools to capture atmospheric physics. The discussion includes additional values that these tools may bring into the energy assessment process to enhance the owners' confidence over the distributed wind power systems.

17 WIND ENERGY↗

DOE OSTI2019

An Evaluation of Advanced Tools for Distributed Wind Turbine Performance Estimation: Preprint

We evaluate various classes of distributed wind turbine performance tools across two sites in the USA. The tools class ranges from the simple mass conservation model to coupled Reynolds Averaged Navier-Stokes (RANS) model, all initiated by the WIND Toolkit dataset. The resource estimation at the site is evaluated against measured data at the mast location. Taking a sample 100 kW wind turbine and constant losses, we evaluate Openwind, Continuum, and WindNinja tools and document Annual Energy Production (AEP) and time series statistics associated with the performance estimation of the wind turbine. Using a methodology that is consistent and unbiased across the three advance options currently available in the industry, we elaborate results at the two sample locations and discuss the probable sources of discrepancy in the AEP estimates. The discussion includes additional values these tools may bring into the energy assessment process to enhance the owners' confidence over the distributed wind power systems.

17 WIND ENERGY↗

DOE OSTI2019

Distributed Wind Resource Assessment Framework: Functional Requirements and Metrics for Performance and Reliability Modeling

The focus of the distributed wind resource assessment (DWRA) performance framework is to first clarify the key parameters that define the wind resource for any distributed wind turbine project and then describe the loss and uncertainty factors associated with long-term performance of wind turbine projects. Next it will identify and define the key parameters required to analyze the operational performance of a project. This framework could then be the foundation for understanding the predictability of the power and revenue from a project. Improving the predictability and reliability of wind power generation and operations will reduce costs and potentially establish a framework to attract new capital to the distributed wind industry sector. This report is the first part of a research effort to investigate the impact that improved resource assessment and energy estimation for distributed wind can have on consumer confidence, cost of capital, cost of ownership and operations, perception of financial risk, and levelized cost of energy.

17 WIND ENERGY↗

DOE OSTI2017

Performance Prediction and Validation: Data, Frameworks, and Considerations

Improving the predictability and reliability of wind power generation and operations will reduce costs and potentially establish a framework to attract new capital into the distributed wind sector, a key cost reduction requirement highlighted in results from the distributed wind future market assessment conducted with dWind. Quantifying and refining the accuracy of project performance estimates will also directly address several of the key challenges identified by industry stakeholders in 2015 as part of the distributed wind resource assessment workshop and be cross-cutting for several other facets of the distributed wind portfolio. This presentation covers the efforts undertaken in 2016 to address these topics.

17 WIND ENERGY↗

DOE OSTI2016

Current and Future Opportunities for Wind Power in the Southeast

This presentation discusses future wind opportunities in the Southeast including factors such as changes in wind turbine technology, historical innovation trends, and forecast demand growth among regions. The presentation covers the current status of wind in the United States at 80-m hub height and the near-future outlook with a hub height at 110 to 140 meters. Future cost reductions in 2030 and beyond are also explored. Heidi Tinnesand presented this information to a utility advisory group meeting in Charlotte, North Carolina, on October 5, 2016.

17 WIND ENERGY↗

DOE OSTI2016

Marine Hydrokinetic Energy Site Identification and Ranking Methodology Part II: Tidal Energy

Marine hydrokinetic energy is a promising and growing piece of the renewable energy sector that offers high predictability and additional energy sources for a diversified energy economy. This report investigates the market opportunities for tidal energy along the U.S. coastlines. It is part one of a two-part investigation into the United States' two largest marine hydrokinetic resources (wave and tidal). Tidal energy technology is still an emerging form of renewable energy for which large-scale grid-connected project costs are currently poorly defined. Ideally, device designers would like to know the resource conditions at economical project sites so they can optimize device designs. On the other hand, project developers need detailed device cost data to identify sites where projects are economical. That is, device design and siting are, to some extent, a coupled problem. This work describes a methodology for identifying likely deployment locations based on a set of criteria that tidal energy experts in industry, academia, and national laboratories agree are likely to be important factors for all technology types. Several factors that will affect tidal project costs and siting have not been considered here -- including permitting constraints, conflicting use, seasonal resource variability, extreme event likelihood, and distance to ports -- because consistent data are unavailable or technology-independent scoring could not be identified. As the industry continues to mature and converge around a subset of device archetypes with well-defined costs, more precise investigations of project siting that include these factors will be possible. For now, these results provide a high-level guide pointing to the regions where markets and resource will one day support commercial tidal energy projects.

16 TIDAL AND WAVE POWER↗

DOE OSTI2016

Wind Turbines in the Built Environment: Summary of a Technical Report

Built-environment wind turbine (BEWT) projects are wind energy projects that are constructed on, in, or near buildings. These projects present an opportunity for distributed, low-carbon generation combined with highly visible statements on sustainability, but the BEWT niche of the wind industry is still developing and is relatively less mature than the utility-scale wind or conventional ground-based distributed wind sectors. The findings presented in this presentation cannot be extended to wind energy deployments in general because of the large difference in application and technology maturity. This presentation summarizes the results of a report investigating the current state of the BEWT industry by reviewing available literature on BEWT projects as well as interviewing project owners on their experiences deploying and operating the technology. The authors generated a series of case studies that outlines the pertinent project details, project outcomes, and lessons learned.

17 WIND ENERGY↗