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At least 91 records · Page 5

Enabling control co-design of the next generation of wind power plants

Abstract. Layout design and wake steering through wind plant control are important and complex components in the design and operation of modern wind power plants. They are currently optimized separately, but with more and more computational and experimental studies demonstrating the gains possible through wake steering, there is a growing need from industry and regulating bodies to combine the layout and control optimization in a co-design process. However, combining these two optimization problems is currently infeasible due to the excessive number of design variables and large solution space. In this article, we present a method that enables the coupled optimization of wind power plant layout and wake steering with no additional computational expense than a traditional layout optimization. We developed a geometric relationship between wind turbines to find an approximate optimal yaw angle, bypassing the need for either a nested or coupled wind plant control optimization. It also provides a significant and immediate improvement to wind power plant design by enabling the co-design of turbine layout and yaw control for wake steering. A small co-designed plant shown in this article produces 0.8 % more energy than its sequentially designed counterpart. This additional energy production comes with no additional infrastructure, turbine hardware, or control software; it is simply the outcome of optimizing the turbine layout and yaw control together, resulting in millions of dollars of additional revenue for the wind power plants of the future.

17 WIND ENERGY↗

Flow Control Leveraging Downwind Rotors for Improved Wind Power Plant Operation

Controlling the air flow within wind power plants has the potential to improve plant performance and is an active area of research in the wind energy control community. In order to develop, test, and tune wind power plant controllers efficiently, an accurate engineering model of the turbine wake dynamics is required. Two elements of flow control are wake steering via yaw and tilt of a turbine. When a turbine is yawed or tilted away from the incoming wind field, the wake shape is changed. This is largely due to shed vortices that produce a curled wake. In this work, the well-known wake engineering model FLOw Redirection and Induction in Steady State (FLORIS) wake engineering model is enhanced to include these curled wake effects due to tilt. Since decay of these vortices has not been previously captured in an engineering model, the authors describe how vortices with decay have been added to FLORIS and how the updated model has been used to study the effects due to tilt in the wake. Results are demonstrated and compared to high-fidelity large-eddy simulations. Potential wind power plant performance gains due to flow control using tilt are investigated across different wind conditions and sites. Preliminary results show power gains by using tilt to implement flow control in a variety of wind distributions and tilt values.

17 WIND ENERGY↗

Grid Integration of Offshore Wind Power: Standards, Control, Power Quality and Transmission

Offshore wind is expected to be a major player in the global efforts toward decarbonization, leading to exceptional changes in modern power systems. Understanding the impacts and capabilities of the relatively new and uniquely positioned assets in grids with high integration levels of inverter-based resources, however, is lacking, raising concerns about grid reliability, stability, power quality, and resilience, with the absence of updated grid codes to guide the massive deployment of offshore wind. To help fill the gap, this paper presents an overview of the state-of-the-art technologies of offshore wind power grid integration. First, the paper investigates the most current grid requirements for wind power plant integration, based on a harmonized European Network of Transmission System Operators (ENTSO-E) framework and notable international standards, and it illuminates future directions. The paper discusses the wind turbine and wind power plant control strategies, and new control approaches, such as grid-forming control, are presented in detail. The paper reviews recent research on the ancillary services that offshore wind power plants can potentially provide, which, when harmonized, will not only comply with regulations but also improve the value of the asset. The paper explores topics of wind power plant harmonics, reviewing the latest standards in detail and outlining mitigation methods. The paper also presents stability analysis methods for wind power plants, with discussions centered on validity and computational efficiency. Finally, the paper discusses wind power plant transmission solutions, with a focus on high-voltage direct-current topologies and controls.

17 WIND ENERGY↗

Outlooks for Wind Power in the United States: Drivers and Trends under a 2016 Policy Environment

Over the past decade, wind power has become one of the fastest growing electricity generation sources in the United States. Despite this growth, the U.S. wind industry continues to experience year-to-year fluctuations across the manufacturing and supply chain as a result of dynamic market conditions and changing policy landscapes. Moreover, with advancing wind technologies, ever-changing fossil fuel prices, and evolving energy policies, the long-term future for wind power is highly uncertain. In this report, we present multiple outlooks for wind power in the United States, to explore the possibilities of future wind deployment. The future wind power outlooks presented rely on high-resolution wind resource data and advanced electric sector modeling capabilities to evaluate an array of potential scenarios of the U.S. electricity system. Scenario analysis is used to explore drivers, trends, and implications for wind power deployment over multiple periods through 2050. Specifically, we model 16 scenarios of wind deployment in the contiguous United States. These scenarios span a wide range of wind technology costs, natural gas prices, and future transmission expansion. We identify conditions with more consistent wind deployment after the production tax credit expires as well as drivers for more robust wind growth in the long run. Conversely, we highlight challenges to future wind deployment. We find that the degree to which wind technology costs decline can play an important role in future wind deployment, electric sector CO 2 emissions, and lowering allowance prices for the Clean Power Plan.

17 WIND ENERGY↗

Nationwide assessment of potential output from wind-powered generators

A method for computing the actual expected power for a wind-powered generator from a given observed speed distribution is described and applied to estimate the potential output for different locations in the continental U.S. A contour map of generator capacity factor values (fraction of the rated output realizable) is obtained for wind-powered generator systems with a cut-in speed of 3.6 m/sec and a rated speed of 8.0 m/sec, and for a unit with hypothetical values for the 1 MW class (cut-in speed, 6.7 m/sec; rated speed, 13.4 m/sec). Results indicate that in the central U.S. and in certain areas of the New England coast at a height of 61 m, over 60% of the rated output power could be obtained on an annual average. In these areas capacity factors of over 20% could be obtained with the 1MW system.

Justus, C. G.↗

Learning Heterogeneous Features Jointly: A Deep End-to-End Framework for Multi-Step Short-Term Wind Power Prediction

Leveraging multiple heterogeneous measurements to predict wind power has long been a challenging task in the electrical community. In this paper, a deep architecture incorporated with multitask learning and multimodal learning for wind power prediction, termed predictive stacked autoencoder (PSAE), is presented. PSAE is a unified framework integrating multiple stacked autoencoders (SAEs), one feature fusion layer, and one prediction terminal layer, which expands the architecture from two spatial dimensions, including the depth and width, compared to conventional prediction models. Initially, the SAEs at the bottom of PSAE extracted features from multiple kinds of measurements respectively. Following, the feature fusion layer encodes the high-order features extracted by different SAEs into a unified feature that is more informative and representative for wind power prediction. Finally, the prediction terminal layer functions as a regression machine which generates the predicted targets based on the fusion features. Trained in an end-to-end (E2E) manner, PSAE is capable of learning heterogeneous features jointly and achieving the prediction task of sequence-to-sequence (S2S). Experiments for multi-step short-term predictions are conducted on real-world data, and the results demonstrate the superiority of PSAE to prior methods.

17 WIND ENERGY↗

Flow Control Leveraging Downwind Rotors for Improved Wind Power Plant Operation: Preprint

Controlling the air flow within wind power plants has the potential to improve plant performance and is an active area of research in the wind energy control community. In order to develop, test, and tune wind power plant controllers efficiently, an accurate engineering model of the turbine wake dynamics is required. Two elements of flow control are wake steering via yaw and tilt of a turbine. When a turbine is yawed or tilted away from the incoming wind field, the wake shape is changed. This is largely due to shed vortices that produce a curled wake. In this work, the well-known wake engineering model FLOw Redirection and Induction in Steady State (FLORIS) wake engineering model is expanded to include these wake effects due to tilt. Since decay of these vortices has not been previously captured in an engineering model, the authors describe how vortices with decay have been added to FLORIS and how the updated model has been used to study the effects due to tilt in the wake. Results are demonstrated and compared to high-fidelity large-eddy simulations. Potential wind power plant performance gains due to flow control using tilt are investigated across different wind conditions and sites. Preliminary results show power gains by using tilt to implement flow control in a variety of wind distributions and tilt values.

17 WIND ENERGY↗

2021 Prototype Manufacture and Installation Awardee: Pecos Wind Power, Inc.

Through the 2021 Competitiveness Improvement Project (CIP), Pecos Wind Power will manufacture a prototype of its 85-kilowatt (kW) horizontal-axis distributed wind turbine, the PW85, a new wind turbine that began development in 2017 when the company was founded. The PW85 wind turbine includes an industry-leading rotor diameter (30 meters) and full-span variable pitch blades to target a levelized cost of energy (LCOE) of $0.103/kilowatt-hour in low annual wind speeds (6 meters per second). This is 55% lower than the average small wind turbine project installed in 2018. The goal of this project is to spur the development of increasingly lower-cost, high-capacity-factor distributed wind turbines. As a result, Pecos Wind Power will manufacture and install wind turbines that increase the geographic area in which distributed wind power is cost competitive with retail-priced electricity and other distributed energy resources - primarily solar energy.

CIP↗

Wind Power Ramping Product for Increasing Power System Flexibility

With increasing penetrations of wind power, system operators are concerned about a potential lack of system flexibility and ramping capacity in real-time dispatch stages. In this paper, a modified dispatch formulation is proposed considering the wind power ramping product (WPRP). A swinging door algorithm (SDA) and dynamic programming are combined and used to detect WPRPs in the next scheduling periods. The detected WPRPs are included in the unit commitment (UC) formulation considering ramping capacity limits, active power limits, and flexible ramping requirements. The modified formulation is solved by mixed integer linear programming. Numerical simulations on a modified PJM 5-bus System show the effectiveness of the model considering WPRP, which not only reduces the production cost but also does not affect the generation schedules of thermal units.

17 WIND ENERGY↗

Wind power costs driven by innovation and experience with further reductions on the horizon

The costs of wind power have declined to levels on par with or below those of conventional sources in many parts of the world. Wind power has become one of the fastest-growing sources of new electricity generation. We take stock of wind power cost evolution over the past 20 years, review methodologies commonly used for cost assessment, discuss the potential for continued cost reduction, and identify anticipated cost and value drivers. Our scope includes both onshore and offshore wind technologies. We draw from a vast body of literature on these topics to highlight key trends, approaches, and limitations. Furthermore, we discuss strategies for wind power assets to enhance their marginal economic value to the broader power system and consumers. We identify a myriad of factors that are expected to influence the future cost and value of wind power, including siting, project scale, turbine size, operational synergies, commodity prices, advancements in turbine technologies, enhanced management of the wind resource, and novel control technologies that provide value for the electricity grid. Because the common methods for forecasting future costs each have their own strengths and weaknesses, we find the best insights are elicited from a combination of methods. Overall, researchers and analysts anticipate further sizable cost reductions for onshore and offshore wind. Midrange forecasts for levelized cost of energy in 2050 are generally between $20 and $30/MWh for onshore wind and $40 and $60/MWh for offshore wind, a reduction to approximately half of today's levels. Optimistic forecasts anticipate these levels as early as 2030.

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The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was compared through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.

17 WIND ENERGY↗

Reference Manual for the System Advisor Model's Wind Power Performance Model

This manual describes the National Renewable Energy Laboratory's System Advisor Model (SAM) wind power performance model. The model calculates the hourly electrical output of a single wind turbine or of a wind farm. The wind power performance model requires information about the wind resource, wind turbine specifications, wind farm layout (if applicable), and costs. In SAM, the performance model can be coupled to one of the financial models to calculate economic metrics for residential, commercial, or utility-scale wind projects. This manual describes the algorithms used by the wind power performance model, which is available in the SAM user interface and as part of the SAM Simulation Core (SSC) library, and is intended to supplement the user documentation that comes with the software.

17 WIND ENERGY↗

Quantifying the Effect of Lidar Turbulence Error on Wind Power Prediction

Currently, cup anemometers on meteorological towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability; however, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install meteorological towers at potential sites. As a result, remote-sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. Although lidars can accurately estimate mean wind speeds and wind directions, there is still a large amount of uncertainty surrounding the measurement of turbulence using these devices. Errors in lidar turbulence estimates are caused by a variety of factors, including instrument noise, volume averaging, and variance contamination, in which the magnitude of these factors is highly dependent on measurement height and atmospheric stability. As turbulence has a large impact on wind power production, errors in turbulence measurements will translate into errors in wind power prediction. The impact of using lidars rather than cup anemometers for wind power prediction must be understood if lidars are to be considered a viable alternative to cup anemometers.In this poster, the sensitivity of power prediction error to typical lidar turbulence measurement errors is assessed. Turbulence estimates from a vertically profiling WINDCUBE v2 lidar are compared to high-resolution sonic anemometer measurements at field sites in Oklahoma and Colorado to determine the degree of lidar turbulence error that can be expected under different atmospheric conditions. These errors are then incorporated into a power prediction model to estimate the sensitivity of power prediction error to turbulence measurement error. Power prediction models, including the standard binning method and a random forest method, were developed using data from the aeroelastic simulator FAST for a 1.5 MW turbine. The impact of lidar turbulence error on the predicted power from these different models is examined to determine the degree of turbulence measurement accuracy needed for accurate power prediction.

17 WIND ENERGY↗

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↗

Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant

Abstract. Optimizing turbine layout is a challenging problem that has been extensively researched in the literature. However, optimizing the number of turbines within a given boundary has not been studied as extensively and is a difficult problem because it introduces discrete design variables and a discontinuous design space. An essential step in performing wind power plant layout optimization is to define the objective function, or value, that is used to express what is valuable to a wind power plant developer, such as annual energy production, cost of energy, or profit. In this paper, we demonstrate the importance of selecting the appropriate objective function when optimizing a wind power plant in a land-constrained site. We optimized several different wind power plants with different wind resources and boundary sizes. Results show that the optimal number of turbines varies drastically depending on the objective function. For a simple, one-dimensional, land-based scenario, we found that a wind power plant optimized for minimal cost of energy produced just 72 % of the profit compared to the wind power plant optimized for maximum profit, which corresponded to a loss of about USD 2 million each year. This paper also compares the performance of several different optimization algorithms, including a novel repeated-sweep algorithm that we developed. We found that the performance of each algorithm depended on the number of design variables in the problem as well as the objective function.

17 WIND ENERGY↗

Economic Impacts from Indiana's First 1,000 Megawatts of Wind Power

The magnitude of Indiana's available wind resource indicates that the development of wind power infrastructure has the potential to support millions of dollars of economic activity in the state. The Jobs and Economic Development Impact (JEDI) models, developed by the National Renewable Energy Laboratory, are tools used to estimate some of the economic impacts of energy projects at the state level. JEDI calculates results in the form of jobs, earnings, and economic output in three categories: project development and onsite labor, local revenue and supply chain, and induced impacts. According to this analysis, the first 1,000 MW of wind power development in Indiana (projects built between 2008 and 2011): supported employment totaling more than 4,400 full-time-equivalent jobs in Indiana during the construction periods; supports approximately 260 ongoing Indiana jobs; supported nearly $570 million in economic activity for Indiana during the construction periods; supported and continues to support nearly $40 million in annual Indiana economic activity during the operating periods; generates more than $8 million in annual property taxes; generates nearly $4 million annually in income for Indiana landowners who lease their land for wind energy projects.

17 WIND ENERGY↗

Spatially-Explicit Prediction of Capacity Density Advances Geographic Characterization of Wind Power Technical Potential

Mounting interest in ambitious clean energy goals is exposing critical gaps in our understanding of onshore wind power potential. Conventional approaches to evaluating wind power technical potential at the national scale rely on coarse geographic representations of land area requirements for wind power. These methods overlook sizable spatial variation in real-world capacity densities (i.e., nameplate power capacity per unit area) and assume that potential installation densities are uniform across space. Here, we propose a data-driven approach to overcome persistent challenges in characterizing localized deployment potentials over broad extents. We use machine learning to develop predictive relationships between observed capacity densities and geospatial variables. The model is validated against a comprehensive data set of United States (U.S.) wind facilities and subjected to interrogation techniques to reveal that key explanatory features behind geographic variation of capacity density are related to wind resource as well as urban accessibility and forest cover. We demonstrate application of the model by producing a high-resolution (2 km × 2 km) national map of capacity density for use in technical potential assessments for the United States. Our findings illustrate that this methodology offers meaningful improvements in the characterization of spatial aspects of technical potential, which are increasingly critical to draw reliable and actionable planning and research insights from renewable energy scenarios.

17 WIND ENERGY↗