Energy by the Numbers: Wind Power
Wind power is growing in America. Fourteen states get more than 10% of their electricity from wind.
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Capacity value assessments of wind power
This article describes some of the recent research into the capacity value of wind power. With the worldwide increase in wind power during the past several years, there is increasing interest and significance regarding its capacity value because this has a direct influence on the amount of other (nonwind) capacity that is needed. We build on previous reviews from IEEE and IEA W ind T ask 25 a and examine recent work that evaluates the impact of multiple‐year data sets and the impact of interconnected systems on resource adequacy. We also provide examples that explore the use of alternative reliability metrics for wind capacity value calculations. We show how multiple‐year data sets significantly increase the robustness of results compared to single‐year assessments. Assumptions regarding the transmission interconnections play a significant role. To date, results regarding which reliability metric to use for probabilistic capacity valuation show little sensitivity to the metric. WIREs Energy Environ 2017, 6:e226. doi: 10.1002/wene.226 This article is categorized under: Wind Power > Economics and Policy Wind Power > Systems and Infrastructure Energy Infrastructure > Economics and Policy
Milligan, Michael↗
Impact of Increasing Distributed Wind Power and Wind Turbine Siting on Rural Distribution Feeder Voltage Profiles: Preprint
Many favorable wind energy resources in North America are located in remote locations without direct access to the transmission grid. Building transmission lines to connect remotely-located wind power plants to large load centers has become a barrier to increasing wind power penetration in North America. By connecting utility-sized megawatt-scale wind turbines to the distribution system, windpower supplied to consumers could be increased greatly. However, the impact of including megawatt-scale wind turbines on distribution feeders needs to be studied. The work presented here examined the impact that siting and power output of megawatt-scale wind turbines have on distribution feeder voltage. This is the start of work to present a general guide to megawatt-scale wind turbine impact onthe distribution feeder and finding the amount of wind power that can be added without adversely impacting the distribution feeder operation, reliability, and power quality.
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WGRID-49 GMLC Project Report: Understanding the Role of Short-Term Energy Storage and Large Motor Loads for Active Power Controls by Wind Power
The GMLC project is built upon the results of pioneering research funded by the U.S. Department of Energy (DOE) and conducted by the National Renewable Energy Laboratory (NREL)—in collaboration with the Electric Power Research Institute and the University of Colorado—on active power controls (APC) by wind power during 2013–2016. The studies detailed in the first APC by wind power project have shown tremendous promise for the potential for wind power plants to provide APC. The goal of this project was to continue the previous work and develop and validate coordinated controls of APC by wind generation, short-term energy storage, and large industrial motor drives for providing various types of ancillary services to the grid and minimizing loading impacts on wind turbines (e.g., drivetrains), thereby reducing operation-and-maintenance (O&M) costs and subsequently reducing the cost of energy generated by wind power. This work used the $30 million, multiyear DOE investments and the unique characteristics of NREL’s existing National Wind Technology Center test site, including a combination of multimegawatt utility-scale wind turbine generators, a 1-MW/1-MWh battery energy storage system (BESS), industrial variable-frequency motor drives, a 1-MW solar photovoltaic (PV) array, and a 7-MVA controllable grid interface. This combination of technologies allows for the optimization, testing, and demonstration of various types of APC by wind power in coordination with other generation sources (including regenerative loads) and energy storage to allow for enhancing or, in some cases, substituting the APC services by wind power and reducing impacts on wind turbine component life and thus increasing the availability and reliability of the power supply from wind
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Simulating wind power forecast error distributions for spatially aggregated wind power plants
Dispersion and aggregation of wind power plants lower the uncertainty of wind power by reducing wind power forecasting errors. Using quantitative methods, this paper studies the dispersion's impact on the uncertainty of the aggregated wind power production. A method to simulate day-ahead forecast error distributions at different dispersion and forecasting skill scenarios is presented. The proposed method models the uncertainty of wind power forecasting on an annual basis and at different levels of production. As a result, the uncertainty in the forecasting of spatially dispersed wind power plants is modelled using two continuous distributions: Laplace and beta distribution. The analysis shows that even the production level uncertainty can be modelled in various dispersion and forecasting skill scenarios. The model for aggregated forecast error distributions requires only four variables: capacity-weighted distance of the total wind power plant fleet, mean of the site-specific mean absolute errors (MAEs), number of aggregated wind power plants, and the mean variability of the elevations from the proximities of the aggregated wind power plants. Here, the results are especially promising when the number of aggregated wind power plants exceeds five, the terrain complexity is low or moderate, and the aggregation region is large. This is demonstrated through a case study for Texas, United States.
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Variability in large-scale wind power generation: Variability in large-scale wind power generation
The paper demonstrates the characteristics of wind power variability and net load variability in multiple power systems based on real data from multiple years. Demonstrated characteristics include probability distribution for different ramp durations, seasonal and diurnal variability and low net load events. The comparison shows regions with low variability (Sweden, Spain and Germany), medium variability (Portugal, Ireland, Finland and Denmark) and regions with higher variability (Quebec, Bonneville Power Administration and Electric Reliability Council of Texas in North America; Gansu, Jilin and Liaoning in China; and Norway and offshore wind power in Denmark). For regions with low variability, the maximum 1 h wind ramps are below 10% of nominal capacity, and for regions with high variability, they may be close to 30%. Wind power variability is mainly explained by the extent of geographical spread, but also higher capacity factor causes higher variability. It was also shown how wind power ramps are autocorrelated and dependent on the operating output level. When wind power was concentrated in smaller area, there were outliers with high changes in wind output, which were not present in large areas with well-dispersed wind power.
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Wind power forecasting: IEA Wind Task 36 & future research issues
Here, this paper presents the new International Energy Agency Wind Task 36 on Forecasting, and invites to collaborate within the group. Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, MetOffice, met.no, DMI,...), operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented.
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PMU-Aided Voltage Security Assessment for a Wind Power Plant
Because wind power penetration levels in electric power systems are continuously increasing, voltage stability is a critical issue for maintaining power system security and operation. The traditional methods to analyze voltage stability can be classified into two categories: dynamic and steady-state. Dynamic analysis relies on time-domain simulations of faults at different locations; however, this method needs to exhaust faults at all locations to find the security region for voltage at a single bus. With the widely located phasor measurement units (PMUs), the Thevenin equivalent matrix can be calculated by the voltage and current information collected by the PMUs. This paper proposes a method based on a Thevenin equivalent matrix to identify system locations that will have the greatest impact on the voltage at the wind power plant's point of interconnection. The number of dynamic voltage stability analysis runs is greatly reduced by using the proposed method. The numerical results demonstrate the feasibility, effectiveness, and robustness of the proposed approach for voltage security assessment for a wind power plant.
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2022 Component Innovation Awardee: Pecos Wind Power
Distributed wind energy systems supply electricity for nearby homes and businesses, often in remote and rural regions. Systems capable of capturing energy in areas with low wind speeds make it possible to deploy these clean energy systems in many geographic locations. Pecos Wind Power's 85-kilowatt (kW) horizontal-axis PW85 wind turbine shows potential to reduce the levelized cost of energy by approximately 55% when compared to typical small wind systems while achieving utility-scale capacity factors of more than 40% in areas with lower wind speeds. To maximize cost competitiveness, the Pecos Wind Power turbine needs a blade optimized for low wind speeds. Through its 2022 Competitiveness Improvement Project (CIP) award, Pecos Wind Power and partner Wetzel Wind Energy Services will model, optimize, fabricate, and validate a 14.5-meter wind turbine blade made specifically for these applications. Three prior CIP awards supported Pecos Wind Power's initial development of the PW85 turbine.
CIP↗
Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting
Wind power forecasting is an important tool in power system operations to address variability and uncertainty. Accurately doing so is important to reducing the occurrence and length of curtailment, enhancing market efficiency, and improving the operational reliability of the bulk power system. This research quantifies the value of wind power forecasting improvements in the IEEE 118-bus test system as modified to emulate the generation mixes of Midcontinent, California, and New England independent system operator balancing authority areas. To measure the economic value, a commercially available production cost modeling tool was used to simulate the multi-timescale unit commitment (UC) and economic dispatch process for calculating the cost savings and curtailment reductions. To measure the reliability improvements, an in-house tool, FESTIV, was used to calculate the system's area control error and the North American Electric Reliability Corporation Control Performance Standard 2. The approach allowed scientific reproducibility of results and cross-validation of the tools. A total of 270 scenarios were evaluated to accommodate the variation of three factors: generation mix, wind penetration level, and wind fore-casting improvements. The modified IEEE 118-bus systems utilized 1 year of data at multiple timescales, including the day-ahead UC, 4-hour-ahead UC, and 5-min real-time dispatch. The value of improved wind power forecasting was found to be strongly tied to the conventional generation mix, existence of energy storage devices, and the penetration level of wind energy. The simulation results demonstrate that wind power forecasting brings clear benefits to power system operations.
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Wind power curve modeling in complex terrain using statistical models
The simplest power curves model wind power only as a function of the wind speed at the turbine hub height. While the latter is an essential predictor of power output, wind speed information in other parts of the vertical profile, as well as additional atmospheric variables, are also important determinants of power. The goal of this work was to determine the gain in predictive ability afforded by adding wind speed information at other heights, as well as other atmospheric variables, to the power prediction model. Using data from a wind farm with a moderately complex terrain in the Altamont Pass region in California, we trained three statistical models—a neural network, a random forest and a Gaussian process model—to predict power output from various sets of aforementioned predictors. The comparison of these predictions to the observed power data revealed that considerable improvements in prediction accuracy can be achieved both through the addition of predictors other than the hub-height wind speed and the use of statistical models. To our knowledge, the use of the Gaussian process model in this context is new, in contrast to neural networks and random forests. The advantage of this model over the other two models is that it provides a much more natural way to estimate the uncertainty associated with its predictions. In addition, this study presents one of the most comprehensive analyses to date of the relative importance of various wind power curve inputs.
Bulaevskaya, V.↗
Different Factors Affecting Short Circuit Behavior of a Wind Power Plant
A wind power plant consists of a large number of turbines interconnected by underground cable. A pad-mount transformer at each turbine steps up the voltage from generating voltage (690 V) to a medium voltage (34.5 kV). All turbines in the plant are connected to the substation transformer where the voltage is stepped up to the transmission level. An important aspect of wind power plant (WPP) impact studies is to evaluate the short-circuit (SC) current contribution of the plant into the transmission network under different fault conditions. This task can be challenging to protection engineers due to the topology differences between different types of wind turbine generators (WTGs) and the conventional generating units. This paper investigates the short circuit behavior of a wind power plant for different types of faults. The impact of wind turbine types, the transformer configuration, and the reactive compensation capacitor will be investigated. The voltage response at different buses will be observed. Finally, the SC line currents will be presented along with its symmetrical components.
Fault contribution, induction generator, protectio↗
Dynamic Model Development of a Wind Power Plant Using Neural Net Method to Forecast Wind Power Output (CRADA Final Report)
This project is intended to model wind power plant based on monitored data at the wind power plant. This project will promote the university research in Renewable Energy area and trains the future highly qualified engineers. The dynamic model will be based on neural net model with the input from the two met towers (12 inputs), and the number of turbines in operation (one input). The overall input will be 13 inputs to drive the simulations. The output power at the point of interconnection will be used to tune the neural net weight coefficients. Two neural net concepts will be investigated (the back propagation neural net and the dynamic recurrent neural net with feedback).
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Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting
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Active Power Controls from Wind Power: Bridging the Gaps
This paper details a comprehensive study undertaken by the National Renewable Energy Laboratory, Electric Power Research Institute, and the University of Colorado to understand how the contribution of wind power providing active power control (APC) can benefit the total power system economics, increase revenue streams, improve the reliability and security of the power system, and provide superior and efficient response while reducing any structural and loading impacts that may reduce the life of the wind turbine or its components. The study includes power system simulations, control simulations, and actual field tests using turbines at NREL's National Wind Technology Center (NWTC). The study focuses on synthetic inertial control, primary frequency control, and automatic generation control, and analyzes timeframes ranging from milliseconds to minutes to the lifetime of wind turbines, locational scope ranging from components of turbines to large wind plants to entire synchronous interconnections, and additional topics ranging from economics to power system engineering to control design.
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