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At least 343 records · Page 19

Performance and Feasibility Analysis of a Wind Turbine Power System for Use on Mars

A wind turbine power system for future missions to the Martian surface was studied for performance and feasibility. A C++ program was developed from existing FORTRAN code to analyze the power capabilities of wind turbines under different environments and design philosophies. Power output, efficiency, torque, thrust, and other performance criteria could be computed given design geometries, atmospheric conditions, and airfoil behavior. After reviewing performance of such a wind turbine, a conceptual system design was modeled to evaluate feasibility. More analysis code was developed to study and optimize the overall structural design. Findings of this preliminary study show that turbine power output on Mars could be as high as several hundred kilowatts. The optimized conceptual design examined here would have a power output of 104 kW, total mass of 1910 kg, and specific power of 54.6 W/kg.

Lichter, Matthew D.↗

Blockage and speedup in the proximity of an onshore wind farm: A scanning wind LiDAR experiment

To maximize the profitability of wind power plants, wind farms are often characterized by high wind turbine density leading to operations with reduced turbine spacing. As a consequence, the overall wind farm power capture is hindered by complex flow features associated with flow modifications induced by the various wind turbine rotors. In addition to the generation of wakes, the velocity of the incoming wind field can reduce due to the increased pressure in the proximity of a single turbine rotor (named induction); a similar effect occurs at the wind-farm level (global blockage), which can have a noticeable impact on power production. On the other hand, intra-wind-farm regions featuring increased velocity compared to the freestream (speedups) have also been observed, which can be a source for a potential power boost. To quantify these rotor-induced effects on the incoming wind velocity field, three profiling LiDARs and one scanning wind LiDAR were deployed both before and after the construction of an onshore wind turbine array. The different wind conditions are classified according to the ambient turbulence intensity and streamwise/spanwise spacing among wind turbines. The analysis of the mean velocity field reveals enhanced induction and speedup under stably stratified atmospheric conditions. Additionally, a reduced horizontal area between adjacent turbines has a small impact on the induction zone but increases significantly the speedup between adjacent rotors.

17 WIND ENERGY↗

Benefit-cost methodology study with example application of the use of wind generators

An example application for cost-benefit methodology is presented for the use of wind generators. The approach adopted for the example application consisted of the following activities: (1) surveying of the available wind data and wind power system information, (2) developing models which quantitatively described wind distributions, wind power systems, and cost-benefit differences between conventional systems and wind power systems, and (3) applying the cost-benefit methodology to compare a conventional electrical energy generation system with systems which included wind power generators. Wind speed distribution data were obtained from sites throughout the contiguous United States and were used to compute plant factor contours shown on an annual and seasonal basis. Plant factor values (ratio of average output power to rated power) are found to be as high as 0.6 (on an annual average basis) in portions of the central U. S. and in sections of the New England coastal area. Two types of wind power systems were selected for the application of the cost-benefit methodology. A cost-benefit model was designed and implemented on a computer to establish a practical tool for studying the relative costs and benefits of wind power systems under a variety of conditions and to efficiently and effectively perform associated sensitivity analyses.

Zimmer, R. P.↗

Characterizing forecastability of wind sites in the United States

With the rapid growth of wind power, managing its uncertainty and variability becomes critical in power system operations. Wind forecasting is one of the enablers to partially tackle challenges associated with wind power uncertainty. To improve the 'forecasting ability', defined as forecastability, different forecasting methods have been developed to assist grid integration of wind energy. However, forecasting performance not only relies on the power of forecasting models, but is also related to local weather conditions and (known as wind characteristics) wind farm properties. In this study, geospatial and instance spatial distributions of six wind characteristics and two forecasting error metrics are first analyzed based on 126,000 + wind sites in the United States. Forecasts in different look-ahead times are generated by using a machine learning based multi-model forecasting framework model and the Weather Research and Forecasting model. A forecastability quantification method is developed by characterizing the relationship between forecastability and wind series entropy using three regression methods, i.e., linear approximation, locally weighted scatterplot smoother nonlinear nonparametric regression, and quantile regression. It is found that the forecastability of a wind site can be successfully characterized by wind series characteristics, thereby providing valuable information at different stages of wind energy projects.

17 WIND ENERGY↗

Techniques for studying gravity waves and turbulence: Vertical wind speed power spectra from the troposphere and stratosphere obtained under light wind conditions

A joint France/U.S. experiment was conducted near the mouth of the Rhone river in southern France as part of the ALPEX program. This experiment used 3 vertically directed 50 MHz radars separated by 4 to 6 km. The main purpose of this experiment was to study the spatial characteristics of gravity waves. The good height resolution (750 meters) and time resolution (1 minute) and the continuous operation over many weeks have yielded high resolution vertical wind speed power spectra under a variety of synoptic conditions. Vertical spectra obtained during very quiet (low wind) conditions in the troposphere and lower stratosphere from a single site are presented.

Ecklund, W. L.↗

Wind wheel electric power generator

Wind wheel electric power generator apparatus includes a housing rotatably mounted upon a vertical support column. Primary and auxiliary funnel-type, venturi ducts are fixed onto the housing for capturing wind currents and conducting to a bladed wheel adapted to be operatively connected with the generator apparatus. Additional air flows are also conducted onto the bladed wheel; all of the air flows positively effecting rotation of the wheel in a cumulative manner. The auxiliary ducts are disposed at an acute angle with respect to the longitudinal axis of the housing, and this feature, together with the rotatability of the housing and the ducts, permits capture of wind currents within a variable directional range.

Kaufman, J. W.↗

Projecting Future Energy Production from Operating Wind Farms in North America. Part I: Dynamical Downscaling

Abstract New simulations at 12-km grid spacing with the Weather and Research Forecasting (WRF) Model nested in the MPI Earth System Model (ESM) are used to quantify possible changes in wind power generation potential as a result of global warming. Annual capacity factors (CF; measures of electrical power production) computed by applying a power curve to hourly wind speeds at wind turbine hub height from this simulation are also used to illustrate the pitfalls in seeking to infer changes in wind power generation directly from low-spatial-resolution and time-averaged ESM output. WRF-derived CF are evaluated using observed daily CF from operating wind farms. The spatial correlation coefficient between modeled and observed mean CF is 0.65, and the root-mean-square error is 5.4 percentage points. Output from the MPI-WRF Model chain also captures some of the seasonal variability and the probability distribution of daily CF at operating wind farms. Projections of mean annual CF (CF A ) indicate no change to 2050 in the southern Great Plains and Northeast. Interannual variability of CF A increases in the Midwest, and CF A declines by up to 2 percentage points in the northern Great Plains. The probability of wind droughts (extended periods with anomalously low production) and wind bonus periods (high production) remains unchanged over most of the eastern United States. The probability of wind bonus periods exhibits some evidence of higher values over the Midwest in the 2040s, whereas the converse is true over the northern Great Plains. Significance Statement Wind energy is playing an increasingly important role in low-carbon-emission electricity generation. It is a “weather dependent” renewable energy source, and thus changes in the global atmosphere may cause changes in regional wind power production (PP) potential. We use PP data from operating wind farms to demonstrate that regional simulations exhibit skill in capturing actual power production. Projections to the middle of this century indicate that over most of North America east of the Rocky Mountains annual expected PP is largely unchanged, as is the probability of extended periods of anomalously high or low production. Any small declines in annual PP are of much smaller magnitude than changes due to technological innovation over the last two decades.

Meteorology & Atmospheric Sciences↗

The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs

The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.

17 WIND ENERGY↗

2015 Renewable Energy Data Book

The Renewable Energy Data Book for 2015 provides facts and figures on energy and electricity use, renewable electricity in the United States, global renewable energy development, wind power, solar power, geothermal power, biopower, hydropower, marine and hydrokinetic power, hydrogen, renewable fuels, and clean energy investment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2014 Renewable Energy Data Book

The Renewable Energy Data Book for 2014 provides facts and figures on energy and electricity use, renewable electricity in the United States, global renewable energy development, wind power, solar power, geothermal power, biopower, hydropower, marine and hydrokinetic power, hydrogen, renewable fuels, and clean energy investment.

2014 data↗

2015 Renewable Energy Data Book

The 2015 Renewable Energy Data Book provides facts and figures on energy and electricity use, renewable electricity in the United States, global renewable energy development, wind power, solar power, geothermal power, biopower, hydropower, marine and hydrokinetic power, hydrogen, renewable fuels, and clean energy investment.

2015 data↗

2016 Renewable Energy Data Book

The 2016 Renewable Energy Data Book provides facts and figures on energy and electricity use, renewable electricity in the United States, global renewable energy development, wind power, solar power, geothermal power, biopower, hydropower, marine and hydrokinetic power, hydrogen, renewable fuels, and clean energy investment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

2018 Renewable Energy Data Book

The 2018 Renewable Energy Data Book provides facts and figures on energy and electricity use, renewable electricity in the United States, global renewable energy development, wind power, solar power, geothermal power, biopower, hydropower, marine and hydrokinetic power, battery storage, hydrogen, renewable fuels, voluntary procurement and clean energy investment.

2018 data↗

Rolling Optimization of Transmission Network Recovery and Load Restoration Considering Hybrid Wind-Storage System and Cold Load Pickup

A common solution to deal with the stochasticity introduced by fast-ramping wind power integration is to equip wind farms (WFs) with energy storage systems (ESSs) to formulate hybrid WF-ESSs. In addition to leveling off wind power fluctuations during normal operations, a hybrid WF-ESS can be a flexible power source to accumulate the power system restoration. In this paper, we propose a rolling optimization model for transmission network recovery and load restoration considering the contributions of WF-ESSs. The proposed model is formulated as a mixed integer linear programming problem that simultaneously optimizes the amount and location of restorable load blocks as well as the restoration lines. The cold load pickup features of interrupted loads considering the outage duration are modeled in detail. A chance-constrained method is employed to deal with the uncertainty of wind power, and a rolling horizon-based framework is adopted to reduce the influence of forecast error. Case studies are conducted on both New England 39-bus system and part of a provincial power system in China. The results show that the load restoration process can be significantly accelerated by employing the proposed method and contributions of hybrid WF-ESSs to power system restoration are validated.

chance-constrained optimization↗

Hurricane Resilient Wind Plant Concept Study Final Report

Hurricanes occur over much of the U.S. Atlantic and Gulf coasts, from Long Island to the U.S.-Mexico border, encompassing much of the nation's primary offshore wind resource. Category 5 hurricanes have made landfall as far north as North Carolina, with Category 3 hurricanes reaching New York with some frequency. Along the US West coast, typhoons strike with similar frequency and severity. At present, offshore wind turbine design practices do not fully consider the severe operating conditions imposed by hurricanes. Although universally applied to most turbine designs, International Electrotechnical Commission (IEC) standards do not sufficiently address the duration, directionality, magnitude, or character of hurricanes. To assess advanced design features that could mitigate hurricane loading in various ways, this Hurricane-Resilient Wind Plant Concept Study considered a concept design study of a 500-megawatt (MW) wind power plant consisting of 10-MW wind turbines deployed in 25-meter (m) water depths in the Western Gulf of Mexico. This location was selected because hurricane frequency and severity provided a unique set of design challenges that would enable assessment of hurricane risk and projection of cost of energy (COE) changes, all in response to specific U.S. Department of Energy (DOE) objectives. Notably, the concept study pursued a holistic approach that incorporated multiple advanced system elements at the wind turbine and wind power plant levels to meet objectives for system performance and reduced COE. Principal turbine system elements included a 10-MW rotor with structurally efficient, low-solidity blades; a lightweight, permanent-magnet, direct-drive generator, and an innovative fixed substructure. At the wind power plant level, turbines were arrayed in a large-scale wind power plant in a manner aimed at balancing energy production against capital, installation, and operation and maintenance (O&M) costs to achieve significant overall reductions in COE.

17 WIND ENERGY↗

Forecastability as a Design Criterion in Wind Resource Assessment: Preprint

This paper proposes a methodology to include the wind power forecasting ability, or 'forecastability,' of a site as a design criterion in wind resource assessment and wind power plant design stages. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology is adopted to maximize the capacity factor of a wind power plant. The 1-hour-ahead persistence wind power forecasting method is used to characterize the forecastability of a potential wind power plant, thereby partially quantifying the integration cost. A trade-off between the maximum capacity factor and the forecastability is investigated.

17 WIND ENERGY↗

Energy optimization for a wind DFIG with flywheel energy storage

The type of distributed generation unit that is the subject of this paper relates to renewable energy sources, especially wind power. The wind generator used is based on a double fed induction Generator (DFIG). The stator of the DFIG is connected directly to the network and the rotor is connected to the network through the power converter with three levels. The objective of this work is to study the association a Flywheel Energy Storage System (FESS) in wind generator. This system is used to improve the quality of electricity provided by wind generator. It is composed of a flywheel; an induction machine (IM) and a power electronic converter. A maximum power tracking technique « Maximum Power Point Tracking » (MPPT) and a strategy for controlling the pitch angle is presented. The model of the complete system is developed in Matlab/Simulink environment / to analyze the results from simulation the integration of wind chain to networks.

17 WIND ENERGY↗