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At least 37 records · Page 2

Calculation of guaranteed mean power from wind turbine generators

A method for calculating the 'guaranteed mean' power output of a wind turbine generator is proposed. The term 'mean power' refers to the average power generated at specified wind speeds during short-term tests. Correlation of anemometers, the method of bins for analyzing non-steady data, the PROP Code for predicting turbine power, and statistical analysis of deviations in test data from theory are discussed. Guaranteed mean power density for the Clayton Mod-OA system was found to be 8 watts per square meter less than theoretical power density at all power levels, with a confidence level of 0.999. This amounts to 4 percent of rated power.

Spera, D. A.↗

A Path to 100 Percent Renewable Energy: Grid-Forming Inverters will Give Us the Grid We Need Now

This is a more urgent problem than it might sound. The westernmost Hawaiian island of significant size, Kauai is home to around 70,000 residents and 30,000 tourists at any given time. Renewable energy accounts for 70 percent of the energy produced in a typical year - a proportion that's among the highest in the world and that can be hard to sustain for such a small and isolated grid. During the day, the local system operator, the Kauai Island Utility Cooperative, sometimes reaches levels of 90 percent from solar alone. But on 2 April, the 26-MW generator was running near its peak output, to compensate for the drop in solar output as the sun set. At the moment when it failed, that single generator had been supplying 60 percent of the load for the entire island, with the rest being met by a mix of smaller generators and several utility-scale solar-and-battery systems.

battery management systems↗

The Value of Increased HVDC Capacity Between Eastern and Western U.S. Grids: The Interconnections Seam Study

The Interconnections Seam Study examines the potential economic value of increasing electricity transfer between the Eastern and Western Interconnections using high-voltage direct-current (HVDC) transmission and cost-optimizing both generation and transmission resources across the United States, proposing, assessing, justifying, and illustrating a major infrastructure change involving two of the worlds largest power grids. The study conducted a multi-model analysis that used co-optimized generation and transmission expansion planning and production cost modeling. Four transmission designs under eight scenarios were developed and studied to estimate costs and potential benefits. The results show benefit-to-cost ratios that reach as high as 2.5, indicating significant value to increasing the transmission capacity between the interconnections under the cases considered, realized through sharing generation resources and flexibility across regions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Grid-Supportive Loads - A New Approach to Increasing Renewable Energy in Power Systems

This paper demonstrates the potential of inverter-based loads to support grid reliability during power system transients thereby enabling reliable integration of renewable energy in power systems. Such loads are referred to in this paper as grid-supportive loads (GSLs). A new GSL model is developed that simulates the transient response capabilities that can be programmed in electronic loads. The model’s design enables it to be easily integrated in widely used commercial power system transient analysis software. Theoretical expressions are derived that explain the workings of the GSL model. The performance, numerical stability, and impact of the GSL model is validated on 9-bus and 2000-bus synthetic power system models using generator tripping and bus fault disturbances. Results on the 2000 bus system show that in the absence of frequency support from wind/solar generation resources, just 20% of loads with grid-supportive capabilities can improve frequency response by up to 2000 MW/0.1 Hz and reduce deviation in frequency at nadir by up to 60% compared to the situation when GSLs are absent. Power system reliability also improves under fault events. Here, it is further shown that GSLs can aid in integrating more renewable generation without degrading the overall transient response of the power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Projecting Future Energy Production from Operating Wind Farms in North America. Part II: Statistical Downscaling

Abstract Capacity factors (CFs) derived from daily expected power at 22 operating wind farms in different regions of North America are used as predictands to train statistical downscaling algorithms using output from ERA5. The statistical downscaling models are then used to make CF projections for a suite of CMIP6 Earth System Models (ESMs). Downscaling is performed using a hybrid statistical approach that employs synoptic types derived using k -means clustering applied to sea level pressure fields with variance corrections applied as a function of the pressure gradient intensity. ESMs exhibit marked variability in terms of the skill with which the frequency of synoptic types and pressure gradients are reproduced relative to ERA5, and that differential skill is used to infer differential credibility in the associated CF projections. Projections of median annual mean CF [P50(CF)] in each 20-yr period from 1980 to 2099 show evidence of declines at most wind farms except in parts of the southern Great Plains, although the magnitude of the changes is strongly dependent on the ESM. For example, P50(CF) in 2080–99 deviate from those in 1980–99 by from −3.1 to +0.2 percentage points in the Northeast. The largest-magnitude declines in P50(CF) ranging from −3.9 to −2 percentage points are projected for the southern West Coast. CF trends exhibit marked seasonality and are strongly linked to changes in the relative intensity of future synoptic patterns, with much less impact from shifts in the occurrence of synoptic types over time. Internal climate modes continue to play a significant role in inducing interannual variability in wind power production, even under high radiative forcing scenarios. Significance Statement We describe how future climate changes may affect wind resources and wind power generation. Near-term changes in projected wind power electricity generation potential at operating wind farms over North America are small, but by the end of the current century electricity production is projected to decrease in many areas but may increase in parts of the southern Great Plains. The amount of change in projected wind power production is a strong function of the Earth system model that is downscaled and also depends on the continued presence of internally forced climate variability. An additional dependence on the amount of greenhouse gas–induced global warming indicates the transition of the energy sector to low-carbon sources may assist in maintaining the abundant U.S. wind resource.

Meteorology & Atmospheric Sciences↗

A Tutorial on the Control of Floating Offshore Wind Turbines: Stability Challenges and Opportunities for Power Capture

Climate change is a serious threat facing humanity. The United States (U.S.) and many other countries are increasing the amount of electrical power generated from renewable energy sources in an effort to combat climate change and ensure energy independence. The U.S. has set goals to achieve a 100% decarbonized electric grid by 2035 and a net-zero emissions economy by no later than 2050. Renewable energy currently accounts for about 20% of the U.S. power grid. According to the U.S. Department of Energy and the National Renewable Energy Laboratory (NREL), in the U.S. in 2021, wind and solar photovoltaic generation supplied 9.1% and 4% of total electricity generation, respectively, and the latest publicly available data show that hydropower represented 6.6% of all electricity generated in the U.S. in 2019. As wind farms have been built in many of the best wind resource areas on land, the U.S. and many other countries are turning to offshore wind for further growth of wind power capacity. Further, the U.S. has committed to deploy 30 GW of offshore wind by 2030, a significant increase from the 0.04 GW of installed offshore wind in the country as of the end of 2023. Many other countries have also established ambitious goals or plans to increase the amount of installed offshore wind power.

17 WIND ENERGY↗

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 Oregon State University wind studies

The economic feasibility of commercial use of wind generated power in selected areas of Oregon is assessed. A number of machines for generating power have been examined. These include the Savonius rotor, translators, conventional wind turbines, the circulation controlled rotor and the vertical axis winged turbine. Of these machines, the conventional wind turbine and the vertical axis winged turbine show the greatest promise on the basis of the power developed per unit of rotor blade area. Attention has been focused on the structural and fatigue analysis of rotors since the economics of rotary winged, wind generated power depends upon low cost, long lifetime rotors. Analysis of energy storage systems and tower design has also been undertaken. An economic means of energy storage has not been found to date. Tower design studies have produced cost estimates that are in general agreement with the cost of the updated Putnam 110-foot tower.

Wilson, R. E.↗

Entropy-Infused Deep Learning Loss Function for Capturing Extreme Values in Wind Power Forecasting

Extreme scenarios in wind power generation occur with higher frequency and larger magnitude in the recent years due to the ever-increasing extreme meteorological factors. Accurate forecasting of the occurrence of extreme values in wind power generation is of great concern to ensure reliable power system operation. Recently, deep learning models have surged in popularity for wind power forecasting, with the mean squared error (MSE) loss function being commonly used. However, the MSE loss function, being sensitive to extreme values, disproportionately penalizes larger errors, cannot adequately capture the extreme values present in wind energy data, and novel loss functions have seldom been tailored for wind power forecasting. To this end, in this paper, we introduce a novel loss function specifically crafted to capture extreme values in wind power forecasting. The experimental results with four fundamental deep learning methods on open source wind power dataset validate that the new loss function is efficient and superior in all cases compared to MSE in capturing extreme values while maintaining forecasting performance.

17 WIND ENERGY↗

Technical feasibility study for the development of a large capacity wind powered electrical generating system

The engineering feasibility of developing a basic mechanical system necessary for extracting large amounts of power (on the order of 10 to 20 MW) from the wind is considered using the concept of vertical airfoils moving along a closed horizontal track system. Attention is focused on those components necessary for the conversion of wind energy to mechanical energy, although the general characteristics and critical aspects of other components are also considered. The four phases of this program are: (1) Establishment of component specifications and interface requirements for major system components; (2) formulation of alternative sets of conceptual designs for major system components; (3) engineering analysis of various components and systems; and (4) re-examination of basic concept and identification of any desirable follow-up work.

Powe, R. E.↗

On-Shore Central Hydraulic Power Generation for Wind and Tidal Energy

Tidal energy, offshore wind energy, and onshore wind energy can be converted to electricity at a central ground location by means of converting their respective energies into high-pressure hydraulic flows that are transmitted to a system of generators by high-pressure pipelines. The high-pressure flows are then efficiently converted to electricity by a central power plant, and the low-pressure outlet flow is returned. The Department of Energy (DOE) is presently supporting a project led by Sunlight Photonics to demonstrate a 15 kW tidal hydraulic power generation system in the laboratory and possibly later submerged in the ocean. All gears and submerged electronics are completely eliminated. A second portion of this DOE project involves sizing and costing a 15 MW tidal energy system for a commercial tidal energy plant. For this task, Atlantis Resources Corporation s 18-m diameter demonstrated tidal blades are rated to operate in a nominal 2.6 m/sec tidal flow to produce approximately one MW per set of tidal blades. Fifteen units would be submerged in a deep tidal area, such as in Maine s Western Passage. All would be connected to a high-pressure (20 MPa, 2900 psi) line that is 35 cm ID. The high-pressure HEPG fluid flow is transported 500-m to on-shore hydraulic generators. HEPG is an environmentally-friendly, biodegradable, watermiscible fluid. Hydraulic adaptations to ORPC s cross-flow turbines are also discussed. For 15 MW of wind energy that is onshore or offshore, a gearless, high efficiency, radial piston pump can replace each set of top-mounted gear-generators. The fluid is then pumped to a central, easily serviceable generator location. Total hydraulic/electrical efficiency is 0.81 at full rated wind or tidal velocities and increases to 0.86 at 1/3 rated velocities.

wind energy↗

On-Shore Central Hydraulic Power Generation for Wind and Tidal Energy

Tidal energy, offshore wind energy, and onshore wind energy can be converted to electricity at a central ground location by means of converting their respective energies into high-pressure hydraulic flows that are transmitted to a system of generators by high-pressure pipelines. The high-pressure flows are then efficiently converted to electricity by a central power plant, and the low-pressure outlet flow is returned. The Department of Energy (DOE) is presently supporting a project led by Sunlight Photonics to demonstrate a 15 kilowatt tidal hydraulic power generation system in the laboratory and possibly later submerged in the ocean. All gears and submerged electronics are completely eliminated.A second portion of this DOE project involves sizing and costing a 15 megawatt tidal energy system for a commercial tidal energy plant. For this task, Atlantis Resources Corporation's 18-m diameter demonstrated tidal blades are rated to operate in a nominal 2.6 m/sec tidal flow to produce approximately one megawatt per set of tidal blades. Fifteen units would be submerged in a deep tidal area, such as in Maine's Western Passage. All would be connected to a high-pressure (20 megapascals, 2900 pounds per square inch) line that is 35 cm ID. The high-pressure HEPG fluid flow is transported 500-m to on-shore hydraulic generators. HEPG is an environmentally-friendly, biodegradable, water-miscible fluid. Hydraulic adaptations to ORPC's cross-flow turbines are also discussed.For 15 megawatt of wind energy that is onshore or offshore, a gearless, high efficiency, radial piston pump can replace each set of top-mounted gear-generators. The fluid is then pumped to a central, easily serviceable generator location. Total hydraulic/electrical efficiency is 0.81 at full rated wind or tidal velocities and increases to 0.86 at 1/3 rated velocities.

wind energy↗

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↗

A Mars 1 Watt vortex wind energy machine

A Martian wind power generator capable of surviving impact and fulfilling the long-term (2-5 yr) low-level power requirements (1-2 W) of an unmanned surface probe is presented. Attention is given to a tornado vortex generator that was chosen on the basis of its capability to theoretically augment the available power that may be extracted for average Martian wind speeds of about 7.5 m/s. The generator offers comparable mass-to-power ratios with solar power sources.

Ralston, Michael↗

Carbon-Free Energy: How Much, How Soon?

The scientific consensus is that carbon emissions need to reach net zero by 2050 to stabilize a global temperature rise below 2 degrees C. Many studies have focused on reaching net zero in the power sector by 2050 and have posited the need for clean firm power sources. Resources such as nuclear, fossil generation with carbon capture and sequestration, or other technologies would be needed to complement variable wind and solar generation.

carbon dioxide↗

Machine Learning–Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations

Abstract The push for increased capacity of renewable sources of electricity has led to the growth of wind-power generation, with a need for accurate forecasts of winds at hub height. Forecasts for these levels were uncommon until recently, and that, combined with the nocturnal collapse of the well-mixed boundary layer and daytime growth of the boundary layer through the levels important for energy generation, has contributed to errors in numerical modeling of wind generation resources. The present study explores several machine learning algorithms to both forecast and correct standard WRF Model forecasts of winds and temperature at hub height within wind turbine plants over several different time periods that are critical for the anticipation of potential blackouts and aiding in black start operations on the power grid. It was found that mean square error for day-2 wind forecasts from the WRF Model can be improved by over 90% with the use of a multioutput neural network, and that 60-min forecasts of WRF error, which can then be used to adjust forecasts, can be made with an LSTM with great accuracy. Nowcasting of temperature and wind speed over a 10-min period using an LSTM produced very low error and especially skillful forecasts of maximum and minimum values over the turbine plant area.

17 WIND ENERGY↗

The useful potential of using existing data to uniquely identify predictable wind events and regimes, part 1

Correlations between standard meteorological data and wind power generation potential were developed. Combined with appropriate wind forecasts, these correlations can be useful to load dispatchers to supplement conventional energy sources. Hourly wind data were analyzed for four sites, each exhibiting a unique physiography. These sites are Amarillo, Texas; Ludington, Michigan; Montauk Point, New York; and San Gorgonio, California. Synoptic weather maps and tables are presented to illustrate various wind 'regimes' at these sites.

Trettel, D. W.↗

WRF Modelling of Deep Convection and Hail for Wind Power Applications

Deep convection and the related occurrence of hail, intense precipitation and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenance costs and energy losses from wind farms. In the United States Southern Great Plains (SGP), where there is widespread wind energy development, deep convection and hail events are common, increasing the potential for precipitation-driven LEE. A 25-day Weather Research and Forecasting (WRF) model simulation conducted at convection-permitting resolution and using a detailed microphysics scheme is carried out for the SGP to evaluate the effectiveness in modelling the wind and precipitation conditions relevant to LEE potential. WRF output for these properties is evaluated using radar observations of precipitation (including hail) and reflectivity, in situ wind speed measurements and wind power generation. This research demonstrates some skill for the primary drivers of LEE. Wind speeds, rainfall rates and precipitation totals show good agreement with observations. Here, the occurrence of precipitation during power producing wind speeds is also shown to exhibit fidelity. Hail events frequently occur during periods when wind turbines are rotating and are especially important to LEE in the SGP. The presence of hail is modelled with a mean proportion correct of 0.77 and an odds ratio 4.55. Further research is needed to demonstrate sufficient model performance to be actionable for the wind energy industry and there is evidence for positive model bias in cloud reflectivity.

17 WIND ENERGY↗