Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Wind Power”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3

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↗

Optimized Swinging Door Algorithm for Wind Power Ramp Event Detection: Preprint

Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time series data are segmented by the original SDA, and then all significant ramps are detected and merged through a dynamic programming algorithm. An application of the optimized SDA is provided to ascertain the optimal parameter of the original SDA. Measured wind power data from the Electric Reliability Council of Texas (ERCOT) are used to evaluate the proposed optimized SDA.

17 WIND ENERGY↗

Long-term implications of sustained wind power growth in the United States: Direct electric system impacts and costs

This paper evaluates potential changes in the power system associated with sustained growth in wind generation in the United States to 35% of end-use demand by 2050; Wiser et al. (2016) evaluate societal benefits and other impacts for this same scenario. Under reference or central conditions, the analysis finds cumulative wind capacity of 404 gigawatts (GW) would be required to reach this level and drive 2050 incremental electricity rate and cumulative electric sector savings of 2% and 3% respectively, relative to a scenario with no new wind capacity additions. Greater savings are estimated under higher fossil fuel costs or with greater advancements in wind technologies. Conversely, incremental costs are found when fossil fuel costs are lower than central assumptions or wind technology improvements are more-limited. Through 2030, the primary generation sources displaced by new wind capacity include natural gas and coal-fired generation. By 2050, wind could displace other renewables. Incremental new transmission infrastructure totaling 29 million megawatt-miles is estimated to be needed by 2050. In conjunction with related societal benefits, this work demonstrates that 35% wind energy by 2050 is plausible, could support enduring benefits, and could result in long-term consumer savings, if nearer-term (pre-2030) cost barriers are overcome; at the same time, these opportunities are not anticipated to be realized in their full form under 'business-as-usual' conditions.

17 WIND ENERGY↗

A Scenario Generation Method for Wind Power Ramp Events Forecasting

Wind power ramp events (WPREs) have received increasing attention in recent years due to their significant impact on the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual data from a wind power plant in the Bonneville Power Administration (BPA) was selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.

17 WIND ENERGY↗

A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps

Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic wind power ramp forecast (cp-WPRF) model based on Copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model (GMM) is adopted to characterize the WPR uncertainty and features. The Canonical Maximum Likelihood (CML) method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion (BIC) from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval (PI) based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.

17 WIND ENERGY↗

Long-term implications of sustained wind power growth in the United States: Potential benefits and secondary impacts

Here, we model scenarios of the U.S. electric sector in which wind generation reaches 10% of end-use electricity demand in 2020, 20% in 2030, and 35% in 2050. As shown in a companion paper, achieving these penetration levels would have significant implications for the wind industry and the broader electric sector. Compared to a baseline that assumes no new wind deployment, under the primary scenario modeled, achieving these penetrations imposes an incremental cost to electricity consumers of less than 1% through 2030. These cost implications, however, should be balanced against the variety of environmental and social implications of such a scenario. Relative to a baseline that assumes no new wind deployment, our analysis shows that the high-penetration wind scenario yields potential greenhouse-gas benefits of 85–1,230 billion USD in present-value terms, with a central estimate of 400 billion USD. Air-pollution-related health benefits are estimated at 52–272 billion USD, while annual electric-sector water withdrawals and consumption are lower by 15% and 23% in 2050, respectively. We also find that a high-wind-energy future would have implications for the diversity and risk of energy supply, local economic development, and land use and related local impacts on communities and ecosystems; however, these additional impacts may not greatly affect aggregate social welfare owing to their nature, in part, as resource transfers.

17 WIND ENERGY↗

A Data-Driven Methodology for Probabilistic Wind Power Ramp Forecasting

With increasing wind penetration, wind power ramps (WPRs) are currently drawing great attention to balancing authorities, since these wind ramps largely affect power system operations. To help better manage and dispatch the wind power, this paper develops a data-driven probabilistic WPR forecasting (p-WPRF) method based on a large number of simulated scenarios. A machine learning technique is first adopted to forecast the basic wind power forecasting scenario and produce the historical forecasting errors. To accurately model the distribution of wind power forecasting errors, a generalized Gaussian mixture model is developed and the cumulative distribution function (CDF) is also analytically deduced. The inverse transform method based on the CDF is used to generate a large number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The p-WPRF is generated based on all generated scenarios under different weather and time conditions. Numerical simulations on publicly available wind power data show that the developed p-WPRF method can predict WPRs with a high level of reliability and accuracy.

17 WIND ENERGY↗

Transient Stability Analysis for Offshore Wind Power Plant Integration Planning Studies - Part I: Short Term Faults

Power system planning is the process of forecasting load demand and ensuring sufficient generation and reserve capacities over some planning horizon. This process includes investigation of steady-state and dynamic operation of power system of interest. This paper addresses the transient stability (also known as large-signal stability) analysis of power systems for offshore wind power plant integration planning studies. In particular, this study develops a comprehensive practical methodology to assess transient stability of power systems, including rotor angle stability, voltage stability and frequency response for all fault types for large scale power systems. This methodology considers variability of the offshore wind power plants as well as the type of any faulted components present and is applicable to the study of both short term and long term faults, though only short term faults are considered here (long term faults are treated sequentially in a companion paper). Furthermore, this paper considers the integration of offshore wind power plants into existing power systems and demonstrates the utility of this methodology through the examination of the specific case of integrating 1,000MW of offshore wind power into the FirstEnergy/PJM service territory using a realistic model of 63k-bus test system that represents the U.S. Eastern Interconnection.

17 WIND ENERGY↗

Transient Stability Analysis for Offshore Wind Power Plant Integration Planning Studies - Part II: Long Term Faults

Here, this paper addresses the transient stability (also called large-signal stability) analysis of power systems for offshore wind power plant integration planning studies. In particular, this study develops a comprehensive practical methodology to assess transient stability of power systems, including rotor angle stability, voltage stability, and frequency response for large scale power systems. This methodology considers variability of the offshore wind power plants as well as the type of any faulted system' components present and is applicable to the study of both short term and long term faults. Part I of this research discussed the short term faults whereas as Part II, the present paper, discusses long term faults. This research considers the integration of offshore wind power plants into existing power systems and demonstrates the utility of this methodology through the examination of the specific case of integrating 1,000 MW of offshore wind power into the FirstEnergy/PJM service territory using a realistic model of 63k-bus test system that represents the U.S. Eastern Interconnection.

17 WIND ENERGY↗

Workforce and Economic Development Considerations from the Operations and Maintenance of Wind Power Plants

As wind capacity in the United States grows, economic opportunities also emerge—supported by the development of the wind workforce, specifically in the operation and maintenance (O&M) phase. Moreover, almost all wind turbines in the United States—about 99%—are located in rural areas (American Wind Energy Association 2018). Communities in these rural areas can anticipate some degree of economic impact from the wind industry. However, the various stakeholders within these communities, such as local businesses and government officials, are not always fully informed of the workforce impacts caused by wind power plants during operational years (Stefek et al. 2019). Although several research studies have focused on workforce and economic impacts of land-based wind facilities (e.g., Tegen et al. 2014; Brown et al. 2012; Halvatzis and Keyser 2013), these studies focused on the quantity of workers and not on O&M workforce interactions with local communities, such as worker commuting and spending patterns. This report is intended to provide stakeholders and community decision makers with qualitative and quantitative information to enable informed decisions that support workforce planning around new wind energy deployment.

17 WIND ENERGY↗

Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach

The significantly increasing deployment of wind power necessitates that system operation considers the spatial-temporal correlation of power forecast from different wind power plants. How to model this spatial-temporal correlation in the actual system dispatch is challenging. In this paper, a sparse correlation matrix is utilized to efficiently model the spatial-temporal correlation of wind power forecast in the generation dispatch model. A novel, adjustable, and distributionally-robust chance-constrained multi-interval optimal power flow (ADRCC-MIOPF) model is proposed to obtain reliable economic dispatch (ED) solutions. The spatial-temporal correlation of wind power plants power forecasts is represented by the sparse correlation covariance matrix obtained from historical, time series wind power forecast error data. The chance constraints in the ADRCC-MIOPF model are transformed into a set of second-order-cone (SOC) constraints in which an adjustable coefficient in the chance constraints controls the robustness of the ADRCC-MIOPF model to the wind power forecast errors distribution. Case studies performed on the PJM 5-bus system and IEEE 118-bus system verify the proposed method to improve the system security and reduce the cost especially under the high wind penetration levels. All the cases can be solved within several minutes for both the small and large cases which validates the efficiency of the proposed sparse matrix model. In addition, considering the spatial-temporal correlation of wind power forecast and the distributional robustness of wind power forecast error leads to a more reliable economic dispatch with lower system violations.

17 WIND ENERGY↗

Value of Improved Wind Power Forecasting in the Western Interconnection (Poster)

Wind power forecasting is a necessary and important technology for incorporating wind power into the unit commitment and dispatch process. It is expected to become increasingly important with higher renewable energy penetration rates and progress toward the smart grid. There is consensus that wind power forecasting can help utility operations with increasing wind power penetration; however, there is far from a consensus about the economic value of improved forecasts. This work explores the value of improved wind power forecasting in the Western Interconnection of the United States.

17 WIND ENERGY↗

Modeling Framework and Validation of a Smart Grid and Demand Response System for Wind Power Integration

Electricity generation from wind power and other renewable energy sources is increasing, and their variability introduces new challenges to the power system. The emergence of smart grid technologies in recent years has seen a paradigm shift in redefining the electrical system of the future, in which controlled response of the demand side is used to balance fluctuations and intermittencies from the generation side. This paper presents a modeling framework for an integrated electricity system where loads become an additional resource. The agent-based model represents a smart grid power system integrating generators, transmission, distribution, loads and market. The model incorporates generator and load controllers, allowing suppliers and demanders to bid into a Real-Time Pricing (RTP) electricity market. The modeling framework is applied to represent a physical demonstration project conducted on the Olympic Peninsula, Washington, USA, and validation simulations are performed using actual dynamic data. Wind power is then introduced into the power generation mix illustrating the potential of demand response to mitigate the impact of wind power variability, primarily through thermostatically controlled loads. The results also indicate that effective implementation of Demand Response (DR) to assist integration of variable renewable energy resources requires a diversity of loads to ensure functionality of the overall system.

Demand Response↗

Environmental Impacts of Wind Power Development on the Population Biology of Greater Prairie-Chickens

1. We investigated the impacts of wind power development on the demography, movements, and population genetics of Greater Prairie-Chickens (Tympanuchus cupido) at three sites in northcentral and eastern Kansas for a 7-year period. Only 1 of 3 sites was developed for wind power, the 201MW Meridan Way Wind Power Facility at the Smoky Hills site in northcentral Kansas. Our project report is based on population data for prairie chickens collected during a 2-year preconstruction period (2007-2008), a 3-year postconstruction period (2009-2011) and one final year of lek surveys (2012). Where relevant, we present preconstruction data from our field studies at reference sites in the northern Flint Hills (2007-2009) and southern Flint Hills (2006-2008). 2. We addressed seven potential impacts of wind power development on prairie chickens: lek attendance, mating behavior, use of breeding habitat, fecundity rates, natal dispersal, survival rates, and population numbers. Our analyses of pre- and postconstruction impacts are based on an analysis of covariance design where we modeled population performance as a function of treatment period, distance to eventual or actual site of the nearest wind turbine, and the interaction of these factors. Our demographic and movement data from the 6-year study period at the Smoky Hills site included 23 lek sites, 251 radio-marked females monitored for 287 bird-years, and 264 nesting attempts. Our genetic data were based on genotypes of 1,760 females, males and chicks that were screened with a set of 27 microsatellite markers that were optimized in the lab. 3. In our analyses of lek attendance, the annual probability of lek persistence during the preconstruction period was ~0.9. During the postconstruction period, distance to nearest turbine did not have a significant effect on the probability of lek persistence. However, the probability of lek persistence increased from 0.69 at 0 m to 0.89 at 30 km from turbines, and most abandoned lek sites were located <5 km from turbines. Probability of lek persistence was significantly related to habitat and number of males. Leks had a higher probability of persistence in grasslands than agricultural fields, and increased from ~0.2 for leks of 5 males, to >0.9 for leks of 10 or more males. Large leks in grasslands should be a higher priority for conservation. Overall, wind power development had a weak effect on the annual probability of lek persistence. 3. We used molecular methods to investigate the mating behavior of prairie chickens. The prevailing view for lek-mating grouse is that females mate once to fertilize the clutch and that conspecific nest parasitism is rare. We found evidence that females mate multiple times to fertilize the clutch (8-18% of broods, 4-38% of chicks) and will parasitize nests of other females during egg-laying (~17% of nests). Variable rates of parentage were highest in the fragmented landscapes at the Smoky Hills field site, and were lower at the Flint Hills field site. Comparisons of the pre- and postconstruction periods showed that wind energy development did not affect the mating behaviors of prairie chickens. 4. We examined use of breeding habitats by radio-marked females and conducted separate analyses for nest site selection, and movements of females not attending nests or broods. The landscape was a mix of native prairie and agricultural habitats, and nest site selection was not random because females preferred to nest in grasslands. Nests tended to be closer to turbines during the postconstruction period and there was no evidence of behavioral avoidance of turbines by females during nest site selection. Movements of females not attending nests or broods showed that females crossed the site of the wind power development at higher rates during the preconstruction period (20%) than the postconstruction period (11%), and that movements away from turbines were more frequent during the postconstruction period. Thus, wind power development appears to affect movements in breeding habitats but not nest site selection of female prairie chickens during the breeding season. 5. We tested the effects of wind power development on five components of female fecundity: timing of clutch initiation, clutch size of first nests and renests, nest survival, and hatchability of eggs. Average date of clutch initiation was 26 April, clutch size was 12.7 and 10.6 eggs for first nests and renests, probability of nest survival was low at 0.18, but egg hatchability was high at 0.79. Wind power development had no impact on reproductive effort or nesting success, and all five components of fecundity were not related to treatment period or distance to turbine. Nest survival was the main factor limiting reproductive output of female prairie chickens and most losses were due to predation. Daily nest survival was strongly related to vegetative cover at the nest. Changes to rangeland management practices that would double nesting cover from 2.5 to 5 dm would triple the probability of nest survival from 0.17 to 0.52. Grass and forb cover had weak positive effects on daily nest survival whereas shrub cover, proximity to woodlands, and recent rainfall had negative effects. Reproductive performance of prairie chickens is low in managed rangelands in northcentral Kansas and efforts to improve range conditions and reduce predator activity would aid recovery of prairie chicken populations. 6. We used molecular methods to investigate patterns of natal dispersal in prairie chickens. High rates of nest failure limited the number of young that we could sample. Direct detections of natal dispersal were limited because survival of newly hatched chicks to become adults were low and because we were unable to detect dispersal distances outside of our study area. Direct observations of natal movements were limited and were inadequate to make conclusions about the potential impacts of wind energy development on natal dispersal. Spatial correlograms of genetic distance among males at leks were a more sensitive measure of population structure, and indicated a weak effect of wind energy development on the spatial genetic structure of prairie chickens. 7. We tested the effects of wind power development on female survival with time-to-event models, and on residual body mass of males with analyses of covariance. Distance to turbine and the interaction of distance and treatment period had no effect on female survival. Contrary to predictions of negative impacts of wind power development, the probability of female was lowest during the preconstruction period (0.274) and increased significantly during the postconstruction period (0.543). Inspection of hazard functions indicated that the difference in annual survival could be attributed to a higher risk of mortality during the lekking season in the preconstruction period. We suggest that wind power development may have improved ecological conditions for prairie chickens by disrupting the foraging behavior of diurnal raptors that kill prairie chickens at lek sites. In support of this idea, raptor kills tended to be farther from turbines during the postconstruction period whereas mammalian kills were closer. Analyses of the major causes of mortality did not support our hypothesis because the odds of raptor predation were greater after development was completed. Most mortality losses of radio-marked females were due to predation, and losses to collision mortality or harvest were rare events. Low rates of natural mortality during fall and winter imply that harvest is likely to be additive mortality in prairie chickens. Wind energy development reduced the residual body mass of male Greater Prairie-Chickens at lek sites near turbines. Low values of residual body mass could have a negative impact on individual survival or fecundity rates, or may be related to predation risk and flight performance of males displaying at open lek sites. 8. We tested for impacts of wind power development on population numbers of prairie chickens with monitoring of male numbers at leks, and with genetic measures of population structure. Lek counts indicated that wind power development did not affect the population size of prairie chickens. Peak counts of males at leks were recorded the first year after construction was completed and the highest rates of population change were observed during the interval when the wind power facility was constructed. Population numbers of prairie chickens near and distant from turbines appeared to covary in parallel, probably because bird numbers were linked by dispersal movements. Estimates of population viability based on genetic diversity, effective population size and rates of population exchange did not show annual changes and were unaffected by wind development during our study. Estimates of relatedness among males at the same and different leks suggested that wind power development has either reduced dispersal rates or changed settlement patterns, leading to higher rates of relatedness among males displaying at the same lek site. 9. Greater Prairie-Chickens were not strongly affected by wind power development in Kansas. Negative impacts of wind power development included a trend for reductions in lek persistence near turbines, behavioral avoidance of turbines by females during their breeding season movements, and changes in the genetic structure of males at leks that were consistent with reduced dispersal or recruitment rates. We found no impacts of wind power development on nest site selection, female reproductive effort or nesting success, or population numbers. Positive impacts of wind power development included an increase in female survival rates. We hypothesized that the unexpected increase in female survival was related to changes in trophic interactions and disruption of the foraging behavior of raptors that kill prairie chickens at lek site. 10. Research funding for this project included a grant from the 20% Wind by 2030 Program of the Department of Energy (this final report), and grants from the Kansas Department of Wildlife, Parks and Tourism, the National Fish and Wildlife Federation, and initial funding from the National Wind Coordinating Collaborative. Research products from data collected during the preconstruction period have included five peer-reviewed research articles and two PhD dissertations at Kansas State University. Additional manuscripts are in review for possible publication in 2013-2014.

17 WIND ENERGY↗

Index for Wind Power Variability

Variability of large scale wind power generation is dependent on several factors: characteristics of installed wind power plants, size of the area where the plants are installed, geographic dispersion within that area and its weather regime(s). Variability can be described by ramps in power generation, i.e. changes from time period to time period. Given enough data points, it can be described with a probability density function. This approach focuses on two dimensions of variability: duration of the ramp and probability distribution. This paper proposes an index based on these two dimensions to enable comparisons and characterizations of variability under different conditions. The index is tested with real, large scale wind power generation data from several countries. Considerations while forming an index are discussed, as well as the main results regarding what the drivers of variability experienced for different data.

17 WIND ENERGY↗

Dynamic land use implications of rapidly expanding and evolving wind power deployment

Abstract The expansion of wind power poses distinct and varied geographic challenges to a sustainable energy transition. However, current knowledge of its land use impacts and synergies is limited by reliance on static characterizations that overlook the role of turbine technology and plant design in mediating interactions with the environment. Here, we investigate how wind technology development and innovation have shaped landscape interactions with social and ecological systems within the United States and contribute to evolving land area requirements. This work assesses trends in key land use facets of wind power using a holistic set of metrics to establish an evidence base that researchers, technology designers, land use managers, and policymakers can use in envisioning how future wind-intensive energy systems may be jointly optimized for clean energy, social, and environmental objectives. Since 2000, we find dynamic land occupancy patterns and regional trends that are driven by advancing technology and geographic factors. Though most historical U.S. wind deployment has been confined to the temperate grassland biome in the nation’s interior, regional expansion has implicated diverse land use and cover types. A large percentage of the typical wind plant footprint (∼96% to > 99%) is not directly impacted by permanent physical infrastructure, allowing for multiple uses in the spaces between turbines. Surprisingly, turbines are commonly close to built structures. Moreover, rangeland and cropland have supported 93.4% of deployment, highlighting potential synergies with agricultural lands. Despite broadly decreasing capacity densities, offsetting technology improvements have stabilized power densities. Land use intensity, defined as the ratio of direct land usage to lifetime power generation of wind facilities, has also trended downwards. Although continued deployment on disturbed lands, and in close proximity to existing wind facilities and other infrastructure, could minimize the extent of impacts, ambitious decarbonization trajectories may predispose particular biomes to cumulative effects and risks from regional wind power saturation. Increased land-use and sustainability feedback in technology and plant design will be critical to sustainable management of wind power.

17 WIND ENERGY↗

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

17 WIND ENERGY↗