scholarly journals Validation of Wind Resource and Energy Production Simulations for Small Wind Turbines in the United States

2021 ◽  
Author(s):  
Lindsay M. Sheridan ◽  
Caleb Phillips ◽  
Alice C. Orrell ◽  
Larry K. Berg ◽  
Heidi Tinnesand ◽  
...  
Keyword(s):  
United States ◽  
Wind Speed ◽  
Wind Turbines ◽  
Energy Production ◽  
The United States ◽  
Wind Integration ◽  
Error Metrics ◽  
Small Wind Turbines ◽  
Annual Capacity ◽  
Wind Resource

Abstract. Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, and MyWindTurbine.com, which are seen to overestimate actual annual capacity factors by 2.5, 4.2, and 11.5 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produce more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.

scholarly journals Wind Resource Assessment and Economic Viability of Conventional and Unconventional Small Wind Turbines: A Case Study of Maryland

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5874
Author(s):  
Navid Goudarzi ◽  
Kasra Mohammadi ◽  
Alexandra St. St. Pé ◽  
Ruben Delgado ◽  
Weidong Zhu
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbines ◽  
Payback Period ◽  
Small Scale ◽  
Small Wind Turbines ◽  
Mean Wind Speed ◽  
Wind Resource ◽  
The Impact

Annual mean wind speed distribution models for power generation based on regional wind resource maps are limited by spatial and temporal resolutions. These models, in general, do not consider the impact of local terrain and atmospheric circulations. In this study, long-term five-year wind data at three sites on the North, East, and West of the Baltimore metropolitan area, Maryland, USA are statistically analyzed. The Weibull probability density function was defined based on the observatory data. Despite seasonal and spatial variability in the wind resource, the annual mean wind speed for all sites is around 3 m/s, suggesting the region is not suitable for large-scale power generation. However, it does display a wind power capacity that might allow for non-grid connected small-scale wind turbine applications. Technical and economic performance evaluations of more than 150 conventional small-scale wind turbines showed that an annual capacity factor and electricity production of 11% and 1990 kWh, respectively, are achievable. It results in a payback period of 13 years. Government incentives can improve the economic feasibility and attractiveness of investments in small wind turbines. To reduce the payback period lower than 10 years, modern/unconventional wind harvesting technologies are found to be an appealing option in this region. Key contributions of this work are (1) highlighting the need for studying the urban physics rather than just the regional wind resource maps for wind development projects in the build-environment, (2) illustrating the implementation of this approach in a real case study of Maryland, and (3) utilizing techno-economic data to determine suitable wind harnessing solutions for the studied sites.

Determination of rated wind speed for maximum annual energy production of variable speed wind turbines

2017 ◽  
Vol 205 ◽  
pp. 781-789 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Arash Hassanzadeh ◽  
Jamaloddin Jamali ◽  
Ali Mostafaeipour ◽  
Wei-Hsin Chen
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Energy Production ◽  
Variable Speed

scholarly journals Comparison of the incentives used to stimulate energy production in Japan, France, West Germany, and the United States

1981 ◽  
Author(s):  
R.J. Cole ◽  
B.W. Cone ◽  
P. Sommers ◽  
C. Eschbach ◽  
W.J. Sheppard ◽  
...  
Keyword(s):  
United States ◽  
Energy Production ◽  
The United States ◽  
West Germany

scholarly journals GoWInD: Wind Energy Spatiotemporal Assessment and Characterization of End-of-Life Activities

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6015
Author(s):  
Francisco Haces-Fernandez
Keyword(s):  
United States ◽  
Wind Energy ◽  
End Of Life ◽  
Wind Turbines ◽  
The United States ◽  
Temporal And Spatial Distribution ◽  
Continuous Growth ◽  
Disposal Options ◽  
Life Options

Concerns on the lack sustainable end-of-life options for wind turbines have significantly increased in recent years. To ensure wind energy continuous growth, this research develops a novel spatiotemporal methodology that sustainably handles end-of-life activities for wind equipment. This research introduces the Global Wind Inventory for Future Decommissioning (GoWInD), which assesses and characterizes wind turbines according to individual spatiotemporal decommissioning and sustainability attributes. Applying data from GoWInD, the research developments networks of end-of-life (EoL) centers for wind turbines. The placement and operational levels of EoL centers optimize sustainable decommissioning according to changing spatiotemporal features of wind turbines. The methodology was evaluated for the United States, developing the United States Global Wind Inventory for Future Decommissioning (US—GoWInD), implementing the network of United States end-of-life (US—EoL) centers. Significant imbalances on the temporal and spatial distribution of US wind decommissioning inventory were revealed by the system. Diverse options to effectively handle these imbalances were highlighted by the methodology, including US—EoL center optimization according to placement, operational levels and potential complementarities. Particular attention was paid to components with challenging disposal options. The system can be implemented for diverse geographical locations and alternative spatial and temporal resolutions.

scholarly journals Verification of the Reliability of Offshore Wind Resource Prediction Using an Atmosphere–Ocean Coupled Model

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 254
Author(s):  
Minhyeop Kang ◽  
Kyungnam Ko ◽  
Minyeong Kim
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Prediction Models ◽  
Wrf Model ◽  
Coupled Model ◽  
Sea Surface ◽  
Offshore Wind ◽  
Wind Effect ◽  
Observation Data ◽  
Wind Resource

An atmosphere–ocean coupled model is proposed as an optimal numerical prediction method for the offshore wind resource. Meteorological prediction models are mainly used for wind speed prediction, with active studies using atmospheric models. Seawater mixing occurring at sea due to solar radiation and wind intensity can significantly change the sea surface temperature (SST), an important variable for predicting wind resources and energy production, considering its wind effect, within a short time. This study used the weather research forecasting and ocean mixed layer (WRF-OML) model, an atmosphere–ocean coupled model, to reflect time-dependent SST and sea surface fluxes. Results are compared with those of the WRF model, another atmospheric model, and verified through comparison with observation data of a meteorological mast (met-mast) at sea. At a height of 94 m, the wind speed predicted had a bias and root mean square error of 1.09 m/s and 2.88 m/s for the WRF model, and −0.07 m/s and 2.45 m/s for the WRF-OML model, respectively. Thus, the WRF-OML model has a higher reliability. In comparing to the met-mast observation data, the annual energy production (AEP) estimation based on the predicted wind speed showed an overestimation of 15.3% and underestimation of 5.9% from the WRF and WRF-OML models, respectively.

scholarly journals Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine

2020 ◽  
Vol 10 (24) ◽  
pp. 9017
Author(s):  
Andoni Gonzalez-Arceo ◽  
Maitane Zirion-Martinez de Musitu ◽  
Alain Ulazia ◽  
Mario del Rio ◽  
Oscar Garcia
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Energy Production ◽  
Cost Effective ◽  
Reanalysis Data ◽  
Small Scale ◽  
Wide Range ◽  
Wind Measurements ◽  
Scale Experiment ◽  
Wind Resource

In this work, a cost-effective wind resource method specifically developed for the ROSEO-BIWT (Building Integrated Wind Turbine) and other Building Integrated Wind Turbines is presented. It predicts the wind speed and direction at the roof of an previously selected building for the past 10 years using reanalysis data and wind measurements taken over a year. To do so, the reanalysis wind speed data is calibrated against the measurements using different kinds of quantile mapping, and the wind direction is predicted using random forest. A mock-up of a building and a BIWT were used in a wind tunnel to perform a small-scale experiment presented here. It showed that energy production is possible and even enhanced over a wide range of attack angles. The energy production estimations made with the best performing kind of calibration achieved an overall relative error of 6.77% across different scenarios.

scholarly journals Wake and Turbulence Analysis for Wind Turbine Layouts in an Island

2018 ◽  
Vol 64 ◽  
pp. 06010
Author(s):  
Bachhal Amrender Singh ◽  
Vogstad Klaus ◽  
Lal Kolhe Mohan ◽  
Chougule Abhijit ◽  
Beyer Hans George
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Wind Farm ◽  
Full Length Paper ◽  
Turbulence Analysis ◽  
Wake Model ◽  
Wind Resources

There is a big wind energy potential in supplying the power in an island and most of the islands are off-grid. Due to the limited area in island(s), there is need to find appropriate layout / location for wind turbines suited to the local wind conditions. In this paper, we have considered the wind resources data of an island in Trøndelag region of the Northern Norway, situated on the coastal line. The wind resources data of this island have been analysed for wake losses and turbulence on wind turbines for determining appropriate locations of wind turbines in this island. These analyses are very important for understanding the fatigue and mechanical stress on the wind turbines. In this work, semi empirical wake model has been used for wake losses analysis with wind speed and turbine spacings. The Jensen wake model used for the wake loss analysis due to its high degree of accuracy and the Frandsen model for characterizing the turbulent loading. The variations of the losses in the wind energy production of the down-wind turbine relative to the up-wind turbine and, the down-stream turbulence have been analysed for various turbine distances. The special emphasis has been taken for the case of wind turbine spacing, leading to the turbulence conditions for satisfying the IEC 61400-1 conditions to find the wind turbine layout in this island. The energy production of down-wind turbines has been decreased from 2 to 20% due to the lower wind speeds as they are located behind up-wind turbine, resulting in decreasing the overall energy production of the wind farm. Also, the higher wake losses have contributed to the effective turbulence, which has reduced the overall energy production from the wind farm. In this case study, the required distance for wind turbines have been changed to 6 rotor diameters for increasing the energy gain. From the results, it has been estimated that the marginal change in wake losses by moving the down-stream wind turbine by one rotor diameter distance has been in the range of 0.5 to 1% only and it is insignificant. In the full-length paper, the wake effects with wind speed variations and the wind turbine locations will be reported for reducing the wake losses on the down-stream wind turbine. The Frandsen model has been used for analysing turbulence loading on the down-stream wind turbine as per IEC 61400-1 criteria. In larger wind farms, the high turbulence from the up-stream wind turbines increases the fatigues on the turbines of the wind farm. In this work, we have used the effective turbulence criteria at a certain distance between up-stream and down-stream turbines for minimizing the fatigue load level. The sensitivity analysis on wake and turbulence analysis will be reported in the full-length paper. Results from this work will be useful for finding wind farm layouts in an island for utilizing effectively the wind energy resources and electrification using wind power plants.

Biomass energy production in the United States: an overview

1994 ◽  
Vol 6 (3) ◽  
pp. 161-173 ◽  
Author(s):  
William G. Hohenstein ◽  
Lynn L. Wright
Keyword(s):  
United States ◽  
Energy Production ◽  
The United States ◽  
Biomass Energy
Sign in / Sign up

Export Citation Format

Share Document