scholarly journals Air Density Induced Error on Wind Energy Estimation

2019 ◽  
Author(s):  
Aurore Dupré ◽  
Philippe Drobinski ◽  
Jordi Badosa ◽  
Christian Briard ◽  
Riwal Plougonven
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Energy Production ◽  
Energy Resource ◽  
Added Value ◽  
Local Analysis ◽  
Mountainous Area ◽  
Air Density ◽  
Temperature And Pressure ◽  
Power Curves

Abstract. In recent years, environmental concerns have encouraged the use of wind power as a renewable energy resource. However, high penetration of the wind power in the electricity system is a challenge due to the uncertainty of wind energy forecast. Estimation of the wind energy production requires a forecast for the wind (the main source of uncertainty) but also of density, often overlooked. Measure of air density is a key for more accurate wind energy prediction. Wind farms often lack instrumentations of temperature and pressure, needed for accurate air density estimation at hub height to be used for locally debiasing air density forecast. In this study, the error budget of air density estimate is computed distinguishing temperature and pressure contributions. The analysis uses measurements for in-depth local analysis as well as meteorological reanalysis to investigate the added-value of a model-based value when measurement is missing. Meteorological reanalysis is also used to study spatial pattern of error budgets (mountainous area, coastal regions, plains, ...). The effect of altitude is carefully accounted for. Temperature is by far the variable inducing the largest errors when it is missing in the air density correction, and replaced by the standard atmosphere value (i.e. 15 °C, used as reference in power curves). It is particularly true for very cold or warm conditions (i.e. far from the standard value), for which the error on wind energy production is nearly halved when an accurate correction of temperature is performed.

scholarly journals Wind Energy Potential on A Highrise Building: A Preliminary Study

2021 ◽  
Vol 88 (3) ◽  
pp. 20-30
Author(s):  
Nofirman Firdaus ◽  
Bambang Teguh Prasetyo ◽  
Hasnida Ab-Samat ◽  
Prayudi ◽  
Hendri ◽  
...  
Keyword(s):  
Wind Energy ◽  
Weibull Distribution ◽  
Wind Turbine ◽  
Energy Production ◽  
Energy Resource ◽  
Limited Area ◽  
Wake Effect ◽  
Horizontal Axis Wind Turbine ◽  
High Rise ◽  
Power Curves

Indonesia has an abundant renewable energy source. One of them is wind energy resources. Unfortunately, Indonesia's wind energy resource is not fully utilized, especially for application in high-rise buildings. The paper investigates the potential of energy production from the horizontal-axis wind turbine (HAWT) and the vertical-axis wind turbine (VAWT) on the rooftop of a university building in Indonesia. The wind speed data were measured on the rooftop of the building for seven months. The data was analyzed using Weibull distribution. Based on the probability density function of the Weibull distribution, the potential energy production was calculated using the power curves from the manufacturer. Comparing energy production between HAWTs and VAWTs has shown that VAWTs can produce more energy than HAWTs. Using six turbines, VAWTs can produce 48,476 kWh. On the other hand, with four turbines, HAWTs can produce 41,729 kWh. The reason is that VAWT requires shorter distance requirements for inter-turbine and between rows. Therefore, VAWT can use more turbines than HAWT in the limited area. In conclusion, VAWT for high-rise buildings is more preferred because VAWT can generate more energy. Further study should investigate the optimal configuration with varying the wind direction and quantifying the wake effect on power output.

scholarly journals Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula

2019 ◽  
Vol 11 (13) ◽  
pp. 3648 ◽  
Author(s):  
Alain Ulazia ◽  
Gabriel Ibarra-Berastegi ◽  
Jon Sáenz ◽  
Sheila Carreno-Madinabeitia ◽  
Santos J. González-Rojí
Keyword(s):  
Wind Energy ◽  
Iberian Peninsula ◽  
Wind Power ◽  
Power Density ◽  
Energy Production ◽  
Offshore Wind ◽  
Power Curve ◽  
Energy Potential ◽  
Air Density ◽  
Wind Energy Potential

A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed’s Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes.

scholarly journals Estimating Air Density Using Observations and Re-Analysis Outputs for Wind Energy Purposes

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2038 ◽  
Author(s):  
Rogier Floors ◽  
Morten Nielsen
Keyword(s):  
Wind Energy ◽  
Energy Resource ◽  
Analysis Data ◽  
Resource Assessment ◽  
Current Version ◽  
Wind Resource Assessment ◽  
Industry Standard ◽  
Air Density ◽  
Wind Resource ◽  
Power Curves

A method to estimate air density as a function of elevation for wind energy resource assessments is presented. The current practice of using nearby measurements of pressure and temperature is compared with a method that uses re-analysis data. It is found that using re-analysis data to estimate air density gives similar or smaller mean absolute errors compared to using measurements that were on average located 40 km away. A method to interpolate power curves that are valid for different air densities is presented. The new model is implemented in the industry-standard model for wind resource assessment and compared with the current version of that model and shown to lead to more accurate assessment of the air density at different elevations.

scholarly journals Wind potential estimation and proposed energy production in Southern Punjab using Weibull probability density function and surface measured data

2020 ◽  
pp. 014459872092074 ◽  
Author(s):  
Muhammad Sumair ◽  
Tauseef Aized ◽  
Syed Asad Raza Gardezi ◽  
Syed Ubaid Ur Rehman ◽  
Syed Muhammad Sohail Rehman
Keyword(s):  
Wind Speed ◽  
Energy Density ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Energy Production ◽  
Economic Assessment ◽  
Weibull Model ◽  
Wind Power Density ◽  
Wind Potential

Current work focusses on the wind potential assessment in South Punjab. Eleven locations from South Punjab have been analyzed using two-parameter Weibull model (with Energy Pattern Factor Method to estimate Weibull parameters) and five years (2014–2018) hourly wind data measured at 50 m height and collected from Pakistan Meteorological Department. Techno-economic analysis of energy production using six different turbine models was carried out with the purpose of presenting a clear picture about the importance of turbine selection at particular location. The analysis showed that Rahim Yar Khan carries the highest wind speed, highest wind power density, and wind energy density with values 4.40 ms−1, 77.2 W/m2 and 677.76 kWh/m2/year, respectively. On the other extreme, Bahawalnagar observes the least wind speed i.e. 3.60 ms−1 while Layyah observes the minimum wind power density and wind energy density as 38.96 W/m2 and 352.24 kWh/m2/year, respectively. According to National Renewable Energy Laboratory standards, wind potential ranging from 0 to 200 W/m2 is considered poor. Economic assessment was carried out to find feasibility of the location for energy harvesting. Finally, Polar diagrams drawn to show the optimum wind blowing directions shows that optimum wind direction in the region is southwest.

A case study of climate variability effects on wind resources in South Africa

2014 ◽  
Vol 25 (3) ◽  
pp. 2-10 ◽  
Author(s):  
Lynette Herbst ◽  
Jörg Lalk
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Power ◽  
Energy Production ◽  
Energy Sector ◽  
Speed Increase ◽  
Wind Resources ◽  
Wind Speed Increase

The wind energy sector is one of the most prominent sectors of the renewable energy industry. However, its dependence on meteorological factors subjects it to climate change. Studies analysing the impact of climate change on wind resources usually only model changes in wind speed. Two elements that have to be calculated in addition to wind speed changes are Annual Energy Production (AEP) and Power Density (PD). This is not only because of the inherent variability between wind speed and wind power generated, but also because of the relative magnitudes of change in energy potentially generated at different areas under varied wind climates. In this study, it was assumed that two separate locations would experience a 10% wind speed increase after McInnes et al. (2010). Given the two locations’ different wind speed distributions, a wind speed increase equal in magnitude is not equivalent to similar magnitudes of change in potential energy production in these areas. This paper demonstrates this fact for each of the case studies. It is of general interest to the energy field and is of value since very little literature exists in the Southern African context on climate change- or variability-effects on the (wind) energy sector. Energy output is therefore dependent not only on wind speed, but also wind turbine characteristics. The importance of including wind power curves and wind turbine generator capacity in wind resource analysis is emphasised.

Wind energy potential assessment for KPT with a comparison of different methods of determining Weibull parameters

2020 ◽  
Vol 14 (1) ◽  
pp. 59-84 ◽  
Author(s):  
Zahid Hussain Hulio ◽  
Wei Jiang
Keyword(s):  
Maximum Likelihood ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Shear ◽  
Oil And Gas ◽  
Social Implications ◽  
Site Specific ◽  
Content Type ◽  
Weibull Parameters ◽  
Air Density

Purpose The purpose of this paper is to investigate wind power potential of site using wind speed, wind direction and other meteorological data including temperature and air density collected over a period of one year. Design/methodology/approach The site-specific air density, wind shear, wind power density, annual energy yield and capacity factors have been calculated at 30 and 10 m above the ground level (AGL). The Weibull parameters have been calculated using empirical, maximum likelihood, modified maximum likelihood, energy pattern and graphical methods to determine the other dependent parameters. The accuracies of these methods are determined using correlation coefficient (R²) and root mean square error (RMSE) values. Findings The site-specific wind shear coefficient was found to be 0.18. The annual mean wind speeds were found to be 5.174 and 4.670 m/s at 30 and 10 m heights, respectively, with corresponding standard deviations of 2.085 and 2.059. The mean wind power densities were found to be 59.50 and 46.75 W/m² at 30 and 10 m heights, respectively. According to the economic assessment, the wind turbine A is capable of producing wind energy at the lowest value of US$ 0.034/kWh. Practical implications This assessment provides the sustainable solution of energy which minimizes the dependence on continuous supply of oil and gas to run the conventional power plants that is a major cause of increasing load shedding in the significant industrial and thickly populated city of Pakistan. Also, this will minimize the quarrel between the local power producer and oil and gas supplier during the peak season. Social implications This wind resource assessment has some important social implications including decreasing the environmental issues, enhancing the uninterrupted supply of electricity and decreasing cost of energy per kWh for the masses of Karachi. Originality/value The results are showing that the location can be used for installing the wind energy power plant at the lower cost per kWh compared to other energy sources. The wind energy is termed as sustainable solution at the lowest cost.

scholarly journals On the Wind Energy Resource and Its Trend in the East China Sea

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Adekunle Ayodotun Osinowo ◽  
Xiaopei Lin ◽  
Dongliang Zhao ◽  
Kaiwen Zheng
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
East China Sea ◽  
Large Scale ◽  
Energy Resource ◽  
East China ◽  
The East China Sea ◽  
Medium Range Weather Forecast ◽  
China Sea ◽  
Wind Potential

This study utilizes a 30-year (1980–2009) 10 m wind field dataset obtained from the European Center for Medium Range Weather Forecast to investigate the wind energy potential in the East China Sea (ECS) by using Weibull shape and scale parameters. The region generally showed good wind characteristics. The calculated annual mean of the wind power resource revealed the potential of the region for large-scale grid-connected wind turbine applications. Furthermore, the spatiotemporal variations showed strong trends in wind power in regions surrounding Taiwan Island. These regions were evaluated with high wind potential and were rated as excellent locations for installation of large wind turbines for electrical energy generation. Nonsignificant and negative trends dominated the ECS and the rest of the regions; therefore, these locations were found to be suitable for small wind applications. The wind power density exhibited an insignificant trend in the ECS throughout the study period. The trend was strongest during spring and weakest during autumn.

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

2015 ◽  
Vol 96 (10) ◽  
pp. 1699-1718 ◽  
Author(s):  
James Wilczak ◽  
Cathy Finley ◽  
Jeff Freedman ◽  
Joel Cline ◽  
Laura Bianco ◽  
...  
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Prediction Models ◽  
Weather Prediction ◽  
Substantial Reduction ◽  
Energy Resource ◽  
The United States ◽  
Department Of Energy ◽  
Improvement Project ◽  
Forecast Models

Abstract 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.

Long-term climate change effects in the European offshore wind energy 

2021 ◽  
Author(s):  
Stefano Susini ◽  
Melisa Menendez
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Climate Change Impact ◽  
Energy Production ◽  
Offshore Wind ◽  
Change Impact

<p>Climate change and offshore renewable energy sector are connected by a double nature link. Even though energy generation from clean marine sources is one of the strategies to reduce climate change impact within next decades, it is expected that large scale modification of circulation patterns will have in turn an impact on the spatial and temporal distribution of the wind fields. Under the WINDSURFER project of the ERA4CS initiative, we analyse the climate change impact on marine wind energy resource for the European offshore wind energy sector. Long-term changes in specific climate indicators are evaluated over the European marine domain (e.g. wind power density, extreme winds, operation hours) as well as local indicators (e.g. gross energy yield, capacity factor) at several relevant operating offshore wind farms.</p><p>Adopting an ensemble approach, we focus on the climate change greenhouse gases scenario RCP8.5 during the end of the century (2081-2100 period) and analyze the changes and uncertainty of the resulting multi-model from seven high resolution Regional Climate Models (RCM) realized within Euro-Cordex initiative (EUR-11, ~12.5km). ERA5 reanalysis and in-situ offshore measurements are the historical data used in present climate.</p><p>Results indicate a small decrease of wind energy production, testified by reduction of the climatological indicators of wind speed and wind power density, particularly in the NW part of the domain of study. The totality of the currently operating offshore windfarms is located in this area, where a decrease up to 20% in the annual energy production is expected by the end of the century, accompanied by a reduction of the operation hours between 5 and 8%. Exceptions are represented by Aegean and Baltic Sea, where these indicators are expected to slightly increase. Extreme storm winds however show a different spatial pattern of change. The wind speed associated to 50 years return period decreases within western Mediterranean Sea and Biscay Bay, while increases in the remaining part of the domain (up to 15% within Aegean and Black Sea). Finally, the estimated variations in wind direction are relevant on the Biscay Bay region.</p>

Design of Wind Turbines for Non-Standard Air Density

2005 ◽  
Vol 29 (2) ◽  
pp. 115-128 ◽  
Author(s):  
Giusepe Soraperra
Keyword(s):  
Wind Energy ◽  
Power Density ◽  
Pitch Angle ◽  
Electric Generator ◽  
Turbine Performance ◽  
Air Density ◽  
Power Curves ◽  
Energy Exploitation ◽  
Standard Density ◽  
Tentative Correlation

New site typologies characterized by non-standard air density have been recently considered for wind energy exploitation. The work attempts to assess the effect of air density on turbine performance, whether with variable or fixed speed. Since the power density of the sites with non-standard density is intrinsically different, it is impossible to reach the standard rated power at the standard rated speed. Three scenarios are possible (i) to keep the standard rated speed of the turbine by changing the size of the electric generator; (ii) to change the rated speed of the turbine by adopting a different pitch angle setting; (iii) adoption of extenders to the blades can also help in restraining the standard rated power at the standard rated speed for ρ less than ρst. The power curves for the three turbine configurations, each in three different air density conformations, have been calculated running BEM simulations. A tentative correlation between the length of extenders and decrease of air density is finally proposed.

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