12 assessment of wep for ssa in ireland using wdm
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This document analyzes wind energy potential in Ireland using Weibull distribution modeling. It examines wind speed data from 2007-2011 at four Irish sites: Malin Head, Dublin Airport, Belmullet, and Mullingar. Annual Weibull parameters show scale and shape values vary between sites and years. Scale generally varies less than shape. Higher shape indicates greater wind power production potential. Analysis of cumulative distribution functions reveals wind speed and power patterns over time and between coastal and inland locations. The west coast Malin Head site shows greatest wind power potential, while inland Mullingar shows lowest.
![The main objectives of this study is
1] Wind Power Potential Assessment of a site for Wind Farm / Mill Projects.
2] Assessment of Wind Pattern Variations over a years with the help of Statistical Parameters
& Models .
3] Calculations of Wind Power Density - Available & Extractable at the Site.
4] Comparative Analysis of the Sites
a
b
c](https://image.slidesharecdn.com/12assessmentofwepforssainirelandusingwdm-131210101523-phpapp02/85/12-assessment-of-wep-for-ssa-in-ireland-using-wdm-3-320.jpg)
![Power in the Wind Wind is a movement of air having kinetic energy. This kinetic energy is converted in to electrical energy
with the help of wind turbine. The amount of theoretical power available in the wind is determined by the
equation
WA = (1/2) x ρ x A x V3
where w is power,
ρ is air density, It is taken as 1.225 kg/m3 , A is the rotor swept area,
Swept Rotor Area = A = π x r2 where r is the rotor radius ]
and V is the wind speed.
If turbine rotor area is constant then theoretical Wind Power Density
Available (WPDA) is WA/A & written as
WPDA = (1/2) x ρ x V3
It is also called as Theoretical Maximum Available Power Density.
It is not possible to extract all the energy available in the wind as it has to move away from the blades of
the turbine & be replaced by the incoming mass of air. Therefore
Theoretical Extractable power is given as
WE = (1/2) x ρ x A x Cp x V3
Cp = Coefficient of Performance taken as 16/27 as per Betz Law. Cp is the ratio of power extracted by a
wind turbine to power available in the wind at the location.
Then theoretical Extractable power density is given as WE/A
WPDE = 0.5 x ρ x Cp x V3
It is also called as Theoretical Maximum Extractable Power Density.](https://image.slidesharecdn.com/12assessmentofwepforssainirelandusingwdm-131210101523-phpapp02/85/12-assessment-of-wep-for-ssa-in-ireland-using-wdm-17-320.jpg)
![There are various methods used for calculations
of empirical estimate F(Xi)
1] Simple Rank Method
i/N
2] Mean Rank Method
i/(N+1)
which is recommended by IEEE Standards
3] Symmetrical CDF Method
( i - 0.5 ) / N
4] Median Rank Method
( i - 0.3 ) / ( N + 0.4 )](https://image.slidesharecdn.com/12assessmentofwepforssainirelandusingwdm-131210101523-phpapp02/85/12-assessment-of-wep-for-ssa-in-ireland-using-wdm-25-320.jpg)
![Distribution of Wind Speeds
•
•
•
•
•
•
As the energy in the wind varies as the cube of the wind speed, an understanding
of wind characteristics is essential for:
1] Identification of suitable sites
2] Predictions of economic viability of wind farm projects
3] Wind turbine design and selection 4] Effects of electricity distribution networks and
consumers
Temporal and spatial variation in the wind resource is substantial
1] Latitude / Climate
2] Proportion of land and sea
3] Size and topography of land mass 4] Vegetation (absorption/reflection of light, surface temp,
humidity)
The amount of wind available at a site may vary from one year to the next, with even larger scale
variations over periods of decades or more
Synoptic Variations
– Time scale shorter than a year – seasonal variations
– Associated with passage of weather systems
Diurnal Variations
– Predicable (ish) based on time of the day (depending on location)
– Important for integrating large amounts of wind-power into the grid
Turbulence
– Short-time-scale predictability (minutes or less)
– Significant effect on design and performance of turbines
– Effects quality of power delivered to the grid
– Turbulence intensity is given by I = σ / V, where σ is the standard deviation on the wind speed](https://image.slidesharecdn.com/12assessmentofwepforssainirelandusingwdm-131210101523-phpapp02/85/12-assessment-of-wep-for-ssa-in-ireland-using-wdm-41-320.jpg)
12 assessment of wep for ssa in ireland using wdm
- 1. Assessment of Wind Energy Potential for Site Selection Assistance in Ireland using Weibull Distribution Model By Parikshit G. Jamdade and Shrinivas G. Jamdade
- 2. Why Wind Energy ? • • • • • • • • • • • Most viable & largest renewable energy resource Plentiful power source Widely distributed & clean Can get started with as small as 100-200 W Produces no green house gas emissions Low gestation period No raw materials & fuels required No pollution No hassles of disposal of waste Quick returns Good alternative for conventional power plants
- 3. The main objectives of this study is 1] Wind Power Potential Assessment of a site for Wind Farm / Mill Projects. 2] Assessment of Wind Pattern Variations over a years with the help of Statistical Parameters & Models . 3] Calculations of Wind Power Density - Available & Extractable at the Site. 4] Comparative Analysis of the Sites a b c
- 4. Description of Ireland • Developing country with increasing energy demand • Member of the European Union (EU), the Organisation for Economic Co-operation and Development (OECD) and the World Trade Organisation (WTO) • In terms of GDP per capita, Ireland is one of the wealthiest countries in the OECD and EU Ireland is a part of the United Kingdom which is having ample amount of sea shores for wind farm developments • Ireland is rich with urban habitats while farmlands in its rural parts In urban areas there is a considerable presence of public parks, church yards, cemeteries, golf courses and vacant areas exist. Some of these locations are ideal to use for development of wind farms. In rural parts considerable presence of farmlands exists. These farmlands are the main source of vegetable crops for Ireland while other parts of rural areas are mostly developed or semi developed grass lands supporting dairy, beef and sheep production. These grass lands are ideal locations for harnessing wind energy because they are having lower surface roughness. • Ireland has rarely had extreme weather events with lower variations in temperatures • The country is one of the largest exporters of related goods and services in the world • Geographic characteristic of Ireland has helped to generate daily wind with reasonable duration and magnitude
- 9. Transmission Network - Ireland
- 10. 2232 MW Energy from Wind Power Plants
- 11. Total Power Generation Plants in Ireland Power Generation Plants Thermal Hydro Wind Pumped Storage Numbers 20 06 10 01 In Percentage 54.05 % 16.22 % 27.03 % 02.70 % a b c
- 13. In this study, data set of 2007 to 2011 years are obtained containing mean wind speed of each month in a year with observation height of 10 m above ground level from “The Irish Meteorological Service online data” site. Data is an open source data and any one can access this data. (http://www.met.ie/climate/monthly-weather-bulletin.asp ) The chosen stations from Ireland are Name Malin Head Co. Donegal Dublin Airport Co. Dublin Belmullet Co. Mayo Mullingar Co. Westmeath Latitude N° 55°23'N 53°21'N 54°14'N 53°31'N Longitude W° 07°23'W 06°15'W 09°58'W 07°21'W
- 16. Annual and Seasonal Variations • • It’s likely that wind-speed at any particular location may be subject to slow long-term variations – Linked to changes in temperature, climate changes, global warming – Other changes related to sun-spot activity, volcanic eruption (particulates), – Adds significantly to uncertainty in predicting energy output from a wind farm Wind-speed during the year can be characterized in terms of a probability distribution
- 17. Power in the Wind Wind is a movement of air having kinetic energy. This kinetic energy is converted in to electrical energy with the help of wind turbine. The amount of theoretical power available in the wind is determined by the equation WA = (1/2) x ρ x A x V3 where w is power, ρ is air density, It is taken as 1.225 kg/m3 , A is the rotor swept area, Swept Rotor Area = A = π x r2 where r is the rotor radius ] and V is the wind speed. If turbine rotor area is constant then theoretical Wind Power Density Available (WPDA) is WA/A & written as WPDA = (1/2) x ρ x V3 It is also called as Theoretical Maximum Available Power Density. It is not possible to extract all the energy available in the wind as it has to move away from the blades of the turbine & be replaced by the incoming mass of air. Therefore Theoretical Extractable power is given as WE = (1/2) x ρ x A x Cp x V3 Cp = Coefficient of Performance taken as 16/27 as per Betz Law. Cp is the ratio of power extracted by a wind turbine to power available in the wind at the location. Then theoretical Extractable power density is given as WE/A WPDE = 0.5 x ρ x Cp x V3 It is also called as Theoretical Maximum Extractable Power Density.
- 18. Month Month 2007 2011 December December November October September 2007 2011 November October September Mullingar 2006 2010 August July June May April Dublin 2006 2010 August July June 2005 2009 May March February January Wind Speed (m/s) 2005 2009 April 20 18 16 14 12 10 8 6 4 2 0 March 2008 35 30 25 20 15 10 5 0 February Wind Speed (m/s) December 2008 January December 2007 2011 November October September August July June May April March February January Wind Speed (m/s) 2007 2011 November October September Belmullet 2006 2010 August July June May 2005 2009 April March 40 35 30 25 20 15 10 5 0 February January Wind Speed (m/s) 50 45 40 35 30 25 20 15 10 5 0 Malin Head 2005 2006 2009 2010 2008 Month Month 2008
- 19. Month 2009 15000 10000 5000 0 Month December November October September August July Month June May April March February January Maximum Available Power Density (w/m2) December Max. Available Power Density Dublin 2007 2008 20000 2010 2011 November October September August July June May April March February January Maximum Available Power Density (w/m2) December November October September August July June May April March February January Maximum Available Power Density (w/m2) December November October September August July June May April March February January Maximum Available Power Density (w/m2) Max. Available Power Density Malin Head 2007 2008 2009 50000 2010 2011 45000 40000 35000 30000 25000 20000 15000 10000 5000 0 Max. Available Power Density Belmullet 2007 2008 2009 25000 2010 2011 20000 15000 10000 5000 0 Month Max. Available Power Density Mullingar 2007 2008 2009 4000 2010 2011 3500 3000 2500 2000 1500 1000 500 0
- 20. Month 8000 6000 4000 2000 0 Month December November October September August July June Month May 2009 April 0 March 5000 February 10000 January 15000 December November October September August July June May April March February January Maximum Exractable Power Density (w/m2) December November October September August July June May April March February January Maximum Exractable Power Density (w/m2) 20000 Maximum Exractable Power Density (w/m2) December Max. Exractable Power Density Dublin 2007 2008 12000 2010 2011 10000 November October September August July June May April March February January Maximum Exractable Power Density (w/m2) Max. Exractable Power Density Malin Head 2007 2008 2009 30000 2010 2011 25000 Max. Exractable Power Density Belmullet 2007 2008 2009 14000 2010 2011 12000 10000 8000 6000 4000 2000 0 Month Max. Exractable Power Density Mullingar 2007 2008 2009 2010 2011 2000 1500 1000 500 0
- 21. Probability Density Function The probability density function (PDF) is the probability that the variate has the value x For distributions, the empirical (sample) PDF is displayed as vertical lines representing the probability mass at each integer x. In the fitting results window, the theoretical (fitted) PDF is displayed as a polygonal line for better perception, though it is defined for integer x values only For continuous distributions, the PDF is expressed in terms of an integral between two points
- 22. Cumulative Distribution Function The cumulative distribution function (CDF) is the probability that the variate takes on a value less than or equal to x. It is an integral of the PDF. It can be drawn by accumulating the probability of the data as it increases from low to high. For distributions, this is expressed as In this case, the empirical CDF is displayed as vertical lines at each integer x, and the theoretical PDF is displayed as a polygonal line: For continuous distributions, the CDF is expressed as so the theoretical CDF is displayed as a continuous curve.
- 23. Wind Speed is a random phenomenon so for predicting wind speeds with good reliability statistical methods are useful. Wind speed probabilities can be estimated by using probability distributions. There are various statistical distributions are available out of them few are chosen for study as they are commonly used by researchers. Weibull Distribution For the study two-parameter Weibull distribution is used, The CDF ( Cumulative Density Function) is The PDF ( Probability Density Function) is Where η is a scale parameter & β is shape parameter
- 24. Scale parameter η & Shape parameter β are calculated by using least square parameter estimation method. If we have data series having Xi & Yi variables then Shape parameter is & Scale parameter is The estimator of ρ is called as correlation coefficient & given as
- 25. There are various methods used for calculations of empirical estimate F(Xi) 1] Simple Rank Method i/N 2] Mean Rank Method i/(N+1) which is recommended by IEEE Standards 3] Symmetrical CDF Method ( i - 0.5 ) / N 4] Median Rank Method ( i - 0.3 ) / ( N + 0.4 )
- 26. Table 1 - Annual Weibull Parameters of Wind Speed estimated for four sites in Ireland Year / Location Malin Head Dublin Airport Belmutt Mullingar c k c k c k c k 2007 30.86 4.61 22.89 5.21 24.28 3.39 13.71 4.89 2008 33.78 5.79 24.13 6.12 25.11 5.31 13.71 5.29 2009 27.81 6.82 22.67 7.39 24.27 6.87 12.58 6.38 2010 27.42 4.60 19.32 7.78 20.72 5.71 10.78 6.31 2011 32.79 3.48 23.88 4.19 25.99 4.09 13.77 3.67
- 27. Fig. 1 Variations in CDF of Wind Speed for Malin Head location Fig. 2 Variations in CDF of Wind Speed for Dublin Airport location
- 28. Fig. 3 Variations in CDF of Wind Speed for Belmullet location Fig. 4 Variations in CDF of Wind Speed for Mullingar location
- 29. Results • Scale parameter (c) varies between 33.78 m/s to 27.42 m/s, 24.13 m/s to 19.32 m/s, 25.99 m/s to 20.72 m/s and 13.77 m/s to 10.78 m/s for Malin Head, Dublin Airport, Belmullet and Mullingar respectively. • Shape parameter (k) varies between 6.82 to 3.48, 7.78 to 4.19, 6.87 to 3.39 and 6.38 to 3.67 for Malin Head, Dublin Airport, Belmullet and Mullingar respectively. • It is clear that the scale parameter (c) has smaller variations in magnitudes than of the shape parameter (k). • In case of Malin Head, the shape parameter is large in the year 2008 while scale parameter is large in the year 2009 which shows that wind power production is large in the year 2008 but wind speed fluctuation is large in the year 2009. • The shape parameter is lower in the year 2010 while scale parameter is lower in 2011 indicating that wind power production is low in year 2010 but wind speed fluctuation is low in the year 2011.
- 30. • In case of Dublin Airport, the shape parameter is large in year 2008 while scale parameter is large in the year 2010 which shows that wind power production is large in year 2008 but wind speed fluctuation is large in the year 2010. • The shape parameter is lower in the year 2010 while scale parameter is lower in 2011 indicating that wind power production is low in the year 2010 but wind speed fluctuation is low in the year 2011. • In case of Belmullet, the shape parameter is large in the year 2011 while scale parameter is large in the year 2009 which shows that wind power production is large in the year 2011 but wind speed fluctuation is large in the year 2009. • The shape parameter is lower in the year 2010 while scale parameter is lower in 2007 indicating that wind power production is low in the year 2010 but wind speed fluctuation is low in the year 2007.
- 31. • In case of Mullingar, the shape parameter is large in the year 2011 while scale parameter is large in the year 2009 which shows that wind power production is large in the year 2011 but wind speed fluctuation is large in the year 2009. • The shape parameter is lower in the year 2010 while scale parameter is lower in 2011 indicating that wind power production is low in the year 2010 but wind speed fluctuation is low in the year 2011.
- 32. • In the year 2010, wind power production is large in all locations while less variation in wind speed is in the year 2011. • It indicates that the north and the west costal sites of Ireland are having variable and gusty wind flow pattern as compared to east coastal sites as they are having high value of μ and S parameters. • Locations in middle land regions of Ireland are having a low speed magnitude of wind with smooth wind flow patterns throughout the study period as μ and S parameters have low values. • In case of Malin Head site CDF plot is having a large magnitude in year 2008 as compared to other years and the CDF plots are located to the left side of the CDF plot of year 2008. This indicates that the magnitude of wind speed is large in the year 2008. • For Dublin Airport site, for the year 2010, CDF plot lies on the extreme left side of other years CDFs. This means that lower values of wind speed occurs in the year 2010 as comp ared to other years which reduce the wind power production in the year 2010. • For Belmullet site, CDF plots of all years are located in the range of wind speed from 20 m/s to 35 m/s except year 2010. So wind power production is almost constant through out the years.
- 33. • For Mullingar site, CDF plots are plotted from wind speed 6.5 m/s to 17.5 m/s and they are always low as compared to other sites. So wind power production is lower as compared to other sites. Owing to this Mullingar site is the least suitable for setting wind power plant as compared to other sites. • In case of Malin Head location during the study period, 20% probability of getting the wind speed varies from 30 m/s to 38 m/s, 50% probability of getting wind speed varies from 26 m/s to 28 m/s while 70% probability of getting wind speed varies from 22 m/s to 28 m/s. • In case of Dublin Airport location during study period, 20% probability of getting wind speed varies from 20 m/s to 28 m/s, 50% probability of getting wind speed varies from 18 m/s to 23 m/s and 70% probability of getting wind speed varies from 17 m/s to 20 m/s. • In case of Belmullet location during study period, 20% probability of getting wind speed varies from 22 m/s to 29 m/s, 50% probability of getting wind speed varies from 19 m/s to 24 m/s while 70% probability of getting wind speed varies from 18 m/s to 20 m/s. • In case of Mullingar location during study period, 20% probability of getting wind speed varies from 12 m/s to 16 m/s, 50% probability of getting wind speed varies from 10 m/s to 13 m/s and 70% probability of getting wind speed varies from 9 m/s to 11 m/s.
- 34. • From Table 1 we can summarize that higher value of the scale parameter with lower value of shape parameter results into high wind power production and assures constant power supply resource. • Existing data resource and CDF variation patterns indicates that out of studying locations Malin Head is the most suitable location for wind power development and Belmullet is the second most suitable site for wind farm development while Mullingar is the least suitable location.
- 35. Conclusions • In case of Malin Head location on an average, 20% probability of getting wind speed is 34 m/s, 50% probability of getting wind speed is 29 m/s and 70% probability of getting wind speed is 24 m/s. • In case of Dublin Airport location on an average, 20% probability of getting wind speed is 25 m/s, 50% probability of getting wind speed is 21 m/s and 70% probability of getting wind speed is 18 m/s. • In case of Belmullet location on an average, 20% probability of getting wind speed is 26.5 m/s, 50% probability of getting wind speed is 22 m/s and 70% probability of getting wind speed is 19 m/s. • In case of Mullingar location on an average, 20% probability of getting wind speed is 14 m/s, 50% probability of getting wind speed is 12 m/s and 70% probability of getting wind speed is 10 m/s. • With increasing wind speed trend over the years boosts the confidence of wind farm developers for developing wind power plant. This wind power potential of Ireland if exploited would help the cottage industries and villages for electrification and water a b pumping. c
- 36. References • • • • • • • Bansal, R.C. Bhatti, T.S. and Kothari, D.P. (2002) On some of the design aspects of wind energy conversion system, Energy Conversion Management, 43(16), pp. 2175 - 2187. Celick, A. N. (2004) A statistical analysis of wind density based on the Weibull and Rayleigh models at the southern region of Turkey, Energy Conversion Management, 29(4), pp. 593 - 604. Carta, J. A. and Ramiez, P. (2005) Influence of the data sampling interval in the estimation of the parameters of the weibull wind speed probability density distribution: a case study, Energy Conversion Management, 46(15), pp. 2419 - 2438. Bansal, R. C. Zobaa, A.F. and Saket, R.K. (2005) Some issues related to power generation using wind energy conversion systems: An overview, International Journal Emerging Electrical Power System, 3(2), pp. 1 - 19. Chang, T. J. and Tu, Y.L. (2007) Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: A case study of Taiwan, Renewable Energy, 32(2), pp. 1999 - 2010. Tingem, M., Rivington, M., Ali, S. A. and Colls, J. (2007) Assessment of the ClimGen stochastic weather generator at Cameroon sites, African Journal of Environmental Science and Technology, 1(4), pp. 86 - 92. Huang, S. J. and Wan, H.H. (2009) Enhancement of matching turbine generators with wind regime using capacity factor curves stratergies, IEEE Transaction Energy Conversion, 24(2), pp. 551 553.
- 37. • • • • Prasad, R. D., Bansal, R.C. and Sauturaga, M. (2009) Wind energy analysis for Vadravadra site in Fiji islands: A case study, IEEE Transaction Energy Conversion, 24(3), pp. 1537 - 1543. Pryor, S. C. and Barthelmie, R. J. (2010) Climate change impacts on wind energy: a review, Renewable and Sustainable Energy Reviews, 14, pp. 430 - 437. Jamdade, S. G. and Jamdade, P. G. (2012) Extreme Value Distribution Model for Analysis of Wind Speed Data for Four Locations in Ireland, International Journal of Advanced Renewable Energy Research, 1(5), pp. 254 - 259. Jamdade, S. G. and Jamdade, P. G. (2012) Analysis of Wind Speed Data for Four Locations in Ireland based on Weibull Distribution’s Linear Regression Model, International Journal of Renewable Energy Research, 2(3), pp. 451 - 455.
- 38. END
- 41. Distribution of Wind Speeds • • • • • • As the energy in the wind varies as the cube of the wind speed, an understanding of wind characteristics is essential for: 1] Identification of suitable sites 2] Predictions of economic viability of wind farm projects 3] Wind turbine design and selection 4] Effects of electricity distribution networks and consumers Temporal and spatial variation in the wind resource is substantial 1] Latitude / Climate 2] Proportion of land and sea 3] Size and topography of land mass 4] Vegetation (absorption/reflection of light, surface temp, humidity) The amount of wind available at a site may vary from one year to the next, with even larger scale variations over periods of decades or more Synoptic Variations – Time scale shorter than a year – seasonal variations – Associated with passage of weather systems Diurnal Variations – Predicable (ish) based on time of the day (depending on location) – Important for integrating large amounts of wind-power into the grid Turbulence – Short-time-scale predictability (minutes or less) – Significant effect on design and performance of turbines – Effects quality of power delivered to the grid – Turbulence intensity is given by I = σ / V, where σ is the standard deviation on the wind speed