Wind Power Analysis Using NonStandard Statistical Models
Niall McCoy
School of Electrical Systems Engineering
Prof Jonathan Blackledge
15th February 2013
Introduction
 Name:
 Niall McCoy.
 Qualifications:
 Degree in Electrical Engineering 2008 (DIT);
 Degree in Energy Management 2010 (DIT);
 Chartered & Professional Engineer 2012 (EI).
 Company & Position:
 Electrical Engineer of Wind Prospect Group, based in
Carrickmines, Co Dublin.
 Roles & responsibilities:
 International Project Management & Electrical Design.
 Academic Works:
 Commenced Part-Time PhD with DIT October 2011;
 Published one academic paper to date in December 2012 in
association with Prof Jonathan Blackledge;
“Analysis of Wind Velocity and the Quantification of Wind Turbulence in Rural and Urban Environments using the
Levy Index and Fractal Dimension - 2012”
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Agenda
 Project Background
 Research Methodologies
 Current Industry Standards
 The Urban vs Rural Resource
 Next Steps
3
Project Background
 Project Background
 Research Methodologies
 Current Industry Standards
 The Urban vs Rural Resource
 Next Steps
4
Why is Wind Energy Analysis Important?
Currently circa 2,158MW of installed
wind generation on the system.
Current energy demand of 35,532GWh¹.
Target of 40% system demand to be via
renewable energy by 2020, 35% of which
to be wind.
Resulting in a requirement of circa
39,852GWh (SD in 2020)² where 35%
must be sourced from wind generation.
Applying capacity factors of 0.31, the
required installed capacity for wind
generation by 2020 is 5,178MW¹.
Le Tene Maps 2013
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¹ EirGrid 2013
² Wind Prospect 2013
Why is Wind Energy Analysis Important?
Main reason – Wind Farm Development &
Financial Risk;
 Wind farm developments require capital investment to be developed;
 Financial return is directly linked to the wind speeds at the site;
 Financial risk is amplified in the energy prediction due to the
relationship between turbine output and wind speed;
 To avoid financial disadvantages, uncertainties must be minimised in:
 Wind resource assessment
 Power curve performance (Turbine output at specific wind
speed)
 Turbine availability
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What has Financial Risk to do with
Wind Energy Analysis?
 To have confidence in an investment, you need confidence in the
wind resource and associated studies;
 Wind studies are performed to understand that return on your
investment;
 All current wind studies carry a degree of uncertainty and potential
for error. All stages of the wind study aim to minimise uncertainty,
resulting in a “best guess”;
 Therefore the Aim of the Project



Design a more accurate model of wind energy analysis with reduced errors;
Provide a reduced risk profile to investors;
Increase funding access to wind projects and increase wind energy
penetration on the Irish system.
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Research Methodologies
 Project Background
 Research Methodologies
 Current Industry Standards
 The Urban vs Rural Resource
 Next Steps
8
Non-Standard Statistical Models
 In order to find a more accurate forecasting model for wind energy
at a potential wind farm location are number of equations have
been looked at;
 Non-Gaussian model for simulating wind velocity data;
 Levy distribution for the statistical characteristics of wind velocity;
 Thus, deriving a stochastic fractional diffusion equation for the wind velocity as a
function of time whose solution is characterised by the Levy index;
 Eventually deriving both to establish Levy index using Betz law to
understand the energy output of a specific turbine.
Betz’s Law – Windpower.de 1999
Illustration of Betz’s Law – Windpower.de 1999
http://eleceng.dit.ie/blackledge/index.php?uid=516&page=publications
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Current Industry Standards
 Project Background
 Research Methodologies
 Current Industry Standards
 The Urban vs Rural Resource
 Next Steps
10
Current Measurement Systems
Met Tower Measurement System
WTG Measurement System
NRG System 2010
Vestas 2013
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Power Law vs Log Law Profiles
• Power law profile:
U1  h 1 
  
U2  h2 

– α = wind shear coefficient
• Log law profile
U 1 h 
  
u*   z0 
– Requires knowledge of u* and z0
– Both must be estimated
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Data Sources & Sets
 Fully calibrated industry standard anemometers;
 10 minute average data set from 80m metrological mast, with cup
anemometers located at heights of, 50m, 65m, 80m & 82.5m;
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The Urban vs Rural Resource
 Project Background
 Research Methodologies
 Current Industry Standards
 The Urban vs Rural Resource
 Next Steps
14
The Urban vs Rural Resource
𝑢 𝑧 =
𝑢∗
𝑧−𝑑
𝐼𝑛
𝑘
𝑧0
– u(z) denotes the wind speed at height z
– u*friction velocity
– κ the Von Karman constant
– z height above the earth’s surface
– d displacement height
– z0 height above the earth’s surface
roughness
Mertens 2006
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The Urban vs Rural Resource
Main Aim of the paper;
 To quantify rural and urban areas in
terms of the Levy index using data
generated from industry standard sources;
The emphasise is based on a theoretical
basis, where;
gamma= 1 for 'perfect' urban area (i.e.
full diffusion)
and = 2 for 'perfect' rural area
(i.e.
perfect laminar flow).
In practice, 'perfect' never exists but the
differences in gamma for the two
environments appears to reflect the
hypothesis.
Greenspec 2011
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Non-Gaussian results of the Urban &
Rural Wind Resource
Project
Urban
Location
CoOrdinates
X
Y
Qmean
Project
Rural
Location
CoOrdinates
X
Y
Qmean
SOLU
WDIT
ICLT
KING
DKIT
Newport
Waterford
Limerick
Cavan
Dundalk
503732
571087
1.4918
659727
610694
1.4628
553282
564665
1.4640
679051
766272
1.4590
704775
806261
1.4110
RABR
KNLR
DUNM
CRIG
DROM
Mayo
Wexford
Louth
Tyrone
Limerick
511370
794745
1.4607
706819
657023
1.4721
695381
785030
1.5235
628938
868785
1.4836
549251
645517
1.4929
Levy index using data generated from industry standard sources
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Five rural and five urban sites were
analysed through determination of
the Levy index over a period of 12
months.
The Table show that, bar one
anomaly, the trend is that the mean
values of the Levy index for the rural
sites is consistently higher in
comparison to the mean values of
the same index for the urban sites.
Resulting in the urban-to-rural ratio
of 0.9832.
Urban vs Rural the Conclusion
 In conclusion, it can be stated that the wind resource in the urban
environment is curtailed due to the influencing factors such a
surface roughness, turbulence intensity, etc...
 When a direct comparison is drawn between the urban and rural
wind resources at selected location across Ireland and the UK, using
similar reference heights, fully calibrated equipment and stochastic
models to define the results. It is evident that the rural resource is
generally of a higher energy yield when compared to the urban
resource.
 For the full paper see - http://users.jyu.fi/~timoh/isast2012.pdf
http://www.isastorganization.org/index.html
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Next Steps
 Project Background
 Research Methodologies
 Current Industry Standards
 The Urban vs Rural Resource
 Next Steps
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Next Steps
 Detailed look at developing a non-Gaussian based energy yield
platform model and possibly CFD software;
 Challenge current industry energy analysis model accuracy, such as
Wind Pro & WaSP, with newly development model/software;
 Introduce more complex influences, such as specific types of
surface roughness, turbulence intensity, etc..;
Wind Pro & WaSP model
Conceptual Non-Gaussian CFD Model
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Q&A
Any Questions?
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