This paper develops a cost model for onshore wind farms in the U.S.. This model is then used to analyze the influence of different designs and economic parameters on the cost of a wind farm. A response surface based cost model is developed using Extended Radial Basis Functions (E-RBF). The E-RBF ap- proach, a combination of radial and non-radial basis functions, can provide the designer with significant flexibility and freedom in the metamodeling process. The E-RBF based cost model is composed of three parts that can estimate (i) the installation cost, (ii) the annual Operation and Maintenance (O&M) cost, and (iii) the total annual cost of a wind farm. The input param- eters for the E-RBF based cost model include the rotor diameter of a wind turbine,the number of wind turbines in a wind farm, the construction labor cost, the management labor cost and the technician labor cost. The accuracy of the model is favorably explored through comparison with pertinent real world data. It is found that the cost of a wind farm is appreciably sensitive to
the rotor diameter and the number of wind turbines for a given desirable total power output.
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
COSTMODEL_IDETC_2010_Jie
1. Response Surface Based Cost Model
for Onshore Wind Farms
Using Extended Radial Basis Functions
Jie Zhang*, Souma Chowdhury*, Achille Messac#
Luciano Castillo* and Jose Lebron*
* Rensselaer Polytechnic Institute, Department of Mechanical, Aerospace, and Nuclear Engineering
# Syracuse University, Department of Mechanical and Aerospace Engineering
ASME 2010 International Design Engineering Technical Conferences
(IDETC) and Computers and Information in Engineering Conference (CIE)
August 15-18, 2010
Montreal, Quebec, Canada
2. Outline
• Motivation
• Research Objectives
• Literature Review
• E-RBF Cost Model
• Concluding Remarks
• Future Work
2
3. Motivation
The global installed wind capacity has been growing at an
average rate of 28% per year.
Wind energy is planed to account for 20% of the U.S. electricity
consumption by 2030.
Efficient planning and resource management is the key to the
success of an energy project.
Accurate (flexible to local market changes) cost models of wind
projects would allow investors to better plan their projects.
Investors can provide valuable insight into the areas that require
further development to improve the overall economics of wind
energy.
3
4. Motivation
International ranking of wind power
Country/Region
Total capacity
end 2009 [MW]
Added capacity
2009 [MW]
Growth rate
2009 [%]
USA 35,159 9,922 39.3
China 26,010 13,800 113.0
Germany 25,777 1,880 7.9
Spain 19,149 2,460 14.7
India 10,925 1,338 14.0
Italy 4,850 1,114 29.8
France 4,521 1,117 32.8
UK 4,092 897 28.1
Portugal 3,535 673 23.5
Denmark 3,497 334 10.6
4
Source: Berkely Lab estimates based on data from BTM Consult and elsewhere
U.S. is lagging behind other countries in wind energy as a percentage of electricity consumption
5. Current
Planned 2030
10 fold increase
Wind Energy
Wind Energy
Motivation
5
• Accurate cost models
6. Research Objectives
• Develop a cost model of wind farms
• Analyze the cost of a wind farm
• Construct a U.S. cost map for wind farm
development
6
7. Literature Review
• Existing Cost Models
• Short-cut Model
• Cost Model for the Greek Market
• OWECOP-Prob Cost Model
• JEDI-Wind
• Opti-OWECS Cost Model
• Existing O&M Cost Models
• The Operation & Maintenance Cost Estimator (OMCE)
• Cost of Grid Connection
7
10. Advantages of E-RBF Cost Model
• Includes life cycle cost
• Considers financial parameters
• Uses appropriate input and output parameters, and
• Provides analytical expression
10
12. E-RBF Method
12
Extended Radial Basis Functions (E-RBF) is a combination of Radial
Basis Functions (RBFs) and Non-Radial Basis Functions (N-RBFs).
Radial Basis Functions
The RBFs are expressed in terms of the Euclidean distance,
y (r) = r2 + c2
where c > 0 is a prescribed parameter.
The final approximation function is a linear combination of these basis
functions across all data points.
( )
% =å -
f x sy x x
1
( )
np
i
i
i
=
r = x - xi
One of the most effective forms is the multiquadric function:
13. 13
E-RBF Method
Non-Radial Basis Functions
N-RBFs are functions of individual coordinates of generic points x
relative to a given data point xi, in each dimension separately
It is composed of three distinct components
( i ) L L ( i ) R R ( i ) ( i )
ij j ij j ij j ij j
f x =a f x +a f x +b f b x
Extended Radial Basis Function (E-RBF)
The E-RBF approach incorporates both the RBFs and the N-RBFs
np np m
( )
% =å - i +ååéë L L i + R R i + i
ùû
f x sy x x a f x a f x b f b x
( ) ( ) ( ) ( )
i ij j ij j ij j
i i j
= = =
1 1 1
Methods: (i) linear programming, or (ii) pseudo inverse.
14. E-RBF Cost Model
• Installation cost,
• Annual O&M cost,
• Total annual cost,
in C
O&M C
t C
The number of coefficients of
the E-RBF cost model,
(3 1) u p n = m+ n
p ( n , Number of data points)
14
Function list of the E-RBF cost model
Model Expression No. of
variables
Data
points
No. of
coefficients
Cin = f(CLC,CLT,CLM) 3 40 400
CO&M = f(N,CLT,CLM) 3 500 5,000
Ct = f(N,D) 2 101 707
Parameter Selection for E-RBF Cost Model
Parameter λ c t
Value 4.75 0.9 2
15. Installation Cost
The installation cost model is developed using data from 40
different states of the U.S.
15
Sample data for developing installation cost model
State Construction
labor cost
($/h)
Technician
labor cost
($/h)
Management
labor cost
($/h)
Installation
cost
($/KW)
California 20.70 30.97 49.56 2,107
Colorado 18.32 25.00 40.00 2,043
Iowa 16.16 22.18 35.28 2,011
Kansas 16.26 26.77 35.49 2,012
Minnesota 19.62 26.77 42.83 2,062
16. Annual O&M Cost
• Training data: cost of all states in the U.S. (except the state
of New York)
Number
of
turbines
Technician
labor cost
($/h)
Management
labor cost
($/h)
O&M
cost
($/KW)
California
20 19.82 49.56 26.58
40 19.82 49.56 25.23
60 19.82 49.56 23.95
Colorado
20 16.00 40.00 25.00
40 16.00 40.00 23.64
60 16.00 40.00 22.36
16
Sample data for developing O&M cost model
17. Annual O&M Cost VS. The Input Factors
The annual O&M cost increases approximately one dollar (per kilowatt installed) for each
20 wind turbines.
When the number of wind turbines increases from 10 to 100, the annual O&M cost
decreases sharply from $26.67/KW to 21.60/KW, approximately 19.01%.
When the number of wind turbines is small (less than 20), the change in the annual O&M
cost is not clearly evident. 17
18. Total Annual Cost
D(m) 49 55 59.2 65 80.5 82
P0(MW) 0.60 0.85 1.00 1.25 1.50 1.65
D(m) 84.25 88 92.13 100 101
P0(MW) 2.00 2.10 2.30 2.50 3.00
18
Relation between rotor diameter and rated power
19. Total Annual Cost VS. The Input Factors
19
The total annual cost deceases from $131.3/KW
to $126.4/KW (approximately 3.73%) when the
rotor diameter of a wind turbine increases from
50m to 100m.
The total annual cost decreases slowly when the
rotor diameter is less than 70m.
The total annual cost begins to decrease sharply
when the rotor diameter changes from 70m to
85m.
If the rotor diameter continues to increase beyond
85m, the change in the total annual cost is
particularly limited.
20. 20
Total Annual Cost VS. The Input Factors
The total annual cost decreases from
$131.48/KW to $126.38/KW (approximately
3.88%) while the number of wind turbines
increases from 10 to 100.
The total annual cost does not change
significantly when the number of wind
turbines increases beyond 60.
22. Conclusion
• An Extended Radial Basis Function (E-RBF) cost model was developed,
which can estimate: (i) the installation cost, (ii) the annual O&M cost,
and (iii) the total annual cost of a wind farm.
• The annual O&M cost roughly increases one dollar (per kilowatt
installed) for each 20 more wind turbines installed.
• The change of the total annual cost depends significantly on the number
and rotor diameters of wind turbines.
• The preliminary cost map shows wide variation in wind farm cost in the
U.S..
• The resulting cost model can be a useful tool for wind farm planning.
22
23. Future Work
Optimization of cost of energy;
Optimization of Operation and Maintenance (O&M)
strategy for offshore wind farms;
Extensive analysis of the demand and general
market for critical energy products (turbines).
23
24. 24
Selected References
Mullur, A. A., and Messac, A., 2005. “Extended radial basis functions: More flexible and effective
metamodeling”. AIAA Journal, 43(6), pp. 1306–1315.
Mullur, A. A., and Messac, A., 2006. “Metamodeling using extended radial basis functions: a
comparative approach”. Engineering with Computers, 21(3), pp. 203–217.
Goldberg, M., 2009. Jobs and Economic Development Impact (JEDI) Model. National Renewable
Energy Laboratory, Golden, Colorado, US, October.
Sisbot, S., Turgut, O., Tunc, M., and Camdali, U., 2009. “Optimal positioning of wind turbines on
gokceada using multi-objective genetic algorithm”. Wind Energy.
Pallabazzer, R., 2004. “Effect of site wind properties on wind-electric conversion costs”. Wind
Engineering, 28(6), pp. 679–694.
Jin, R., Chen, W., and Simpson, T., 2001. “Comparative studies of metamodelling techniques under
multiple modelling criteria”. Structural and Multidisciplinary Optimization, 23(1), pp. 1–13.
Lindenberg, S., 2008. 20% wind energy by 2030: Increasing wind energy contribution to u.s. electricity
supply. Tech. Rep. DOE/GO-102008-2567, U.S. Department of Energy: Energy Efficiency &
Renewable Energy, July.
Cockerill, T. T., Harrison, R., Kuhn, M., and Bussel, G. V., 1998. Opti-owecs final report vol. 3:
Comparison of cost of offshore wind energy at european sites. Tech. Rep. IW-98142R, Institute for
Wind Energy, Delft University of Technology, August.
Kiranoudis, C., Voros, N., and Maroulis, Z., 2001. “Shortcut design of wind farms”. Energy Policy, 29,
pp. 567–578.
Andrawus, J. A., Watson, J., Kishk, M., and Adam, A., 2006. “The selection of a suitable maintenance
strategy for wind turbines”. Wind Engineering, 30(6), pp. 471–486.
These are four types of active windows. These are others efforts to decrease the energy lost through windows.
We sought to design a window system that will compensate for all of the heat gained through the glass and maintain a thermal balance. This is our window design that improves upon the current passive window model.
We chose thermoelectric units for our system because they are very small and are solid state.