Assessment of Off-shore Wind Farms in Malaysia
S. Mekhilef
D. Chandrasegaran
Department of Electrical Engineering
University of Malaya, Kuala Lumpur, Malaysia
[email protected]
Department of Mechanical Engineering
University of Malaya, Kuala Lumpur, Malaysia
[email protected]
Abstract— A lot of attention is being paid to wind energy due to
the focus on renewable energy. Malaysia is situated in the
equatorial region and its climate is governed by the monsoons.
Wind resource in Malaysia varies from one location to another.
Wind speed is better offshore compared to onshore, so more
contribution by offshore wind powers in terms of electricity
generation.
Wind energy potentiality and techno- economic feasibility of
offshore wind farms in Malaysia is investigated in this paper.
Research was conducted using HOMER software to assess the
potential of wind energy along the South China Sea coastline.
This study indicates the best sites to set up offshore wind farms
in Malaysia while costs associated for wind energy generation are
calculated. Two model of the wind turbines Vestas V-47 and V80 are taken for economic feasibility analyses. Investment,
operation and maintenance costs have been evaluated for
offshore wind energy. Feed-in tariff policy effects energy price
and wind farms profitability were analyzed.
Offshore wind speed better than onshore wind and it is less
turbulent, thus making turbines to generate more electricity.
Studies done in Malaysia indicated that only a few places in
the East Coast have sufficient wind energy for utilization. In
Malaysia there are wind turbines installed in remote areas in
Sabah and Sarawak.
The design of an offshore wind project needs prerequisite
study and expectations of some environmental conditions at a
proposed site over the project’s lifetime.
These
environmental conditions are primarily defined by the wind,
wave, current, water depth and soil and seabed characteristics.
Wind turbine manufacturing industry is another concern of the
wind power utilization. It is favorable to build and construct
the wind turbines in vicinity of the wind sites where the
system is going to set up [6-8]
In this paper, current situation of global wind energy
utilization is reviewed. Potential of wind energy, technoeconomic analysis, annual energy yield and energy price for
offshore wind installations are presented. Assessments are
applied to six selected sites with high potential of harnessing
offshore winds in Malaysia. Results offer promising potentials
of introducing this technology for harnessing wind in the
selected sites.
Keywords-component; Malaysia; offshore; South China Sea;
wind energy, wind turbine.
I.
INTRODUCTION
The resources of energy used today are non-renewable such as
coal, oil, natural gas and uranium all of which are finite
resources and will be depleted [1-5]. In addition the use of
these types of fuel has side effects on health and environment.
Renewable energy on the other hand has the potential to
produce clean energy for our use, for all time for everyone.
Renewable energy systems play an integral role in the
reduction of greenhouse gas emissions [1]. And are considered
a major source of energy for the 21st century and beyond.
Renewable and alternative sources of energy include solar
energy, wind energy, ocean wave energy, geothermal energy
and biogas[7].
Wind energy has conspicuous growth in the last decade. It is
plenty in source and clean. According to statistics made in
2005 about 58,982 MW of wind capacity installed. Therefore
Wind energy makes the contribution of 1% of the world
electricity[8].
In offshore wind power, wind farms are constructed in bodies
of water for the purpose of generating electricity from wind.
978-1-4577-0255-6/11/$26.00 ©2011 IEEE
II. ENERGY ANALYSIS
To determine the annual energy (E) yield of an offshore
wind farm, there are some technical aspects that should be
appraised.
A. Offshore Wind Power Prediction
Predictions of wind flow for a particular site is a crucial factor
in order to determine the feasibility of a project. Therefore, a
detailed knowledge of wind characteristics and historical data
is required for efficient planning and implementation of wind
farms[5-7]. These data can be sourced from meteorological
department of the locality and marine surface observation
reports. Fig.1 shows the coastline of Malaysia that faces that
South China Sea. Numbers 1 to 16 were assigned to each
location. Grids 1-7 represent area covering the east peninsular
Malaysia coastline that faces east of South China Sea. Grids 816 represent area covering the north-west side of Borneo that
1351
TENCON 2011
N = A/ (48. D2)
forms pall of Sarawak and Sabah coast line [9-12]. Sites with
numerical identification of 1, 2, 3, 4, 8 and 13 are selected.
These sites face the South China Sea and present a potential
offshore wind resource. The criterion for selecting these places
is that during the Northeast monsoon season wind speeds at
these sites reach more than 5 m/s; however, wind speed has
been marked low for the rest of the year. The directions of the
wind are from the northeast and east quadrant during the
northeast monsoon season and south and southwest during the
southwest monsoon season [13] [14]. Currently, there is no
available or precise bathymetry survey conducted in South
China Sea for these sites.
Also, the array efficiency (ηL) is often accessed via software
programs considering the sheltering effects of the WTGs and
wind flow characteristics, so the value is assumed to be 0.9.
D. Wing Farm Electrical Transmission Losses Cooficient ηE
A 20kV AC transmission line is the best solution for a
wind farm size of 10-20 MW with estimated distance to coast
of 0.5–2.0 km [11]. Hence, the electrical transmission losses
coefficient (ηE) is expressed by (2) where (d) is distance to the
shore (km).
ηE = 0.98 – (d/ 600)
B. Gross Energy Assessment EG
Gross energy of the wind turbines can be calculated using
wind flow information and the Wind Turbine Generators
(WTGs) power curve. The HOMER software [14] has been
used considering the WTGs power curves, prevailing wind
directions and the Weibull distribution parameter of selected
sites. The main parameters of selected sites are described
E = EG x N x ηL x ηE x ηA
TABLE I.
1
2
3
4
8
13
kg/ m3
1.08
1.08
1.08
1.08
1.08
1.08
(3)
III. ECONOMIC ANALYSIS
HOMER software has been employed to calculate offshore
wind energy costs. Total capital costs to establish offshore
wind energy systems are comprised of the following items[1519].
in Table 1.
Air
Density
(2)
E. Wind Farm Availability ηA
Wind farm availability refers to the availability of plant to
produce electricity in percentage. Studies have shown that
availability of the plant can significantly affect the cost of
electricity. Hence, the system shall be sufficiently designed
using high quality and reliable components. Wind farm
availability considers both electrical system and WTGs
availability. The availability is assumed to be 95% of the
annual energy yield.
Consequently, the annual gross energy yield, E (GWh/ year)
can be concluded using previous assumptions in (3):
Figure 1. Selected offshore wind farm sites
Site
(1)
MAIN PARAMETERS OF SELECTED SITES
Weibull
parameter
2
2
2
2
2
2
Average
Wind
Speed
m/ s
3.5
4.1
3.8
3.3
3.1
3.8
Available
Area
km2
2
2
2
2
2
2
Mean
Water
Depth
Coast
Distance
m
20
20
20
20
20
20
km
2
2
2
2
2
2
A.
Wind Turbine Cost CT
Wind turbine costs include the tower, shell and electrical
devices of the WTGs which mainly depend on the size of the
turbine. Another factor that affects the wind turbine cost is the
hub height of the WTG. The cost particularly increases due to
the adaption WTGs required to the sea conditions. According
to literature data CT is in the range of RM 3,750,000 to
4,500,000/MW[15].
B. Support and Instalation Cost CS
Support and installation costs comprise of material,
construction and installation costs. Material cost is factored by
hub height and site conditions such as water depth and
climate, meanwhile, the installation cost is a function of
number of WTGs erected:
C. Wing Farm Design
Offshore wind farm layouts can be optimized to enhance
the energy generation. Water depth and sea bed conditions
also shall be considered to reduce the overall project costs.
The available space is assumed to be 2km2. Layout is arranged
by arrays distance (D) between rows (dr) and columns (dc) of
6D and 8D, respectively. With these assumptions, the number
of wind turbines (WTGs) in a wind farm (N) is calculated
using:
CS = (H/0.5) 0.3 [(1700 W2 - 9455W+ 21836)/ 1000]
1352
(4)
Where, (W) is the water depth and (H) stands for the wind
turbine hub height.
COE = Total annualized cost of the system/ Total electricity
produced
(8)
The economic parameters are defined in Table 2
C. Grid Connection Cost CG
Grid connection costs are subject to the transmission
system, distance from the shore-based station and also the
distance from onshore point. A 20kV/ 150kV transformer
costs around RM42,500/MW and the additional costs of other
devices are of RM500,000/MW.
TABLE II.
D. Operation and Maintainance Cost CM
CM is ties up with the overall operational and maintenance
strategy employed by the plant operator. In addition, distance
from shore points and plant reliability affect the cost. It is
estimated to be RM250, 000/ MW.
Parameter
Value (unit)
Economic lifetime
20 years
Discount rate
4%
Electricity Price
RM 0.29
Feed-in- Tariff
RM 0.29
IV.
E. Project and Development Cost CP
(5)
TABLE III.
Where PR is the WTG rated power
F. Operational ans Maintenance Annual Cost
The O & M cost is about 2% of total investment costs.
However, total operating cost is the sum of the annual O & M
costs, total fuel cost, and annualized replacement cost minus
the annualized salvage value. For grid-connected systems, the
operating cost includes the annualized cost of grid purchases
minus grid sales.
The total Net Present Cost (NPC) is the current value of the
total costs minus current value of total revenues that has
earned during the system lifetime. Costs consist of investment,
operation and maintenance, replacement and fuel costs. In
addition, emission penalties and the prices of the power
bought from the grid should be considered. Revenues include
salvage value and grid sales revenue:
NPC = Total annualized cost of the system/ CRF
CRF = i(1+i)n/[(1+i)n – 1]
CASE STUDY
The economic feasibility analyses are considered for two
model of the wind turbines. Vestas V-47 and V-80.Table 3
represents the technical data for each wind turbine. Investment
and also O & M costs for each wind turbine are presented in
Table 4.
The project and development cost constitute about 4% of
the total investment cost. The total investment cost (I) is:
I = N [PR (CT + CG + CM +CP) + CS]
ECONOMIC PARAMATERS
Parameters
Vestas
V-47
Vestas
V-80
Rated power (kW)
660
2000
Rotor diameter (m)
47
80
Hub height (m)
50
78
Number of WTGs
19
7
Availability
0.95
0.95
Array efficiency
Transmission efficiency
Plant size (MW)
0.93
0.978
12.54
0.95
0.978
14
TABLE IV.
IVESTMENT O & M COSTS (1RM = 0.16 €)
Description
Investment cost (kRM)
(6)
Vestas
V-47
162,828
Vestas
V-80
132,750
CT (%)
36
56
CS (%)
50
26
CG (%)
9
10
CM (%)
2
3
3
3,258
5
2,658
CP (%)
O&M cost (kRM/yr)
(7)
WIND TURBINE PARAMATERS
To assess the monthly average electric production by each
wind turbine, site 2 is adopted. Fig. 2 shows the results that
confirm electricity production during the Northeast monsoon
season is the highest and decreases for the rest of the year.
Where CRF is the Capital Recovery Factor, (i) is discount rate
and (n) is the number of years.
Cost of Energy (COE) is average cost of the efficient
generated electricity per kWh and can be calculated as the
result of the annualized cost of producing electricity divided
by total efficient electric energy production.
1353
Monthly Average Electric Production
8,000
Wind
Grid
6,000
Wind
Grid
P o w e r (k W )
P o w e r (k W )
6,000
4,000
4,000
2,000
0
Monthly Average Electric Production
8,000
2,000
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
0
Jan
Feb
Mar
Apr
May
Jun
(a)V-47
Jul
Aug
Sep
Oct
Nov
Dec
(b)V-80
Figure 2. Monthly average electric production for site 2: (a)V47, (b)V80
Wind farm capacity, costs of energy and energy
generation for all the selected sites are tabulated in Table 5.
Each site is considered for using both models of wind turbines.
Results indicates that larger sized WTG produces higher
energy output compared to the smaller sized WTG,
corresponding to Site 1, 4, 8 and 13. However, the variances
are between the two models is less than 5%. The COE for both
models of WTG in all the investigated sites is presented as
well. The results confirm that the higher rated WTGs are
TABLE V.
Site
Model
1
1
2
2
3
3
4
4
8
8
13
13
V-47
V-80
V-47
V-80
V-47
V-80
V-47
V-80
V-47
V-80
V-47
V-80
More competitive at approximately 33% lower against the
lower rated WTGs, due to their lower energy system cost. As
it shows, the lowest cost of energy system is achieved at Site
2. Meanwhile, highest cost of energy system is found on Site
8. The reason is differences available in wind resources for a
particular site.
TECHNO-ECONOMIC ANALYSIS FOR SELECTED SITES
Wind Farm Capacity
Initial capital
Operating cost
Total NPC
COE
MW
12.54
14
12.54
14
12.54
14
12.54
14
12.54
14
12.54
14
RM
162,828
132,750
162,828
132,750
162,828
132,750
162,828
132,750
162,828
132,750
162,828
132,750
RM/ year
614,232
871,438
1,439,335
1,856,770
628,527
1,043,950
472,174
20,300
806,762
309,527
599,816
1,030,091
RM
154,480,404
83,294,371
97,132,399
69,903,402
108,151,545
80,949,874
123,110,393
94,861,629
127,657,583
99,344,070
108,541,727
81,137,637
RM/kWh
0.85
0.64
0.55
0.40
0.77
0.58
1.40
1.04
1.77
1.32
0.79
0.59
Net specific production results in having smaller rated WTG
with higher value for all sites as shown in Fig. 3. Influence of
the feed-in tariffs in the energy price for Site 2 using the V-80
wind turbine is explored for the sensitivity analysis. Table 6
shows the variation in the cost of energy vs. feed-in tariff
ratio.
TABLE VI.
M Wh/ M W
1600
1400
1200
1000
800
600
400
200
0
1
2
3
4
8
13
Site
V-47
V-80
Figure 3. Net spesific production for different sites
1354
Net Specific Production
kWh/ yr
13,445,249
13,451,246
18,286,012
18,225,138
14,357,679
14,287,065
9,024,842
9,327,512
7,403,764
7,729,525
14,218,581
14,220,128
MWh/ MW
1,072
961
1,458
1,302
1,145
1,021
720
666
590
552
1,134
1,016
SENSIVITY ANALYSIS FOR V-80 MODEL SITE 2
TNB Commercial
Sellback
(RM/kWh)
0.29
0.43
0.58
0.65
0.68
0.69
0.71
0.72
0.73
0.77
0.78
0.86
Net Spec Production for Different Site
E
COE
(RM/kWh)
0.40
0.25
0.11
0.01
0.00
-0.01
-0.02
-0.04
-0.05
-0.08
-0.10
-0.18
FeedIn Tariff Ratio
1.00
1.50
2.00
2.35
2.38
2.40
2.46
2.50
2.54
2.67
2.71
3.00
V.
[9]
CONCLUSION
In the presented paper, preliminary feasibility of offshore
wind energy for 6 selected sites in Malaysia were
conducted. Locations facing South China Sea are the best
choices for offshore wind farm implementations with the
maximum potential during Northeast monsoon season in
November to February. The highest annual vector resultant
wind speed of 4.1 mls is recorded in the East peninsular
Malaysia. Results indicate that Site 2 is the best location due
to high wind resources availability[20-21]. The 2 MW rated
wind turbines, provides the lowest energy cost at RM0.40.
However, higher net specific production is provided by the
0.66 MW rated wind turbine. The sensitivity analysis
confirms that the feed-in tariff is a significant criterion to
determine the feasibility of offshore wind farm in Malaysia.
Feed-in tariff higher than the breakeven point, would attract
private sectors to invest on this type of energy system. An
attractive policy would determine the profitability of an
investment in the offshore wind farms and encourage
private sectors to invest here.
Although there are several strategies to encourage
communities utilizing wind energy, there is no energy
policy specifically approved for wind energy in Malaysia
due to the new-fangled characteristics of the wind energy
technology in the country. Thus, to commercialize wind
energy utilization a comprehensive wind energy policy can
boost the country among the leaders in this field.
[10]
[11]
[12]
[13]
[14]
[15]
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