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www.elsevier.com/locate/enbuild
Energy and Buildings 39 (2007) 154–165
Urban energy generation: Influence of micro-wind turbine
output on electricity consumption in buildings
A.S. Bahaj, L. Myers *, P.A.B. James
Sustainable Energy Research Group, School of Civil Engineering and the Environment,
University of Southampton, Southampton SO17 1BJ, UK
Received 30 April 2006; received in revised form 19 May 2006; accepted 1 June 2006
Abstract
Small scale wind turbines installed within the built environment is classified as microgeneration technology. Such turbines may soon become a
commercial reality in the UK as a result of both advancements in technology and new financial incentives provided by the government. In addition,
microgeneration technologies, especially those with appreciable resource, have the potential to reduce built environment related CO2 emissions
coupled with reductions in consumers’ electricity costs. In many cases payback on capital investment is within the lifetime of the device. Micro-
wind turbines installed in certain areas in the UK will fit within such criteria. The work presented here addresses modelling of such installations
around the UK and presents a methodology to assess the suitability and the economic viability of micro-wind turbines for domestic dwellings. A
modelling tool ‘‘m-Wind’’ has been developed specifically for studying both energy yields and the payback periods for micro-wind turbines. m-
Wind predicts wind turbine performance prior to installation according to specific power curves either defined by turbine manufacturers or the user.
Numerical consideration of wind speed data at specific UK sites was used to estimate energy yields and the results are projected to real electricity
demand data from monitored dwellings in the UK. The results show that it is possible to predict with a good degree of accuracy the expected
financial payback period for a typical domestic dwelling. Furthermore, the paper postulates that micro-wind technology could have the potential to
make a significant impact upon domestic electricity generation when located at the windiest sites in the UK. The likelihood of a proliferation of
these turbines in the urban or suburban environment is low but at coastal or inland high elevation sites the technology appears to have a promising
future.
# 2006 Elsevier B.V. All rights reserved.
Keywords: Micro-wind; Domestic; Microgeneration
1. Introduction
Urban energy generation such as that produced by small
scale wind turbines or photovoltaic systems installed on or
around buildings can be defined as microgeneration [1]. The
term applied equally for the generation of energy – heat or
electricity – by individual buildings or small groups of
buildings. Such technologies also include micro-combined
heat and power (CHP), solar thermal, photovoltaics, fuel cells
and micro-hydro systems. In contrast to the traditional
centralised energy supply, microgeneration technologies
bring power generation close to the user to sustain their
homes or buildings. It is estimated that there is a huge
potential to utilise this type of technology in the urban built
* Corresponding author. Tel.: +44 23 8059 3941.
E-mail address: [email protected] (L. Myers).
0378-7788/$ – see front matter # 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.enbuild.2006.06.001
environment not only to satisfy demand and provide
decentralised generation but also to help tackle fuel poverty
and achieve reductions in emissions [2].
The UK Government has set targets of reducing the
country’s carbon dioxide emissions to 60% below 1990 levels
by 2050 and to achieve 10% of electricity generation from
renewable sources by 2010. The Energy White Paper and
subsequently, the UK Energy Act of 2004 have set the initiative
to encourage the installation of microgeneration technology,
clear Skies2 (solar thermal), and the Major Photovoltaic
Demonstration Programme with total funding over 4 years of
around s 62 m (£ 42.5 m)1 [3]. Such initiatives aim to drive
microgeneration technologies in conjunction with energy
efficient measures so that a real impact on overall energy
demand in buildings can be achieved.
1 1 GBP (£) = 1.46 EUR (s), 9 May 2006.
A.S. Bahaj et al. / Energy and Buildings 39 (2007) 154–165 155
Micro-scale wind turbines in the UK are an emerging
technology driven by advances in device design, increasing
energy prices and the financial incentives offered to aid their
uptake in buildings. At present there are a few devices that are
almost market-ready and there will be a number of factors that
will affect the uptake and success of the technology. Micro-
wind turbine developers have quickly realised that units must
be self contained requiring little if no input from the consumer.
‘Plug & Play’ type devices are presently marketed in the UK
and are likely to be widely available by mid-to-late 2006 [4,5].
The direct benefit in utilising micro-wind turbines in the built
environment is clearly one of sustainable electrical power gener-
ation and hence CO2 abatement and also in financial savings.
Indirect benefits are more subtle and span ‘softer’ issues such as
pride in housing and increased energy awareness to technical
issues such as generation at point of use and the potential for
demand reduction. It can also be argued that the use of microg-
eneration technologies when combined with occupier perception
and behaviour can result in further environmental benefits or
additionality that cannot be achieved with traditional supply.
The work presented here is targeted at the modelling of
micro-wind installations around the UK and the assessment of
their economic viability for domestic dwellings. m-Wind
predicts wind turbine performance prior to installation
corrected for the specific location and topography where the
turbine will be installed. Numerical consideration of wind
speed data at specific UK sites is used to estimate energy yields
and the results are projected to real electricity demand data
from monitored dwellings in the UK. The paper also presents
analysis of financial payback periods and carbon savings under
various scenarios applicable to micro-wind devices and the
urban environment. The aim of this work is to present an
unbiased estimation of turbine energy yields and the financial
reward to the consumer based on actual electricity demand data.
Fig. 1. Locations of wind data sites used in this paper.
2. Wind energy resource
The UK has the most intense wind energy resource in
Europe due to its western location that is subjected to the main
Atlantic weather fronts [6]. Mean wind speeds at 50 m above
open ground have been measured at 6.5–7.5 m/s over a large
area of the country [6]. However, micro-wind turbines will not
enjoy as favourable locations as large scale devices due to their
siting at low altitude and in perhaps dense urban terrain.
Baseline wind data for this study has been gathered for a
number of locations around the UK (Fig. 1). All site measured
data represents wind speeds recorded 10 m above ground level
(AGL). The selected sites shown in Fig. 1 give a spread of the
UK distributed at inland, coastal and upland areas. Fig. 2 shows
the variation in mean hourly wind speed throughout the day at
these sites during 2003. All sites show a similar trend of
increasing wind speed peaking in the late afternoon.
Domestic electricity demand generally peaks twice every day
in the morning and evening as occupants depart and return from
work/school, etc. The stronger wind speeds that occur from mid-
afternoon onwards are well suited to provide power during the
evening period. Therefore, despite the relatively low wind
intensity inurban terrain, thewind resource iswell suited tomatch
domestic electricity demand. A good match also occurs when
studying the monthly mean wind speeds throughout the year.
Fig. 3 highlights that; as one might expect stronger winds occur
during winter months and the calmer conditions during the
summer. Again, domestic electricity usage follows a similar
trend, with reduced lighting requirements in the summer coupled
with lower usage ofhigh energy appliances such as tumble dryers.
3. Domestic electricity demand
Forecasting or predicting domestic electricity demand on an
hourly basis is very difficult unless a great deal is known about
the dwelling, such as the number of occupants, age, lifestyle
habits and the quantity and nature of electrical devices. Even
with all this information there are other factors that serve to
make predicting electricity demand a very uncertain process.
Fig. 2. Annual mean wind speeds for each hour of the day at five sites in the UK
during 2003.
A.S. Bahaj et al. / Energy and Buildings 39 (2007) 154–165156
Fig. 3. Monthly average wind speeds at several UK sites during 2003.
Fig. 4. Havant low energy housing scheme. Schematic map of the development
(top) and image of ‘Eco home’ photovoltaic array (bottom).
These may include irregular routines, occasional working from
home, holidays and over occupancy due to visitors. However,
despite all these factors it is possible to see general trends in
demand. There are the typical weekday patterns of high
morning and evening demand as people arise, go to work and
return in the early evening. Weekends tend to have a higher
daily demand as people stay in during the day more especially
during the winter months. Such a pattern is applicable for
example, to families with children. It follows that other
behavioural trends exist for differing lifestyle groups such as
shift workers, the retired or a mother at home with a young
family [1]. The base load will depend upon the number and
nature of consumer appliances in the dwelling and will
gradually increase over time as more are added and efficiencies
of certain devices (such as freezers) degrade.
3.1. Energy demands in a housing development test site
Energy demand for the analysis presented here is measured
at nine dwellings that form a social housing development
located on the western side of Leigh Park in Havant, NH. The
scheme was developed by Hermitage Housing Association [7]
working closely with the Local Authority, Havant Borough
Council [8]. The houses are located in Havant, near Portsmouth
on the South Coast of the UK (longitude 0.988W, latitude
50.88N). Seven of the houses are identical in construction with
Table 1
Overview of the four varying types of domestic electricity demand measured in th
Dwelling no.
Unit 4 Unit 5
Occupancy Young couple with
two pre-school
children
Family w
child of s
Typical demand trends High peak use often
late at night, little
diurnal pattern
Strong diu
trends, hig
daytime d
at weeken
Approximate base load (W) 100 200
Annual electricity demand (kWh) 7100 6060
two additional south facing ground floor flats or apartments
built for the benefit of mobility-impaired tenants [9,10].
Each house has an identical photovoltaic (PV) system
consisting of two SMA-SWR700 string inverters [11] each
connected to a string of nine BP585L roof mounted laminates
[9,12]. The rated output of the PV system of each house is
1530 Wp. There are six south, two west and one east facing
array. The arrays are inclined at 458, with no shading from
surrounding buildings or trees (Fig. 4). All tenants were
provided with a home PV user guide to explain the system
installed on their houses. This guide included information on
selecting the best import–export tariff regime for their needs
and how to avoid export of electricity to maximise financial
return. In addition the performance of the systems on a month-
by-month basis was published on a web site to enable system
performance and house electricity usage to be observed. A kWh
display meter was installed in the entrance hall of each house to
show cumulative yield of the PV electricity generated since
commissioning.
Table 1 shows the occupancy and energy demand detail for
four of the low-energy social housing dwellings that are
e UK
Unit 6 Unit 7
ith one
chool age
Single retired person Young professional couple
rnal
her
emand
d
Strong diurnal
trends, small
peaks at 8 a.m., 8 p.m.
As unit 6 but with
lower peak use
160 70
3660 2620
A.S. Bahaj et al. / Energy and Buildings 39 (2007) 154–165 157
Fig. 5. Monthly electricity demand of four low-energy domestic dwellings at
New Lane, Havant. (Recorded data, April 2004–March 2005.)
currently being monitored as part of a UK Government study
[10]. These four houses have a wide range of occupancy,
demand trends and annual electricity consumption despite the
houses being of the same construction. Fig. 5 shows the
monthly electricity demand for the identical construction
dwellings. Unit 4 has a very unpredictable monthly demand
whilst units 5–7 demonstrate the more expected annual trend to
their electricity usage.
4. Micro-wind turbine energy analysis
In order to assess the suitability of micro-wind electrical
energy generation in the built environment the modelling tool
m-wind was developed. m-Wind is composed of four modules.
Fig. 6. Turbine performance component of the modelling tool. User can define win
output onscreen.
(1) W
d sh
ind resource module. The wind resource module brings up
a diagram of the UK (similar to Fig. 1) allowing the user to
select one of nine UK sites. It then uploads wind speed and
direction data to the model. Currently, the data is held in
yearly sets composed of 30 or 60 min averages depending
upon the site.
(2) T
urbine performance module. The front end for this moduleis depicted in Fig. 6. Within m-wind, the wind speed data is
corrected for terrain roughness and the height of the turbine
above ground level using the Prandtl boundary layer theory
[13]. There is also the option of defining wind shadow to
model the effects of tall buildings/trees that may reduce the
wind resource from a certain direction. In addition, the user
can specify the performance of the micro-wind turbine by
defining the electrical power generated over a range of wind
speeds (power curves). This may be estimated figures or
from manufacturers literature contained within the ‘turbine
library’.
(3) E
lectricity demand data module. This module allows theuser to upload electricity demand data. Electricity demand
data from the housing scheme described in Section 3 was
gathered and used in m-wind. Yearly data sets composed of
30-min demand averages are used. A number of different load
profiles are available to simulate the variation in domestic
electricity demand. The user can also upload their own data as
monthly totals that they may obtain from utility bills.
(4) F
inancial and carbon savings analysis module. Thefinancial module requires input of the nature of the electricity
tariff, the costs of the wind turbine and any financial rewards
from generation/export of electricity. Annual savings, device
payback times and carbon savings are calculated.
ear, shadow effects and the power curve of the turbine and view graphical
A.S. Bahaj et al. / Energy and Buildings 39 (2007) 154–165158
Fig. 7. Input parameters and the outputs of the m-wind model.
Fig. 8. Estimated typical power curves for four sizes of micro-wind turbines.
Fig. 9. Wind speed and simulated power output for a 400 W turbine on two
building types in Aberdeen 2003.
Fig. 7 summarises all the input parameters that can be
defined in m-wind and the associated outputs.
5. Characterising turbine performance
Power curves depict the relationship between wind speed
and power and the range of operation of the turbine [13]. Such
curves are normally supplied by the turbine manufacture. At
present many new devices are under test/field trial condition so
there is a paucity of accurate curve data available to include in
m-wind. Hence, simulated power curves are given as option
within the software. Such power curves, for a set of example
micro-wind turbines, were generated using the following
relationship:
CP ¼P
0:5rV3A(1)
where P is the power (W), r the density of air, A the rotor area, V
the wind speed and CP is the coefficient of power. CP is a
dimensionless term that has a theoretical maximum value of
0.59 [13]. In reality the value of CP also includes mechanical
and electrical losses so the maximum value is always less than
the theoretical limit.
For this work a stall-regulated rotor was designed using
commercial numerical turbine design software [14]. The
dimensionless CP curves were then applied to four different
sizes of rotor for a micro-wind turbine device. Fig. 8 shows the
resultant power curves which were considered representative of
micro-scale devices expected to reach the market in the future.
5.1. Micro-wind turbine output for selected sites
To determine the power output of a specifically characterised
turbine modules 1 and 2 of m-wind are used. As an example a
400 W commercially available turbine [15] was located in
Aberdeen and Coleshill for two types building: building 1 roof
level is 20 m AGL in suburban terrain, building 2 is 10 m AGL
in suburban terrain with 50% wind speed reduction within an
arc of 180–2408 representing a structure creating a wind
shadowing effect. The measured wind speed, corrected wind
speed (at the turbine) and the resultant power is shown in Figs. 9
and 10 for the scenarios detailed above. It can be seen that
increasing the installed height of the turbine by 10 m in heavy
terrain has the effect of more than doubling the annual energy
yield. There is also a five-fold increase in generation between
good and bad locations (Aberdeen building 1 versus Coleshill
building 2). Thus, extreme care is required when estimating
annual energy yields for micro-wind turbines.
5.2. Commercially available micro-wind turbines
At present there are a number of micro-wind turbines
available aimed specifically at domestic properties. Rated
power ranges from 400 W to 1.5 kW. Larger devices are
available but are better suited to larger multiple occupancy
buildings. Whereas traditional horizontal axis rotors seem to be
favoured for domestic applications vertical axis devices are
appearing upwards of 1.5 kW rated power. The vertical axis
design has a performance advantage over horizontal axis
devices [16] when operating in skewed flow where the rotor is
A.S. Bahaj et al. / Energy and Buildings 39 (2007) 154–165 159
Fig. 10. Wind speed and simulated power output for a 400 W turbine on two
building types in Coleshill 2003.
Fig. 12. Non-dimensionalised characterisation of several micro-wind turbine
performance scenarios.
not aligned perpendicular to the wind direction. Field trials of
two horizontal axis devices [4,5] aimed at the domestic market
have commenced in the UK during 2006. Fig. 11 shows some
of the main devices that are already available or close to
market.
6. Matching micro-wind turbine generation with
demand
The domestic electricity demand data used in this analysis
corresponds to that expended by the four different dwellings
described in Table 1. The aim here is to match this demand to
that is likely to be generated by micro-wind turbines, from the
various sites in the UK specified in Fig. 1. m-Wind was used to
predict electricity generation and calculate electricity import/
export for the selected four dwellings based on the five different
wind resource sites (Fig. 1) and the four discrete sizes of turbine
described in Section 5 (Fig. 8).
The analysis assumes wind speeds were corrected to 10 m
above ground level for flat grassy terrain using the Prandtl
wind shear theory [13] incorporated in the model. All data
was loaded into m-wind and the results (electricity demand,
import, export, generation) were produced for the duration of
1 year.
Fig. 11. Several micro-wind turbines (L–R): D400 StealthGen (400 W 1.1 m diamet
(2.1 m diameter 1.5 kW) [5], Turby vertical axis (2.6 m high, 2 m diameter, 2.5 kW
Fig. 12 shows an interesting relationship that characterises
the general usage and generation by a micro-wind turbine for
any given location and dwelling. Data points shown are
monthly totals (electricity export, demand and generation).
The electricity demand data is measured data over 5 min
intervals from the Havant dwellings whilst generation was
that calculated by m-wind. Import or export is simply the net
balance of electricity flow over each time step; thus if
demand > generation then the difference between these is
imported, export occurs when demand < generation. In
Fig. 12 the demand/generation (D/G) ratio can be thought
of as a sizing parameter and a measurement of the wind
resource. If the ratio is low (�1) then demand is equal to
generation and it follows that it is likely that the level of
export will be high. This means the consumer is not making
the best use of the generated power by using it in the home. If
the D/G ratio is high (>10) then it is quite likely that the wind
resource is poor. Here we are assuming that the majority of
UK domestic properties use 3000–4000 kWh/yr [18]. There
are a few caveats to this: Firstly, for properties with a very
high demand (>7000 kWh/yr) a higher D/G ratio is not such a
handicap. Secondly, smaller turbines will naturally generate
er) [15], Windsave (1 kW 1.75 m diameter) [4], renewable devices swift turbine
) [17].
A.S. Bahaj et al. / Energy and Buildings 39 (2007) 154–165160
less power leading to a lower D/G ratio compared to a larger
turbine. In such cases it is useful to correct the D/G ratio for
an equivalent 1000 W rated turbine so generation is multi-
plied by 2.5 for a 400 W turbine and divided by 0.66 for a
1.5 kW device. This will give a more representative D/G
ratio.
The export/demand (E/D) ratio is a measure of the
matching suitability between the device yield and the
dwelling consumption. If the ratio approaches zero then this
is very favourable as it means export is low and nearly all
electricity generated is used. This generated electricity from
the turbine can be utilised so that it offsets imported power
(currently valued at s 0.12 kWh�1 for UK domestic
unrestricted). In the UK, this has good financial implications
for consumers on a tariff that promotes fixed payment
(currently �s 0.06 kWh�1) for electricity generated from
renewable sources. Hence, the maximum possible gain by the
consumer per unit of electricity could be around s0.18 kWh�1. If the E/D ratio is large then this is bad for
the consumer who fails to maximise the financial benefits of
using generated power on site. Thus, from Fig. 12 a
compromise between the D/G and E/D ratio is most
favourable close to the circled region shown.
6.1. Assessing the suitability of a micro-wind turbine
system
The analysis presented above provides the basis for
constructing a methodology for assessing the suitability of a
micro-wind turbine system. The approach is summarised in the
flowing steps:
Fig. 13. Financial analysis module
� O
of
btain local wind speed measurements at small time steps
(not annual average values).
� E
stimate monthly energy yields (generation) from turbine,correcting for terrain and height above ground level.
� O
btain monthly electricity demands for the building (eitherfrom measurement or utility bills).
� C
alculate demand/generation ratio.� A
pply to Fig. 12 to give an estimate electricity export permonth.
� C
onduct financial analysis considering electricity export/generation/import costs.
� A
ssess yearly savings from turbine and calculate paybackperiod considering turbine installed cost.
The above steps can be easily conducted with m-wind. This
methodology is incorporated into the model as the user can
enter monthly electricity demand data with the relationship
shown in Fig. 12 utilised to provide an estimate of electricity
export.
7. Financial and carbon savings analysis
Accurate economic analysis of microgeneration technol-
ogies is important as it allows prospective buyers to asses
the financial outlay and the annual and total payback period
of the turbine. A secondary concern may be towards
carbon savings. Such functionality is provided within
m-wind and allows various scenarios to be explores by the
user.
Fig. 13 shows a screen shot of the financial analysis and
carbon savings module of m-wind. Many combinations of input
micro-wind modelling tool.
A.S. Bahaj et al. / Energy and Buildings 39 (2007) 154–165 161
Tab
le2
Pay
bac
kp
erio
din
yea
rsfo
r‘‘
pai
dto
gen
erat
e’’
tari
ffan
dvar
iab
lein
stal
led
cost
s
Sit
e6
10
0(U
nit
5)a
36
60
(Un
it6
)a2
62
0(U
nit
7)a
s1
.2W�
1s
1.5
W�
1s
1.8
W�
1s
2.2
W�
1s
1.2
W�
1s
1.5
W�
1s
1.8
W�
1s
2.2
W�
1s
1.2
W�
1s
1.5
W�
1s
1.8
W�
1s
2.2
W�
1
Ab
erd
een
15
19
24
29
16
20
25
30
17
22
27
33
Cal
shot
23
29
36
43
24
30
37
45
25
31
39
47
Co
om
be
34
42
52
63
34
43
53
64
36
44
56
67
Bri
dli
ngto
n3
03
84
75
73
23
94
95
93
34
25
26
2
Cole
shil
l44
55
69
82
44
56
69
84
46
58
72
86
Key
var
iab
les:
Tu
rbin
e7
mA
GL
ino
pen
cou
ntr
ysi
de,
10
00
Wex
amp
lep
ow
ercu
rve
(Fig
.7).
aA
nn
ual
dw
elli
ng
sco
nsu
mp
tio
n(k
Wh
/yr)
(dw
elli
ng
no
.).
parameters were specified in the modelling tool to assess the
relative impacts upon device payback time.
Examples of payback times and carbon savings are
presented in the tables below. In the example calculations,
the set variables that do not change are given below:
(1) A
ll data is from m-wind. Measured wind and electricitydemand data is combined with simulated turbine power
outputs at small time steps over one full year (2003).
(2) S
tandard two-tier electricity tariff: the first 600 kWh arecalculated at s 0.19 kWh�1 (£ 0.13). Further units cost at s0.10 kWh�1 (£ 0.07). This closely represents the current
UK tariff.
(3) T
urbine installed costs expressed as s/W of rated power.The costing includes taxes (VAT in the UK), installation
costs, and allowances for grants or subsidies.
(4) ‘‘
Paid to generate’’ tariff. All generation (whether used orexported) is paid s 0.06 kWh�1 (£ 0.04). This value is
approximately that of renewable obligation certificate
(ROC) [19].
(5) Z
ero annual maintenance costs unless stated.(6) ‘‘
Paid to export’’ tariff. Only exported electricity isrewarded at a varying rate. Generated electricity used in
the home receives no additional reward.
(7) ‘‘
Zero payment’’ tariff. No financial rewards for generatedor exported electricity. Only benefit for the owner is the
reduced import costs when generated power is used in the
building.
7.1. Domestic single story dwellings
Tables 2 and 3 show the payback periods for a turbine at a
height of 7 m above ground level, in relatively open countryside
topography. The results in Table 2 show the effect that varying
the installed cost of the turbine will have upon the payback
period. Data is shown for three different domestic dwellings
(based upon the demand profiles of units 4–6 discussed in
Section 3, Table 1) located at the five UK locations shown in
Fig. 1.
The data in Table 2 shows the following:
� V
ariations in wind speed around the UK are significant (forsimilar height AGL and terrain type). Payback periods have
the expected linear relationship with device install costs
(assuming no additional annual costs). This means subsidy
driven aid, such as upfront capital grants or reduced VAT can
greatly reduce payback periods.
� A
lthough units 6 and 7 have poorer demand/generationmatching than unit 5, the export from micro-wind is low
enough that it does not considerably affect payback periods
(for a paid to generate tariff).
Table 3 presents results for the same conditions as in Table 2
but for a ‘‘paid to export’’ tariff such that only generated
electricity that is not used within the property but exported
warrants payment. The results for the payback period are shown
for varying export tariff fixed at s 0.00, 0.06 and 0.10 per unit
A.S. Bahaj et al. / Energy and Buildings 39 (2007) 154–165162
Table 3
Payback period in years for ‘‘paid to export’’ tariff for 1000 W turbine costing s 1500
Site 6100 (Unit 5)a 3660 (Unit 6)a 2620 (Unit 7)a
s 0.0 kWh�1 s 0.06 kWh�1 s 0.10 kWh�1 s 0.0 kWh�1 s 0.06 kWh�1 s 0.10 kWh�1 s 0.0 kWh�1 s 0.06 kWh�1 s 0.10 kWh�1
Aberdeen 29 29 29 31 30 29 36 32 29
Calshot 44 44 44 47 45 44 50 47 44
Coombe 64 64 64 65 65 64 70 67 65
Bridlington 58 58 57 62 59 58 68 62 58
Coleshill 84 84 83 86 85 84 89 87 86
Key variables: turbine 7 m AGL in open countryside.a Annual dwellings consumption (kWh/yr) (dwelling no.).
of electricity exported (kWh). Zero export reward is equivalent
to the zero payment option detailed above. The s 0.06 export
rewards is approximately equal to the UK ROC price, the third
to an upper-bound realistic estimate one could expect to be paid
for export (close to the import price). The results in Table 3
minimises the effects of varying turbine installed cost by
including a relatively optimistic price of s 1.5 W�1 of rated
power for the turbine costs in the analysis.
The data in Table 3 shows the following:
� H
Ta
Pa
Si
A
Ca
Co
Br
Co
K
Ta
A
Si
A
Ca
Co
Br
Co
igh demand households still have shorter payback times
under paid to export tariff as the offset cost of import is
greater than any export reward.
� E
nergy efficient users (low demand) show a greater reductionin payback times as the export price increases. Houses with a
higher demand demonstrate virtually no change in benefit due
to the very small amount of export.
� P
ayback periods for ‘‘paid to export’’ are almost twice that ofthe ‘‘paid to generate’’ tariff (Table 2 for s 1.5 W�1 installed
figures).
ble 4
yback period in years for multi-occupancy building, 1000 W turbine variable co
te Multi-occupancy building (generation tariff 4 p/kWh)
s 1.2 W�1 s 1.5 W�1 s 1.8 W�1 s 2.2 W
berdeen 8 10 12 14
lshot 11 13 16 20
ombe 15 19 23 28
idlington 16 20 25 30
leshill 23 28 35 43
ey variables: turbine 20 m AGL, open countryside terrain.
ble 5
nnual CO2 savings (kg) from micro-wind turbines, in domestic and multi-occup
te Domestic property
(7 m AGL in open terrain)
Multi-occupancy
(25 m AGL, subu
600 Wa 1000 Wa 1500 Wa 2000 Wa 600 Wa 1000
berdeen 124 198 346 393 409 650
lshot 81 129 227 258 219 349
ombe 55 89 155 177 141 224
idlington 62 99 174 198 215 341
leshill 42 68 118 135 173 275
a Device rated power.
� I
st
�
anc
bu
rb
Wa
n contrast to the paid to export tariff, the paid to generate
tariff is far more beneficial to the consumer.
7.2. Multi-occupancy buildings
Table 4 shows the effects of varying the wind resource at
double the turbine elevation used in the domestic single storey
dwellings (Tables 2 and 3). The height of the turbine above
ground level is increased to 20 m representing taller multi-
occupancy buildings in open (non-urban) terrain. The
economics of such larger buildings are much simpler as the
base-load demand is generally greater than that of the rated
output of the turbine. Therefore, in the analysis for this case, no
electricity export is considered within m-wind. Two different
tariffs are defined, the first claiming ROCs for generation the
second receiving no financial gains from the turbine. From the
conditions depicted in Table 4, one observes payback periods
less than the expected lifetime of the turbine. This is due to the
substantial increase in wind speed with increasing height above
ground level. All payback periods less than 15 years (our
and electricity tariffs
Multi-occupancy building (zero payment tariff)
1 s 1.2 W�1 s 1.5 W�1 s 1.8 W�1 s 2.2 W�1
12 15 18 22
16 20 26 31
24 29 37 44
25 32 40 48
37 47 58 70
y settings
ilding
an terrain)
Multi-occupancy building, 70% wind speed
reduction in 180–2408 arc (25 m AGL,
suburban terrain)
1500 Wa 2000 Wa 600 Wa 1000 Wa 1500 Wa 2000 Wa
1139 1294 317 504 883 1004
611 695 171 272 476 541
392 446 112 178 312 355
598 680 179 284 498 566
482 548 110 175 307 349
A.S. Bahaj et al. / Energy and Buildings 39 (2007) 154–165 163
Tab
le6
Co
stp
erk
go
fC
O2
(s)
saved
over
the
life
tim
eo
fa
1k
Wtu
rbin
e(1
4y
ears
)fo
rvar
yin
gtu
rbin
eco
stin
do
mes
tic
and
mu
lti-
occ
up
ancy
bu
ild
ing
s
Sit
eD
om
esti
cp
rop
erty
(7m
AG
Lin
op
ente
rrai
n)
Mult
i-occ
upan
cybuil
din
g
(25
mA
GL
,su
bu
rban
terr
ain
)
Mu
lti-
occ
up
ancy
bu
ild
ing
,7
0%
spee
dre
du
ctio
nin
18
0–
24
08
arc
(25
mA
GL
,su
burb
ante
rrai
n)
s1
.2W�
1s
1.5
W�
1s
1.8
W�
1s
2.2
W�
1s
1.2
W�
1s
1.5
W�
1s
1.8
W�
1s
2.2
W�
1s
1.2
W�
1s
1.5
W�
1s
1.8
W�
1s
2.2
W�
1
Ab
erd
een
0.3
10
.38
0.5
50
.74
0.1
00
.13
0.1
90
.25
0.1
30
.16
0.2
50
.32
Cal
shot
0.4
40.5
50.8
31.1
10.1
80.2
20.3
40.4
40.2
30.2
90.4
20.5
7
Co
om
be
0.6
00
.74
1.1
21
.50
0.2
90
.37
0.5
40
.72
0.3
70
.45
0.6
70
.91
Bri
dli
ngto
n0
.61
0.7
71
.15
1.5
30
.19
0.2
30
.37
0.4
80
.23
0.2
90
.44
0.5
7
Cole
shil
l1.0
81.3
62.0
32.7
20.2
30.2
90.4
40.5
80.3
70.4
70.6
90.9
2
estimate of device life based upon experience of equivalent size
photovoltaic inverter mean time to failure [20]) are shaded grey
to illustrate favourable scenarios presented at such tall
buildings.
7.3. Carbon savings
The predicted carbon or CO2 savings as a function of micro-
turbine device size (based on the example turbine power curves
shown in Fig. 8) are shown in Table 5. In the analysis, CO2
displacement has been taken as 0.43 kg CO2/kWh [19]
generated energy. It is apparent from the results that carbon
savings from micro-wind turbines are resource driven with high
wind speed sites giving the largest savings.
In the last column of Table 5, m-wind was used to arrive at
realistic estimates of savings in the urban built environments by
imposing a 70% wind speed reduction in a 180–2408 arc.
7.4. Carbon saved over turbine life time
One important aspect of microgeneration technologies is the
cost associated reductions on CO2. This may be cheaper than
other measures to reduce national emissions and in any case can
be quite easily quantified for micro-wind assuming electricity
generation can be accurately quantified. It is also useful to
compare micro-wind costs to those of other microgeneration
technologies.
Table 6 shows the cost per kg CO2 saved over the lifetime
of the device. This has been estimated as being equal to the
mean time to failure (MTF) of a typical PV inverter (14
years) [20]. In the analysis the condition are as those in
Table 5. A 1 kW turbine has been simulated (from Fig. 8) for
a range of installed costs. It can be seen in Table 6 that for
windy locations atop of tall buildings that the cost falls as
low as s 0.10 kg�1 CO2. At present it is expected that
installed turbine costs may be towards the upper estimates
within this analysis.
For comparison purposes, a PV system with good
orientation in the UK is likely to achieve an annual generation
close to 1000 kWh/kWpeak installed (the UK annual PV
resource is not as variable as wind). The most cost effective
PV system in the UK has a full installed cost of around s 6.60
per Wpeak and a mean time to failure (MTF) of the inverter of
14 years. In this case the cost per kg of CO2 saved over this
period is approximately s 1.10. Hence, the cost per kg (s) of
CO2 saved over the lifetime of the micro-wind turbine shown
in Table 6 compare favourably to those predicted for PV
systems.
8. Conclusions
Micro-scale wind turbines in the UK are an emerging
technology driven by advances in design, increasing energy
prices and the financial incentives offered by the government.
At present there are few devices that are almost market-ready
and there will be a number of factors that will affect the uptake
and success of the technology. On the other hand, a large
A.S. Bahaj et al. / Energy and Buildings 39 (2007) 154–165164
vociferous section of the community feel that micro-wind
generation will be appropriate for the built environment;
judging by the number of politicians interested in installing
such turbines on their houses the technology is already enjoying
a good deal of popularity. Nevertheless, currently there is a
paucity of knowledge in the public domain to confirm such
premise. The work presented here highlighted some issues
which are likely to contribute to the debate and investigation of
micro-wind turbine in the built environment. The conclusions
of this work as summarised below:
� T
he methodology presented in Section 6 of this paperprovides a first step for assessing the suitability of micro-
wind devices for domestic dwellings in the UK. It provides a
good estimate of the amount of power likely to be exported
from a property and enables more accurate financial
calculations to be performed to assess the financial payback
period of a micro-wind device. If this payback period is
greater than the device life then micro-wind power is not
suitable from a financial perspective.
� T
his methodology presented in the paper relies upon the useof good wind data (1 h mean averages corrected for terrain
and height) and not annual mean wind speeds. Turbine energy
yields based upon overly optimistic annual wind speeds are
already in evidence and may lead to consumer scepticism and
disillusionment in the long term.
� A
nalysis of device payback times has demonstrated that thekey parameter in ensuring the success of the technology is the
wind resource itself. Turbines must be placed in windy
locations (preferably at maximum height above ground level
in smooth terrain) if payback within the lifetime of the device
is to be achieved.
� A
paid to generate tariff is also a key variable in reducingpayback times. A larger than average electricity demand
with a strong base load will help to minimise export and
therefore increase the financial benefits to the homeowner.
Energy efficient consumers are unlikely to realise the
financial rewards of micro-wind devices as there is a much
greater chance of export (and hence lower financial reward)
due to the lower demand/generation ratio. Turbine
sizes should be small if devices are to be installed on
properties with low demand in order to minimise electricity
export.
� M
etering costs were not included in the analysis. However, ameter reading charge of s 30 per year could increase payback
times by 20%, more so if the wind resource is poor.
� I
n any case, annual charges and red tape will increasepayback times significantly and may be a considerable barrier
to uptake of the technology.
� F
urthermore, it is highly unlikely that micro-wind turbineswill proliferate among UK urban areas. Output from the
numerical modelling in m-wind has demonstrated that
accounting for wind shear and shadow effects can reduce
the annual energy output by up to 50%. Standard empirical
models break down close to terrain level meaning generalised
quantification of urban energy yields is uncertain [21]. At
such low heights in complicated terrain the wind resource will
be governed by local effects. Turbulence and wind shadow
effects will be prominent in such cases. This would favour
dwellings located on sea fronts where sea breezes occur and
for countryside and suburban locations where fields, large
open spaces and low objects have a smaller effect upon wind
speed at typical rotor heights on domestic properties.
� T
he increase in wind speed with height above ground levelcan significantly increase turbine energy yields. Micro-wind
turbines would be better suited to taller buildings in the urban
environment where a better wind resource and larger
structures will allow the installation of larger rotor diameters
with better annual energy yields, financial and carbon
payback times.
In summary, the analyses presented in the paper gives
realistic estimates of both energy yields and economics of
micro-wind turbines. This is because the analyses are based
on real hourly wind speed data (rather than the optimistic
annual data) with appropriate correction for the height above
ground level and the type of terrain. However, there is no
substitute to real measured performance data obtained from
quantifiable environments and devices. However, in the
absence of real operational data full, constructive, and
verifiable judgement on the appropriateness of the technol-
ogy in the built environment will remain unfulfilled. This is
real shame as our analyses indicate that if micro-wind
devices can pay back their costs within the lifetime of the
device, then the potential market in the UK will be large. In
spite of the above shortcomings, our work will progress with
aim of capturing operational data to validate m-wind for the
built environment.
Acknowledgements
This work was conducted as part of the project ‘‘unlocking
the power house—integrating microgeneration into energy
networks and building’’. Funded by the UK Economic and
Social Research Council (ESRC) as part of the Sustainable
Technologies Programme [22].
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