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: luke@soton.ac.uk (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 module

is 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 the

user 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. The

financial 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 (either

from measurement or utility bills).

� C

alculate demand/generation ratio.

� A

pply to Fig. 12 to give an estimate electricity export per

month.

� C

onduct financial analysis considering electricity export/

generation/import costs.

� A

ssess yearly savings from turbine and calculate payback

period 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 electricity

demand 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 are

calculated 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 or

exported) 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 is

rewarded at a varying rate. Generated electricity used in

the home receives no additional reward.

(7) ‘‘

Zero payment’’ tariff. No financial rewards for generated

or 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 (for

similar 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/generation

matching 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 reduction

in 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 of

the ‘‘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 paper

provides 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 use

of 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 the

key 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 reducing

payback 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, a

meter 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 increase

payback times significantly and may be a considerable barrier

to uptake of the technology.

� F

urthermore, it is highly unlikely that micro-wind turbines

will 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 level

can 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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