1. Wind Resource Potential
Wind speed dispersion follows a continuous probability frequency of values
occurrence at different sample spaces of the density function
(IbTroen.et.al, 1989).
𝑓 𝑢 =
𝑘
𝐴
(
𝑢
𝐴
) 𝑘−1
exp[−(
𝑢
𝐴
) 𝑘
]
(eq.2) Sound power level d(IEC-61-400-14)
Figure 2 Weibull distribution
Feasibility analysis of the power potential of small and medium scale wind projects in Leeds area
Author: Antonios Phinios STUDENT ID: 200728144, Supervisor: Prof. Alison Tomlin
0
20
40
60
80
100
120
0 5 10 15 20 25 30
PowerOutput(kW)
Wind speed (m/s)
GHRE-FD-100
ACKNOWLEDGEMENTS AND REFERENCES
ACKNOWLEDGEMENTS: Professor Alison Tomlin /James Goodwin/ Katrina Adam /University of Leeds/ Phinios
Demetrios
REFERENCES: J.T Millward Hopkins.et.al, A.S.T., L. Ma, D.B Ingham, M. Pourkashanian. 2012. Mapping the wind resource over UK
cities. 55, p9.
Noise and Statutory Nuisance Act 1993.
Ib Troen.et.al, E.L.P. 1989. European Wind Atlas.
IEC.61400. 2005. Wind Turbines: Design Requirements (IEC 61400-1)
DECC. 2015a. Feed In Tariff Scheme.
Wind.Measurement.International. 2015. Operational and Maintenance Costs for Wind Turbines. [Online]. Available from:
http://www.windmeasurementinternational.com/wind-turbines/om-turbines.php
OBJECTIVES
1. Identify Leeds urban wind resource availability for small
medium development with respect to legislative restrictions.
2. Convert wind resource potential to power potential
3. Specify potential sites for development.
4. Investigate investment potential for the purposed
development.
5. Extrapolate financial benefits to development potential.
RESULTS
1. Five sites of interest were identified with mean wind speeds
range of 6-8m/s applicable for wind turbine development of 16-
40% capacity factors (Table 2).
2. Most of the turbines have a payback period time of 6-7 years,
and a 500kW investment turbine is returned at year 5 (Figure 3).
3. Wind turbine of 20kW can provide electricity at 14 households
per year with average consumption of 3.3GWhe (Table 3).
4. Wind turbine of 100kW nominal power can support 250 vehicle
charging requirement (Figure 4).
5. Wind turbine of 500kW nominal power can support charging
requirements of 68 electric buses/ year with energy demands
of 3kWh/km on a 24km/day route (Figure 4)..
FUTURE WORK PROPOSALS
Comprehensive study to determine development potential of
Micro-Grid public transport charging infrastructure in Leeds.
INTRODUCTION
Effective energy systems desire certainty, versatility, sustainability and stability to meet both ends supply and demand. As power demand increases in urban areas, network operators find it difficult to stabilise both variables; often resulting to degradation of the
local network. Distributed generation is the key factor providing versatility, whereas wind energy integration supports the decentralisation of power dependence.
Investment Potential
Restrictions
Sites specification
RESEARCH ACTIVITY
Restriction: Input information , Turbine Selection Site Identification: Wind Resource Potential (m/s) Site Specification: Power potential (m/s)→(kW)→(kWh)
Investment potential (kWh)→(£) →(utilities)
Investment Potential
• Net Present Value (NPV) sum of all discounted annual cash flows
associated with the project.
• N lifetime of the project
• discount rate (d) Net product prices annual (i) reductions
• Export Fraction (x): Supply effectiveness of the turbine in terms utilization
Low export fraction: (0%) HIGH utilization
High export fraction (100%) high TRANSMMITANCE
Setting a step of 20% export fraction the influence of direct utility on turbine
investment is estimated and presented (Figure 3)
Generated electricity (Table 2) from wind turbines at all
heights is assumed to be employed to support
either community energy or electric mobility.
1. Community Energy:
Average household consumption: 3,300kWh/year
(OFGEM, 2011) , Grid carbon intensity (UK) is
426gCO2/kWh (Earth Notes, 2015) the benefits can
compromise from environmental and technical
perspective (Table 3).
Table 3: Community energy utilization scenario
2. Transport Electrification
Request of charge calculations for Light Duty
Vehicles (LDV) and High Duty Vehicles (HDV) are
summarised (Tables 4,5).and graphically represented
(Figure 4).
Restrictions were extracted as map
information in accordance with eq.1
Turbine Selection
Turbine selection was based primarily on nominal power and secondarily on
their technical specifications as provided by the manufacturers.
Acoustic assessment was performed for each turbine to evaluate the
minimum acceptable distance (r) from households.
(eq. 1) Sound power level dispersion (IEC-61-400-14)
Input Information
Leeds urban mean wind speed and direction
information was assessed from a previous
study.(J.T Millward Hopkins.et.al, 2012)
Therefore the broad area of investigation is
restricted based on the information provided
(Figure 1).
(Figure 1) Area of investigation
Restrictions with
correspondence to the limit of
42dBLAeq,5 mins(Regulatory Office,1993)
[Noise and statutory nuisance
act 1993]
Lp (dB) 42
Lw (dB) Sound power level
Q Directivity factor (=1)
r (m) Slant distance
𝐿 𝑝 = 𝐿 𝑤 − 10 ∗ log(
𝑄
4𝜋𝑟2
)
In accordance with the UK Weibull
Specifications(IbTroen.et.al, 1989)
mean wind speed distribution is
estimated for all potential sites and at
all heights:
h(m): 20 25 30 35 50
Weibull Distribution
Specifications (UK)
Shape factor
(k)
1.8
Scale factor
(A)
1.124
WindATLAS exclusions:
Frequencies
(f)
<0.01
umean (m/s) ≤3m/s
Height (m) >50
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0 1.5 3 4.5 6 7.5 9 10.5 12 13.5
Probability
Wind Speed (m/s)
POINT A
POINT B
Poly. (POINT A)
Poly. (POINT B)
Heights
(m)
[LONG. , LAT.] [0] Umean
Turbine
Type
Annual
Generated
Energy
(GWh/year
)
Capacity
Factor
(Cp)
20 [53.762 , -1.675] 6.02
CF20 52 29%
WindEN-45 62 16%
25 [53.767 , -1.683] 7.1
GHRE-FD-
100
309 37%
30 [53.764 , -1.676] 6.8
GHRE-FD-
101
288 35%
35 [53.844 , -1.428] 7.7
NPS-60 299 44%
GHRE-FD-
100
399 42%
GEV-MP-
250
429 20%
50 [53.840 , -1.428] 8
GEV-MP-
275
545 23%
WTN-500 2148 40%
Power Potential
Power performance tests as provided by
manufacturers(IEC61-400-12) specify the power
output of a turbine at various speeds,
therefore:
• wind speed information (m/s)
is extracted to power (kW)
• and the distributive characteristics
of wind speed f(1)
• are converted to f(hours/annum)
The outcome is annual energy generated
(kWh/year) for all turbine types at all
specified sites and heights(Table 2).
Table (2) Annual energy generated for all turbine types
CF20
(20m)
GHRE-
FD-100
(25m)
GHRE-
FD-100
(30m)
NPS-60
(35m)
GHRE-
FD-100
(35m)
GEV-
MP-
250
(35m)
GEV-
MP-
275
(50m)
WTN-
500
(50m)
x(0%) 12 7 7 5 5 10 11 4
x(20%) 13 7 7 6 5 11 11 4
x(40%) 14 7 8 6 6 11 11 4
x(60%) 15 8 8 6 6 12 12 5
x(80%) 16 8 9 7 6 13 13 4
x(100%) 17 9 9 7 6 14 13 5
0
2
4
6
8
10
12
14
16
18
20
Years
x(0%)
x(20%)
x(40%)
x(60%)
x(80%)
x(100%)
𝑵𝑷𝑽 =
𝑵=𝟏
𝑵
𝑪𝒂𝒔𝒉 𝑭𝒍𝒐𝒘 𝑵
(𝟏 + 𝒅) 𝑵
𝐶𝑎𝑠ℎ 𝐹𝑙𝑜𝑤 £ = 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐶𝑜𝑠𝑡 𝑜𝑓 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 − 𝐼𝑛𝑐𝑜𝑚𝑒 𝐹𝑙𝑜𝑤
𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐶𝑜𝑠𝑡 𝑜𝑓 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 £ = 𝐶𝐴𝑃𝐸𝑋 + 𝑂𝑃𝐸𝑋
𝐼𝑛𝑐𝑜𝑚𝑒 𝐹𝑙𝑜𝑤 = 𝐹𝑔 + 𝐹𝑒 𝑥 + 𝐸 1 − 𝑥 𝑌𝑖(1 − 𝑓)𝑖−1
Figure 3 payback period for all turbine types with varying export fraction
Development potential
Community Energy
Height (m) 20 25 30 35 50
Annual electricity
generated (GWh/year)
43 238 256 355 1779
Households/year 13 75 81 112 566
tnCO2/year (savings)
1833 10145. 10891 15126 75780
HDV
Energy
Demands
(kWh/km)
Average
Route
Daily (km)
Energy
Consumption
(kWh/year)
3 25 27375
LDV
BEV Diesel Car
Battery
Capacity
(kWh)
Consum
ption
(kWh/km
)
Daily
distance
Travel
Consum
ption
(kWh/ye
ar)
Gco2/km
Daily
Distance
(km)
Annual
Emissions
(tnCO2/yea
r
25 0.2 15 1095 110 15 6.03
0 1000 2000
20
35
50
Height (m)
20 35 35 50 50
HDV 2 10 14 17 68
LDV 41 245 340 433 1706
HDV
LDV
Figure 4 LDV HDV charging availability per year