In urban area, sitting renewable energy (RE) can be a challenging issue because only few spacious land is available but the demand of the energy is high. Hence the proper selection of RE technology is important to ensure plenty of energy are delivered from limited site area. This paper present how does the local climate condition in typical urban area, Auckland Central Business District, affect annual electricity production and energy production of PV or Wind Power system. The analysis is then extended to find the energy density for respective RE system.The result are strategic to advise which renewable energy system can actually optimize energy production in the small land area.

1. TRADEOFF BETWEEN PV AND WIND
TURBINE: DG SYSTEM SELECTION IN
URBAN RESTRICTED SPACE
Presented by:
Haryo Agung Wibowo (15862703)*
Danilo Coelho Mendes (15853609)
* Email: disiniharyo@yahoo.com

2. STUDY MOTIVATION
• Lack of spacious land in typical urban region but
high demand to explore more renewable energy
(RE)
• To advise which technology among PV and Wind
Turbine gives more energy per m2 of land area

3. THE ATTRACTIVENESS OF RE
• RE devices are now sold freely in the generic
market store, and the price is affordable.
• People thinking RE to reduce electric bill, or
even making profit by selling energy.
• Utilities + own RE = redundant system to ensure
continuous energy supply

4. THE UNIQUENESS OF URBAN AREA
• High rise building, great number of residential
house, affect RE primary energy sources quality:
1. Wind gust >> subject to turbulence
2. Solar illumination >> shaded by adjacent
building
• To represent this situation, weather data taken
from AUT weather station (WS Building) are
used in the analysis

5. BUILDING COMPLEX

6. AUT WEATHER STATION

7. PARAMETER ACQUIRED FROM AUT
WEATHER STATION
• Wind Speed
• Solar Intensity
• Ambient Temperature
• Wind Direction
• Humidity
• Barometric Pressure
All data are presumably measured 2 m above WS
Building
INPUT FOR WIND TURBINE SOLUTION
INPUT FOR PV SOLUTION

8. CALCULATING ENERGY DENSITY
• First, predict the annual energy produced by
single RE unit (1 wind turbine or 1 solar module)
• Where the point of energy prediction will be
made
Load

9. CALCULATING ENERGY DENSITY
• First, predict the annual energy produced by
single RE unit (1 wind turbine or 1 solar module)
• Where the point of energy prediction will be
made
Power Converter
+
-
Assume:
Converter efficiency 90%

10. CALCULATING ENERGY DENSITY
• Relying on study proposed by literatures [1-2],
derive a minimum necessary area that should be
provided to build single RE unit
• Energy density is then expressed as
Annual Energy
Energy Density
Minimum Area per Single RE


11. ENERGY FROM WIND TURBINE
• Five different type of wind turbine ranging from 5 –
25 kW at hub height 18 m, are used in assessment.
• Original wind speed data therefore must be
corrected from 2 to 18 m altitude.
• Energy calculation are based on power curve and
time span between successive wind speed data
sampling.
• Sum it up for one year to get annual production.
• From [1], the spacing between wind turbine to
enable wind speed recover in the downstream side
should be 9Dx5D

12. WIND TURBINE PERFORMANCE ANALYSIS
No Wind Turbine Annual
Electricity
kWh
Annual
Wind Energy
kWh
Turbine
efficiency (ηWT)
Energy Density
(εwind) - kWh/m2
1. Eoltec Wind Runner 25 kW 13915.49 35832.56 38.83% 3.87
2. Fortis Montana 5.6 kW 1986.54 8958.14 22.18% 2.21
3. Gaia 11 kW 8689.72 60557.02 14.35% 1.43*
4. Iskra 5 kW 3119.72 10448.77 29.86% 2.97
5. Aircon 10 kW 5360. 02 18063.19 29.67% 2.95

13. WIND TURBINE PERFORMANCE ANALYSIS
No Wind Turbine Annual
Electricity
kWh
Annual
Wind Energy
kWh
Turbine
efficiency (ηWT)
Energy Density
(εwind) - kWh/m2
1. Eoltec Wind Runner 25 kW 13915.49 35832.56 38.83% 3.87
2. Fortis Montana 5.6 kW 1986.54 8958.14 22.18% 2.21
3. Gaia 11 kW 8689.72 60557.02 14.35% 1.43*
4. Iskra 5 kW 3119.72 10448.77 29.86% 2.97
5. Aircon 10 kW 5360. 02 18063.19 29.67% 2.95
• From the table: A bigger wind turbine capacity is not assuring better energy density.
• Better performance wind turbine, are normally posing:
low cut in speed, high cut out speed, high power to rotor diameter ratio

14. ENERGY FROM PHOTOVOLTAIC
• Energy calculation are based on corrected power
output from STC rating
• Multiplying power output with time span between
successive solar data sampling and sum it up for one
year to get annual energy production.
• It is suggested in [2], the ratio between land
occupied for module installation to land for
maneuver, access, services facilities, etc are 1:1.89.
max * max, STC
2
1000
G
P P
W m
    max max* 1 25cellP P T C    

15. PV SYSTEM PERFORMANCE ANALYSIS
• Refer to that assumption, the kWh/m2 of PV
system then become: 34.6%
.
Total PV EnergyOutput
panel year
Surface Area of Single PV Module

Photovoltaic Annual Electricity
kWh/Module
Annual Solar Energy
kWh/Module
Module
efficiency (ηPV)
Energy Density
(εPV) - kWh/m2
ND-220E1J 180.68 1575 11.46% 38.07

16. PV SYSTEM PERFORMANCE ANALYSIS
• Refer to that assumption, the kWh/m2 of PV
system then become:
• Best Wind Turbine Energy Density: 3.87kWh/m2
• Best Wind Turbine Efficiency: 38.83 %
34.6%
.
Total PV EnergyOutput
panel year
Surface Area of Single PV Module

Photovoltaic Annual Electricity
kWh/Module
Annual Solar Energy
kWh/Module
Module
efficiency (ηPV)
Energy Density
(εPV) - kWh/m2
ND-220E1J 180.68 1575 11.46% 38.07

17. THE ANALYSIS RESULT INTERPRETATION
Why WT comes with low power density?
• The quality of wind speed in urban area overall is poor. It
causes WT annual production is low as well
• Although efficiency of a wind turbine is better than solar
panel, wind turbine demand large area free from interference.
Why PV better?
• Radiation received in WS building at 0.5 sun is up to
1000hrs/year, and PV works at cell temperature less than
25⁰C (yield better power output) have 75%+ of chance, make
this place suitable for PV operation
• Though PV has lower efficiency, it doesn’t require so much
space, as long as it fits with the module size plus for
miscellaneous services

18. CONCLUSION
• From the analysis it has been shown that in urban
area, PV outrank wind turbine energy density
(kWh/m2) performance by the ratio of 38.07 : 3.87
• With less spacious land available in the urban area,
PV is the best solution if RE are preferred for
generating electricity.
• The presented result need further improvement,
because as introduced earlier, several key factor that
may cause reduction in PV and wind power output:
humidity and wind direction so far remain
unconsidered in calculation.

19. REFERENCES
[1] G. M. Masters, Renewable and Efficient Electric Power Systems:
Wiley, 2004.
[2] K. Kurokawa, Energy from the Desert: Feasibility of Very
Large Scale Photovoltaic Power Generation (VLS-PV) Systems:
James & James (Science Publishers), 2003.

20. THAT’S ALL