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Geospatial Modeling of Potential
Renewable Energy in Papua New
Guinea
Dr. Sailesh Samanta
Dilip Kumar Pal, Sammy Samun Aiau & Babita Palsamanta
PAPUA NEW GUINEA UNIVERSITY OF TECHNOLOGY
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Introduction • The energy sector in Papua New Guinea mostly depends on three main types of
energy:
– Electricity
– Oil
– Gas
• The energy sector accounts for 14% of the country’s GDP (PNG‘s GDP US$ 12.937
billion with a growth rate of 8.9%).
• PNG Power Limited is the sole national electricity company responsible for
generation, transmission, distribution and retail of electricity in PNG
• Oil Search and Inter Oil are the oil companies, also dominate the gas market.
• The PNG LNG Project in 2014 is contributing PNG’s energy sector.
• But renewable Energy sector remains severely underexploited.
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Introduction: Underexploited ! • Absence of proper Electricity Industry Policy, Energy Policy, Rural Electrification
Policy
• High investment costs associated with establishing transmission lines due to the PNG’s
rugged topography.
Recent initiative in 2006
– Electricity Industry Policy (endorsed 2011)
– National Energy Policy (early draft stage)
– Rural Electrification Policy & Strategy (early draft stage)
– Geothermal Energy Policy
– Renewable Energy Policy
• The delay in the formulation and implementation of these policies
• Rural areas of Papua New Guinea, without access to electricity.
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Introduction: Electricity Situation Name of Province Population % with Electricity % without Electricity
Central 183983 1.7 98.3
Gulf 106898 0.4 99.6
Milne Bay 210412 0.6 99.4
National Capital 254158 16.4 83.6
Oro 133065 0.7 99.3
Western 153304 0.4 99.6
Eastern Highlands 432972 1.3 98.7
Enga 295031 0.5 99.5
Simbu 259703 0.7 99.3
Southern Highlands 546265 0.2 99.8
Western Highlands 440025 1.4 98.6
East Sepik 343181 0.7 99.3
Madang 365106 0.9 99.1
Morobe 539404 2.2 97.8
Sandaun 185741 0.6 99.4
Bougainville 175160 0.3 99.7
East New Britain 220133 3.0 97.0
Manus 43387 7.7 92.3
New Ireland 118350 1.0 99
West New Britain 184508 1.1 98.1
PNG 90% of the population of 7 million lack access to electricity services. The progress of rural electrification has lagged over the years. PNG electrification rate is only 7%.
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PNG’s Energy Sector Policies
PNG’s Vision 2050:Government sets vision for
long term development and prosperity 2010-50
Strategic Development Plan 2010-30:
High level of strategic support for energy
development.
All households have access to reliable and
affordable energy supply.
70% of PNG be electrified by 2030.
Solar Energy
Largest potential source in PNG.
Average insolation in most parts of PNG is 400–
800 W/m²
Average 8 sunshine hours per day all year round
To date no solar electricity grids have been
installed
Solar Irradiances 22 years Avg.
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Study area
• This study was focused on GIS modeling and
mapping of potential solar energy for entire
Papua New Guinea.
• The solar radiation model has been used for
Papua New Guinea, which is located in the
north of Australia, with a geographical
Extension of 0 degree to 12 degree south and
141 degree east to 160 degree east.
• In this study, we evaluated a methodology for
obtaining monthly and annual radiation maps
based on digital topographic data, satellite
measured climatic parameters, field data and
GIS tools.
Digital elevation model and the 1 x 1 degree grid point used for modeling
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Data and Software • The solar radiation model used in this research is completely based on mathematical
computations using a digital elevation model (resolution of 90 m).
• Other elements are taken into account, like
1) the solar constant,
2) the Earth-Sun distance,
3) solar azimuth angle,
4) solar elevation,
5) the incident angles for each grid point (1 x 1 degree),
6) topographic slope and aspect,
7) proportion of visible sky,
8) diffuse radiation from sky sector
9) the transmitivity of the atmosphere and
10) finally cloud cover.
• ArcGIS spatial analyst tool was used to run the entire model for this research
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Methodology
• The solar constant used in the analysis is 1367 W/m2.
• Maximum Sun altitudes (in degrees) for selected latitudes (Source: Pidwirny 2006)
Latitude (in
degrees) March 21
(Vernal Equinox) June 21 (Summer Solstice of
North hemisphere) September 23
(Autumnal Equinox) December 21 (Summer Solstice
of South hemisphere)
0 º 90 º 66.5 º 90 º 66.5 º
23.5 S 66.5 º 43 º 66.5 º 90 º
50 S 40 º 16.5 º 40 º 63.5 º
60 S 30 º 6.5 º 30 º 53.5 º
66.5 S 23.5 º 0 º 23.5 º 47 º
70 S 20 º -3.5 º 20 º 43.5 º
90 S 0 º - 23.5 º 0 º 23.5 º
Latitude Z-factor Latitude Z-factor Latitude Z-factor
0 0.00000898 30 0.00001036 60 0.00001792
10 0.00000912 40 0.00001171 70 0.00002619
20 0.00000956 50 0.00001395 80 0.00005156
• Appropriate z-factors for particular latitudes (Source: ESRI, 2012)
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Methodology • To produce an accurate insolation map, modeling of
direct, diffuse, and total radiation for each grid point
location viewshed analysis is essential. The
viewshed refers the portion of sky which is viewed
from a particular location.
• A sunmap displays the sun track, or apparent
position of the sun. It varies from time to time, like
different hours of a day and different days of the
year.
• Modeling of diffuse radiation for a location skymap
is required which represents a hemispherical view
of the entire sky. It can be divided into a numbers of
sky sectors based on zenith and azimuth angles.
Schematic Overlay of a sunmap
and a skymap on viewshade
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Methodology: Solar radiation model equations
• Global radiation (Globaltot) is
calculated as the sum of direct
(Dirtot) and diffuse (Diftot) radiation
of all sun map and sky map
sectors, respectively based on the
Solar Analyst 1.0 Manual.
Globaltot = Dirtot + Diftot Dirθ,α = SConst * βm(θ) * SunDurθ,α * SunGapθ,α * cos(AngInθ,α) Difθ,α = Pdif * Dur * SkyGapθ,α * cos(AngInθ,α) * Weightθ,α * Rglb
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Results • An output point layer with four columns: (i) global
radiation, (ii) direct radiation, (iii) diffuse radiation and (iv)
direct insolation duration are generated through the
modeling and followed by Kriging interpolation.
• Total solar radiation is higher in the month of September
than other months. The mean monthly average daily
duration of direct insolation is 6.10 hours/day for
September.
• Maximum daily total solar insolation was modelled as
7.31 Kw/ m2/day (brown) and minimum of 6.45
Kw/m2/day (blue) for the month.
• The direct insolation ranged from 4.76 Kw/ m2/day (blue
colour) to 5.36 Kw/ m2/day (brown), while the diffuse
solar insolation ranged from 1.70 Kw/ m2/day (green
colour) to 1.95 Kw/ m2/day (red) for the month.
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Comparison between SSE and modeled total solar insolation
Validation of our model output (point
layer) is established based on
minimum, maximum, mean and
standard deviation statistics for all
months after comparing with
NASA-SSE data sets.
While comparing with NASA-SSE
data sets, we received good
prediction for the months January
to April and August to December.
Months Interpolated NASA-SSE (1 degree) Interpolated modelled (0.5 degree)
Min Max Mean Std. Dev Min Max Mean Std. Dev
January 7.02 7.84 7.38 0.22 6.10 7.15 6.43 0.16
February 7.25 7.92 7.54 0.18 6.11 7.01 6.35 0.18
March 5.72 7.30 6.30 0.20 6.40 7.24 6.61 0.18
April 6.36 7.46 6.88 0.27 5.85 6.65 6.08 0.18
May 5.55 7.10 6.29 0.33 5.17 6.12 5.43 0.19
June 5.05 7.07 6.15 0.40 4.83 5.83 5.10 0.19
July 5.27 6.90 6.08 0.32 5.01 5.98 5.28 0.20
August 5.89 7.28 6.59 0.29 5.63 6.50 5.89 0.19
September 6.52 7.99 7.16 0.22 6.46 7.31 6.68 0.19
October 6.93 7.70 7.32 0.21 6.15 7.02 6.37 0.18
November 6.92 7.75 7.28 0.23 6.16 7.19 6.47 0.19
December 5.99 7.33 6.48 0.27 6.00 7.09 6.35 0.20
As the general condition of Earth-Sun angular relationship and insolation,
during June Solstice (May to July) monthly values of available insolation around
Equator and south of it must be less as mentioned in the table.
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Discussion
Comparison of total solar insolation (Kw/ m2/day ): point feature (known sample locations) and raster based model
Months Raster Based total insolation Point Based total insolation
RB1
(146.5, -6.5)
RB2
(147, -6.5)
RB3
(146.5, -7)
PB1
(146.5, -6.5)
PB2
(147, -6.5)
PB3
(146.5, -7)
January 6.35 5.66 5.43 6.35 5.70 5.62
February 6.28 5.76 5.60 6.26 5.79 5.74
March 6.56 6.20 6.21 6.50 6.19 6.29
April 6.01 5.97 6.06 5.94 5.92 6.05
May 5.37 5.54 5.74 5.27 5.48 5.64
June 4.90 5.13 5.38 4.93 5.21 5.41
July 5.21 5.43 5.65 5.11 5.35 5.54
August 5.81 5.85 5.97 5.73 5.80 5.93
September 6.60 6.36 6.38 6.55 6.34 6.44
October 6.32 5.85 5.72 6.28 5.87 5.84
November 6.39 5.73 5.52 6.39 5.77 5.70
December 6.27 5.53 5.30 6.27 5.59 5.50
Average 6.01 5.75 5.75 5.97 5.757 5.81
Average difference: 0.01
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Discussion Comparison of total solar insolation (Kw/ m2/day) using point-interpolation at unknown sample location and raster based model
Months Raster Based total insolation Point Based total insolation
(Interpolation)
RB4 RB5 RB6 RB7 PB4 PB5 PB6 PB7
January 6.42 6.38 6.80 7.91 6.10 6.31 6.10 6.14
February 6.08 6.22 6.85 7.53 6.09 6.25 6.09 6.14
March 5.94 6.31 7.38 7.45 6.40 5.22 6.40 6.39
April 5.10 5.67 7.02 6.46 5.84 6.01 5.84 5.85
May 4.30 4.96 6.48 5.54 5.13 5.38 5.13 5.17
June 3.83 4.48 6.00 4.98 4.78 5.06 4.78 4.83
July 4.12 4.79 6.33 5.33 4.96 5.22 4.96 5.06
August 4.83 5.45 6.86 6.15 5.61 5.81 5.61 6.42
September 5.89 6.34 7.51 7.4 6.46 6.60 6.46 7.11
October 6.01 6.22 6.93 7.46 6.14 6.28 6.14 6.15
November 6.41 6.40 6.86 7.9 6.16 6.16 6.16 6.58
December 6.42 6.32 6.67 7.9 6.31 6.44 6.36 6.41
Average 5.45 5.80 6.81 6.83 5.84 5.90 5.84 6.02
Average difference: 0.32
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Conclusion The proposed methodology allows computing solar
radiation values on the Earth surface on a monthly and
on annual basis giving valuable results from a DEM of
medium resolution (SRTM DEM data of 90 m). Given
the DEM data of extreme topography prevalent in the
study area, 'unnatural' peaks are automatically
identified on the ASTER (30 m) data sets prompting us
to avoid these data sets in this research.
This type of research on solar insolation modeling can
enthuse researchers towards a better understanding of
climate change scenarios.
The solar model also can be useful in decision making
for power production and might justify investment in
rural electrification in remote areas.
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Conclusion To carry out ground based measurements of solar irradiances
and temperature (as well as wind speed and wind
directions) installation of the portable weather stations are
being conducted by the department of Electrical
engineering of the PNG University of Technology at Umi,
Markham valley under Morobe province.
In extension of the current work we propose to work further on a
void-free high resolution DEM data (10 to 20 m in spatial
resolution) complimented with station data from larger
geographical area in order to improve the veracity of
radiation map. Meteosat second generation seviri images
can also be used to refine the cloudiness component and
that is expected to yield better results to produce radiation
maps for operational use in atmospheric science.
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Thank you
Questions ?