geospatial modeling of potential renewable energy … · geospatial modeling of potential renewable...

Add your company logo here on master slide

Add your company logo and other footers here on master slide

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


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.


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.


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%.


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.


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


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


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)


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


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


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.


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.


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


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


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.


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.


Thank you

Questions ?

geospatial modeling of potential renewable energy … · geospatial modeling of potential renewable...

Documents