By

Saheed RajiPhD Student, Department of Geography,

University of Lagos, Nigeria.

Department of Environmental Management &Toxicology.

Federal University of Petroleum Resources, Effurun Nigeria.

Modelling Potential Sites for Solar PV

Plants in Northwest Nigeria Using

Geoinformation Tools

A Paper Presented at the 40th IAEE International Conference, 18–21 June 2017

Marina Bay Sands, Singapore

Outline

• Introduction

• Study Area

• Materials & Methods

• Results & Discussion

• Conclusion

Introduction

• Accessibility of reliable energy sources is fundamental

towards attainment of meaningful development.

• However, challenges surrounding provision of energy

within the developing nations of the world is getting much

more enormous.

• Global attention has shifted from reliance on fossil fuels to

renewables such as solar energy.

• In Sub-Saharan Africa, specifically in Nigeria energy

provision remain a herculean task.

• Solar energy is an all-year-round readily available

renewable natural resource within the northern axis of

Nigeria aided by local geography.

• Geoinformation technologies have been found very useful

for modelling probable location of solar photovoltaic

equipment (Jun, 2000; Malczewski, 2004; Ramachandra &

Shruthi, 2005; Ramachandra, 2007; Janke, 2010).

• Various geoinformation techniques have been devised such

based on MCE including WLS, WLC, and fuzzy AHP

(Saaty, 1997; Jun, 2000; Carrion et al., 2008; Effat, 2013;

2014).

• In this study, potential sites for exploitation of solar

resources for energy generation was demonstrated in the

northwestern axis of Nigeria using geoinformation tools.

Introduction

Study Area

Materials and Methods

• Data Sources and Characteristics

s/N Data Data Scale/Resolution Data extracted Data Source Remarks

1 Climate data Point based data from

meteorological stations

Solar radiation

Average

temperature

Sunshine

Relative

humidity

Nigeria

Meteorological

Agency (NIMET),

Lagos

Analogue data

(from 1985-

2015)

2 Digital Elevation

Model from

SRTM

30 metre spatial resolution Elevation

Slope

United States

Geological Survey

(USGS)

EarthExplorer ® web

portal

Digital data

3 Topographic

Maps of Kaduna,

and Sokoto

1:50,000 Waterbodies

Streams

Office of the

Surveyor-General of

the Federation

(OSGOF)

Analogue data

4 Gazaette of

place names

Point based with longitude

and latitude

Urban centres National Population

Commission (NPC)

Analogue data

5 Landsat ETM+

(2016)

32 metre spatial resolution

Bands 2, 4, and 7

Land use and

cover

United States

Geological Survey

(USGS)

EarthExplorer ® web

portal

Digital data

Materials and Methods• Research framework

• The suitability criteria is based on NREL guidelines:

– A region must record solar radiation value above 1300 (kWh.m-2

year-1);

– A region should have a distance above 2 km from protected

regions;

– The minimum distance requirement for populated areas is 2000 m

from cities and 500 m from villages;

– Roads and transmission lines should be at a buffer of 50 km away

from the solar PV farm;

– Faults and roads should be within distances of less than 0.5 km and

0.1 km respectively;

– Rivers, lakes, wetlands and dams should be within a 1 km buffer;

– Elevation/altitude above sea level of over 2200 m are appropriate;

– Slope should not exceed 11%.

Materials and Methods

• Evaluation criteria are based on the following:

– Climate factor consists of the succeeding variables;

• Solar radiation

• Average temperature

• Sunshine

• Relative humidity

– Environmental factor includes the following variables;

• Elevation

• Slope

• LULC

– Proximity (distance) operators consist of;

• Distance to stream and waterbody

• Distance to roads

• Distance to urban

Materials and MethodsModelbuilder framework for computing the climate factor

Modelbuilder framework for computing the environmental factor

Modelbuilder framework for computing the proximity factor

Materials and Methods

• Fuzzy Analytical Hierarchy Process (AHP)

This was used to attach weights to the factors and variables

using a classification based analysis as defined by the

equation;

𝑾𝒔. 𝑤𝑠 = 𝜆𝑚𝑎𝑥. 𝑤𝑠 (1)

where,𝑾𝒔 is the aggregated comparison matrix, 𝑤𝑠 is the eigenvector, and

𝜆𝑚𝑎𝑥 is the largest eigenvalue of matrix 𝑾𝒔.

For accuracy assessment, consistency ratio is devised and

computed as; 𝑪𝑰 = (𝜆

𝑚𝑎𝑥−𝑛)/(𝑛 − 1)

𝑪𝑹 = 𝐶𝐼/𝑅

These were effected using TerrSet Software.

Results and Discussion

• Weight used for Factor and Variable Modelling

Variable Eigenvector of (AHP derived)

Solar radiation 0.3049

Temperature 0.0915

Roads 0.0661

Urban 0.0714

Streams 0.0547

Waterbody 0.0587

Elevation 0.0638

Slope 0.0645

LULC 0.0855

Humidity 0.0645

Sunshine 0.0743

Results and DiscussionFactor Factor

weight

Variables Variable weight Attribute values Attribute value

weight

Final weight

Climate

0.5102

Solar radiation

(Global irradiance)

(kWh.m-2 year-1)*100

0.3049

19.91-20.03

20.04-20.16

20.17-20.28

20.29-20.40

20.41-20.52

0.018

0.035

0.044

0.141

0.762

0.005

0.011

0.013

0.043

0.232

Temperature (0C) 0.0915

28.00-28.18

28.19-28.36

28.37-28.54

28.55-28.72

28.73-28.90

0.020

0.037

0.040

0.245

0.658

0.018

0.034

0.037

0.022

0.060

Sunshine (hours) 0.0743

0.072-12.97

12.98-25.87

25.88-38.78

38.79-51.68

51.69-64.58

0.005

0.051

0.041

0.345

0.558

0.0003

0.004

0.003

0.026

0.041

Humidity (%) 0.0645

0.047-8.36

8.37-16.66

16.67-24.97

24.98-33.28

33.29-41.59

0.658

0.345

0.040

0.037

0.020

0.042

0.022

0.003

0.002

0.001

Environmental

0.2390

Elevation (m) 0.0638

204-243

244-282

283-321

322-360

361-400

0.001

0.006

0.131

0.241

0.621

0.000

0.0003

0.008

0.015

0.040

Slope (%) 0.0645

0-12.3

12.4-24.6

24.7-36.8

36.9-49.2

49.3-61.5

0.422

0.245

0.191

0.101

0.041

0.027

0.016

0.012

0.007

0.003

LULC 0.0855

Built-up Areas

Forest reserve

Waterbody

Savanna

Wetland

0.000

0.000

0.000

0.519

0.258

0

0

0

0.044

0.022

Factor Factor

weight

Variables Variable weight Attribute values Attribute value

weight

Final weight

Proximity

0.2508

Distance to waterbody

(m)

0.0587

200-400

400-600

600-800

800-1000

>1000

0.011

0.007

0.045

0.269

0.668

0.0006

0.0004

0.003

0.016

0.039

Distance to stream (m) 0.0547

200-400

400-600

600-800

800-1000

>1000

0.011

0.007

0.045

0.269

0.668

0.0006

0.0004

0.002

0.015

0.037

Distance to urban (m) 0.0714

500-1000

1000-1500

1500-2000

2000-2500

>2500

0.001

0.036

0.020

0.265

0.678

0.00007

0.003

0.001

0.019

0.048

Distance to roads (m) 0.0661

100-500

500-1000

1000-2000

2000-3000

>3000

0.020

0.037

0.040

0.331

0.572

0.001

0.002

0.003

0.022

0.039

Results and Discussion

Solar radiation (kWh.m-2 year-1*100 ) Sunshine (hours) Temperature (0C)

Humidity (%) Elevation (m) Slope (%)

Results and Discussion• Modelling potential sites for solar PV plants

Results and Discussion

LULC Distance to waterbody (m) Distance to stream (m)

Distance to urban (m) Distance to road (m)

Results and Discussion

3.62%

7.86%

27.92%

37.79%

22.80%

0 5 10 15 20 25 30 35 40

Not suitable

Marginally suitable

Fairly suitable

Moderately suitable

Excellently suitable

Percentage

Su

itab

ilit

y r

an

kin

g

Results and Discussion

Local government

area

Percentage of Suitability for Solar PV Plants (%)

Unsuitable Marginally suitable Fairly suitable Moderately

suitable

Excellently

suitable

Sokoto North 88.4 9.8 1.8 - -

Sokoto South 93.0 5.0 1.2 0.8 -

Wamakko 58.2 40 1.8 - -

Kware 3.0 38.2 52.8 4.9 1.1

Silame 5.2 54.5 38.3 1.0 1.0

Augie 1.4 7.6 50.5 18.2 22.3

Arewa 0.1 2.9 27.5 30.5 38.0

Gudu 1.2 7.8 31.6 50.4 8.0

Tangazar 0.9 7.1 33.5 53.0 5.5

Wurno 2.6 13.3 44.2 25.2 14.7

Goronyo 0.4 2.5 23.8 40.2 33.1

Gwadabawa 0.6 0.5 5.8 43.3 49.8

Gada 0.8 0.2 6.0 47.3 45.7

Illeila 2.1 3.3 11.6 33.8 49.2

Rabah 1.1 7.2 56.3 31.2 4.2

Binji 0.5 36.1 38.1 20.2 3.1

Results and DiscussionClassification of suitability classes at the local government level

Conclusion

• There is the urgent need for Nigeria to consider large scale

investment in renewable energy particular solar energy to

tackle energy issues.

• This study has demonstrated the potency of

geoinformation tools in selection of possible sites for

exploitation of solar energy.

• The results of the study has indicated that local geography

factors (solar radiation and sunshine) are essential in

expansion of the study to other areas of the country.

• Also relevant policies aimed at regulating the utility of

renewable energy to resolve energy issues must be

addressed urgently.