Article
Assessment of Suitable Areas for Smart Grid of Power
Generated from Renewable Energy Resources in
Western Uganda
Jane Rose Atwongyeire 1, Arkom Palamanit 2,*, Adul Bennui 3, Mohammad Shakeri 4, Kuaanan Techato 5
and Shahid Ali 6
Sustainable Energy Management Program, Faculty of Environmental Management, Prince of Songkla
University, 15 Karnjanavanich Rd., Hat Yai, Songkhla 90110, Thailand; roseatwongyeire@gmail.com
2 Energy Technology Program, Department of Specialized Engineering, Prince of Songkla University,
15 Karnjanavanich Rd., Hat Yai, Songkhla 90110, Thailand
3 Southern Regional Center of Geo‐Informatics and Space Technology, Faculty of Environmental
Management, Prince of Songkla University, 15 Karnjanavanich Rd., Hat Yai, Songkhla 90110, Thailand;
adul.b@psu.ac.th
4 School of Computer Science and Electronic Engineering (CSEE), University of Essex, Wivenhoe Park,
Colchester, Essex CO4 3SQ, UK; m_shakeri63@yahoo.com
5 Faculty of Environmental Management, Prince of Songkla University, 15 Karnjanavanich Rd., Hat Yai,
Songkhla 90110, Thailand; uhugua@hotmail.com
6 School of Engineering and Built Environment, Griffith University, Southport, QLD 4222, Australia;
shahid.ali@griffithuni.edu.au
* Correspondence: arkom.p@psu.ac.th
1
Citation: Atwongyeire, J.R.;
Palamanit, A.; Bennui, A.; Shakeri,
M.; Techato, K.; Ali, S. Assessment of
Suitable Areas for Smart Grid of
Power Generated from Renewable
Energy Resources in Western
Uganda. Energies 2022, 15, 1595.
https://doi.org/10.3390/en15041595
Academic Editors: Mahdi Alipour,
Majid Khazaee, Komeil Kohansal
Sadetmahaleh, Hosein
Gholami‐Khesht, Mohammad
Alipour and K. T. Chau
Received: 15 November 2021
Accepted: 15 February 2022
Published: 21 February 2022
Publisher’s Note: MDPI stays neu‐
tral with regard to jurisdictional
claims in published maps and institu‐
tional affiliations.
Abstract: This study assessed suitable smart grid areas for power generation and distribution from
solar and small hydro energy resources in Western Uganda by employing the fuzzy analytic hier‐
archy process (AHP) based on geographic information system (GIS) data. This was performed based
on the selected economic, environmental, and technical criteria by the authors guided by the ex‐
perts’ judgements in the weighing process. The main criteria also included various sub‐criteria. The
sub‐criteria of the economic criterion included distance from transmission lines, topography, and
distance to roads. The environmental sub‐criteria covered land use, sensitive areas, and protected
areas. The technical sub‐criteria were on distance from demand centers, available potential energy
resources (solar and hydro), and climate (rainfall and sunshine). The weights of the main criteria
and the sub‐criteria were calculated by using the fuzzy AHP. These weights were then used in the
GIS environment to determine both the potential for power generation from the solar energy re‐
source and the smart grid suitable areas. According to the weight results, the economic criteria has
the highest weight, followed by environmental and technical criteria. The validation of the experts’
judgements for each criterion by comparing the results from fuzzy AHP with AHP confirmed in‐
significant differences in weights for all criteria. The obtained suitable smart grid areas in Western
Uganda have been classified into three parts, that is, the South, North, and Central. Therefore, this
is a one‐of‐a‐kind study that, in the authors’ view, will provide the initial insights to the govern‐
ment, policymakers, renewable energy practitioners, and researchers to investigate, map, and em‐
brace decarbonization strategies for the electricity sector of Uganda.
Keywords: Africa; GIS; multi‐criteria decision making; renewable energy; power generation and
distribution; smart grid; smart grid suitable area
Copyright: © 2022 by the authors. Li‐
censee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con‐
ditions of the Creative Commons At‐
tribution (CC BY) license (https://cre‐
ativecommons.org/licenses/by/4.0/).
Energies 2022, 15, 1595. https://doi.org/10.3390/en15041595
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Energies 2022, 15, 1595
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1. Introduction
Energy is one of the key factors driving global development and economic growth.
Most energy in its various forms, such as heat, electricity, and vehicle fuels, is obtained
from fossil fuels. However, overdependence on fossil fuels has proved to not only be as‐
sociated with effects on the environment (climate change and global warming) and hu‐
man health but also influences the social and energy aspects. Some of the concerns related
to fossil fuels include depletion of resources, low energy security and sustainability, low
social acceptance, and high environmental impacts. The issues associated with conven‐
tional fossil fuel resources continue to become more complex around the globe and need
immediate solutions. This has led to the growing interest in the development of sustaina‐
ble energy systems in different countries and regions to replace fossil fuel energy re‐
sources with renewable energy resources. However, this remains a challenge because the
intermittent nature of renewable energy resources is far different from the steady reliabil‐
ity of fossil fuel resources [1,2]. Hence, appropriate applications of renewable energy re‐
sources for energy utilization, particularly in power generation, need to be designed and
demonstrated.
Recent projections have indicated an increase in energy intake of 776.52 × 106 tera‐
joules (TJ) (736 × 1015 British thermal units (BTU)) by 2040 from renewable energy re‐
sources. The expected increase in power generation from these resources is about 30% by
2022 as well as 60% by 2050 [3,4]. Consequently, flexible, and reliable energy systems for
power generation and distribution from renewable energy resources are very important.
Moreover, integration of suitable power generation technologies with appropriate distri‐
bution systems like smart grids have gained interest because of their ability to overcome
the limitations associated with renewable energy resources [3–7]. A smart grid, for exam‐
ple, is an innovative technology propelled by connective power systems associated with
the integration of information, communication, and technology (ICT). The system has the
ability to enhance sustainable, proper management of power generation, transmission,
and distribution in a two‐way power flow network [8–11]. Likewise, Zhang et al. [12] re‐
vealed that smart grid is a crucial development in the management of energy. Moreover,
technology advancements associated with smart grids have enabled renewable energy in‐
tegration, hence relieving conventional systems of some demand [13]. Generally, the
smart grid is a system concept designed to smartly and efficiently manage power genera‐
tion, transmission, and distribution for secure, reliable, efficient, and quality power [6].
Several studies have been carried out on smart grids to reveal that renewable energy re‐
sources can be efficiently and smartly utilized in smart grids [14–16].
Jiang and Low, [17] undertook a study to design and evaluate distributed algorithms
for optimal energy procurement and demand response in the presence of uncertain re‐
newable supply and time‐correlated demand. Lurwan et al. [18] presented a geographic
information system (GIS) based model for optimal site selection for suitable large‐scale
smart grid connected photovoltaic power plants. Furthermore, Islam et al. [19] concluded
that power generation from renewable energy sources integrated into a smart grid system
is the best option for future energy security. Therefore, integrating a smart grid concept
in power generation for developing countries like Uganda, still predominantly relying on
conventional systems, is ideal. Moreover, studies have revealed that power generation
using a smart grid approach allows integration of different renewable energy resources
on both small and large scales. To this effect, selecting sites for power generation from
renewable energy resources using a smart grid strategy is crucial. The approach is expe‐
dient for future energy systems to improve electricity access and the quality of living, es‐
pecially in developing regions like the sub‐Saharan Africa, which includes Uganda [20].
Hence, application of this approach is suitable for a country like Uganda with abundant
underutilized renewable energy resources, such as biomass, solar, hydro, wind, and geo‐
thermal, for the management of power generation and distribution [21].
Energies 2022, 15, 1595
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Uganda’s energy sector under the mandate of the Ministry of Energy and Mineral
Development (MEMD) needs to promote the utilization of these renewable energy re‐
sources sustainably, as formulated in the renewable energy policy, 2007 [22]. Following
the vision and the goals of the country, planning power generation incorporating a smart
grid concept would be one way to enhance the utilization of renewable energy resources
with an appropriate distribution system [23]. However, power generation requires site
selection planning. Site selection for power generation is very complex and involves the
participation of different experts and stakeholders. Nevertheless, GIS has been widely
used as a support tool in planning energy projects [24,25]. GIS can be combined with the
site selection based on a decision‐making method. This tool has the ability to manage,
evaluate, and display geospatial reference data. It has normally been integrated with
multi‐criteria decision‐making (MCDM) techniques for solving complex situations in en‐
ergy planning, especially in site selection [25,26]. Among the MCDM methods, analytic
hierarchy process (AHP) has been widely used in many studies to select suitable sites.
AHP has been used extensively in site selection for renewable energy power generation
as well as for energy planning due to its ability to deliver appropriate decisions [24–26].
However, AHP cannot capture the uncertainties associated with human judgement, hence
the need for alternative methods such as fuzzy AHP. Fuzzy AHP is another MCDM
method that has been widely used with GIS to solve complex problems. The method uses
pairwise comparisons in a fuzzy environment to capture the vagueness associated with
human judgements[27] . Moreover, the study by [28] revealed that fuzzy AHP is the most
preferred MCDM method for energy policy.
Therefore, this paper presents a study where a combination of GIS and fuzzy AHP
has been used to select sites for power generation from solar and hydro using a smart grid
approach. To demonstrate this strategy, a case study in the western region of Uganda was
considered, and further details of the study area are provided later. The novelty of this
study is in the identification of areas where solar and hydro can be integrated within a
smart grid system. This strategy aims to enhance power generation, power quality, relia‐
bility, accessibility, proper data management (both supply and demand data), and proper
management of the resources’ sustainably. To the best of the authors’ knowledge, no sim‐
ilar previous studies have been carried out in the study area. In this study, there are five
main sections. The first one is the introduction, already provided above. Section 2 supplies
comprehensive background information on the Uganda energy situation as well as the
study area. Section 3 shows the data sources and the taxonomy of siting criteria, while
Section 4 presents the research methodology. The last one is Section 5, which reports the
results and discussion and the conclusion of the study.
2. Uganda Energy Situation
Western Uganda has been considered as the study region and comprised 29 districts
at the time of the study. It covers a land area of 55,276.6 square kilometers (km2) (see Fig‐
ure 1) [29]. The study area was selected due to its abundant solar and hydro potentials
[21,30,31]. However, before proceeding to the next step, it is important to understand the
energy situation and the regulations for the development of energy projects in Uganda.
In terms of regulations, the National Environment Management Authority (NEMA)
Uganda, a government‐backed institution, ensures all projects are subjected to environ‐
mental impact assessments before implementation [31]. In Uganda, power generation is
predominantly from hydropower complemented by solar, thermal, and cogeneration
(from bagasse). Previously, according to [30], the power generated was 82% hydropower,
10% thermal, 5% mini and micro‐hydro, and 3% cogeneration. Total installed capacity at
the time was about 820.5 megawatts (MW), and only 558.5 MW was utilizable [30]. Ac‐
cording to the Electricity Regulatory Authority (ERA), the country’s current installed ca‐
pacity stands at 1252.4 MW, 1246.5 MW connected to the grid and 5.9 MW off the grid.
The installed capacity includes 80% hydro (1004.2 MW), 8.1% thermal (100 MW), 7.7%
cogeneration (96.2 MW), 4.1% solar PV (50.8 MW), and 0.1% others (1.1 MW) [32]. The
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power generated in the country is both utilized locally and exported to the neighboring
countries [29].
In as much as power generation has been predominantly hydropower, solar energy
has become an important renewable energy resource for electricity generation because of
favorable variations in solar power throughout the year. Recently, the country has in‐
stalled two 10 MW solar photovoltaic (PV) farms, including the Soroti and Tororo solar
farms connected to the national power grid [33,34]. However, the solar energy resources
still have high remaining potential for power generation with solar PV or solar cells. More‐
over, the government of Uganda has a development plan to implement more hydropower
plants and solar projects in the concentrated region [31,32].
Figure 1. Map of study area.
3. Data Sources and Siting Criteria
3.1. Data Sources
Data used for this study were both qualitative and quantitative. Qualitative data
were obtained from survey questionnaires. The questionnaires were prepared by the au‐
thors based on related studies and energy content in Uganda. The prepared question‐
naires were sent to ten experts in the energy sectors of Uganda for their opinions. The
number of questionnaires used in this study was modified from Watson and Hudson,
(2015) and Waewsak et al., (2020), who consulted only seven experts. However, in this
study, there were ten experts from the energy sectors, selected based on their knowledge
and experience in the energy fields. This included experts from academia and profession‐
als from government institutions and from utilities. On the other hand, quantitative data
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were obtained from publicly available online resources and government institutions (see
Appendix A Table A1). The data on solar energy was obtained from the Global Atlas
online portal while small hydropower data was obtained from the Ministry of Energy and
Mineral Development, Uganda. Both the expert‐based data and the online data were used
to generate the data sets for this research.
3.2. Siting Criteria
In power generation siting, there are considerations that are general to energy gener‐
ation projects [35–37]. In this study, selecting suitable sites for power generation from so‐
lar and hydro renewable energy resources using the smart grid approach was done based
on economic, environmental, and technical factors, which were selected by the authors
according to the previous studies [24,36,38–43]. Both main criteria and sub‐criteria were
considered by the authors based on the available literature for energy planning. The rank‐
ing of the parameters used was based on literature and the data used as indicated in Table
1 below. These ranking parameters were used to generate individual sub‐criteria maps in
GIS.
Table 1. Ranking of the parameters.
Parameter
Ranges
500–2000
Distance from transmission 2000–5000
lines (meters)
5000–10,000
500<, >10,000
0–5
5–10
Slope (degrees)
10–15
>15
574–947
949–1325
Elevation (meters)
1325–1700
>1700
500–2000
2000–3500
Distance to roads (meters)
3500–5000
500<, >5000
Bare areas, grasslands
Shrub cover areas
Croplands, lichen, mosses,
Land use
tree cover areas
Built‐up areas, aquatic vege‐
tation, regularly flooded
Sensitive and protected areas Excluded
500–1500
Distance to demand centers 1500–2500
(meters)
2500–3500
500<, >3500
4.90–5.97
Solar‐GHI (kWh/m2)
3.83–4.90
2.76–3.83
Ranking
3
2
1
0
3
2
1
0
3
2
1
0
3
2
1
0
3
2
1
0
Excluded
3
2
1
0
3
2
1
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Small hydro (MW)
Climate
Available data used
Available data used
Available data used
Available data used
Sources: [42,44,45–52].
3.2.1. Economic Criteria
The economic criteria consisted of distance from transmission lines, topography, and
distance to roads, as these have also been considered in other studies [38,39,44,45]. The
data used in our study for the transmission lines was obtained from the Energydata.info
portal to prepare the map in Figure 2. Topography (DEM) data from the U.S. Geological
Survey (USGS) were used to generate slope and elevation maps in Figures 3 and 4. Finally,
the data for roads used were obtained from the World Food Geospatial data portal
(WFPGeoNode) to generate the map in Figure 5.
Figure 2. Distribution map of transmission lines in the study area.
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Figure 3. Suitability map of study area by slope.
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Figure 4. Suitability map of study area by elevation.
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Figure 5. Road network map of study area.
3.2.2. Environmental Criteria
This criterion considered land use and sensitive and protected areas such as rivers,
wetlands, forests, lakes, etc. According to NEMA, it is very important to put into consid‐
eration the sensitivity of the environment while planning any project [44]. In this study,
the sensitive and protected areas were therefore excluded, and the data used to identify
these areas, as shown in Figures 6 and 7, were obtained from Diva GIS and World Re‐
sources Institute Portal. In addition, in the planning of power generation, land use is key.
A land use map helps in the management and modification of the natural environment
into a built environment in a sustainable way. Therefore, data for land use were obtained
from Servir Global Portal to generate the map in Figure 8 [38,39,44,45].
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Figure 6. Map of protected areas in the study area.
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Figure 7. Map of waterbodies in the study area.
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Figure 8. Map of study area land use.
3.2.3. Technical Criteria
The technical criterion included distance from demand centers, available potential
energy resources (solar and hydro), and climate. Under the climate sub‐criterion, rainfall
and sunshine elements were considered. The proximity of renewable energy resources for
power generation to demand centers is very important in planning a smart grid network.
Data for demand centers (towns) used were obtained from the Energy Sector GIS Working
Group, Uganda, to generate the map in Figure 9. Climate has an important role in power
generation, especially regarding weather forecasts, in order to manage the power pro‐
duced and dispatched in a smart grid. Moreover, climate is also an important factor in the
determination of solar incoming energy. In this study, the rainfall and sunshine data ob‐
tained from the Uganda National Meteorological Authority (UNMA) were used to select
suitable sites for solar [38,39,44,45,47]. Figures 10 and 11 show maps of rainfall and sun‐
shine distribution, respectively. Available potential energy resources being the reason for
site selection, existing data for small hydropower potential used was obtained from the
Ministry of Energy and Mineral Development (MEMD), Uganda, as represented in the
map (Figure 12), and the data on Global Horizontal Irradiation (GHI) to obtain the solar
potential map (Figure 13) were obtained from Global Solar Atlas.
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Figure 9. Map showing demand centers in the study area.
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Figure 10. Map showing rainfall distribution in the study area.
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Figure 11. Map showing sunshine distribution in the study area.
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Figure 12. Map showing small hydro potential sites in the study area.
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Figure 13. Map showing solar potential in the study area.
4. Methodology
Fuzzy Analytic Hierarchy Process (Fuzzy AHP)
In 1970, Saaty introduced an MCDM technique known as analytic hierarchy process
(AHP), designed to deal with complexity in decision‐making processes [48]. AHP is one
of the commonly used MCDM techniques in planning renewable energy investments
based on several factors such as environmental, technical, economic, and social, among
others [49]. However, realistically, in situations that involve multiple opinions for a single
decision, vagueness is inevitable. This is because human judgements are uncertain and, in
such cases, it is useful to employ fuzzy logic in uncertain valuations and priorities [50].
Considering fuzzy logic, the membership function μB(x) of a fuzzy set B gets values in the
interval [0, 1], and when used to depict the fuzziness for selecting alternatives in the
weighing process, common shapes of the membership function are triangular and trape‐
zoidal fuzzy numbers (TFNs). This study used triangular numbers characterized by num‐
bers l, m, n in the membership function, as shown in Equation (1) [51,52].
μB 𝑥
𝐵 → 0, 1
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𝑙
μB 𝑥
𝑥
𝑚
0
𝑥
𝑚
𝑛
(1)
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
A combination of fuzzy logic with the AHP method is called fuzzy AHP. This method
combined with GIS is a powerful tool. It allows the use of geospatial and non‐geospatial
data based on experts’ judgements to make important decisions, especially in renewable
energy site selection processes [24]. The study procedure from the set objective to the final
result is summarized in Figure 14.
Figure 14. Conceptual flow chart of study.
In this study, a questionnaire based on the economic, environmental, and technical
criteria was used to obtain comments and opinions from experts in the energy field. The
experts represented academia, government institutions, and utilities. In the questionnaire,
the experts were required to score the criteria and the sub‐criteria based on Saaty’s AHP
pairwise comparison scale (Appendix A, Table A2), giving reasons for their scoring ac‐
cording to their expertise.
Based on the scores and comments given for the criteria and the respective sub‐crite‐
ria from the experts, weights of the criteria and sub‐criteria were firstly calculated using
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the AHP method to examine the consistency in the experts’ judgements. To capture the
vagueness in human judgements, the scores were then transformed to equivalent triangu‐
lar fuzzy numbers (TFNs) (see Appendix A Table A3). These were used in the fuzzy AHP
model to calculate the final weights. To demonstrate how the scores by experts using
AHP‐Saaty’s pairwise scale in the AHP model were transformed into equivalent TFNs,
criteria A, B, C are metaphorically used. After determination of consistency in the AHP
model, the scores were transformed into TFNs as shown in Appendix B, Figure A1.
As earlier mentioned, calculations were firstly done using the AHP model to deter‐
mine the consistency in the decision‐making process using the experts’ judgements. A
matrix Ax was obtained for pairwise comparisons of the criteria in the AHP model, as
shown in Equation (2).
𝐴𝑥
𝑑
𝑑
𝑑
𝑑
⎡
𝑑
𝑑
⎢𝑑
𝑑
𝑑
⎢𝑑
⎢… . . … . . … . .
𝑑
𝑑
⎣𝑑
…..
…..
…..
…..
…..
𝑑
⎤
𝑑 ⎥
𝑑 ⎥
… . .⎥
𝑑 ⎦
(2)
In the matrix Ax, n represents the number of criteria, dij for i, j = 1, 2, 3,…n signifies
the importance of criterion di over criterion dj, and vice versa: dji signifies the importance
of criterion dj over di as a reciprocal of dij, i.e., as 1/dij. The criteria and sub‐criteria were
weighed using the matrix Ax in Equation (2). Relative weights of each criterion were ob‐
tained through a normalized calculation of principal eigen factors of the matrix. Con‐
sistency check was later performed based on the consistency ratio, CR, using the expres‐
sion CI/RI, where CI denotes consistency index and RI stands for random index. CI was
calculated using the expression (λmax − n)/(n − 1), where λmax represents the dominant
eigenvalue, and n is the number of criteria used [48]. On the other hand, the random index,
RI, was obtained from Table 2 shown below.
Table 2. The random consistency index (RI) [53].
N 1 2 3
4
5
6
7
RI 0 0 0.58 0.90 1.12 1.24 1.32
8
1.41
9
1.45
10
1.49
11
1.51
12
1.53
13
1.56
14
1.57
15
1.59
While determining consistency, if the consistency ratio, CR, is less than 0.1, it means
the comparison matrix is consistent. If greater than 0.1, it implies inconsistencies in the
matrix, hence the need for repetition of the process. However, the AHP method does not
account for the variations in multi‐opinions as also acknowledged by Chaudhary et al.
(2015). Human judgements carry a lot of uncertainties and cannot be easily captured in
the AHP model, hence the need for the fuzzy environment. In fuzzy AHP, a fuzzy set,
membership function, and fuzzy numbers are used. This study considered triangular
fuzzy numbers (TFNs) with a membership function defined as in Equation (1). The scores
by experts using the AHP pairwise comparison scale were transformed to equivalent
TFNs, likewise used in [54–56] as illustrated in Appendix B, Figure A1. Following the ob‐
jective of the study, a new matrix, 𝐴𝑥 in Equation (3), for the fuzzy AHP model was
constructed.
𝐴𝑥
𝑑
𝑑
⎡
⎢𝑑
⎢𝑑
⎢…..
⎣𝑑
𝑑
𝑑
𝑑
…..
𝑑
𝑑
𝑑
𝑑
…..
𝑑
𝑑
𝑑
𝑑
…..
….. 𝑑
…..
…..
…..
…..
⎤
⎥
⎥
⎥
⎦
(3)
In the same way as matrix Ax (AHP model), n in the matrix 𝐴𝑥 represents the num‐
ber of criteria, 𝑑 for i, j = 1, 2, 3…, n signifies the preference of criterion 𝑑 i over criterion
𝑑 j, and vice versa: 𝑑 ji, signifies the preference of criterion 𝑑 j over 𝑑 i as the reciprocal
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of 𝑑 ij or 1/𝑑 ij in an increasing order in the TFN parameters. After the transformation of
AHP scores to equivalent TFNs to derive matrix 𝐴𝑥 , using the geometric method, the
crisp numbers were obtained according to Buckley’s concept [57] and normalized to ob‐
tain the final weights (Appendix B, Figures A2 and A3).
The final weights were then applied to the map layers of each criterion in ArcGIS
10.5. Using a weighted overlay function, the normalized weights were used to generate a
map for solar suitable potential sites in the western region of Uganda. Finally, available
data for small hydropower potential sites, the obtained solar potential sites, the transmis‐
sion lines layer map, road network, and demand centers were combined in ArcGIS 10.5.
This was to identify suitable sites demonstrating possible power generation from solar
and hydro in a connective smart grid system.
5. Results and Discussion
5.1. Parameter Weight Calculation Results
In this study, the weights of main criteria and sub‐criteria were calculated using both
the ordinary AHP and the fuzzy AHP to compare the weight results for a conclusive
study. The results from weights calculation from AHP and fuzzy AHP are shown in Ta‐
bles 3 and 4. These tables indicate that the economic criteria had the highest weight fol‐
lowed by environmental and technical criteria. The weights of economic, environmental,
and technical criteria were 0.470283, 0.308290, and 0.221427 for AHP, and these were
0.469979, 0.310541, and 0.219480 for fuzzy AHP. The consistency (CR) of weight determi‐
nation from main criteria and sub‐criteria was lower than 0.05, which is consistent with
the requirement of the decision‐making process performed by the AHP model (CR < 0.1).
Considering the weights of sub‐criteria, it was found that the distance to the roads had
the highest weight, followed by land use, distance to transmission lines, and then other
aspects. The weights of main criteria obtained from this study were different from previ‐
ous studies [41,42]. This is because of differences in considering criteria, aspects, and fac‐
tors. In this study, the weights calculated for AHP and for fuzzy AHP were also assessed
in comparison to each other. As shown in Figure 15, the trend of calculated weights ob‐
tained from AHP and fuzzy AHP was similar. This indicates that the opinions of the ex‐
perts had no dramatic uncertainties. This comparison serves as a validation of the models.
Table 3. Criteria weights on using AHP and fuzzy AHP models.
Criteria
Environmental
Economic
Technical
AHP
0.308290
0.470283
0.221427
Fuzzy AHP
0.310541
0.469979
0.219480
CR
0.0458
Difference (±)
0.002252
0.000305
0.001947
Table 4. Sub‐criteria weights on using AHP and fuzzy AHP models.
Sub‐Criteria
AHP
Fuzzy AHP
CR
Difference (±)
Distance from transmission lines
0.182083
0.172938
0.009146
Topography (slope)
0.083215
0.082858
0.0543 0.000358
Topography (elevation)
0.083215
0.082858
0.000358
Distance to roads
0.204985
0.214183
0.009199
Land use
0.188895
0.189720
0.0000 0.000825
Sensitive and protected areas
0.119395
0.120821
0.001427
Distance from demand centers
0.057504
0.056688
0.000816
Available solar potential
0.103789
0.103021
0.0516 0.000768
Available small hydro potential
0.103789
0.103021
0.000768
Climate
0.060134
0.059771
0.000363
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0.25
AHP
0.20
Fuzzy AHP
Weights
0.15
0.10
0.05
0.00
Land use
Sensitive
and
protected
areas
Environmental
Distance to
roads
Distance Topography
(slope and
from
transmission elevation)
lines
Distance
from
demand
centers
Economic
Available
solar and
hydro
potentials
Climate
Technical
Criteria and respective sub‐criteria
Figure 15. The patterns of weight results for AHP and fuzzy AHP models.
5.2. Smart Grid Suitable Sites
The calculated weights of each parameter were first used to obtain the suitable areas
for solar power generation in ArcGIS 10.5. Suitability of the areas was categorized as
highly suitable, moderately suitable, or low suitable, as shown in Figure 16 (more details
in Appendix C Table A4). This study considered 1.4 hectares to represent 1 MW based on
the area covered by the solar projects already existing in Uganda (Soroti and Tororo solar
power plants). This project indicated that 10 MW of the Soroti solar power plant was sit‐
ting on 13 hectares of land area, while 10 MW of the Tororo solar power plant was on 14
hectares of land area [33,58]. In this consideration, sensitive, protected, and unsuitable
areas were excluded from the final solar potential sites map. After obtaining solar poten‐
tial sites, the layers for solar potential areas, small hydro potentials sites, transmission
lines, road network, and demand centers (towns) were combined in ArcGIS 10.5. This was
to generate a final suitability map for demonstrating possible power generation from solar
and hydro in a smart grid connective system. The whole conceptualized architectural lay‐
out demonstrating a connective solar‐hydro smart grid system is shown in Figure 17.
Moreover, the study area was divided into three sections, North, Central, and South, to
generate the final suitability maps (see Figures 18–20). The area was divided based on the
nearness to energy resources, transmission lines, road network, and demand centers. Ta‐
ble 5 shows the total potential capacity for power generation from both solar and hydro
in each smart grid sectioned area.
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Figure 16. Map showing solar potential areas in the study area.
Solar
Solar
Solar
Hydro
Hydro
Transmission
network
Solar
Solar
Hydro
Solar
Figure 17. A layout of power generation in a smart connective system.
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Figure 18. Final map of North smart grid suitability area.
Figure 19. Final map of Central smart grid suitability area.
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.
Figure 20. Final map showing South smart grid suitability area.
Table 5. Potential power generation capacity of solar and hydro per areal smart grid (SG).
Sub‐Region
North SG
Central SG
South SG
Solar: MW
Solar (MW)
319,555.19
517,655.67
558,267.39
.
Small Hydro (MW)
4.7
5.56
18.32
[33,58], small hydro (data obtained from MEMD).
5.3. Study Results for Future Challenges
According to the results, the southern smart grid division had the highest maximum
potential capacity (558,285.71 MW) for power generation from both solar and hydro re‐
newable energy resources. The northern part had the lowest maximum potential capacity
(319,559.89 MW), which can be attributed to the size of the area. Among the section areas,
it was also noticed that the areas in the far northern part have poor transmission and road
network systems. Therefore, these study results can help the government of Uganda and
investors to use the identified suitable areas to design suitable smart grid systems in the
area. Using suitable and appropriate technologies, power can be generated from both hy‐
dro and solar energy resources using a smart grid strategy. Moreover, among the solar
technologies, Yuan et al. [59] mentioned that integration of photovoltaic power plants and
hydro power plants is very efficient and promising. This study can also be used as a guide
in suitability determination of implementation areas for smart grid connective systems in
other parts of the country and other developing countries. However, it is important to
note that smart grid designs differ from country to country, and factors of a smart grid
implementation depend on the purpose [60]. For example, Lurwan et al. [18] carried out
a study for site selection using GIS for large‐scale smart grid‐connected photovoltaic (PV)
power plants in Selangor, Malaysia, based on grid lines, land use, slope and elevation,
road network, and solar radiation.
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During smart grid site selection, it was noticed that there is a need for improved in‐
frastructure, such as the transmission network system, especially in the northern part of
the study area. Therefore, authorities and utility operators can use the results to improve
the power system in the western region of Uganda. However, the smart grid strategy in
this study only demonstrates the possibility of distributed power generation from renew‐
able energy resources (solar and hydro). The investigation does not include detailed tech‐
nicalities and information, communication, and technological systems within a smart grid
system. This is due to it being difficult to find related studies in Uganda; therefore, some
of the classification intervals used to obtain the suitability results were obtained from pre‐
vious studies in other countries, which could have induced uncertainties and inaccuracy.
The considered criteria only covered the economic, environmental, and technical factors.
In practice, smart grid implementation also depends on other factors, such as political and
social, among others. Therefore, these limitations will remain as challenges for future re‐
search.
6. Conclusions
This study investigated the suitable areas for power generation and distribution from
renewable energy resources (RERs) by applying a smart grid concept in the western re‐
gion of Uganda using a GIS‐based fuzzy AHP multi‐criteria decision‐making method. The
investigated RERs for power generation included solar and small hydro energy resources.
A questionnaire was used to collect opinions from experts in different energy fields in
Uganda. The criteria for considering the suitable smart grid areas covered environmental,
economic, and technical aspects selected by the authors. It is based on the selected criteria
that the questionnaire was designed and distributed to the experts for their opinions. Ac‐
cording to the design of the questionnaire, the experts were required to score the criteria
and the respective sub‐criteria using Saaty’s AHP scale as well as give the reason for their
scoring. The scores were then used to calculate the weights using both AHP and fuzzy
AHP approaches to validate the opinions of the experts. The fuzzy AHP calculated
weights were then used in ArcGIS 10.5 to identify the suitable area for power generation
from solar energy resources, as well as in the identification of suitable smart grid areas.
The obtained suitable solar power generation areas were combined with existing data on
small hydropower sites to generate final smart grid suitability areas. The final smart grid
suitability maps were generated by GIS using layers for road network, transmission lines,
demand centers, and resource potentials. The results showed that the criteria with the
highest weight were for economics (particularly distance to the road with 0.214183 fuzzy
AHP), followed by environmental and technical criteria. The validation of the opinion
weights from different experts indicated insignificant concerns for all criteria. With these
results, the suitable smart grid areas in Western Uganda were grouped into three loca‐
tions: south, north, and central. Most power generation and distribution for smart grid
application would be obtained from solar energy resources.
The results will be beneficial for government and investors for planning and imple‐
mentation of smart grid systems. This concept can be used to enhance power generation
and distribution from available renewable energy resources. The results can support strat‐
egies to combat energy insecurity, poor electrification rates, climate change, and to en‐
hance demand and supply management and system monitoring. This study, being the
first of its kind in Uganda, presents opportunities to explore other techniques in future
studies, such as the suitability of smart grid technologies, social acceptance, a combination
of GIS with other multi‐criteria methods, and cost analysis. The study can also be a con‐
ceptualized guide to smart power generation and utilization of renewable energy re‐
sources to the government, investors, policymakers, and energy planners interested in
smart grid and multi‐energy resources for power generation. As a recommendation, the
government of Uganda should consider putting in place a legislative and regulatory
framework favorable for smart grid initiatives for sustainable energy developments. In
addition, the results from this study will be useful and an example for applying in other
Energies 2022, 15, 1595
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countries, especially the developing countries which have low electricity access and high
potential of renewable energy resources.
Author Contributions: Conceptualization, J.R.A. and A.P.; funding acquisition, A.P.; investigation,
J.R.A.; methodology, J.R.A. and A.B.; supervision, A.P., A.B., M.S., K.T., and S.A.; writing—original
draft, J.R.A.; writing—review & editing, A.P., M.S., K.T., and S.A. All authors have read and agreed
to the published version of the manuscript.
Funding: This research was funded by the Thailand International Cooperation Agency (TICA).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Publicly available datasets were analyzed in this study.
Acknowledgments: The authors appreciate the participation of the experts, government institu‐
tions, and researchers towards the success of this study. Special thanks to the Thailand International
Cooperation Agency (TICA) under Thailand International Postgraduate Programme for the support
of Ms. Atwongyeire Jane Rose’s study in the Master of Science in Sustainable Energy Management
Program at Prince of Songkla University.
Conflicts of Interest: The authors declare no conflict of interest. In addition, the funders had no role
in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the
manuscript; or in the decision to publish the results.
Appendix A
Table A1. Data and sources.
Variables
Topography (DEM, Elevation)
Transmission lines
Format
Raster
Shapefile
Data source
U.S. Geological Survey (USGS) [61]
Energydata.info [62]
World Food Geospatial data portal
Roads
Shapefile
(WFPGeoNode) [63]
Land use
Shapefile
Servir Global Portal [64]
World Resources Institute Portal,
Sensitive and protected areas
Shapefile
Diva GIS [65,66]
Climate (rainfall and sunshine) Excel converted to shapefile Uganda National Meteorological Authority
Global Solar Atlas [67]
Solar GHI
Raster
Ministry of Energy and Mineral Development, MEMD.
Hydro potential
Shapefile
Demand centers
Shapefile
Energy Sector GIS Working Group, Uganda [68]
Table A2. AHP‐Saaty’s pairwise comparison scale [41].
Intensity of
Importance
1
3
5
7
Judgement Description Explanation
Equal importance
Weak dominance
Strong dominance
Demonstrated
dominance
9
Absolute dominance
2,4,6,8
Intermediate values
Equal contribution of two elements to the objective
The judgement of one element slightly preferred over another
The judgment of one element strongly dominant over another
The judgement of one element very strongly preferred over another
The judgement of one element extremely preferred over another and
is the highest
Representative of compromises between the preferences in weights 1,
3, 5, 7 and 9.
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Table A3. AHP‐Saaty pairwise comparison scale and the equivalent TFNs [55].
Intensity of Importance
1
3
5
7
9
2
4
6
8
Judgement Description
Equal importance
Weak dominance
Strong dominance
Demonstrated dominance
Absolute dominance
Intermediate values
Fuzzy Triangular Numbers
(1,1,1)
(2,3,4)
(4,5,6)
(6,7,8)
(9,9,9)
(1,2,3)
(3,4,5)
(5,6,7)
(7,8,9)
Appendix B
Figure A1. Transformation of AHP scores into equivalent TFNs.
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Figure A2. An illustration for individual criterion geometric mean calculation.
Figure A3. Computation of final weights in fuzzy AHP model.
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Appendix C
Table A4. Suitability of solar sites for power generation.
District
Buhweju
Buliisa
Bundibugyo
Bushenyi
Hoima
Ibanda
Isingiro
Kabale
Kabarole
Kagadi
Kakumiro
Kamwengye
Kanungu
Kasese
Kibaale
Kiruhura
Kiryandongo
Kisoro
Kyegegwa
Kyenjojo
Masindi
Mbarara
Mitooma
Ntoroko
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Low Suitability
1873.55
1338.25
915.93
654.24
3117.92
2227.08
2003.28
1430.92
15,640.76
11,171.97
2245.71
1604.08
6562.03
4687.16
1026.69
733.35
4751.83
3394.16
6107.65
4362.61
10,504.77
7503.41
2988.46
2134.62
2266.52
1618.95
2170.33
1550.24
16,852.73
12,037.66
20,465.71
14,618.37
6805.21
4860.86
220.19
157.28
15,397.38
10,998.13
20,475.86
14,625.61
15,431.51
11,022.50
4568.28
3263.06
4707.96
3362.83
762.95
Moderate Suitability
2797.26
1998.05
16,664.43
11,903.16
9151.06
6536.47
23,253.33
16,609.52
110,920.51
79,228.94
34,687.26
24,776.62
73,587.96
52,562.83
4381.34
3129.53
47,253.81
33,752.72
71,984.02
51,417.15
78,174.32
55,838.80
76,884.49
54,917.49
29,999.31
21,428.08
20,346.38
14,533.13
45,150.19
32,250.14
152,024.11
108,588.65
56,459.57
40,328.26
‐
‐
82,490.76
58,921.97
105,410.24
75,293.03
74,998.09
53,570.07
52,833.17
37,737.98
22,013.87
15,724.19
16,166.07
High Suitability
282.18
201.56
13,946.25
9961.61
4907.81
3505.58
1000.87
714.91
52,561.05
37,543.61
9,568.37
6,834.55
51,058.29
36,470.21
371.63
265.45
1429.35
1020.97
3134.24
2238.74
20,054.71
14,324.79
18,227.76
13,019.83
1890.96
1350.69
12,324.15
8802.97
232.08
165.77
75,025.33
53,589.52
33,555.54
23,968.24
‐
‐
11,791.89
8422.78
1158.63
827.59
49,478.41
35,341.72
26,722.70
19,087.65
649.99
464.28
15,316.09
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Rubanda
Rubirizi
Rukungiri
Sheema
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Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
Area (ha)
Megawatts
544.96
7959.78
5685.56
78.84
56.31
923.74
659.82
2009.41
1435.29
2819.13
2013.67
11,547.19
79,131.91
56,522.79
0.48
0.34
6654.63
4753.31
34,093.07
24,352.19
22,400.34
16,000.24
10,940.06
10,997.03
7855.02
‐
‐
1439.57
1028.27
1421.17
1015.12
3557.40
2541.00
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