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 www.mdpi.com/journal/energies Energies 2022, 15, 1595 2 of 32 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 3 of 32 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 Energies 2022, 15, 1595 4 of 32 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 Energies 2022, 15, 1595 5 of 32 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 Energies 2022, 15, 1595 6 of 32 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. Energies 2022, 15, 1595 7 of 32 Figure 3. Suitability map of study area by slope. Energies 2022, 15, 1595 8 of 32 Figure 4. Suitability map of study area by elevation. Energies 2022, 15, 1595 9 of 32 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]. Energies 2022, 15, 1595 10 of 32 Figure 6. Map of protected areas in the study area. Energies 2022, 15, 1595 11 of 32 Figure 7. Map of waterbodies in the study area. Energies 2022, 15, 1595 12 of 32 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. Energies 2022, 15, 1595 13 of 32 Figure 9. Map showing demand centers in the study area. Energies 2022, 15, 1595 14 of 32 Figure 10. Map showing rainfall distribution in the study area. Energies 2022, 15, 1595 15 of 32 Figure 11. Map showing sunshine distribution in the study area. Energies 2022, 15, 1595 16 of 32 Figure 12. Map showing small hydro potential sites in the study area. Energies 2022, 15, 1595 17 of 32 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 Energies 2022, 15, 1595 18 of 32 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 Energies 2022, 15, 1595 19 of 32 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 Energies 2022, 15, 1595 20 of 32 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 Energies 2022, 15, 1595 21 of 32 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. Energies 2022, 15, 1595 22 of 32 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. Energies 2022, 15, 1595 23 of 32 Figure 18. Final map of North smart grid suitability area. Figure 19. Final map of Central smart grid suitability area. Energies 2022, 15, 1595 24 of 32 . 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. Energies 2022, 15, 1595 25 of 32 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 26 of 32 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. Energies 2022, 15, 1595 27 of 32 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. Energies 2022, 15, 1595 28 of 32 Figure A2. An illustration for individual criterion geometric mean calculation. Figure A3. Computation of final weights in fuzzy AHP model. Energies 2022, 15, 1595 29 of 32 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 Energies 2022, 15, 1595 Ntungamo Rubanda Rubirizi Rukungiri Sheema 30 of 32 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 References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 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