Multi-Criteria Decision Making for Selecting the Location of a Solar Photovoltaic Park: A Case Study in UAE

Abstract

The high availability of solar energy in the Gulf Cooperation Council (GCC) makes it the most attractive source of energy in this region, especially due to the global shift toward eco-friendly systems. A significant increase in the implementation of solar PV projects has been noticed in the United Arab Emirates (UAE). For this reason, this study conducted a multi-criteria decision-making process to compare four locations for building a solar PV park in the UAE, namely, Abu Dhabi, Dubai, Sharjah, and Umm Al Quwain. Various criteria were taken into account, including the solar radiation, wind speed, distance from the electricity grid, distance from seaports, and land cost. A hybrid AHP-TOPSIS model was employed to evaluate the criteria weights and alternatives’ scores, which were also compared with the no priority-TOPSIS to check the effects of the criteria weights on the overall ranking. According to the findings, Dubai and Abu Dhabi were ranked first and second, with final scores of 0.7108 and 0.4427, respectively, when using the AHP-TOPSIS model. Furthermore, Umm Al Quwain scored slightly less than that of second place, with a value of 0.4252. The top two ranked alternatives were consistent between the two compared models, as Dubai also performed the best among all locations, which confirmed the reliability of the proposed approach and verified the obtained results and rankings.

1. Introduction

Energy is a pivotal factor in driving worldwide economic and industrial advancement. The majority of global energy generation relies on conventional fossil fuels, such as coal, oil, and natural gas [1]. Heightened environmental apprehensions, diminishing reserves, and soaring energy costs have spurred efforts to explore renewable energy alternatives. Among the various forms of renewable energy, photovoltaic solar power is demonstrating reliability. Solar energy is poised to emerge as the leading technology that will facilitate the transition to a decarbonized energy supply among the array of renewable options. It boasts versatility in installation potential across the globe [2]. Furthermore, advancements in photovoltaic solar cell technology have led to an increased power conversion efficiency, which enhances the overall efficiency of solar energy systems. As these trends continue, solar power is anticipated to become increasingly affordable in the years ahead, and significant investments are likely to ensue [3].

Selecting sites for renewable energy projects is recognized as a complex issue that involves various criteria. This drives the adoption of a growing variety of multi-criteria decision-making (MCDM) methods [4,5,6]. MCDM methods play a crucial role in addressing the complexity of the site selection problem by enabling the structured management of multiple, sometimes conflicting, criteria. Unlike single-criterion approaches, MCDM methods offer the unique advantage of incorporating multiple criteria or attributes to derive a comprehensive decision-making outcome.

Various research studies employed MCDM approaches to select suitable locations for solar power plants. Ghasempour et al. [7] reviewed the MCDM methods that were previously used by researchers to effectively select solar plant sites and solar plant technologies. According to their findings, the weight of each criterion depends on different factors, including the region, economy, accessibility, power network, maintenance cost, and operational cost. In [8], an assessment of three distinct locations in Turkey was conducted to determine the optimal site to establish a solar PV power plant. The analytic hierarchy process (AHP) was utilized to evaluate these locations by considering both quantitative and qualitative factors that significantly influence electricity production. The problem was addressed using two approaches: (a) employing precise electricity production data obtained through measurement and (b) utilizing linguistic data supplied by a decision maker. Wang et al. [9] introduced an MCDM model that integrated three methodologies: fuzzy AHP, data envelopment analysis (DEA), and the technique for order of preference by similarity to ideal solution (TOPSIS). This model was used to identify the optimal location for constructing a solar power plant based on both quantitative and qualitative criteria. Potential locations were selected from 46 sites in Vietnam using various DEA models. Fuzzy AHP was employed to determine the weight of each factor, and the TOPSIS was subsequently used to rank the locations in the final step. In [10], an indirect approach is outlined to identify regions in Iran with brackish water sources and to assess areas’ potentials for solar energy to power reverse osmosis desalination devices. The primary goal of their study was to utilize water from ponds, wells, or surface sources that are unsuitable for residential or agricultural use. They employed an MCDM approach by considering 16 data layers that encompassed economic, environmental, technical, and geological factors. Geographic information systems (GISs) were used to create data layers and apply elimination criteria and constraints. Hassan et al. [11] developed a hybrid framework to evaluate suitable sites and technical potentials for large-scale solar PV systems by combining two MCDM techniques. The assessment considered a variety of variables, including climate, technical, geographical, and economic factors, with the factor weights determined using the CRITIC method. The framework was illustrated using five Saudi Arabian cities with high solar radiation. The TOPSIS method was used to rank the five alternatives, and a sensitivity analysis was conducted to examine the reliability and robustness of the results. Wang et al. [12] proposed an optimized methodology to select solar power plant sites in Indonesia by integrating DEA, fuzzy AHP, and fuzzy measurement of alternatives and ranking according to a compromise solution (F-MARCOS) models. DEA was utilized to identify the most efficient locations based on quantitative measures, like solar radiation, land availability, and grid connectivity. Qualitative factors, including technological, economic, environmental, and socio-political aspects, were evaluated using fuzzy AHP to prioritize the most significant criteria for site selection. Finally, F-MARCOS was applied to rank the potential locations based on the selected criteria.

As MCDM methods have proven to be effective tools for selecting the location of solar power plants, the aim of this study was to identify and evaluate criteria that could be used to select the best location to build a PV plant in the United Arab Emirates (UAE). To achieve this, a hybrid AHP-TOPSIS model was employed to evaluate the weights of the criteria and the final alternatives’ scores and rankings. This hybrid model was chosen for several reasons. First, it is a validated and widely used model in similar applications, which ensures the reliability and robustness of the decision-making process. Additionally, it is well suited to the specific context of the case study, as the weighting method incorporates expert opinions, providing a nuanced and tailored assessment of the criteria. This approach allowed for a more accurate reflection of the unique factors relevant to the UAE, which enhanced the overall effectiveness of the site selection process for the PV plant. This study considered nine different criteria that encompassed technical, financial, and social factors. Four potential site alternatives were evaluated: Abu Dhabi, Dubai, Sharjah, and Umm Al Quwain (UAQ). The results of this study are intended to provide a comprehensive framework for decision makers to optimize the site selection process for PV plants in the UAE to ensure sustainable and efficient energy production.

2. Methodology

Selecting the optimal solution in energy systems, particularly in renewable-energy-based and energy storage systems, is crucial due to the complex interplay of technical, economic, and environmental factors that must be balanced to achieve sustainable outcomes. Optimization procedures play a vital role in identifying solutions that maximize the efficiency, reduce the costs, and minimize the environmental impact. Various techniques are employed in this context, including multi-criteria decision-making methods [13], mixed-integer linear programming [14], genetic algorithms [15], particle swarm optimization [16], and fuzzy logic [17]. These techniques can aid in optimizing energy system designs by solving problems with both continuous and discrete variables and can be utilized to address the complexities and uncertainties inherent in renewable energy systems to ensure that the most effective and feasible solutions are implemented.

Multi-criteria decision making (MCDM) is one of the most common methods that are employed to evaluate and prioritize different options based on multiple criteria. In this study, an MCDM procedure was applied, which could be divided into three main steps: data collection, criteria weighting, and scores evaluation. Figure 1 illustrates these steps in which they are colored red, blue, and green, respectively. Data collection was carried out through two procedures, where the first one included comparing the different criteria and the second one was to collect the exact data related to each alternative. After this, the AHP model utilized the initial data that involved criteria comparisons to determine the weight of each criterion. The resulting weights and the collected data related to the alternatives were used to evaluate the scores and rankings of the solar PV locations based on the TOPSIS model. A detailed explanation of the two employed models is presented in the next sections.

As reported in [18], a cooperative strategy for managing energy systems is a crucial element in the success of projects. For this reason, building a framework that involves key stakeholders, such as government entities, energy companies, and local communities, can significantly contribute to the location selection process. Therefore, in the current research, the collected data were received from expertise in the solar PV projects field in the UAE. In addition to this, a pairwise comparison matrix was completed by PV experts from one of the main developers in renewable energy and a project management consultancy firm.

2.1. AHP Weighting Model

As mentioned previously, the AHP is used to determine the weights of each criterion prior to the scores’ evaluation. This model starts by preparing a pairwise comparison matrix, which lists all the criteria against each other (one by one). This matrix was first shared with solar PV experts in the UAE to fill the required data. After receiving these samples, the matrices are normalized as shown below:

$$a_{n,ij}={\displaystyle \frac{a_{ij}}{\sum _{i=1}^n{a}_{ij}}}$$

(1)

where a_n,ij is the normalized value, a_ij is the original value, i is the row number, and j is the column number. After normalizing the pairwise comparison matrix, the criteria weights can be calculated based on Equation (2), which involves adding all normalized values related to each criterion alone and dividing the summed value by the total number of criteria:

$$w_i={\displaystyle \frac{1}{n}}\sum _{j=1}^n{a}_{n,ij}$$

(2)

where w_i and j are the weight of each criterion and the column number, respectively. The resulting weights cannot be used directly for the scores evaluation since it is necessary to first check the consistency of the pairwise comparison matrices (each matrix/sample alone). To ensure that the data are reliable and consistent, the consistency ratio of each matrix needs to be less than 0.1, which can be calculated as shown below:

$$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}={\displaystyle \frac{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}}{\mathrm{R}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}}}$$

(3)

where the consistency index is calculated as shown in Equation (4) and the random index represents a constant value based on the total number of criteria. In this study, nine criteria were taken into account, which corresponded to a random index of 1.45. The value of the random index is an empirical average and might vary slightly depending on the number of random matrices generated and the method used for the eigenvalue computation. However, due to the random nature and the statistical process, an RI value of 1.45 is commonly accepted for a 9 × 9 matrix, as shown in [19].

$$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(4)

where ${\lambda }_{max}$ is the maximum eigenvalue and n is the number of criteria. After calculating the consistency ratio, final checking needs to be done in order to ensure that all matrices have a value less than 0.1. However, if any matrix has a consistency ratio greater than this threshold, minor changes must be carried out without affecting the importance order of the criteria. At this stage, a deep analysis of the matrices is needed to know what modifications can be done to make the data more consistent. Additionally, after checking each matrix alone, the data consistency between all matrices needs to be checked such that remarkable outliers can be removed.

2.2. TOPSIS

In this study, the evaluation of scores and rankings was carried out by employing the TOPSIS model, which is one of the most commonly used models in MCDM studies [20,21,22]. Similar to the AHP model, the first step is to normalize the data; however, in the TOPSIS model, the normalization is different, as it uses the summation of the squared value:

$$X_{n,ij}={\displaystyle \frac{X_{ij}}{\sqrt{\sum _{i=1}^n{X^2}_{ij}}}}$$

(5)

where X_n,ij is the normalized entity, n is the number of solar PV location alternatives, i is the row number, and j is the column number. The next step is to prepare the weighted normalized matrix, which involves multiplying the weights derived from the AHP model by the normalized data, as shown below:

$$V_{ij}=X_{n,ij}\times W_j$$

(6)

where V_ij, X_n,ij, and W_j are the weighted normalized value, normalized data, and criterion weight, respectively. At this stage, the best and worst values need to be determined by considering the weighted normalized data for each criterion. These values can be either the maximum or minimum value depending on the effect of the criterion, i.e., whether it is beneficial or non-beneficial. Assuming that a criterion has a negative impact on selecting the PV plant location, then the best value will be the minimum obtained value between the different alternatives. After this, the distances between the data of each alternative and the best and worst values are calculated, as shown in Equations (7) and (8), respectively:

$${S_i}^{+}=\sqrt{\sum _{j=1}^m{\left(V_{ij}-{V_j}^{+}\right)}^2}$$

(7)

$${S_i}^{-}=\sqrt{\sum _{j=1}^m{\left(V_{ij}-{V_j}^{-}\right)}^2}$$

(8)

where S_i⁺ is the distance from the best value, S_i⁻ is the distance from the worst value, m is the number of criteria, V_ij is the weighted normalized value, V_j⁺ is the best value, and V_j⁻ is the worst value. The final step is to evaluate the score of each alternative (P_i), which represents the ratio of the distance from the worst value to the summation of both distances. The alternatives’ rankings are determined by this score such that higher scores correspond to higher rankings.

$$P_i={\displaystyle \frac{{S_i}^{-}}{{S_i}^{+}+{S_i}^{-}}}$$

(9)

3. Criteria and Alternatives

In the MCDM method employed in this study, nine criteria and four alternatives were taken into consideration. A detailed representation of the AHP model is depicted in Figure 2, which was used to achieve the primary objective of this research, that is, to identify the best solar PV location in the UAE between these four alternatives: Abu Dhabi, Dubai, Sharjah, and UAQ. The upcoming sections illustrate the difference between these locations and justify the reasons behind choosing these nine criteria.

3.1. Criteria Description

Selecting the best location for solar parks is affected by various factors, including the ambient temperature, soil condition, land surroundings and orientation, costs, and demand fluctuations. In the current study, 17 criteria were considered in the first stage to compare the different alternatives; however, it was found that almost half of these criteria were consistent between the different locations, and hence, they were excluded. The removed criteria were the ambient temperature, soil condition, land surroundings, labor cost, maintenance cost, land availability, water consumption, and susceptibility to natural disasters.

The average ambient temperature per year for the four alternatives ranges between 28.2 and 28.3 °C. The soil conditions are similar between the four alternatives since they are in a desert area. The four alternatives are located in remote areas; as a result, there are no land surroundings that could affect the shading. The costs of labor and maintenance within the UAE are the same, which will not make any difference that can affect the selection of the alternatives. Utility-scale solar PV projects in the UAE are managed by the government; therefore, the land is provided by the government in long-term land lease agreements. Dry cleaning is used in PV projects; therefore, water consumption will be minimal. Water consumption will only be used for the maintenance of buildings. Hence, the difference between the four alternatives for water consumption will be minimal. Since the four alternatives are located in the desert area, there will be the same potential for sandstorms and windstorms. Therefore, the susceptibility to natural disasters between the four alternatives will be the same. The descriptions of all excluded factors are presented below:

Ambient temperature: Ambient temperature affects the module efficiency, thus affecting the generation of the plant. The capacities of the electrical equipment, such as inverters, transformers, and cables, are affected by the ambient temperature. Especially for inverters, the capacities at 50° and 30° have significant differences.

Soil condition (geotechnical): The bearing capacity of soil decides the depth, length, and section of the module-mounting structure foundation, equipment, and building foundations. The hardness of the ground can affect the cost and time of foundation construction. Soil thermal resistance and electric resistance affect the underground cable and earthing design.

Land surroundings (shadings): if tall objects are proximate to PV panels, especially when the objects are in the south, the shading projected on the PV panels by these objects will reduce the generation.

Labor cost: This is one of the main factors of construction cost. The unit price of labor cost in the region will affect the construction cost.

Maintenance cost: This is a major factor in operation expenditure, which happens every year after the plant is put into operation. The maintenance cost impacts the long-term return rate of the plant.

Land availability: This decides the volume of a plant, as well as the potential for extension. If the land is sufficient, the distance between adjacent modules/planes can be designed to an optimal value to achieve an optimal yield.

Water consumption: If the dry-cleaning method is adopted for module cleaning, the water consumption of the plant will be very limited since only a very small frequency of water cleaning is required (e.g., twice a year). If a water-cleaning method is adopted, the water consumption volume is high and must be considered. The dry-cleaning method is used in most utility-scale projects in the UAE.

Susceptibility to natural disasters: When selecting the location of a plant, it shall be ensured that the area has a low risk and extent of natural disasters, such as floods, earthquakes, storms, and hail. If the area has a high risk or extent of natural disaster, the design must be robust enough, which will significantly increase the cost of the plant.

After excluding these, nine criteria remained: solar radiation, wind speed, slope of the terrain, distance from the electricity grid, distance from seaports, distance from a road, land cost, distance from the sea, and distance from neighbors and protected areas. A brief description of these factors is presented in

Table 1. Definitions of the different criteria considered in the current study.

	Criteria	Description
C1	Solar radiation (W/m2)	This represents the received power
C2	Wind speed (m/s)	This reduces the PV cell’s temperature
C3	Slope of the terrain (%)	Inclination of the land where the solar park is placed affects the mounting structure type
C4	Distance from the electricity grid (m)	Proximity to the electricity grid
C5	Distance from seaports (m)	Proximity to seaports for the transportation of equipment
C6	Distance from a road (m)	Proximity to the road infrastructure for equipment transportation
C7	Land cost (AED/m2)	Cost of leasing land per year
C8	Distance from the sea	Impact on the corrosivity category of the mounting structure
C9	Distance from neighbors and protected areas (m)	Noise, dust during construction, and visual impact on habitats and species

.

The selected criteria were carefully evaluated to provide the best location, as they all have a significant impact on selecting the suitable location for a solar PV park, as illustrated below:

Solar radiation: This reflects the density of solar energy received on the PV panel plane. The annual generation of the PV system is positively correlated to the yearly accumulated irradiance. Solar radiation is the most important factor in deciding the amount of energy generated by the system. The data related to solar radiation were obtained from a solar resource database that SolarGIS (Bratislava, Slovakia) owns and maintains. It provides the estimated solar resource, air temperature data, and potential solar power output for the selected location and input parameters of a PV power system. The data were obtained from SolarGIS [23], where their model was compared with high-quality ground measurements at 228 sites across various climates. A summary of the accuracy of the SolarGIS solar radiation data is presented in

Table 2. Summary of SolarGIS model accuracy [23].

	Solar Radiation (GHI)	Description
Number of validation sites	228	Sites where data can be open to public access
Mean bias for all sites	0.5%	Tendency to overestimate or to underestimate the measured values, on average
Standard deviation of biases	±3.0%	Range of deviation of the model estimates assuming a normal distribution of bias (68% occurrence)

.

Wind speed: This affects the system efficiency and energy generation by changing the heat dissipation rate of the PV module. The module temperature in working conditions is always higher than the ambient temperature. The timely dissipation of heat can avoid an efficiency reduction caused by high temperatures. The wind speed was obtained from SolarGIS’s average yearly wind speed at 10 m above the ground. The wind speed data were calculated by SolarGIS from outputs of Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and Climate Forecast System Version 2 (CFSv2) models (© 2024 National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA)). To reduce the uncertainty, an anemometer should be installed at the site for at least 12 months to capture data.

Slope of the terrain: The type and slope of terrain are important for the system design. Undulated terrain can cause shading effects, as well as mismatch loss due to the different tilts of each string of modules.

Distance from the electricity grid: This decides the length of the transmission system. The cost, corridor land, and construction period of the transmission system are all directly related to the distance from the grid substation. If the distance is too far, the transmission loss and fault rate of the system must be considered.

Distance from seaports: Logistics cost and time are important factors for building a PV plant. Tremendous containers and large bulk equipment, such as transformers, need to be delivered to the site. The distance from seaports is the most important criterion for the logistics.

Distance from a road: If the distance from a public road is too long, the last kms can become the bottleneck for the logistics. Terrible road conditions may damage PV modules and other equipment in transportation. The cost of building an access road and convenience for staff commutes also need to be considered.

Land cost: The cost of leasing land per year is one of the main expense items in a PV project financial model. The land lease cost varies from one location to another, in which the gap can be very huge. The land costs were obtained from local authorities for one of the currently built projects in the same area as A2.

Distance from the sea: The distance from the sea is a decisive factor in determining the corrosivity category of the area. All metal facilities, including the mounting structure, must be designed according to the area corrosivity category.

Distance from neighbors and protected areas: The noise, dust, and traffic impacts during construction can cause problems if neighbors or protected areas are close to the plant location. A vast spread of PV plants may have a visual impact on habitats and species. Such criteria are major factors that contribute to the environmental and social impacts.

3.2. Alternatives

The used alternatives were chosen in the UAE in four different emirates. The UAE consists of seven emirates: Abu Dhabi, Dubai, Sharjah, Ajman, Umm Al Quwain, Ras Al Khaimah, and Fujairah. UAE is located in the Middle East and West Asia, as shown in Figure 3. Out of the seven emirates, four emirates were chosen. Developers have expressed interest in these identified locations. The reason is that these four emirates have a high demand for electricity and a higher population and have significant amounts of available desert land. In addition, each of the chosen emirates has different off-takers. Abu Dhabi has the Emirates Water and Electricity Company (EWEC) as a utility company. Dubai has the Dubai Electricity and Water Authority. Sharjah has the Sharjah Electricity, Water, and Gas Authority (SEWA). Ajman, Umm Al Quwain, Ras Al Khaimah, and Fujairah have the same off-taker, which is the Etihad Water and Electricity (EtihadWE). In the UAE, there are multiple PV utility-scale projects. Regarding Abu Dhabi “A1”, this site was located in the eastern area of Abu Dhabi in an area called Sweihan. It was chosen due to the land availability in that area in the midst of the desert. The site in Dubai “A2” was located in the southern side of the emirate. This location was chosen due to the availability of the land and to connect it to the existing solar park, where multiple phases of solar power plants have been developed. The sites in Sharjah “A3” and Umm Al Quwain (UAQ) “A4” were located in the eastern side of the emirates. The exact locations of these alternatives are presented in Figure 4.

According to the collected data, installing a solar park in Abu Dhabi can result in the highest expected power production since it corresponds to the highest solar radiation and average wind speed. However, the amount of power produced in the current case study was not the most influential factor since it does not vary significantly between the investigated alternatives. For example, the amounts of solar radiation received were 2201 and 2195 kWh/m² in Abu Dhabi and Dubai, respectively (see

Table 3. Criteria values for the different solar PV park location alternatives in the UAE [25,26].

	Criteria	Abu Dhabi “A1”	Dubai “A2”	Sharjah “A3”	UAQ “A4”
C1	Solar radiation 1 (kWh/m2)	2201	2195	2176	2170
C2	Wind speed 2 (m/s)	3.5	3.4	3.5	2.9
C3	Slope of the terrain (%)	2.6%	1.5%	2.5%	1.9%
C4	Distance from the electricity grid (m)	11,000	12,400	4800	2500
C5	Distance from seaports (m)	83,000	49,000	45,000	43,000
C6	Distance from a road (m)	470	240	100	500
C7	Land cost (AED/m2)	5.25	5	4.75	4.5
C8	Distance from the sea	83,000	49,000	4800	2500
C9	Distance from neighbors and protected areas (m)	9700	19,000	1500	7200

¹ Average monthly sum of global horizontal irradiation (© 2024 SolarGIS) [kWh/m²]. ² Average monthly wind speed at 10 m above ground. Calculated from outputs of MERRA-2 and CFSv2 models (© 2024 NOAA and NASA) [m/s].

). Similarly, the wind speeds were almost equal for A1, A2, and A3, with a value of ~3.5 m/s, while it was lower in the case of UAQ (2.9 m/s). Thus, a solar park in UAQ was expected to generate less power compared with the other alternatives; however, it held the lowest values regarding three negative factors, which were the distance from the electricity grid, distance from a road, and land cost, with values of 2500 m, 43,000 m, and 4.5 AED/m², respectively. Even though A1 and A2 corresponded to the highest expected power generation, they had a few drawbacks, such as the distance from the electricity grid since they were relatively far from the grid compared with the two other alternatives. Furthermore, they had the highest land costs, where the cost was 5.25 AED/m² in Abu Dhabi and 5 AED/m² in Dubai. In addition, the site in Abu Dhabi was very far from the seaports, which could result in higher expenses during installation. On the other hand, this was reflected positively in another criterion (C8), which represented the distance from the sea, thereby reducing the impact of corrosivity.

4. Rankings

According to the proposed AHP-TOPSIS model, A2 was ranked the highest with a score of 0.711, followed by A1, with a score of 0.4427. Sharjah (A3) was ranked fourth, with the lowest score of 0.2432. However, if the no priority weighting method was used, the score of A3 increased up to 0.4036, which resulted in having the third rank. This was mainly due to the low weight of C6 (distance from a road) in this case study, which resulted in the insignificant effect on the total score, noting that Sharjah had the considerably best value between the different alternatives, with a value of 100 m. Therefore, when the no priority method was employed, the effect of C6 on the final score increased, which led to an improved overall ranking of A3. On the other hand, the scores of A1 and A4 were not significantly affected by the change in criteria weights between the two methods. In addition, the rankings of the top two location alternatives were also not affected, as A2 and A1 were still ranked first and second, respectively, when considering the no priority-TOPSIS method. A comparison between the results of the proposed AHP-TOPSIS and no priority-TOPSIS models is presented in

Table 4. Scores and rankings of the solar PV park location alternatives in the UAE.

Method	AHP-TOPSIS		No Priority-TOPSIS
Alternatives	Score	Rank	Score	Rank
A1	0.4427	2	0.4986	2
A2	0.7108	1	0.602	1
A3	0.2432	4	0.4036	3
A4	0.4252	3	0.3934	4

. The no priority method considered that all criteria had the same weight, which was one-ninth in the current study. Additionally, a detailed representation of criteria scores is depicted in Figure 5, which demonstrates the effect of each criterion on the overall ranking of the compared alternatives.

In order to further examine the impacts of the criteria weights on the decision-making process, a sensitivity analysis was carried out, as presented in Figure 6. It involved changing the weight of each criterion from 0 to 0.9, while the remaining weight was distributed equally between the other criteria. As an example, if the weight of the investigated criterion was 0.5, all the other criteria had a weight of 0.5/8. In this sensitivity analysis, the scoring method was the same as was used in the previously employed model, which was the TOPSIS technique. It can be noticed that altering the criteria weights had a huge impact on the final score of the solar PV plant location alternatives. The lowest effect corresponded to C1, which could be predicted due to the insignificant variation in the solar radiation between the compared alternatives, as shown in Table 3. Even though the scores and rankings were affected by the criteria weights, A2 was still the most favorable option since it performed very well in most of the cases when considering the variations observed in Figure 6. This verified the reliability of the proposed model and confirmed that this was the best location for installing a solar PV park among the compared alternatives in the UAE.

In the sensitivity analysis, Dubai was consistently ranked the highest, especially when the distance from neighbors and protected areas were heavily weighted, where its score increased from 0.51 to 0.99. Abu Dhabi showed significant improvement when the proximity to the sea was prioritized, where the scores rose from 0.29 to 0.98. Sharjah gained an advantage when the proximity to roads and proximity to seaports were emphasized, where its scores increased from 0.33 to 0.97 and from 0.38 to 0.93, respectively. UAQ emerged as the top alternative when the distance from the electricity grid was prioritized, where its score increased from 0.29 to 0.97.

5. Conclusions

In this study, a hybrid AHP-TOPSIS model was employed to conduct an MCDM analysis to select the best solar PV park location in the UAE. The four compared alternatives were Abu Dhabi (A1), Dubai (A2), Sharjah (A3), and Umm Al Quwain (A4). Nine criteria were involved in the comparison, which included technical, financial, and social factors. Since the weather conditions did not vary significantly between these locations, the amount of power that could be generated from the solar park did not considerably affect the ranking of these alternatives, which was mainly represented by the solar radiation and wind speed. According to the results, A2 was ranked first, followed by A1, A4, and A3, with final scores of 0.7108, 0.4427, 0.4252, and 0.2432, respectively. The ranking of the top two alternatives was the same when utilizing the no priority-TOPSIS model, while A3 performed slightly better than A4. This confirmed the reliability of the proposed model, as the difference between the results of the two models was very acceptable, noting that the criteria weights are usually affected by the investigated case study and available data. A comprehensive sensitivity analysis was also conducted to evaluate the impact of varying the criteria weights on the decision-making process for selecting the best location for a solar PV park in the UAE. The analysis involved adjusting the weight of each criterion from 0 to 0.9, with the remaining weight distributed equally among the other criteria. The results indicated that altering the criteria weights significantly affected the final scores and rankings of the location alternatives. However, A2 consistently emerged as the most favorable option, which demonstrated the robustness and reliability of the proposed model.

As the proposed MCDM method presented its effectiveness in selecting the best location for a solar PV park location, future research could apply the same approach to select the most suitable site for other renewable energy projects in the UAE, such as wind and hydropower. Moreover, exploring the integration of real-time data and predictive analytics could enhance the model’s responsiveness to dynamic conditions and future trends. Another avenue for future work includes validating the model’s performance in different regions with varying geographic and socio-economic characteristics, which could further strengthen its generalizability and utility across diverse contexts. By advancing the methodology and applying it to a broader range of energy projects, decision makers can better navigate the complexities of renewable energy development and contribute to the UAE’s transition toward a sustainable energy landscape.