Next Article in Journal
Sorption-Enhanced Water-Gas Shift Reaction for Synthesis Gas Production from Pure CO: Investigation of Sorption Parameters and Reactor Configurations
Next Article in Special Issue
A Review of Optimization of Microgrid Operation
Previous Article in Journal
A Study on the Numerical Performances of Diffuse Interface Methods for Simulation of Melting and Their Practical Consequences
Previous Article in Special Issue
A Feasibility Analysis of Controlling a Hybrid Power System over Short Time Intervals
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Optimal Decision-Making in Photovoltaic System Selection in Saudi Arabia

Department of Electrical Engineering, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
GS E&C Institute, GS E&C Corp., 33, Jong-ro, Jongno-gu, Seoul 03159, Korea
Author to whom correspondence should be addressed.
Energies 2021, 14(2), 357;
Submission received: 1 December 2020 / Revised: 5 January 2021 / Accepted: 7 January 2021 / Published: 11 January 2021
(This article belongs to the Special Issue Advanced Analytics in Renewable Energy)


In this study, optimal decision-making process in photovoltaic (PV) system location selection in Saudi Arabia is described. First, to identify the criteria that influence the decision of selecting a suitable location for the PV system, the geographical information system (GIS)-based multi-criteria decision making (MCDM) approach is used. Next, to assess the weights of the criteria that present different aspects of the investigated locations, four major criteria and 11 sub-criteria are proposed, and analytic hierarchy process (AHP) is applied to develop comparison decision matrix. Finally, the order preference by similarity to ideal solution (TOPSIS) technique evaluates and classifies 17 cities (such as Riyadh, Jeddah) in Saudi Arabia. The result shows that Tabuk city in the northern of Saudi Arabia is the best location. Among the 17 cities, the performance score of seven cities is above or equal 80%, and Tabuk city has the highest score with 87%. This analytical approach could contribute as an early planning to locate suitable sites for the selection of PV system region in Saudi Arabia.

1. Introduction

Saudi Arabia is the biggest electricity provider in the Middle East and northern Africa region. The total capacity of the power generation reached 76.9 GW in 2018, while the total load peak reached 61.7 GW [1]. The electrical power demand in Saudi Arabia is growing on an annual basis, which results in burning more barrels of carbon-based fuel, and oil burning to produce electricity, which has a harmful effect to the economy of Saudi Arabia as the oil is recognized as the backbone of the economy. Moreover, the general health will be affected as well due to the emission of the CO2 gaseous [2]. Therefore, Saudi Arabia has stepped toward a big transformation of changing the current situation of full dependency on oil to new horizons of exploring other sources of renewable energies. Among all the renewable resources of energy, the PV solar energy is the most attractive one to be harnessed in Saudi Arabia. Fortunately, the geographical location of Saudi Arabia is one of the best for solar insolation in the world. The average daily global horizontal irradiance (GHI) received by the lands of Saudi Arabia, is about 6.2 kWh/m2 with a clear sky during the year [3]. Moreover, the costs of manufacturing the PV modules has decreased globally during the current decade to an attainable limit, whereas the life span of the modules has increased to reach almost to 30 years [4]. The total installed capacity of a PV solar energy indicator is a growing arrow over time, as it was 40.3 GW in 2010 and went up to 480.6 GW in 2018. Nevertheless, more capacities are expected to be installed in the coming decade to generate approximately 3278 TWh by 2030, which is almost six times the 585 TWh generated in 2018 [5].
The warm climate of Saudi Arabia and the high temperature at the PV module surface may cause a decrease in the module performance [6]. Power flow from the PV system in a direction opposite that of conventional power flow may lead to changes to network voltage profiles and are perhaps in violation of node voltage limits, a reduction or an increase in line losses, and increased fault current levels [7]. Thus, the integration of large-scale PV systems to the existing electrical power networks of Saudi Arabia could have positive or negative impacts on the network, depending on the network configuration and the solar resource of the location. The importance of creating new regulations and policies to integrate a large scale of solar systems with the existing network in Saudi Arabia is highly required. Likewise, conducting research studies by the aid of developed software programs and tools to analyze the potential risks, to evaluate the technical impacts from/on the existing network and to study the economic feasibility of constructing such projects will surely pave the road for the decision makers to choose the suitable decision. To formulate appropriate policies and regulations, the decision makers require data on the impacts of network integration of the PV systems under the meteorological conditions of the country. It means that the PV system location problem needs to solve those related elements to overall goals and for evaluating alternative solutions. The analytic hierarchy process (AHP) is a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology. The AHP method is used widely in integration with GIS for assigning weights to the criteria and analyzing data in many studies due to its solidity of the analysis procedure [6,8,9,10,11,12,13,14,15,16,17,18,19,20]. The technique for order of preference by similarity to ideal solution (TOPSIS) is a different approach and an important MCDM technique as well. The authors in [9,11,12,15] have proven the value of utilizing the TOPSIS technique, integrated with GIS, in the data analysis process of evaluating the location of the PV system. However, there are very few detailed studies conducted to assess the impact of network integration of the large-scale PV systems in Saudi Arabia or to justify the benefits of building such projects [21,22,23,24]. Through references, there are many studies to use AHP and TOPSIS approaches for PV system location, but this article suggests 11 characterized criteria for Saudi Arabia such as solar irradiation and dust storms and applies 17 locations such as Riyadh, Jeddah, etc., in Saudi Arabia. In this paper, the selection of the optimal site for installing the PV system in Saudi Arabia is studied by the following means:
  • Environmental, location, climate and orographic criteria are proposed to identify the suitable locations by a GIS based multi criteria decision making techniques (MCDM).
  • To obtain the weights of the criteria which influence the proposed locations, the analytic hierarchy process (AHP) is used. Then, the suitable locations will be evaluated and classified using one of the multi-criteria decision methods, The technique for order preference by similarity to ideal solution (TOPSIS).
This paper is composed as follows. Section 2 presents the literature review and selection methodology. In the literature review, references are classified as four major criteria and 11 sub-criteria and assessment of the weighting and analysis method. In selection methodology, GIS-MCDM for criteria and restriction date collection, AHP for criteria weighting, and TOPSIS for performance matrix generation and evaluation are explained. Section 3 provides results that shows optimum region of Saudi Arabia to host PV system facilities. Finally, the conclusion is given in Section 4.

2. PV System Site Selection

The selection of a suitable site is the basic important step towards developing a feasible and efficient PV System project. The process of conducting a solar site analysis considers decision criteria and restriction factors that may have technical, financial, social, and environmental impacts [25]. However, it was recognized by many researchers that site selection along with careful design, allow the impacts to be mitigated [26]. It is a crucial, strategic step for Saudi Arabia to document as much as possible of potential sites for which certain criteria are applied to achieve the highest possible production with less negative impacts as possible. Therefore, when it comes to solar farm site selection, there are two key tools available to the decision maker: MCDM techniques and GIS software [8]. Their complimentary nature makes them very useful tools in the analysis process of selecting the best potential site [9].
MCDM is a form of a numerical integrated technique that can provide solutions to evaluate the sustainability. In addition, it helps to deal with the operations of decision making, in the presence of multiple alternatives featuring high uncertainty. However, a decision-maker is required to choose among those alternatives, which usually contain complex problems, conflicting objectives, and different forms of data. Therefore, the solution is highly dependent on the preferences of the decision-maker and it is normally aimed at identifying the most efficient options at a low cost. In most of the cases, several decision-makers cooperated to identify the criteria. Each group brings along different criteria and points of view that must be resolved within a framework of understanding and mutual compromise [27].
A GIS can be defined as an information system that is designed to work with spatially referenced data. Furthermore, it can deal with those data, by analyzing, storing, modeling, managing, and mapping them [9].
However, employing MCDM techniques, integrated with GIS, leads to choose the suitable decision regardless the effect of many factors in the decision process. Hence, the site selection of large-scale PV system, depending on the analyzed data by GIS and MCDM techniques, offers significant advantages as follows [28]:
  • Enhancing the performance of the system; if the site has suitable climate conditions such as high solar irradiance, moderate temperatures, and long days hours per year.
  • Optimizing the installations by building the PV system on a flat ground, facing the south and avoiding the shadow.
  • Minimizing the cost impact; if the system is built near urban areas, power transmission and constructed roads are the main consumption points.
  • Reducing the impact on the environment, society, and infrastructures.

2.1. Literature Review

The main objective of the literature review in this section is to emphasize the common decision criteria in this research and to avoid the restriction factors, to highlight the MCDM methodology for criteria weighting and analysis technique to find the best alternative location. In fact, in most of the solar PV site analysis studies, decision criteria for site selection fall into general major criteria, and each general criterion contains sub-criteria as shown in Table 1.
However, not all the criteria have the same weight of importance. Therefore, the criteria should be selected based on the subjective circumstances and accessibility to the database [10]. Moreover, the selected criteria should not exceed more than 15 criteria to avoid the complexity of the analysis [39]. Most of the criteria are associated with essential factors and boarders that guarantee the quality and the efficiency of the PV system plant location. It is important that to receive high solar irradiation in the location, acceptable distance, and connections to a grid point source, as well as taking into account the environmental conditions for the installation in the area [11]. In the literature, there are several studies that specified a wide range of the determinant factors that must be avoided during the plan of choosing the optimum location of the PV system plant. The authors of [37] have advised that high risk or frequency areas, which resulted from natural or weather conditions like wildfires, earthquake, dust storm and flash floods, are hazard zones for PV development. Flat areas without shadows are a most preferable location for installing PV system projects, so as per [45], hills or big mountains, lakes, sand dunes, permanent snow areas and natural forest are restricted due to their location or topographic status. In [29], the restriction factors should include military zones, cattle trails, watercourses and streams, archaeological sites, paleontological sites, cultural heritage, railroad network, and community interest sites. In fact, the restriction factors can go beyond the geographical conditions as it is mentioned in [30] that, the unavailability of skilled manpower, noise and visual negative impact, and toxin emissions can be considered as restricted factors as well. However, as the restriction factors are specified, the major role of GIS-analysis is to exclude those areas that are unsuitable for installing large-scale solar power plants.
After defining the problem, identification of alternatives, criteria selection, and restriction factors determination, MCDM are also important methods for assigning weights to the criteria and analyzing data with the aid of GIS. Table 2 shows various MCDM methods classification in the references.
The authors of [12,29] analyzed technique in integration with GIS. They have concluded how it is worth to utilize the field of renewable energy system location identification. The author in [46] shows that two-sided matching decision making (TSMDM) problems exist widely in the daily lives of humans. An approach to TSMDM with multi-granular hesitancy and thus provide hesitant fuzzy linguistic term sets is developed and an example for the matching of green building technology supply and demand is provided to demonstrate the characteristics of the proposed approach. In fact, only one study in the literature that utilized the GIS-MCDM integration technique identified the best location of PV system in Saudi Arabia by considering five criteria only [6]. Therefore, it is of vital contribution for this research to be one of the first studies to utilize the GIS-MCDM integration technique in Saudi Arabia to find the best location of the large-scale PV system by using different approaches than the other conducted researches.

2.2. Selection Methodolgy

This section explores, investigates, and evaluates the optimum location of a large-scale grid connected PV system among 17 cities distributed all over the Saudi Arabia country. The access to the available geodatabase is the limitation of the research expansion. The proposed framework is presented in Figure 1, and summarizes as the following steps:
  • Identifying the criteria that influence the decision of selecting the PV plant location.
  • Loading the geodata of the identified criteria into the GIS. The important restriction inputs are applied to discard the unsuitable locations.
  • Applying the AHP technique for determining the criteria weights, based on the relative importance of each one.
  • Evaluating the remaining alternatives using TOPSIS analysis method.

2.2.1. Criteria and Restrictions

Since each region has different environmental, cultural, climate, orography, political, and economic aspects, the following 11 sub-criteria and four major criteria suggested in this study research are: solar irradiation; average temperature; relative humidity; dust storm; distance to power lines; distance to main roads; distance to residentials areas; slope; orientations (aspect angles); population density; and carbon emissions reduction. These sub-criteria were subset of four major criteria: environmental; location; climate; and, orographic, as shown in Figure 2. The explanation of each factor is presented in the following:
  • Solar irradiation (m1): The continuous operation of a PV plant depends on the received solar irradiance, the annual solar radiation is considered as a climatic factor which is used to measure the intensity of sunshine for a candidate site and is normally expressed as an average of several years. The PV power output is related proportionally to the solar radiation. In this study, the annual average daily solar irradiation was calculated for all the locations chosen using PVGIS, which is a web-based calculator developed by the JRC (Joint Research Centre) of the European Commission.
  • Average temperature (m2) and relative humidity (m3): As the temperature increases, the output power and the efficiency of the modules decreases [18]. In the contrary, high relative humidity is unlikable for PV module efficiency. Both descend factors are classified as climatic factors too. In this study, the data of the annual temperature and relative humidity were driven from the new network monitoring systems which have been developed by K.A. CARE as part of the Renewable Resource Monitoring and Mapping (RRMM) program.
  • Dust storm (m4): The third climatic factor in this study is the dust storms. The Arabian Peninsula has been recognized as one of the five world regions where sand and dust storm generation are especially extreme [47]. Thus, this factor cannot be neglected in regions that have a climate such as Saudi Arabia. In this study, the data of the dust storm frequencies were driven from the presidency of the meteorology and environment of Saudi Arabia over the period of 2013–2016.
  • Slope (m5) and orientations (aspect angles) (m6): The slope and aspects of the land are crucial topographical factors which play a considerable role for land suitability for PV system location [37]. Therefore, a land with a slope of more than 5 degree and non-south facing will be eliminated in order to avoid the shadow effect on the generation of the PV system [10]. In this study the data of slope and topographic orientation of Saudi Arabia have been downloaded as a digital elevation data form Alaska Satellite facilities database.
  • Distance to power lines (m9), main roads (m10), and residentials areas (m7): The three mentioned sub-criteria here are sometimes classified as a location criterion and economic criterion. Generally, the importance of a solar power plant being close to the road network is for facilitating the transportation of modules and equipment, and the maintenance during and after the construction phase [14]. Moreover, it has to be close enough to residential areas and to the electrical grid to supply the electricity and to avoid constructing new power transmission lines [25]. In this study, the data of the power lines locations were collected from the internal website of Saudi Electricity company, and from KAPSARK data portal for the main roads, and Google Maps for residentials areas. A buffer distance restriction of 2 km for power lines, 0.5 km for main roads, and 50 km for residential areas has been applied to get an optimum location of the PV system.
  • Population density (m8): is the fourth sub-criteria that classified as a location criterion. The establishment of a solar system must be close enough to a location that has adequate consumers and well skilled manpower to lower the cost of employing nonlocality manpower to construct, operate and maintain the PV system [30,41]. In this study, the data of the population for each alternative has been derived from KAPSARK data portal.
  • Carbon emissions reduction (m11): is one of the critical sub-criteria that classified as an environmental criterion. Many studies discussed the carbon emission savings in terms of the manufacturing supply chain [44]. In this study, a different approach in minimizing the carbon emissions is considered by installing the PV system in the locations that have higher emissions of CO2. Thus, the data of total CO2 of power plants which are speared all over Saudi Arabia has been derived from the KAPSARK data portal and Google Maps.
The area of Saudi Arabia is big enough to host large solar system projects, as its area is almost 2.15M km2. Therefore, once the problem has been defined, we must solve the problem of identifying various alternatives. GIS is a suitable tool that can be employed to enable us to obtain such alternatives and considering the criteria and restrictions which are mentioned in Section 2.2 and Figure 2 that affect the study area. These alternatives represent the suitable surface area available in Saudi Arabia to host these facilities after eliminating the restricted areas and are defined in plots of Figure 3 and Figure 4.
The decision matrix is generated as a form of B = (n × m), where n represents the number of the selected alternatives and m represents the number of the selected criteria listed in Section 2.2.1. Seventeen alternatives (cities) have accessible data and satisfied the purpose of this study. In Table 3, the entry values bij which represent the collected data value of the corresponding criterion ith (row), at the alternative location jth (column) are presented.

2.2.2. Criteria Weighting Using AHP Technique

To assess the plots of Figure 3 and Figure 4 based on the criteria that have been mentioned in the Section 2.2.1, the weights of the criteria must be obtained. Thus, for this purpose, the analytical hierarchy process (AHP) technique that was created by Saaty [48] is used. The main concept behind this technique is to develop a comparison decision matrix that allows to compare each criterion with its pair, by applying certain calculations and procedures, to obtain consisted weights for each criterion. The following steps explains the required AHP procedure to obtain the criteria weights:
  • Pairwise comparison scaling:
Table 4 shows and describes the pairwise comparison scaling values of the criteria and the meaning of each value. The scale starts from 1 to 9, as Saaty suggested [48], which basically represents the relative importance for each criterion.
Pairwise comparison matrix (A):
The pairwise comparison matrix is generated as a form of a square matrix A = (m × m), where m represents the number of criteria. In the comparison process, the entry value aij, which represents the comparison value between the ith (row) criterion relative to the jth (column) criterion, must achieve the condition in the following Equation (1). The comparison matrix was applied to all the criteria listed in Section 2.2.1 as shown in Table 5.
aij × aji = 1
Comparison matrix normalization ( a i j ¯ ) :
To establish a normalized pairwise comparison matrix A, it has to be done by dividing each entry value aij with the sum of the entry values in the belonging column as shown in Table 6 by using the following Equation (2):
a i j ¯ = a i j l = 1 m a l j
Compute the overall weight vector (wi):
The overall weight vector wi can be calculated by taking the average for each row in the matrix as shown Table 7 by using the following Equation (3):
w i = l = 1 m a i l ¯ m
Comparison matrix consistency verification:
Finally, to verify the consistency of the pairwise comparison matrix, the consistency ratio (CR) must be calculated. If CR < 0.1, the calculation consistency is acceptable to be used in the analysis, but if CR > 0.1, the procedure must be revised to find the reason of the inconsistency and correct it. CR is given in the following Equation (4):
C R =   C I R I ,
where (RI) is the random index and (CI) is the consistency index and can be calculated by using the following Equation (5):
C I =   λ m a x m m 1 ,
where λ m a x is the maximum eigenvalue of the comparison matrix and m is the number of criteria in the comparison matrix. The λ m a x is calculated as the average of the elements of the vector whose ith element is the ratio of the ith element of the vector (A·wi) to the corresponding element of the vector (wi), as shown in Table 8.
In Equation (4), the random index (RI) according to Saaty [48], changes subject to the number of the criteria as shown in Table 9.

2.2.3. Technique for Order Preference by Similarity to Ideal Solution

In Section 2.2.2 the criteria weights have been obtained, the next step is to evaluate the alternatives to find the optimum location to host the PV system plant. The technique for order preference by similarity to ideal solution (TOPSIS), which was created by Hwang and Yoon [49], is applied. The fundamental concept of this technique is to find an alternative whose distance from the positive ideal solution is the shortest while the distance from the negative ideal solution is the longest. The computational steps of the TOPSIS method are the following:
Performance matrix creation and normalization ( b i j ¯ ) :
Table 3. The decision matrix B is used as a performance matrix for TOPSIS. To establish a normalized performance matrix B ¯ , each entry value xij is divided with the sum of the squared entry values, under the squared root, in the same column as shown in Table 10 by using the following Equation (6):
b i j ¯ = b i j i = 1 n b i j 2 .
Weighted normalized performance matrix ( B ¯ × wi):
The weighted normalized performance matrix is calculated by multiplying the weight vector wi by the normalized performance matrix B ¯ as per the following Equation (7), and the results are shown in Table 11.
Z = B ¯ × w i
Determining the positive and negative ideal solutions (zj+ & zj):
To find the positive ideal solution in the jth (column), find the maximum cell value in the same column of the matrix Z. To find the negative ideal solution in the jth (column) find the minimum cell value zij in the same column of the matrix Z. Note that this is only true for the beneficial criteria while for the non-beneficial criteria the opposite procedure is to be followed. The desired positive and negative ideal solution are shown in Table 12.
Finding the Euclidean distance from positive and negative ideal solutions (Si+ & Si):
This can simply be found through calculating the measurement of the alternatives with respect to zj+ and zj, as shown in Table 13, by applying Equations (8) and (9):
S i + = j = 1 n ( z i j z l + ) 2 ,
S i = j = 1 n ( z i j z l ) 2 .
Calculating the Performance score (Pi):
The performance score can be found throughout the calculation of the relative proximity of each alternative to the positive and negative ideal solutions, by dividing the negative ideal solution over the positive and negative ideal solutions, as shown in Table 14, by using the following Equation (10):
P i = S i S i + + S i

3. Results and Discussion

Once the criteria have been identified, the integrated MCDM-GIS methodology is applied to weight those criteria, to eliminate the restricted locations, and to evaluate the remaining alternatives locations based on the data availability. The AHP technique is used to weight the identified criteria that were mentioned in Section 2.2.1. The given weights, as shown in Figure 5, are consistent and acceptable since the consistency ratio CR was 0.08, a resulting value of Equation (4).
It is obvious that, the climate and the orography criteria have the highest weight over the location and environmental criteria so that solar irradiation, air temperature and humidity, land slope and land aspects have a direct impact of the power production and modules performance of the PV system. TOPSIS technique has been applied to evaluate the 17 alternatives locations with those which were mentioned in Section 2.2.3. The evaluation procedure was accomplished based on the weighted criteria. The performance score for each alternative demonstrated on a scale range (0–1), which is defined in Equation (10), is presented in Figure 6 where Tabuk has the highest score. The results show that the region of Saudi Arabia is very promising to host PV system facilities. All the alternatives locations got evaluation scores above 50% based on the identified weighted criteria.
Regions such as Tabuk, Taif, Qassim, Al-Baha, Najran, Abha and Hail are considered to be the most suitable locations since their scores are above or equal 80%. However, there are three cities that scores below 65%, which are Jeddah, Jizan, and Al-Ahsa. That means 82% of the alternative locations can be classified as suitable locations to host PV system facilities since they got performance scores above 65%. Finally, the distribution of the locations is demonstrated on the map shown in Figure 7.

4. Conclusions

In this paper, the potential sites to host the PV system facility in Saudi Arabia were examined throughout the application of MCDM techniques integrated with the GIS software tool. The identification of the criteria was comprehensive and included as much criteria as possible, such as climate, location, orography, and environmental criteria with a total of 11 sub-criteria. The weights given to each sub-criterion by using the AHP technique were acceptable since the results of the consistency ratio were less than 0.1. Sub-criteria such as solar irradiation, average temperature, dust storm and slope were given accumulative weights of 71%, which were much higher than the remaining sub-criteria, which is due to their direct technical impact on the power output and the performance of the modules of the PV system. The TOPSIS technique was used to evaluate the alternatives location for the first time in Saudi Arabia. Seventeen sites were analyzed based on the weighted criteria and results of the performance score for each alternative site was optimistic. All of the candidate sites got scores above 50%, out of which seven sites got scores above 80%. The Tabuk site was selected to be the optimum location among all the other alternatives since it got the highest score of 87%. In conclusion, the Tabuk site is the best location to host the Large-Scale PV system grid connected to the network of Saudi Arabia. In the early planning and feasibility study, there are limitations to gathering data and time is needed to evaluate where the suitable location to implement Renewable Energy could be, and this study helps to decide the suitable location for PV site in an early stage. Existing and available GIS information could provide a fundamental data before taking measurements of each region and give a basic direction for the feasibility study. In this article, 17 cities were considered to implement a PV system facility and choose seven sites for a suitable place. It implies that in a practical case, it saves time and budget to select a suitable site for PV implementation. In addition, attention should be given based on local experts’ assistance, and more and different criteria should be considered as well. In a future study, the potential technical impact and economic assessment will be investigated for the selected regions after integrating large-scale PV solar system to the electric grid in Saudi Arabia with real network parameters.

Author Contributions

The paper was a collaborative effort between the authors. The authors contributed collectively to the data analysis, modeling, simulation, and manuscript preparation. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.


The authors extend their appreciation to the Deputyship for Research and Innovation, “Ministry of Education” in Saudi Arabia for funding this research work through the project number IFKSURG-170.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Annual Report 2018. Saudi Electricity Company, Saudi Arabia. 2018. Available online: (accessed on 20 August 2020).
  2. Global Carbon Atlas. 2018. Available online: (accessed on 5 September 2020).
  3. Zell, E.; Gasim, S.; Wilcox, S.; Katamoura, S.; Stoffel, T.; Shibli, H.; Engel-Cox, J.; Al Subie, M. Assessment of solar radiation resources in Saudi Arabia. Sol. Energy 2015, 119, 422–438. [Google Scholar] [CrossRef] [Green Version]
  4. Renewable Energy Cost Analysis—Solar Photovoltaics. IRENA. Available online: (accessed on 10 July 2020).
  5. Renewable Power Generation Costs in 2019. IRENA. 2019. Available online: (accessed on 30 July 2020).
  6. Al Garni, H.Z.; Awasthi, A. Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia. Appl. Energy 2017, 206, 1225–1240. [Google Scholar] [CrossRef]
  7. Cohen, M.A.; Callaway, D.S. Effects of distributed PV generation on California’s distribution system, Part 1: Engineering simulations. Sol. Energy 2016, 128, 126–138. [Google Scholar] [CrossRef]
  8. Watson, J.J.; Hudson, M.D. Regional Scale wind farm and solar farm suitability assessment using GIS-assisted multi-criteria evaluation. Landsc. Urban Plan. 2015, 138, 20–31. [Google Scholar] [CrossRef]
  9. Sanchezlozano, J.M.; Teruel-Solano, J.; Soto-Elvira, P.L.; García-Cascales, M.S. Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain. Renew. Sustain. Energy Rev. 2013, 24, 544–556. [Google Scholar] [CrossRef]
  10. Charabi, Y.; Gastli, A. PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation. Renew. Energy 2011, 36, 2554–2561. [Google Scholar] [CrossRef]
  11. Rediske, G.; Siluk, J.C.M.; Michels, L.; Rigo, P.D.; Rosa, C.B.; Cugler, G. Multi-criteria decision-making model for assessment of large photovoltaic farms in Brazil. Energy 2020, 197, 117167. [Google Scholar] [CrossRef]
  12. Sánchez-Lozano, J.M.; García-Cascales, M.S.; Lamata, M.T. Evaluation of suitable locations for the installation of solar thermoelectric power plants. Comput. Ind. Eng. 2015, 87, 343–355. [Google Scholar] [CrossRef]
  13. Uyan, M. GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renew. Sustain. Energy Rev. 2013, 28, 11–17. [Google Scholar] [CrossRef]
  14. Merrouni, A.A.; Elalaoui, F.E.; Mezrhab, A.; Mezrhab, A.; Ghennioui, A. Large scale PV sites selection by combining GIS and Analytical Hierarchy Process. Case study: Eastern Morocco. Renew. Energy 2018, 119, 863–873. [Google Scholar] [CrossRef]
  15. Sánchez-Lozano, J.; García-Cascales, M.S.; Lamata, M.T. Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain. J. Clean. Prod. 2016, 127, 387–398. [Google Scholar] [CrossRef]
  16. Suh, J.; Brownson, J.R.S. Solar Farm Suitability Using Geographic Information System Fuzzy Sets and Analytic Hierarchy Processes: Case Study of Ulleung Island, Korea. Energies 2016, 9, 648. [Google Scholar] [CrossRef] [Green Version]
  17. Doljak, D.; Stanojević, G. Evaluation of natural conditions for site selection of ground-mounted photovoltaic power plants in Serbia. Energy 2017, 127, 291–300. [Google Scholar] [CrossRef] [Green Version]
  18. Habib, S.M.; Suliman, A.E.-R.E.; Al Nahry, A.H.; El Rahman, E.N.A. Spatial modeling for the optimum site selection of solar photovoltaics power plant in the northwest coast of Egypt. Remote Sens. Appl. Soc. Environ. 2020, 18, 100313. [Google Scholar] [CrossRef]
  19. Aly, A.; Jensen, S.S.; Pedersen, A.B. Solar power potential of Tanzania: Identifying CSP and PV hot spots through a GIS multicriteria decision making analysis. Renew. Energy 2017, 113, 159–175. [Google Scholar] [CrossRef]
  20. Yunna, W.; Geng, S. Multi-criteria decision making on selection of solar–wind hybrid power station location: A case of China. Energy Convers. Manag. 2014, 81, 527–533. [Google Scholar] [CrossRef]
  21. Ko, W.; Al-Ammar, E.A.; Almahmeed, M. Development of Feed-in Tariff for PV in the Kingdom of Saudi Arabia. Energies 2019, 12, 2898. [Google Scholar] [CrossRef] [Green Version]
  22. Al-Saleh, Y.M.; Taleb, H.M. The Economic Viability of Solar Photovoltaics within the Saudi Residential Sector. In Proceedings of the Conference on Technology & Sustainability in the Built Environment, King Saud University, Riyadh, Saudi Arabia, 3–6 January 2010; p. 12. [Google Scholar]
  23. Alharthi, Y.Z.; Siddiki, M.K.; Chaudhry, G.M. Resource Assessment and Techno-Economic Analysis of a Grid-Connected Solar PV-Wind Hybrid System for Different Locations in Saudi Arabia. Sustainability 2018, 10, 3690. [Google Scholar] [CrossRef] [Green Version]
  24. Rehman, S.; Ahmed, M.; Mohamed, M.H.; Al-Sulaiman, F.A. Feasibility study of the grid connected 10 MW installed capacity PV power plants in Saudi Arabia. Renew. Sustain. Energy Rev. 2017, 80, 319–329. [Google Scholar] [CrossRef]
  25. Al Garni, H.Z.; Awasthi, A. Solar PV Power Plants Site Selection. In Advances in Renewable Energies and Power Technologies; Elsevier: Amsterdam, The Netherlands, 2018; pp. 57–75. [Google Scholar]
  26. Chiabrando, R.; Fabrizio, E.; Garnero, G. The territorial and landscape impacts of photovoltaic systems: Definition of impacts and assessment of the glare risk. Renew. Sustain. Energy Rev. 2009, 13, 2441–2451. [Google Scholar] [CrossRef]
  27. Pohekar, S.; Ramachandran, M. Application of multi-criteria decision making to sustainable energy planning—A review. Renew. Sustain. Energy Rev. 2004, 8, 365–381. [Google Scholar] [CrossRef]
  28. Carrión, J.A.; Estrella, A.E.; Toro, F.A.D.M.Z.; Rodríguez, M.; Ridao, A.R. Environmental decision-support systems for evaluation the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants. Renewa. Sustain. Energy Rev. 2008, 12, 2358–2380. [Google Scholar]
  29. Sanchezlozano, J.M.; Antunes, C.H.; García-Cascales, M.S.; Dias, L.C. GIS-based photovoltaic solar farms site selection using ELECTRE-TRI: Evaluating the case for Torre Pacheco, Murcia, Southeast of Spain. Renew. Energy 2014, 66, 478–494. [Google Scholar] [CrossRef]
  30. Sindhu, S.; Nehra, V.; Luthra, S. Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: Case study of India. Renew. Sustain. Energy Rev. 2017, 73, 496–511. [Google Scholar] [CrossRef]
  31. Aydin, N.Y.; Kentel, E.; Duzgun, H.S. GIS-based site selection methodology for hybrid renewable energy systems: A case study from western Turkey. Energy Convers. Manag. 2013, 70, 90–106. [Google Scholar] [CrossRef]
  32. Wang, Q.; M’Ikiugu, M.M.; Kinoshita, I. A GIS-Based Approach in Support of Spatial Planning for Renewable Energy: A Case Study of Fukushima, Japan. Sustainability 2014, 6, 2087–2117. [Google Scholar] [CrossRef] [Green Version]
  33. Lee, A.H.I.; Kang, H.-Y.; Lin, C.Y.; Shen, K.-C. An Integrated Decision-Making Model for the Location of a PV Solar Plant. Sustainability 2015, 7, 13522–13541. [Google Scholar] [CrossRef] [Green Version]
  34. Lee, A.H.I.; Kang, H.-Y.; Liou, Y.-J. A Hybrid Multiple-Criteria Decision-Making Approach for Photovoltaic Solar Plant Location Selection. Sustainability 2017, 9, 184. [Google Scholar] [CrossRef]
  35. Borgogno-Mondino, E.; Fabrizio, E.; Chiabrando, R. Site Selection of Large Ground-Mounted Photovoltaic Plants: A GIS Decision Support System and an Application to Italy. Int. J. Green Energy 2014, 12, 515–525. [Google Scholar] [CrossRef] [Green Version]
  36. Piyatadsananon, P. Spatial Factors Consideration in Site Selection of Ground-mounted PV Power Plants. Energy Procedia 2016, 100, 78–85. [Google Scholar] [CrossRef] [Green Version]
  37. Majumdar, D.; Pasqualetti, M.J. Analysis of land availability for utility-scale power plants and assessment of solar photovoltaic development in the state of Arizona, USA. Renew. Energy 2019, 134, 1213–1231. [Google Scholar] [CrossRef]
  38. Boran, F.E.; Menlik, T.; Boran, K. Multi-criteria Axiomatic Design Approach to Evaluate Sites for Grid-connected Photovoltaic Power Plants: A Case Study in Turkey. Energy Sources Part B Econ. Plan. Policy 2010, 5, 290–300. [Google Scholar] [CrossRef]
  39. Kereush, D.; Perovych, I. Determining criteria for optimal site selection for solar power plants. Geomat. Landmanag. Landsc. 2017, 4, 39–54. [Google Scholar] [CrossRef]
  40. Janke, J.R. Multicriteria GIS modeling of wind and solar farms in Colorado. Renew. Energy 2010, 35, 2228–2234. [Google Scholar] [CrossRef]
  41. Kengpol, A.; Rontlaong, P.; Tuominen, M. Design of a decision support system for site selection using fuzzy AHP: A case study of solar power plant in north eastern parts of Thailand. In Proceedings of the 2012 Proceedings of PICMET ’12: Technology Management for Emerging Technologies, Vancouver, BC, Canada, 29 July–2 August 2012. [Google Scholar]
  42. Massimo, A.; Dell’Isola, M.; Frattolillo, A.; Ficco, G. Development of a Geographical Information System (GIS) for the Integration of Solar Energy in the Energy Planning of a Wide Area. Sustainability 2014, 6, 5730–5744. [Google Scholar] [CrossRef] [Green Version]
  43. Sabo, M.L.; Mariun, N.; Hizam, H.; Radzi, M.A.M.; Zakaria, A. Spatial energy predictions from large-scale photovoltaic power plants located in optimal sites and connected to a smart grid in Peninsular Malaysia. Renew. Sustain. Energy Rev. 2016, 66, 79–94. [Google Scholar] [CrossRef]
  44. Liu, J.; Xu, F.; Lin, S. Site selection of photovoltaic power plants in a value chain based on grey cumulative prospect theory for sustainability: A case study in Northwest China. J. Clean. Prod. 2017, 148, 386–397. [Google Scholar] [CrossRef]
  45. Anwarzai, M.A.; Nagasaka, K. Utility-scale implementable potential of wind and solar energies for Afghanistan using GIS multi-criteria decision analysis. Renew. Sustain. Energy Rev. 2017, 71, 150–160. [Google Scholar] [CrossRef]
  46. Zhang, Z.; Gao, J.; Gao, Y.; Yu, W. Two-sided matching decision making with multi-granular hesitant fuzzy linguistic term sets and incomplete criteria weight information. Expert Syst. Appl. 2020, 114311. [Google Scholar] [CrossRef]
  47. Butt, M.J.; Mashat, A.S. MODIS satellite data evaluation for sand and dust storm monitoring in Saudi Arabia. Int. J. Remote Sens. 2018, 39, 8627–8645. [Google Scholar] [CrossRef]
  48. Saaty, T.L. Multicriteria Decision Making: The Analytic Hierarchy Process: Planning, Priority Setting Resource Allocation; Thomas, L., Ed.; SAATY McGraw-Hill: New York, NY, USA, 1990. [Google Scholar]
  49. Hwang, C.-L.; Yoon, K. Methods for Multiple Attribute Decision Making. In Lecture Notes in Economics and Mathematical Systems; Springer: Berlin/Heidelberg, Germany, 1981. [Google Scholar]
Figure 1. The flowchart of the proposed process.
Figure 1. The flowchart of the proposed process.
Energies 14 00357 g001
Figure 2. The major and sub-criteria classifications.
Figure 2. The major and sub-criteria classifications.
Energies 14 00357 g002
Figure 3. The generated GIS maps after applying the restriction factors.
Figure 3. The generated GIS maps after applying the restriction factors.
Energies 14 00357 g003
Figure 4. The generated GIS maps after applying the restriction factors.
Figure 4. The generated GIS maps after applying the restriction factors.
Energies 14 00357 g004
Figure 5. The weights values given to each criterion.
Figure 5. The weights values given to each criterion.
Energies 14 00357 g005
Figure 6. The performance score for each alternative by Rank.
Figure 6. The performance score for each alternative by Rank.
Energies 14 00357 g006
Figure 7. Suitable locations to harvest solar PV system in Saudi Arabia.
Figure 7. Suitable locations to harvest solar PV system in Saudi Arabia.
Energies 14 00357 g007
Table 1. Reference classification by four major criteria and 11 sub-criteria.
Table 1. Reference classification by four major criteria and 11 sub-criteria.
Major CriteriaSub-CriteriaReferences
ClimaticSolar irradiation[6,9,10,12,13,14,15,16,29,30,31,32,33,34,35]
Average temperature[6,9,10,12,15,29,33,34,35]
Relative humidity[17,18,36]
Wind speed[18,33,34,36]
Dust storm[37]
Sun hour duration[17,18,33,34,38]
Aspect angles[15,26,27,35,39]
Plot area[9,12,15,29]
LocationDistance from residential areas (m)[6,8,9,11,12,13,15,17,29,30,31]
Visual impact[9,30]
Population density[40,41,42,43]
Distance to power lines[8,9,10,12,13,14,15,29,30,31,33,34,40,41,43]
Distance to historical areas[8,37]
Distance to wildlife designations[8,16,37]
Distance to main roads[8,9,10,11,12,13,14,15,29,40,43]
Land cover[40]
Distance to substations[9,12,15,29]
EconomicLand cost[41]
Construction cost[41]
EnvironmentalCarbon emission savings[44]
Agrological capacity[9,12,29]
Table 2. MCDM methods classification in the references.
Table 2. MCDM methods classification in the references.
RES TechnologySite LocationWeighting MethodAnalysis MethodReferences
PVSaudi ArabiaAHPWSM (Weighted Sum Model)[6]
PV-CSP-WindSouthern EnglandAHPAHP[8]
PVMurcia, SpainAHPTOPSIS[9]
PVOmanAHPFLOWA(Flow Analysis)[10]
Solar ThermoelectricMurcia, SpainAHPFuzzy TOPSIS-ELECTRE[12]
PVKonya, TurkeyAHPAHP[13]
PVEastern MoroccoAHPAHP[14]
PVSouth KoreaFuzzy SetAHP[16]
PVNorthwest cost of EgyptAHPWSM (Weighted Sum Model)[18]
PVIndiaAHPFuzzy TOPSIS[30]
HybridWestern TurkeyFuzzyOWA (Ordered Weighted Averaging)[31]
PV-CSP-WindColorado, USAWLCWLC[40]
PVNorthwest China Grey cumulative[44]
PV-CSP-WindAfghanistan WLC(Weighted Linear Combination)[45]
Table 3. The decision matrix.
Table 3. The decision matrix.
m1 (Wh/m2/d)m2 (C°)m3 (%)m4 (#)m5 (°)m6m7 (Km)m8 (people)m9 (Km)m10 (Km)m11 (Kg)
Table 4. The importance pairwise comparison scaling.
Table 4. The importance pairwise comparison scaling.
The Importance Scale (aij) of The CriteriaThe Description
1Criteria i and j are of equal importance
3Criteria i is slightly more important than j
5Criteria i is moderately more important than j
7Criteria i is strongly more important than j
9Criteria i is extremely more important than j
2, 4, 6, 8Intermediate values
Table 5. The pairwise comparison matrix.
Table 5. The pairwise comparison matrix.
Table 6. The normalized pairwise comparison matrix.
Table 6. The normalized pairwise comparison matrix.
Table 7. The overall weight vector wi.
Table 7. The overall weight vector wi.
Table 8. The maximum eigenvalue of the comparison matrix λmax.
Table 8. The maximum eigenvalue of the comparison matrix λmax.
m1m2m3m4m5m6m7m8m9m10m11 λ m a x
Table 9. The random index.
Table 9. The random index.
Table 10. The performance matrix normalization. * Blank control.
Table 10. The performance matrix normalization. * Blank control.
Table 11. The weighted normalized performance matrix. * Blank control.
Table 11. The weighted normalized performance matrix. * Blank control.
Table 12. The positive and negative ideal solutions. * Blank control.
Table 12. The positive and negative ideal solutions. * Blank control.
zj+0.06090.02940.00930.00000.0329 *0.00690.01520.01290.01110.0104
zj0.05110.04940.03870.09930.0329 *0.00690.00010.01290.01110.0000053
Table 13. The Euclidean distance from positive and negative ideal solutions.
Table 13. The Euclidean distance from positive and negative ideal solutions.
Table 14. The performance scores.
Table 14. The performance scores.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Al-Shammari, S.; Ko, W.; Al Ammar, E.A.; Alotaibi, M.A.; Choi, H.-J. Optimal Decision-Making in Photovoltaic System Selection in Saudi Arabia. Energies 2021, 14, 357.

AMA Style

Al-Shammari S, Ko W, Al Ammar EA, Alotaibi MA, Choi H-J. Optimal Decision-Making in Photovoltaic System Selection in Saudi Arabia. Energies. 2021; 14(2):357.

Chicago/Turabian Style

Al-Shammari, Sultan, Wonsuk Ko, Essam A. Al Ammar, Majed A. Alotaibi, and Hyeong-Jin Choi. 2021. "Optimal Decision-Making in Photovoltaic System Selection in Saudi Arabia" Energies 14, no. 2: 357.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop