Site-selection procedures of PV and Concentrated Solar Power technologies

Solar energy is one of the leading renewable energy sources in terms of installed power capacity on a global scale. Scientific research on the site-selection procedures of solar photovoltaics (PV) and concentrated solar power (CSP) technologies is of significant importance, contributing to environmentally sustainable, technically and economically viable, and socially acceptable solar energy projects. This systematic review provides direct analysis and assessment of existing site-selection procedures and addresses a gap in knowledge in the solar energy research.

Among a total of 10,121 scientific studies filtered and investigated, a total of 152 scientific studies were identified as eligible and reviewed in detail. Each selected study was further investigated through 11 key thematic modules: (1) site-selection methodologies; (2) type, number, and exclusion limits of exclusion criteria; (3) type, number, importance, priority, and suitability classes of assessment criteria; (4) optimization modules and criteria; (5) studies’ geographic locations; (6) spatial planning or reference scales; (7) solar radiation data estimation and analysis; (8) sensitivity analysis related to site-selection procedure; (9) participatory planning approaches, groups, and contributions; (10) laws, regulations, and policies related to site-selection procedure; (11) suitability indexes (linguistic or/and numeric) and ranking procedures. Important insights and useful data trends are identified and highlighted in these key thematic modules of site-selection issue, enhancing future studies and globally improving siting implementations.

Renewable energy was the only source of electricity generation to register a net increase in total capacity in 2020, despite the political focus to COVID-19 crisis and the impacts of this pandemic (REN21, 2021). Solar energy is one of the leading renewable energy sources in terms of installed power capacity on a global scale. In particular, solar photovoltaics (PV) had another record-breaking year with the installation of 139 GW in 2020 (REN21, 2021). Accordingly, the global solar PV energy market reached the milestone of 760 GW cumulative installed capacity at the end of 2020. At the same time, global concentrated solar power (CSP) capacity increased a mere 1.6% in 2020 to 6.2 GW, by installing a single 100 MW parabolic trough project in China, while more than 1 GW of CSP projects was under construction in the United Arab Emirates, Chile, China, and India during this year (REN21, 2021). Contrary to PV accelerated growth, this was the lowest annual CSP market growth in over a decade, due to several challenges faced by the CSP energy market in recent years (i.e., cost competition from PV and a range of operational issues at existing power facilities). Despite that, CSP is a proven technology for large-scale conversion of solar energy into electricity and remains more competitive than PV in several geographical areas globally (Dupont et al., 2020, Koberle et al., 2015). Additionally, CSP costs decreased 50% during the 2010s and there are several existing CSP installations with thermal energy storage co-located with PV in order to lower costs and increase capacity (REN21, 2021).

In this accelerated spatial deployment of PV farms and stepwise deployment of CSP farms globally, all site-selection aspects that lead to environmentally sustainable, technically and economically viable, and socially acceptable solar energy projects, should be considered and investigated in detail. Numerous studies on PV (e.g., Brewer et al., 2015, Marques-Perez et al., 2020) and CSP (e.g., Dawson and Schlyter, 2012, Wu et al., 2019) siting have aimed to solve this quite complex siting problem by: (i) developing innovative site-selection methodologies (Brewer et al., 2015, Dawson and Schlyter, 2012, Marques-Perez et al., 2020, Wu et al., 2019); (ii) defining and applying numerous exclusion criteria (EC) (Brewer et al., 2015, Dawson and Schlyter, 2012, Marques-Perez et al., 2020, Wu et al., 2019) and assessment criteria (AC) (Brewer et al., 2015, Dawson and Schlyter, 2012, Marques-Perez et al., 2020, Wu et al., 2019); (iii) determining the relative importance of each AC (Brewer et al., 2015, Dawson and Schlyter, 2012, Marques-Perez et al., 2020, Wu et al., 2019); (iv) conducting solar radiation data analysis (Brewer et al., 2015, Dawson and Schlyter, 2012, Marques-Perez et al., 2020); (v) considering laws, regulations or policies related to sustainable solar technologies siting (Marques-Perez et al., 2020); (vi) conducting sensitivity analysis on PV or CSP siting results (Wu et al., 2019); (vii) incorporating experts, stakeholders, or public views and concerns in the site-selection process (Brewer et al., 2015, Dawson and Schlyter, 2012, Marques-Perez et al., 2020, Wu et al., 2019); and (viii) prioritizing the suitable sites for solar technologies installation in order to pinpoint the most suitable ones (Marques-Perez et al., 2020, Wu et al., 2019). A systematic review of site-selection procedures that have globally been applied in different geographic locations and a direct analysis of all their key aspects could reveal critical insights and useful data trends for the improvement of existing siting procedures and the fulfillment of international energy target goals.

Preceding reviews conducted on solar PV (Lara and García, 2021, Maleki et al., 2020, Sreenath et al., 2021) and CSP (Fernández et al., 2019, Islam et al., 2018, Mohammadi et al., 2019, Powell et al., 2017) research field have provided useful insights on: (i) the viability and implementation of residential PV systems by identifying and comparing various related aspects (i.e., self-consumption without and with batteries, electricity rates, incentive programs and analysis tools) (Lara and García, 2021); (ii) the methods used for cooling PV panels (i.e., active and passive methods), their effect on the efficiency of PV panels and the practices that could be applied for achieving further enhancement in their efficiency and for reducing their operating temperature (Maleki et al., 2020); (iii) the approaches and metrics applied for assessing the glare impact from the solar PV array in the airport environment and the existing solar glare guidelines in different countries (Sreenath et al., 2021); (iv) the current status and technology trends in CSP systems by analyzing the technology cost and their growth during the last years, with special focus on thermal storage (Fernández et al., 2019); (v) the status of state-of-the-art hybrid CSP-desalination systems and the practices for potentially hybridizing CSPs with desalination systems (Mohammadi et al., 2019); (vi) global CSP technology implementations, future research trends in the CSP research field and major findings of previously published review articles on this issue (Islam et al., 2018); and (vii) CSP hybridization strategies with coal, natural gas, biofuels, geothermal, PV and wind, and the practices of different configurations for hybridizing CSP with other energy sources (Powell et al., 2017). However, preceding reviews on site-selection topic of PV technologies are limited and focus on very few aspects of site-selection procedures. Specifically, Rediske et al. (2019) analyzed the factors that have been used for choosing the most suitable locations for PV projects by reviewing a total of 27 research studies on PV siting. Malemnganbi and Shimray (2020) analyzed and classified the criteria that have been used for pinpointing suitable locations for PV and examined the multicriteria decision-making techniques that have been applied in PV applications by reviewing a total of 31 PV siting studies. Al Garni and Awasthi (2018) reviewed about 50 research studies on PV site-selection topic by determining the restricting criteria (i.e., EC), the decision criteria (i.e., AC) and the methodologies that have been used for estimating the suitability of potential areas for PV siting. It should be noted that no reviews on site-selection of CSP technologies can be found in the international literature.

The present systematic review addresses an important gap in knowledge in the solar PV and CSP siting research. Specifically, it provides a direct analysis and assessment on 11 key thematic modules (e.g., optimization modules and criteria, spatial planning or reference scales, EC and their related exclusion limits (min, max, mean and mode values)) of existing site-selection procedures by reviewing a total of 104 PV and 48 CSP research studies. Some advantages of this systematic review are as follows: (i) it focuses on both PV and CSP siting research; (ii) it investigates the existing site-selection procedures in an integrated manner, since it develops a workflow that examines qualitatively and quantitatively all key thematic modules of site-selection issue; (iii) it identifies insights and useful data trends in the site-selection procedures, contributing to the knowledge and improvement of future studies and global solar technologies implementations.

A total of 152 research studies on PV and CSP site-selection topic were identified as eligible and reviewed in detail on the basis of the proposed workflow of this systematic review. Each selected study was investigated in depth through the following key thematic modules: (1) site-selection methodologies per stage of the siting applications and combinations of the methods; (2) type, number, and exclusion limits (min, max, mean and mode values) of EC; (3) type, number, importance, priority, and suitability classes (optimal and poor values) of AC; (4) optimization modules and criteria; (5) geographic locations of the study areas; (6) spatial planning or reference scales; (7) solar radiation data estimation and analysis; (8) sensitivity analysis related to site-selection procedure; (9) participatory planning approaches, groups, and contributions; (10) laws, regulations, and policies related to site-selection procedure; and (11) suitability indexes (linguistic and/or numeric) and ranking procedures. Important insights and useful data trends identified and highlighted in these 11 key thematic modules of site-selection issue, motivating the conduction of new updated site-selection analyses of PV and CSP technologies and globally improving siting implementations.

The remainder of the article is structured as follows. Section 2 presents the workflow followed for the systematic review and the thematic modules reviewed in detail in each selected PV and CSP siting study. Section 3 presents the results of qualitative synthesis and quantitative meta-analysis of this systematic review per thematic module. Section 4 introduces and discusses critical insights and useful data trends that are revealed from the synthesis and analysis of the systematic review. Lastly, Section 5 provides concluding remarks and key findings.

2. Materials and methods

The main objective of the systematic review of existing site-selection procedures of PV and CSP technologies is to identify potential research gaps and shortages as well as useful data trends, in order to reveal valuable knowledge and insights for: (i) the development of new innovative site-selection tools, methodologies, criteria, approaches, or policies; (ii) the improvement of key aspects of existing siting procedures; and (iii) the fulfillment of a sustainable land use allocation in the framework of the accelerated solar technologies deployment. In accordance with the above, the present review addresses four main research questions: (1) Are there data trends in the site-selection procedures of PV and CSP systems? (2) Can these trends reveal valuable knowledge and provide a basis to inform and/or improve future studies and siting implementations? (3) Are there potential research gaps and shortages in the existing site-selection procedures? (4) Can these research gaps reveal valuable knowledge and insights for the deployment of new and innovative site-selection planning tools, methodologies, criteria, or policies and/or for the improvement of key aspects of the existing siting procedures and/or for the fulfillment of a sustainable land use allocation?

Specific search terms had been used for the systematic review in order to properly identify the studies that focus on site-selection of PV or/and CSP technologies (see Section 2.1). All searches were conducted during April 2019 and April 2020 in various scientific databases (e.g., ScienceDirect (Elsevier), MDPI) and in selected scientific collections of high-quality peer-reviewed international conference proceedings (e.g., Energy Procedia by Elsevier, Proceedings by MDPI). Hence, the remaining international conference proceedings, national or local conference proceedings and gray literature were eliminated.

Research filters used for the systematic review were: (i) review search terms (Filter.1); (ii) review criteria (Filter.2); and (iii) thematic modules of the systematic review (Filter.3). The schematic workflow of the systematic review, and the thematic modules reviewed in each PV and CSP siting study that was considered as eligible are presented and analyzed in detail below (See Fig. 1).

Fig. 1. Schematic depiction of workflow followed for the systematic review.

2.1. Filter.1 — Review search terms

Search terms used for the systematic review were: (i) photovoltaic siting; (ii) PV siting; (iii) photovoltaic GIS; (iv) PV GIS; (v) photovoltaic spatial planning; (vi) PV spatial planning; (vii) photovoltaic site selection; (viii) PV site-selection; (ix) photovoltaic site suitability; (x) PV site suitability; (xi) photovoltaic siting participation; (xii) PV siting participation; (xiii) photovoltaic site selection participation; (xiv) PV site selection participation; (xv) photovoltaic site suitability participation; (xvi) PV site suitability participation; (xvii) concentrated solar power siting; (xviii) CSP siting; (xix) concentrated solar power GIS; (xx) CSP GIS; (xxi) concentrated solar power spatial planning; (xxii) CSP spatial planning; (xxiii) concentrated solar power site selection; (xxiv) CSP site selection; (xxv) concentrated solar power site suitability; (xxvi) CSP site suitability; (xxvii) concentrated solar power siting participation; (xxviii); CSP siting participation; (xxix) concentrated solar power site selection participation; (xxx) CSP site selection participation; (xxxi) concentrated solar power site suitability participation and (xxxii) CSP site suitability participation. Among numerous scientific studies, a total of 10,121 scientific studies are filtered and further investigated (i.e., title, abstract and/or main text) in the next stage of systematic review.

2.2. Filter.2 — Review criteria

The 10,121 scientific studies revealed from the search results of Filter 1 are further filtered according to the three following review criteria: the study should focus on (i) site-selection issue; (ii) PV or/and CSP systems; and (iii) non-hybrid systems. Accordingly, all studies were either oriented toward different scientific topics (e.g., environmental impact assessment) or conducted site-selection analysis for other renewable energy systems (e.g., biogas power plants, offshore wind turbines) or investigated the siting of hybrid systems (e.g., site-selection analysis for hybrid CSP-desalination systems) were excluded. As a result, a total of 104 PV (Aghbashlo et al., 2020, Akkas et al., 2017, Akkaş et al., 2017, Al Garni and Awasthi, 2017, Ali et al., 2019, Aly et al., 2017, Anwarzai and Nagasaka, 2017, Arnette and Zobel, 2011, Asakereh et al., 2014, Asakereh et al., 2017, Awan et al., 2018, Bissiri et al., 2020, Brewer et al., 2015, Cai et al., 2020, Calvert and Mabee, 2015, Carrion et al., 2008, Charabi and Gastli, 2011, Charabi and Gastli, 2013, Chen et al., 2019, Chu and Hawkes, 2020, Colak et al., 2020, Dagdougui et al., 2011, Dehghani et al., 2018, Deshmukh et al., 2019, Dhunny et al., 2019, Dias et al., 2019, Doljak and Stanojevic, 2017, Domínguez Bravo et al., 2007, Doorga et al., 2019, Dupont et al., 2020, Fang et al., 2018, Fernandez-Jimenez et al., 2015, Firozjaei et al., 2019, Freire et al., 2019, Gastli and Charabi, 2010, Georgiou and Skarlatos, 2016, Ghasemi et al., 2019, Giamalaki and Tsoutsos, 2019, Gómez et al., 2010, Gunderson et al., 2015, Habib et al., 2020, Hafeznia et al., 2017, Haurant et al., 2011, Hazaymeh et al., 2018, Hott et al., 2012, Huang et al., 2018, Janke, 2010, Jung et al., 2019, Kereush and Perovych, 2017, Khan and Rathi, 2014, Kim et al., 2020, Koberle et al., 2015, Kolendo et al., 2019, Lee et al., 2015, Liu et al., 2017, Majumdar and Pasqualetti, 2019, Maleki et al., 2020, Marques-Perez et al., 2020, Mensour et al., 2019, Merrouni et al., 2016, Merrouni et al., 2018b, Messaoudi et al., 2019, Mierzwiak and Calka, 2017, Mokarram et al., 2020, Mondino et al., 2014, Morelli et al., 2015, Mostegl et al., 2017, Nguyen and Pearce, 2010, Noorollahi et al., 2016, Ogbonnaya et al., 2020, Ozdemir and Sahin, 2018, Palmer et al., 2019, Perpiña Castillo et al., 2016, Pillot et al., 2020, Piyatadsananon, 2016, Polo et al., 2015, Rahnama et al., 2019, Rediske et al., 2019, Sabo et al., 2016, Sabo et al., 2017, Sacchelli et al., 2016, Sadeghi and Karimi, 2017, Sánchez-Lozano et al., 2013, Sánchez-Lozano et al., 2014, Sánchez-Lozano et al., 2016, Saracoglu et al., 2018, Shiraishi et al., 2019, Shorabeh et al., 2019, Sindhu et al., 2017, Singh Doorga et al., 2019, Suh and Brownson, 2016, Sultan et al., 2018, Sun et al., 2013, Sward et al., 2019, Tahri et al., 2015, Tavana et al., 2017, Uyan, 2013, Vafaeipour et al., 2014, Wang et al., 2016, Watson and Hudson, 2015, Yang et al., 2019, Yimen and Dagbasi, 2019, Yushchenko et al., 2018, Zoghi et al., 2017) and of 48 CSP (Al-Soud and Hrayshat, 2009, Aly et al., 2017, Anwarzai and Nagasaka, 2017, Aqachmar et al., 2019, Azoumah et al., 2010, Badran and Eck, 2006, Beltagy et al., 2015, Boukelia et al., 2015, Broesamle et al., 2001, Charabi and Gastli, 2010, Chu and Hawkes, 2020; Clifton and Boruff, 2010, Dawson and Schlyter, 2012, Deshmukh et al., 2019, Djebbar et al., 2014, Domínguez Bravo et al., 2007, Dupont et al., 2020, Enjavi-Arsanjani et al., 2015, Falter et al., 2020, Fluri, 2009, Ghasemi et al., 2019, Giamalaki and Tsoutsos, 2019, Hanel and Escobar, 2013, Hang et al., 2008, Hott et al., 2012, Janjai et al., 2011, Koberle et al., 2015, Larraín et al., 2010, Larraín and Escobar, 2012, Martins et al., 2012, Mehos and Owens, 2014, Merrouni et al., 2018a, Merrouni et al., 2014, Mohammadi and Khorasanizadeh, 2019, Noone et al., 2011, Omitaomu et al., 2015, Polo et al., 2015, Purohit et al., 2013, Rijanto et al., 2013, Saracoglu, 2020, Servert et al., 2014, Shiraishi et al., 2019, Tlhalerwa and Mulalu, 2019, Wanderer and Herle, 2015, Wu et al., 2019, Wu et al., 2014, Yushchenko et al., 2018, Ziuku et al., 2014) siting studies (137 peer-reviewed journal articles and 15 peer-reviewed international conference papers in both PV and CSP siting research) were identified as eligible and selected for further analysis.

2.3. Filter.3 — Thematic modules of systematic review

Each selected study identified as eligible from the review criteria results of Filter 2 was further investigated through 11 key thematic modules of site-selection issue, addressing various aspects of site-selection procedure of PV and CSP technologies.

A plethora of essential datasets are produced and used for qualitative synthesis and quantitative meta-analysis. The datasets were structured into (i) qualitative, and (ii) quantitative data and are presented in detail below (Table 1).

Table 1. Datasets produced in accordance with the selected thematic modules of this systematic review.

No.	Name of thematic module	Data parameter	Data type
TM.1	Site-selection methodologies	Frequency of occurrence per methodological stage	Qualitative, Quantitative
TM.1	Site-selection methodologies	Combinations of site-selection methods	Qualitative, Quantitative
TM.2	EC	EC type	Qualitative
		EC numerical ranges	Quantitative
		Frequency of occurrence of EC	Qualitative, Quantitative
		Exclusion limits (mean, min, max and mode values)	Quantitative
TM.3	AC	AC type	Qualitative
		AC numerical ranges	Quantitative
		Frequency of occurrence of AC	Qualitative, Quantitative
		Importance of AC based on their mean weights and priority position	Quantitative
		Optimal AC Values	Quantitative
		Poor AC Values	Quantitative
TM.4	Optimization modules and criteria	Optimization Methods	Qualitative
		Optimization Criteria and their Objective(s)	Qualitative
		Objective Functions	Qualitative, Quantitative
TM.5	Geographic location of the study area	Frequency of occurrence on global, continental and national scale	Quantitative
TM.5	Geographic location of the study area	Countries investigated on site-selection issue	Qualitative
TM.6	Spatial planning or reference scale	Frequency of occurrence of each spatial planning or reference scale	Qualitative, Quantitative
TM.6	Spatial planning or reference scale	Correlation analysis with studies’ geographic location	Qualitative, Quantitative
TM.7	Solar radiation data estimation and analysis	Approaches for solar radiation data estimation	Qualitative, Quantitative
		Methodologies of processing solar radiation data	Qualitative, Quantitative
		Time-period of solar radiation analysis	Quantitative
		Gap period	Quantitative
		Spatial resolution of solar radiation data	Quantitative
TM.8	Sensitivity analysis related to site-selection procedure	Types of sensitivity analysis	Qualitative, Quantitative
		Methodologies of sensitivity analysis	Qualitative, Quantitative
		Objective(s) of the sensitivity analysis	Qualitative, Quantitative
		Number of “what-if” scenarios	Quantitative
TM.9	Participatory planning	Methodologies of participatory planning	Qualitative, Quantitative
		Participatory groups	Qualitative
		Number of participants	Quantitative
		Contributions of each participant and participation	Qualitative, Quantitative
TM.10	Laws, regulations or policies related to site-selection procedure	Frequency of occurrence of legal siting requirements considered per methodological stage	Qualitative, Quantitative
TM.10		Correlation analysis with studies’ geographic location	Qualitative, Quantitative
TM.11	Suitability indexes and ranking procedures	Frequency of occurrence of suitability indexes and ranking procedures	Qualitative, Quantitative
TM.11	Suitability indexes and ranking procedures	Scales and Classifications in numeric and linguistic terms	Qualitative, Quantitative

Note: EC, exclusion criteria; AC, assessment criteria.

3. Results

The systematic review of mainly peer-reviewed journal articles and of high-quality peer-reviewed international conference papers yielded a total of 152 scientific studies that were oriented toward the site-selection issue of PV or/and CSP technologies. The selected key thematic modules yielded a thorough investigation and analysis of existing site-selection procedures. The overall workflow proposed for the systematic review gave credence, quality assurance, and accuracy to the authors’ qualitative synthesis and quantitative meta-analysis.

Fig. 2. Frequency of occurrence of methodologies applied per siting stage of PV applications. Methodologies used in combination with other approaches within the relevant stages denoted with *. Note: TOPSIS, Technique for Order Preference by Similarity to Ideal Solution; ELECTRE, ÉLimination Et Choix Traduisant la REalité; VIKOR, Vise Kriterijumska Optimizacija I Kompromisno Resenje; GIS, Geographic Information System; PROMETHEE, Preference Ranking Organization Methods for Enrichment Evaluations; AHP, Analytic Hierarchy Process; WASPAS, Weighted Aggregates Sum Product Assessment; MAUT, Multi-Attribute Utility Theory; OWA, Ordered Weighted Averaging; FLOWA, Fuzzy Logic Ordered Weighted Averaging; WLC/SAW, Weighted Linear Combination/Simple Additive Weighting; ANN, Artificial Neural Network; SWARA, Step-wise Weight Assessment Ratio Analysis; ANP, Analytic Network Process; AR, Assurance Region.

3.1. Thematic module 1 — Site-selection methodologies

3.1.1. Frequency of occurrence per methodological stage

The proposed site-selection methodologies in each study were investigated and analyzed in accordance with the methodological stage (i.e., Exclusion Stage (ES), Assessment Stages Part A (ASPA) and Part B (ASPB), and Optimization Stage (OS); Fig. 2), in which they deployed and applied. ES refers to the exclusion of unsuitable areas for solar farm siting. ASPA refers to the assessment of AC, while ASPB refers to the assessment of suitable sites. OS refers to the optimization of site-selection results in order to pinpoint the optimal sites for solar farm installation.

A variety of different methodologies have been applied in the PV siting studies for the exclusion of unsuitable sites and the determination of suitable ones (ES; 16 different methodological approaches). GIS-based methodologies are the most frequently applied (69 and 13 studies used them, individually or combined with other approaches respectively), followed by fuzzy logic membership functions (e.g., Mamdani fuzzy logic membership function; 6 studies), primary data-collection methods (i.e., interviews, meetings and interactive discussions; 3 studies), and ordered weighted averaging method (OWA; 2 studies). At the ASPA, the most frequently applied methodology is the analytic hierarchy process (AHP; 36 and 5 studies used it, individually or combined with other approaches respectively), followed by primary data-collection methods (i.e., interviews, questionnaire surveys, Delphi method, workshops and meetings; 7 studies), direct weights technique (i.e., weights directly assigned to the AC; 6 studies), and fuzzy AHP (3 studies used it, individually or combined with other methods). In total 11 different methodological approaches applied at the ASPA for the determination of AC weights. Numerous methodologies applied for the assessment of suitable areas for siting PV (ASPB; 42 different methodological approaches). At the ASPB, GIS-based methodologies are the most frequently applied (25 and 42 studies used them, individually or combined with other approaches respectively), followed by primary data-collection methods (i.e., interviews, questionnaire surveys, Delphi method, workshops and meetings; 16 studies), fuzzy logic membership functions (11 studies), and weighted linear combination (WLC) method (8 studies). At the OS, barely three different methodologies applied for the determination of the optimal sites, since only three studies (Dhunny et al., 2019, Gómez et al., 2010, Pillot et al., 2020) incorporated an optimization stage within their site-selection methodological approach. Therefore, the OS is the stage that is mostly omitted in the site-selection procedure (101 studies), followed by the ASPA (49 studies), ASPB (17 studies), and lastly by the ES (13 studies).

In the CSP systems siting studies, a variety of different methodologies have been also applied for the determination of suitable sites (ES; 14 different approaches). GIS-based methodologies are also the most frequently applied (26 and 6 studies used them, individually or combined with other approaches respectively), followed by geo-spatial multi-criteria data analyses (5 studies used them, individually or combined with other methods), field investigation surveys (3 studies), techno-economic feasibility modeling and analyses (2 studies), and system feasibility analyses (2 studies). A geo-spatial multi-criteria data analysis differs from a GIS-based analysis in that an information system is always being used for the identification of suitable sites by the latter methodologies, while the use of a such system is missing by the former ones. At the ASPA, five different methodologies used for the assessment of AC and the determination of their relevant weights. The most frequently applied methodology is also AHP (7 studies), while the remaining methodologies applied only once (1 study). Several different methodologies applied for the assessment of suitable areas for CSP siting (ASPB; 18 different methodological approaches). GIS-based methodologies are also the most frequently applied (7 and 10 studies used them, individually or combined with other approaches respectively), followed by primary data-collection methods (i.e., interviews, questionnaire surveys, and meetings/interactive discussions; 4 studies), techno-economic analyses (3 studies), economic modeling and analyses (3 studies), system performance modeling and analyses (3 studies), and fuzzy logic membership functions (2 studies). It should be noted that there is no study that incorporates an OS within the site-selection methodological approach for the determination of the optimal sites (see Fig. 3). Therefore, the OS is the stage that is always omitted in the site-selection procedure of CSP (48 studies), followed by the ASPA (38 studies), ASPB (21 studies), and lastly by the ES (3 studies).

It should be mentioned that some studies focus on site- selection issue of PV and CSP systems by identifying and clustering site-selection factors, in a systematic way. In particular, Saracoglu et al. (2018) applied the 1st generation original Anatolian honeybees’ investment decision support methodology (1GOAHIDSM) by combining several useful methods (such as grey systems theory, fuzzy logic theory) for the development of a framework that could be used for selecting and clustering site-selection factors for PV installations. Additionally, Saracoglu (2020) applied the 1GOAHIDSM by combining several other practical methods (such as fuzzy decision-making trial and evaluation laboratory, WLC) for the development of a framework that could be used for selecting and clustering site-selection factors for CSP installations. Lastly, Kereush and Perovych (2017) focused also on the determination of the most proper criteria that could be used for PV siting and they applied the Method of Multiple Coefficient of Rank Correlation — Coefficient of Concordance for the estimation of reliability level of AC results.

3.1.2. Combinations of site-selection methodologies

GIS-based site-selection methodologies (the most frequently applied methods for the execution of ES and ASPB) combined frequently with other methodological techniques, especially at the ASPB (Fig. 4, Fig. 5). In particular, in PV siting studies, GIS is mostly combined with fuzzy logic membership functions (11 studies), primary data-collection methods (10 studies), and WLC method (8 studies) at the ASPB, while it is most frequently combined with fuzzy logic membership functions (5 studies), primary data-collection methods (3 studies), and OWA (2 studies) at the ES. In CSP siting studies, GIS is mostly combined with primary data-collection methods (3 studies), economic modeling and analysis (2 studies), and techno-economic analysis (2 studies) at the ASPB, while it is most frequently combined with field investigation surveys (2 studies) at the ES. In several cases, more than one methodologies were combined with GIS for the identification of the most suitable sites in PV and CSP siting studies (e.g., GIS-based method was combined with primary data-collection methods and WLC by Noorollahi et al. (2016) or with PROMETHEE and Economic Analysis by Marques-Perez et al. (2020) or with System Performance Modeling and Analysis, and Economic Modeling and Analysis by Broesamle et al., 2001). In total, twenty (20) and nine (9) diverse methodologies are combined with GIS in PV and CSP siting studies, respectively.

Fig. 3. Frequency of occurrence of methodologies applied per siting stage of CSP applications. Methodologies used in combination with other approaches within the relevant stages denoted with *. Note: PROMETHEE, Preference Ranking Organization Methods for Enrichment Evaluations; TIFGOWA, Triangular Intuitionistic Fuzzy Generalized Ordered Weighted Averaging; 4E, Energy Exergy Environment Economic; GIS, Geographic Information System; AHP, Analytic Hierarchy Process.

3.2. Thematic module 2 — EC

3.2.1. EC for PV siting

The EC applied in each PV siting study varied in number, type, and in the exclusion limits that were defined for each criterion. The determination of EC and of exclusion limits were carrying out based on various essential factors, such as the unique characteristics and climatic conditions of each geographic location, the policies and/or laws associated with each country, the available geographic information data, and the objectives of each site-selection procedure. In total, 83 exclusion criteria for photovoltaics (ECPV) siting are identified and presented in Table 2, Table 3. The mean number of ECPV applied in the PV siting studies was 8, and the mode was 6. Additionally, the maximum number of ECPV applied in a study was 20 (Marques-Perez et al., 2020), while there were studies with no ECPV used (Aghbashlo et al., 2020, Akkas et al., 2017, Akkaş et al., 2017, Awan et al., 2018, Fang et al., 2018, Fernandez-Jimenez et al., 2015, Freire et al., 2019, Liu et al., 2017, Ogbonnaya et al., 2020, Ozdemir and Sahin, 2018, Tavana et al., 2017, Vafaeipour et al., 2014). It should be noted that Kolendo et al. (2019) refer that they considered and used 27 ECPV in their study, but they mention only few of them. Thus, there is no possibility to include the remaining criteria in the systematic analysis both quantitatively and qualitatively.

The most restrictive lower limits applied, in terms of distance, were 10,000 m from protected areas (Shiraishi et al., 2019) and 8000 m from urban and residential areas (Aly et al., 2017), while the least restrictive lower limit which also considered as mode value limit was 0 m and applied from numerous land uses/covers, such as urban and residential areas, water surfaces, protected areas, road network, agricultural land and croplands, electricity grid, bird habitats and wetlands, forests, archaeological, historical and cultural heritage sites, and many other areas as it can be seen in Table 2. The most restrictive upper limits applied, in terms of distance, were 500 m from road network (Kereush and Perovych, 2017, Marques-Perez et al., 2020) and 600 m from electricity grid (Kereush and Perovych, 2017), while the least restrictive upper limit was 50,000 m from urban and residential areas, water surfaces, road network, electricity grid, and railway network. Observing the remaining minimization EC (i.e., the lower the value, the better the performance of the criterion), a quite restrictive limit was identified for ECPV.4 (i.e., a limit of 0.60%), since they considered as suitable only the very flat sites. Observing the remaining maximization EC (i.e., the higher the value, the better the performance of the criterion), a quite restrictive limit was identified for ECPV.10 (i.e., a limit of 2374 kWh/m²/year), since the values of over 1826 and 2191 kWh/m²/year are considered of very good and excellent solar energy potential respectively, by National Renewable Energy Laboratory (NREL) (Sabo et al., 2016).

Table 2. Exclusion criteria applied more than once for PV siting in accordance with their frequency of occurrence and min, max, mean, and mode values of the defined exclusion limits.

No.	Description	FO	Min/Max values	Mean value	Mode value(s)
ECPV.1	Distance from Urban and Residential Areas (Lower Limits)	71	<0/8000 m	<611.69 m	0 m
ECPV.1	Distance from Urban and Residential Areas (Upper Limits; FO: 8)	71	>2500/50,000 m	>22,812.5 m	>10,000 m
ECPV.2	Distance from Water Surfaces (Lower Limits)	64	<0/1000 m	<152.5 m	0 m
ECPV.2	Water Availability (Upper Limits; FO: 4)	64	>17,300/50,000 m	>33,650 m	–
ECPV.3	Distance from Protected Areas^a (e.g., of environmental or ecological importance)	61	<0/10,000 m	<396 m	0 m
ECPV.4	Slope of Terrain^b	58	>0.60%/100%	>12.84%	>5%
ECPV.5	Distance from Road Network (Lower Limits)	51	<0/1500 m	<166.41 m	0 m
ECPV.5	Distance from Road Network (Upper Limits; FO: 19)	51	>500/50,000 m	>18,186.67 m	>10,000 and >20,000 m
ECPV.6	Distance from Agricultural Land^c $^{,}$

^d and Croplands^c $^{,}$

^e	42	<0/500 m	<24.42 m	0 m
ECPV.7	Distance from Electricity Grid (Lower Limits)	36	<0/1000 m	<115.15 m	0 m
ECPV.7	Distance from Electricity Grid (Upper Limits; FO: 20)	36	>600/50,000 m	>21,128.27 m	>50,000 m
ECPV.8	Distance from Bird Habitats and Wetlands	35	<0/1000 m	<131.20 m	0 m
ECPV.9	Distance from Forests^c	34	<0/1000 m	<114.29 m	0 m
ECPV.10	Global Horizontal Irradiance	33	<1000/ 2374 kWh/m²/year	<1477 kWh/m²/year	<1643 kWh/m²/year
ECPV.11	Other Land uses	26	DOLU^f/DOLU^f	DOLU^f	DOLU^f
ECPV.12	Distance from Archeological, Historical and Cultural Heritage Sites	18	<0/1000 m	<194.11 m	0 m
ECPV.13	Distance from Railway Network (Lower Limits)	17	<0/500 m	<120.32 m	0 m
ECPV.13	Distance from Railway Network (Upper Limits; FO: 4)	17	>8046.72/50,000 m	>34,511.68 m	>40,000 m
ECPV.14	Elevation (Lower Limits; FO: 1)	15	—/—	–	<300 m
ECPV.14	Elevation (Upper Limits)	15	>60/5000 m	>1717.66 m	>1500 m
ECPV.15	Farm Minimum Required Area	14	<0.001/2.5 km²	<0.396868 km²	<0.001 km², <0.4 km², <0.6677 km², and <0.0404 km²
ECPV.16	Distance from Flood (Hazard) Zones^g	13	<0/500 m	<133.33 m	0 m
ECPV.17	Distance from Vegetation Coverage Areas	12	<0/500 m	<143.75 m	0 m
ECPV.17	Classes of Vegetation Coverage Areas^c	12	—/—	–	Dense Surface, Mosaic, High, Natural Aquatic and Sparse Vegetation Areas, and High NDVI Values Areas
ECPV.18	Land Orientation	11	North/East, West, North, Northeast, Northwest, Southwest and South Aspect	–	East, West, North, Northeast, and Northwest Aspect
ECPV.19	Distance from Industrial Zones and Economic Activities (Lower Limits)	11	<0/1000 m	<250 m	0 m
ECPV.19	Distance from Economic Activities (Upper Limits; FO: 1)	11	—/—	–	>3500 m
ECPV.20	Other Land Covers	11	DOLC^h/DOLC^h	DOLC^h	DOLC^h
ECPV.21	Distance from Woodlands and Tree Regions^c	10	<0/500 m	<65 m	0 m
ECPV.22	Average Air Temperature (Lower Limits; FO: 2)	9	<10/25 °C	<17.5 °C	–
ECPV.22	Average Air Temperature (Upper Limits)	9	>20/45 °C	>28.625 °C	–
ECPV.23	Distance from Civil/Military Aviation Areas	9	<0/3000 m	<1889 m	<3000 m
ECPV.24	Distance from Military Zones	9	<0/1000 m	<222.22 m	0 m
ECPV.25	Mountain Zones	9	0/0 m	0 m	0 m
ECPV.26	Distance from Areas of Landscape Value (Landscape Protection)	8	<0/1000 m	<277.77 m	0 m
ECPV.27	Distance from Shoreline	7	<0/500 m	<106.25 m	0 m
ECPV.28	Snow-Covered and Permafrost Areas	7	0/0 m	0 m	0 m
ECPV.29	Shrublands^c	7	0/0 m	0 m	0 m
ECPV.30	Distance from Touristic Zones	6	<0/500 m	<83.33 m	0 m
ECPV.31	Distance from Hazard Zones of Other Natural Phenomena	6	<0/10 m	<3.33 m	0 m
ECPV.31	Hazard Zones of Other Natural Phenomena	6	—/—	–	Hazard Zones of any Natural Phenomena (e.g., Dust storm, Slip Risk, Torrent)
ECPV.32	Distance from Sand Dunes and Sandy Lands	6	<0/200 m	<28.57 m	0 m
ECPV.33	Marshlands, Swamplands and Boglands	6	0/0 m	0 m	0 m
ECPV.34	Distance from Religious Sites	5	<0/1000 m	<260 m	<100 m
ECPV.35	Distance from Faults	5	<200/500 m	<440 m	<500 m
ECPV.36	Distance from Orchards and Other Plantations	5	<0/100 m	<41.66 m	0 m
ECPV.37	Distance from Rural Areas	5	<0/500 m	<260 m	0 m and <500 m
ECPV.38	Livestock Trails	4	0/0 m	0 m	0 m
ECPV.39	Distance from Mineral Extraction Sites	4	<0/1000 m	<250 m	0 m
ECPV.40	Rangelands^c	4	0/0 m	0 m	0 m
ECPV.41	Grasslands^c	4	0/0 m	0 m	0 m
ECPV.42	Relative Humidity (Lower Limits; FO: 1)	4	—/—	–	<44%
ECPV.42	Relative Humidity (Upper Limits)	4	>50%/62.6%	>54.87%	–
ECPV.43	Vineyards	3	0/0 m	0 m	0 m
ECPV.44	Population Density	3	>500/800 Inhabitants/km²	>650 Inhabitants/km²	–
ECPV.45	Restrictive Areas Due to Planning Policies and Laws	3	0/0 m	0 m	0 m/Zones that are under laws and regional planning or government policies
ECPV.46	Savannas	3	0/0 m	0 m	0 m
ECPV.47	Geological Conditions	3	—/—	–	Hard excavations areas or areas of geological importance
ECPV.48	Distance from Landfill (FO: 1)	2	—/—	–	<200 m
ECPV.48	and/or Dump Sites (FO: 2)	2	0/0 m	0 m	0 m
ECPV.49	Rocky Lands	2	0/0 m	0 m	0 m
ECPV.50	Wind Velocity (m/s)	2	—/—	–	–
ECPV.51	Net Present Value (NPV)	2	<0/0 € (Negative NPV)	<0 €	<0 €
ECPV.52	Shadow Degree	2	—/—	–	Shadow zones throughout the year/Dark pixels values
ECPV.53	Annual Average Precipitation (mm)	2	—/—	–	–
ECPV.54	Distance from Meadows	2	<150/500 m	<325 m	–
ECPV.55	Sunshine Hours per Year	2	—/—	–	–
ECPV.56	Soil Conditions	2	—/—	–	–
ECPV.57	Land Ownership	2	—/—	–	Public lands have priority for PV siting
ECPV.58	Practical Potential of Photovoltaic Electricity Production (PVOUT)	2	—/—	–	–
ECPV.59	Land Use Discount Factor	2	75%/90%	83%	–

Note: ECPV, exclusion criteria for photovoltaics; FO, frequency of occurrence.

a: Only one study applied 10,000 m of exclusion limit from a specific protected area, the remaining studies applied exclusion limits in the range from 0 to 2000 m.
b: Two studies examined the PV siting around of highway networks, in that cases all cut slopes considered as unsuitable and the highway fill slopes considered as suitable.
c: Some studies defined a proportion of these areas as suitable (e.g., 10% of grasslands considered as suitable and 90% of them as unsuitable).
d: Several studies excluded specific classes of agricultural land, such as Prime Agricultural Land, and Agricultural Land of Grades 1, 2 and 3.
e: Several studies excluded specific classes of croplands, such as Seasonal Crop Cultivation Land, and Kharif.
f: DOLU, Depending on land uses of the study area, the exclusion classes defined and their exclusion limits determined in accordance with these classes.
g: Very few studies excluded classes of flood hazard zones, such as High Flood Hazard Zones or types of Flood Zones, such as Flood Zones larger than 40 km $^{2}$

.
h: Depending on land covers of the study area, the exclusion classes defined and their exclusion limits determined in accordance with these classes.

Regarding one of the most crucial criteria in terms of energy efficiency, ECPV.10, has a moderate frequency of occurrence (33) as EC in the PV siting studies and the mean and mode values used for this factor were 1477 and 1643 kWh/m²/year, respectively. Regarding an essential issue of the international relevant literature (e.g., Dias et al., 2019, Sacchelli et al., 2016) of social and economic importance, namely PV installations in agricultural land, the majority of the studies that considered and referred to agricultural land as EC, excluded all types of agricultural land. The remaining studies considered as suitable the agricultural land with low productivity or only a part of agricultural land (e.g., 25% of the land) or certain classes and types of agricultural land (e.g., agricultural land of grade 5). The “agricultural land and croplands” criterion has moderate to high frequency of occurrence as EC in the PV siting studies. Lastly, Table 3 presents the EC that used only once for PV siting and these criteria with the respective limits could be used as a basis from the future studies for investigating further their influence on the determination of the most proper land sites for PV installations.

Table 3. Exclusion criteria applied only once for PV siting in accordance with their frequency of occurrence and the defined exclusion limits.

No.	Description	Exclusion limit
ECPV.60	Distance from Solitary Dwellings	<500 m
ECPV.61	Visibility Degree	Areas visible from most-visited sites in number
ECPV.62	Distance from Underground Pipes	<70 m
ECPV.63	Simple Payback Period	>6 years
ECPV.64	Cloudy days	>70 days
ECPV.65	Wet days	N/A
ECPV.66	Dusty days	>70 days
ECPV.67	Availability of Additional Power Capacity in the Electricity Grid	Larger capacity than the maximum value of node fixed by Power Distributor Company
ECPV.68	PV Dismantling Guarantee	Sites with no PV dismantling guarantee in the exploitation contracts
ECPV.69	Solar Radiation Exergy (kWh/m²/year)	N/A
ECPV.70	Average Unit Exergoeconomic Cost of Electricity Generation (USD/GJ)	N/A
ECPV.71	Annual Average Exergy Efficiency (%)	N/A
ECPV.72	Annual Average Unit Exergoenvironmental impact of Electricity Generation (mPts/GJ)	N/A
ECPV.73	Political and Security Conditions	Unstable locations based on these conditions
ECPV.74	Supergrid Business Climate and Conditions	Unstable locations based on these conditions
ECPV.75	Climate Conditions (Not-specified)	N/A
ECPV.76	Return on Investment	N/A
ECPV.77	Drainage System	N/A
ECPV.78	Geographic Boundaries	Administration Boundaries of Study Area
ECPV.79	Annual averaged insolation on tilted at latitude angle (TI)	<5 kWh/m²/day
ECPV.80	Annual averaged insolation clearness index (K)	<0.55 dimensionless
ECPV.81	Climatic Conditions Related to Module Soiling	Dusty areas or areas in which the modules could be soiled to the extent that their efficiency will be reduced
ECPV.82	Generation Costs of Electricity	>0.35 US$/kWh
ECPV.83	Capacity Factor

10%

Note: ECPV, exclusion criteria for photovoltaics.

3.2.2. EC for CSP siting

The EC applied in each CSP siting study varied in number, type, and in the exclusion limits that were defined for each criterion. The determination of EC and of exclusion limits were carried out based on various essential factors, such as the factors referred in Section 3.2.1. for the PV siting procedure. In total, 66 exclusion criteria for concentrated solar power (ECCSP) systems siting are identified and presented in Table 4, Table 5. The mean number of ECCSP applied in the CSP siting studies was 8, and the mode was 9. Additionally, the maximum number of ECCSP applied in a study was 17 (Anwarzai and Nagasaka, 2017), while there were studies with no ECCSP used (Beltagy et al., 2015, Hanel and Escobar, 2013, Larraín et al., 2010).

The most restrictive lower limits applied, in terms of distance, were 10,000 m from protected areas (Shiraishi et al., 2019) and 8000 m from urban and residential areas (Aly et al., 2017), while the least restrictive lower limit which also considered as mode value limit was 0 m and applied from numerous land uses/covers, such as water surfaces, urban and residential areas, protected areas, woodlands and tree regions, civil and military aviation areas, wetlands, military zones, industrial zones and economic activities, forests, and many other areas as it can be seen in Table 4. The most restrictive upper limit applied, in terms of distance, was 4000 m from road network (Giamalaki and Tsoutsos, 2019), while the least restrictive upper limits were 50,000 m from electricity grid (Aly et al., 2017) and 45,000 m from urban and residential areas (Aly et al., 2017). Observing the remaining minimization EC, a quite restrictive limit was also applied for ECCSP.1 (i.e., a limit of 1%), since they considered as suitable only the very flat and flat sites. Observing the remaining maximization EC, a restrictive limit was also used for ECCSP.5 (i.e., a limit of 2557 kWh/m²/year), since a threshold of 2000 kWh/m²/year is globally approved for commercial CSP projects (Dawson and Schlyter, 2012).

Table 4. Exclusion criteria applied more than once for CSP siting in accordance with their frequency of occurrence and min, max, mean, and mode values of the defined exclusion limits.

No.	Description	FO	Min/Max values	Mean value	Mode value(s)
ECCSP.1	Slope of Terrain	36	>1%/28%	>5%	>3%
ECCSP.2	Distance from Water Surfaces (Lower Limits)	31	<0/1000 m	<94.117 m	0 m
ECCSP.2	Water Availability^a (Upper Limits; FO: 9)	31	>9000/30,000 m	>16,333.33 m	–
Empty Cell	Water Availability (gpm; FO: 1)		–	–	Areas with <15,000 gpm of stream-flow within 32,186.88 m from water sources
ECCSP.3	Distance from Urban and Residential Areas (Lower Limits)	28	<0/8000 m	<761.290 m	0 m
ECCSP.3	Distance from Urban and Residential Areas (Upper Limits; FO: 4)	28	>10,000/45,000 m	>28,333.33 m	–
ECCSP.4	Distance from Protected Areas^b (e.g., of environmental or ecological importance)	27	<0/10,000 m	<463 m	0 m
ECCSP.5	Direct Normal Irradiance	26	<1000/ 2557 kWh/m²/year	<1830 kWh/m²/year	<1800 kWh/m²/year
ECCSP.6	Distance from Electricity Grid (Lower Limits)	21	<0/1000 m	<55.55 m	0 m
ECCSP.6	Distance from Electricity Grid (Upper Limits; FO: 14)	21	>10,000/50,000 m	>31,000 m	–
ECCSP.7	Distance from Agricultural Land^c and Croplands^c	19	0/0 m	0 m	0 m
ECCSP.7	Classes of Agricultural Land and Croplands	19	–	–	Irrigated, and High Productive Agricultural Land and Fenced Ranches/Farms. Irrigated Herbaceous, Permanent, Cultivated, Shifting Cultivated, Mosaic, Kharif, Rabi, Zaid, Double/Triple Irrigated, and Current Fallow Croplands and Degraded Land Under Plantation Crops
ECCSP.8	Forests^c	16	0/0 m	0 m	0 m
ECCSP.9	Distance from Road network (Lower Limits)	15	<0/1500 m	<157.14 m	0 m
ECCSP.9	Distance from Road network (Upper Limits; FO: 8)	15	>4000/20,000 m	>13,500 m	>20,000 m
ECCSP.10	Other Land Uses	12	DOLU^d/DOLU^d	DOLU^d	DOLU^d
ECCSP.11	Snow-Covered and Permafrost Areas	9	0/0 m	0 m	0 m
ECCSP.12	Other Land Covers	9	DOLC^e/DOLC^e	DOLC^e	DOLC^e
ECCSP.13	Distance from Wetlands	9	<0/500 m	<55.55 m	0 m
ECCSP.14	Distance from Military Zones	7	<0/500 m	<71.43 m	0 m
ECCSP.15	Distance from Industrial Zones and Economic Activities (Lower Limits)	7	<0/500 m	<71.43 m	0 m
ECCSP.15	Proximity to Industrial Consumption Zones (Upper Limits; FO: 1)	7	—/—	–	–
ECCSP.16	Farm Minimum Required Area	7	<0.4/10 km²	<3.157 km²	–
ECCSP.17	Shrublands^c	6	0/0 m	0 m	0 m
ECCSP.18	Sand Dunes and Sandy Lands	6	0/0 m	0 m	0 m
ECCSP.19	Distance from Vegetation Coverage Areas^c	6	<0/500 m	<83.33 m	0 m
ECCSP.19	Classes of Vegetation Coverage Areas	6	—/—	–	Critically Endangered, Endangered, Vulnerable, Natural Aquatic, Mosaic, Sparse Vegetation Areas
ECCSP.20	Elevation	5	>1500/5000 m	>2666.66 m	>1500 m
ECCSP.21	Marshlands and Swamplands	5	0/0 m	0 m	0 m
ECCSP.22	Distance from Railway Network (Lower Limits)	5	<0/500 m	<220 m	0 and <500 m
ECCSP.22	Distance from Railway Network (Upper Limits; FO: 2)	5	—/—	–	–
ECCSP.23	Archeological, Historical and Cultural Heritage Sites	4	0/0 m	0 m	0 m
ECCSP.24	Woodlands and Tree Regions^c	4	0/0 m	0 m	0 m
ECCSP.25	Grasslands^c	4	0/0 m	0 m	0 m
ECCSP.26	Distance from Hazard Zones of Other Natural Phenomena	4	0/0 m	0 m	0 m
ECCSP.26	Hazard of Other Natural Phenomena	4	—/—	–	Hazard Zones of any Natural Phenomena (e.g., Wind Load, Acid Rain, Seismic Activity, Volcanic Activity; Natural Disasters Index can be used)
ECCSP.27	Distance from Flood (Hazard) Zones	4	<0/500 m	<125 m	0 m
ECCSP.27	Flood Hazard Zones	4	—/—	–	Areas classified as 100 year Flood Zones by the Federal Emergency Management Agency
ECCSP.28	Population Density^f	3	>193/ 800 Inhabitants/km²	>498 Inhabitants/km²	–
ECCSP.29	Distance from Civil/Military Aviation Areas	3	<0/3000 m	<1000 m	0 m
ECCSP.30	Availability of Power Suppliers/Electricity Demand Profile	3	—/—	–	Areas that the electricity demand is covered
ECCSP.31	Savannas^c	3	0/0 m	0 m	0 m
ECCSP.32	Soil Structure (FO: 1)	3	—/—	–	–
ECCSP.32	and Geological Conditions	3	—/—	–	–
ECCSP.33	Global Horizontal Irradiance	2	<1753/ 2191 kWh/m²/year	<1972 kWh/m²/year	–
ECCSP.34	Distance from Tundra Areas	2	0/N/A m	–	–
ECCSP.35	Livestock Trails	2	0/0 m	0 m	0 m
ECCSP.36	Rangelands^c	2	0/0 m	0 m	0 m
ECCSP.37	Distance from Orchards and Other Plantations^c	2	0/0 m	0 m	0 m
ECCSP.38	Geomorphology	2	—/—	–	–
ECCSP.39	Land Use Discount Factor	2	75%/90%	83%	–
ECCSP.40	Land Orientation	2	North Aspect/East, West, North, Northeast, Northwest Aspect	–	–

Note: ECCSP, exclusion criteria for concentrated solar power; FO, frequency of occurrence.

a: Six of nine studies not refer a specific upper exclusion limit qualitatively, only quantitatively.
b: Only one study applied 10,000 m of exclusion limit from a specific protected area, the remaining studies applied exclusion limits in the range from 0 to 500 m.
c: Only few studies defined a proportion of these areas as suitable (e.g., 10% of grasslands considered as suitable and 90% of them as unsuitable).
d: DOLU, Depending on land uses of the study area, the exclusion classes defined and their exclusion limits determined in accordance with these classes.
e: DOLC, Depending on land covers of the study area, the exclusion classes defined and their exclusion limits determined in accordance with these classes.
f: Only a study also applied a buffer from areas with specific population density (buffer of 32,186.88 m).

Regarding one of the most crucial criteria in terms of energy efficiency, ECCSP.5, has high frequency of occurrence (26) as EC in the CSP siting studies, and the mean and mode values used for this factor were 1830 and 1800 kWh/m²/year respectively. Additionally, it should be noted that the “bird habitats” criterion is missing as an EC in the CSP siting studies; however birds commonly use the wetlands as habitats and “wetlands” included as EC in the CSP siting procedure. “Distance from touristic zones” criterion is also missing as EC in the CSP siting studies, since applied only once in the existing siting applications. Regarding the issue of “CSP siting in agricultural land”, several studies excluded all classes of agricultural land, and several other studies selected to exclude certain classes of agricultural land. The classes of agricultural land and croplands, that are excluded in the CSP siting studies, can be seen in Table 4. The “agricultural land and croplands” criterion has moderate to high frequency of occurrence as EC also in the CSP siting studies. Lastly, Table 5 presents the EC used only once for CSP siting and these criteria with the respective limits could be used as a basis from the future studies for investigating further their influence on the determination of the most proper land sites for CSP installations.

Table 5. Exclusion criteria applied only once for CSP siting in accordance with their frequency of occurrence and the defined exclusion limits.

No.	Description	Exclusion Limit
ECCSP.41	Generation Costs of Electricity	>0.35 US$/kWh
ECCSP.42	Visibility Degree	Areas visible from most-visited sites in number
ECCSP.43	Mountain Zones	0 m
ECCSP.44	Vineyards	0 m
ECCSP.45	Distance from Shoreline	<50 m
ECCSP.46	Mining Wastelands	0 m
ECCSP.47	Capacity Factor	<25%
ECCSP.48	Minefields	0 m
ECCSP.49	Geographic Boundaries	Administration Boundaries of Study Area
ECCSP.50	Precipitation	>6 mm/day
ECCSP.51	Distance from Drainage Pipelines	0 m
ECCSP.52	Average Annual Cosine Efficiency (%)	N/A
ECCSP.53	Distance from Touristic Zones	<500 m
ECCSP.54	Political, War, Terror and Security Conditions	Unstable locations based on these conditions
ECCSP.55	Supergrid Business Climate and Conditions	Unstable locations based on these conditions
ECCSP.56	Climate Conditions (Not-specified)	N/A
ECCSP.57	Proximity to Mining Clusters	N/A
ECCSP.58	Proximity to Harbors	N/A
ECCSP.59	Governments’ Supergrid Integration Policy	Unsuitable locations based on this policy
ECCSP.60	Maximum Wind Velocity	N/A (maximum of 12 m/s had the suitable site)
ECCSP.61	Energy Return Over Investment (EROI)	EROI $\leq$

1
ECCSP.62	Energy Payback Time (years)	N/A
ECCSP.63	Proximity to Natural Gas Pipelines (m)	N/A (upper exclusion limit defined)
ECCSP.64	Net Energy (TJ)	N/A/ Ineffective energy resource locations
ECCSP.65	Meteorological Data	N/A
ECCSP.66	Availability of Fuel Back-Up (natural gas)	N/A

Note: ECCSP, exclusion criteria for concentrated solar power.

3.3. Thematic module 3 — AC

3.3.1. AC for PV siting

The AC in each PV siting study varied in number, type, assessment weights, priority position, and in their optimal and poor values. In total, 105 assessment criteria for photovoltaics (ACPV) siting were identified. Fifty-three (53) ACPV were used in more than one study as it can be seen in Table 6. The remaining 51 AC were applied only once and indicatively include: land value (Georgiou and Skarlatos, 2016), proximity to mines (Aly et al., 2017), economic risk (Vafaeipour et al., 2014), time delay risk (Vafaeipour et al., 2014), noise impact (Sindhu et al., 2017), skilled manpower (Sindhu et al., 2017), service life (Lee et al., 2015), shadow degree (Habib et al., 2020), distance from forests (Ali et al., 2019), and environmental risk (Vafaeipour et al., 2014). The mean number of AC applied in the PV siting studies was 5, and the mode ones were 0 (16 studies) and 7 (14 studies). Additionally, the maximal number of ACPV applied in a study was 19 (Sindhu et al., 2017), while there were studies with no ACPV used (e.g., Arnette and Zobel, 2011, Calvert and Mabee, 2015, Dias et al., 2019, Merrouni et al., 2016, Piyatadsananon, 2016).

The five most important criteria based on their mean weight and their median priority position were: (1) ACPV.1, (2) ACPV.11, (3) ACPV.3, (4) ACPV.4, and (5) ACPV.6. AC with frequency of occurrence less than five, excluded from the ranking due to very few studies used these criteria and assigned specific weights to them. ACPV.1, ACPV.3, and ACPV.4 are three of the five most frequently used and important ACPV in terms of weight and priority position. Although ACPV.2 and ACPV.5 were highly frequently used for the assessment of the suitable sites, considered as criteria of moderate to low importance, in terms of weight and priority position, in the international literature.

Table 6. AC applied more than once for PV siting in accordance with their frequency of occurrence, mean weight, median priority position, and the defined mean optimal and poor values.

No.	Description	FO	Mean weight	Median priority position	Mean optimal values	Mean poor values
ACPV.1	Global Horizontal Irradiance	64	33.14%	1°	>1895 kWh/m²/year	<1408 kWh/m²/year
ACPV.2	Proximity to Road Network (FO: 51)	52	8.30%	5°

3124.3 m

16,777.6 m
Distance from Road Network (FO: 1; Off-Grid PV)	N/A	N/A

15,000 m

2000 m
ACPV.3	Proximity to Electricity Grid (FO: 49)	50	14.88%	3°

4048 m

25,181 m
Distance from Electricity Grid (FO: 3; Off-Grid PV)	23.1% (FO:1)	2° (FO:1)

17,500 m

900 m
ACPV.4	Slope of Terrain	45	10.60%	4°

4.92%

18.10%
ACPV.5	Distance from Urban and Residential Areas (FO: 17)	40	8.07%	5°

5212.15 m

3389.15 m
Proximity to Urban and Residential Areas (FO: 21)	6.23%	5 $°$

–6°

6969.1 m

20,561.10 m
Distance from/Proximity to Urban and Residential Areas (FO: 3)	N/A	N/A	4500–10,000 m (from urban areas); and 200–1500 m (from villages)	500–4500 m and 10,000–20,000 m (from urban areas); and 101–200 m and 1500–3000 m (from villages)
ACPV.6	Land Orientation	23	13.49%	4°	Horizontal and South Aspect (Northern Hemisphere) and North Aspect (Southern Hemisphere)	North Aspect (Northern Hemisphere) and South Aspect (Southern Hemisphere)
ACPV.7	Average Temperature (Minimization; FO: 16)	21	13.05%	4°

C
Average Temperature (Maximization; FO: 5)	10.51%	5°	>28 °C (FO: 1)	<24 °C (FO: 1)
ACPV.8	Land Uses/Covers for PV Siting (FO: 18)	19	13.82%	4°	Barren/Bare Lands, Areas without vegetation, Areas with little/short vegetation (e.g., grasslands)	Agricultural Areas and Cultivations, Forests/Woody Crops
ACPV.8	Distance from Other Land Uses (FO: 1)	19	N/A	N/A

1000 m	<100 m
ACPV.9	PV Energy Output/Expected Electricity Generation	15	N/A (26.09%; FO: 3)	N/A (1°; FO: 3)	N/A ( $\geq$

18.56 kWh/m²/month, FO: 1)

N/A (

\leq

16.15 kWh/m²/month; FO: 1)
ACPV.10	Elevation (Maximization; FO: 9)	13	5.47%	6°	>1127 m	<336 m (and >4500 m; FO: 1)
ACPV.10	Elevation (Minimization; FO: 4)	13	6.29%	5°	<117 m	>1000 m
ACPV.11	Annual Sunshine Hours	11	18.45%	2°

2702.58 Hours

2371.388 Hours
ACPV.12	Water Availability/Proximity to Water Surfaces (FO: 6)	9	5.47% (1.06% to groundwater; FO: 1)	5° (8° to groundwater; FO: 1)	<6800 m to water surfaces (<2500 m to groundwater; FO: 1)	>21,200 m to water surfaces (>5000 m to groundwater; FO: 1)
ACPV.12	Distance from Water Surfaces (FO: 3)	9	8%	4 $°$

–5°	>700 m	<267 m
ACPV.13	Proximity to Railway Network	8	8.2%	4°	<10,431.64 m	>24,554.672 m
ACPV.14	Distance from Protected Areas (e.g., of environmental, ecological importance)	7	15.33% (FO: 3)	3° (FO: 3)

2761.68 m

652.336 m
Proximity to Protected Areas (e.g., of environmental, ecological importance) (FO: 1; Off-Grid PV)	16.8%	3°	Inside of Protected Areas	Outside of Protected Areas
ACPV.15	Relative Humidity (%)	7	3.73%	9°	<51%	>60%
ACPV.16	Visual Impact/Observability	6	7.3%	7°	Zero or Minimum Visibility	N/A
ACPV.17	Vegetation Index (FO: 3)	5	12.73%	4°	N/A (<0.2; FO: 1)	N/A ( $\geq$

0.4; FO: 1)
Distance from Vegetation Coverage Areas (FO: 2)	N/A	N/A

1000 m

500 m
ACPV.18	Investment Costs (€)^a	5	8.18%	5°	N/A	N/A
ACPV.19	Farm Minimum Required Area	5	12.3%	4°	N/A (>1.5 km²; FO: 1)	N/A (<1 km²; FO: 1)
ACPV.20	Distance from Faults	5	2.8%	10°

7333.33 m

1900 m
ACPV.21	Public/Social Acceptance	5	16.47%	10°	N/A	N/A
ACPV.22	Population Density (Minimization; FO: 2)	4	8.73%	3 $°$

–4°	N/A (0 Inhabitants/km²; FO:1)	N/A (>500 Inhabitants/km²; FO:1)
Population Density (Maximization; FO: 3)	N/A (48.4%; FO:1)	N/A (1°; FO:1)	N/A (>500 Inhabitants/km²; FO:1)	N/A (0 Inhabitants/km²; FO:1)
ACPV.23	Distance from Agricultural Land (FO: 3)	4	N/A	N/A

400 m

75 m
Classes of Agricultural Land (FO: 1)	12.5%	2°	Poor and Non Agricultural Land	N/A
ACPV.24	Precipitation (mm or days)	4	16.56%	4°	N/A ( $\leq$

40 days; FO:1)

N/A (

\geq

60 days; FO:1)
ACPV.25	Agrological Capacity (Class)	4	8.581%	5°	Class VIII or Very Low	Excellent/High or Class I
ACPV.26	Net Present Value (€)	4	N/A (7.3%; FO:1)	N/A (5°; FO:1)	N/A	N/A
ACPV.27	Effect on Economic Benefits of Surrounding Regions	4	4.838%	10 $°$

–11°	N/A	N/A
ACPV.28	Capacity Factor (%)	4	N/A	N/A	N/A	N/A
ACPV.29	Electricity Demand Density	3	N/A (7.3%; FO: 1)	N/A (5°; FO: 1)	N/A ( $\geq$

102.8 MWh/year/km²; FO: 1)	N/A (0 MWh/year/km²; FO: 1)
ACPV.30	Distance from Rural Areas	3	N/A (1%; FO: 1)	N/A (10°; FO: 1)

1250 m and $\leq$

3000 m

750 m and $\geq$

5000 m
ACPV.31	Distance from Bird Habitats and Wetlands	3	15.18%	4°	>1304.672 m	<451.168 m
ACPV.32	Cloudy Days	3	6.43%	9°

26.67 days

73.33 days
ACPV.33	Dusty Days	3	3.91%	10°

26.67 days

73.33 days
ACPV.34	Distance from Orchards	3	N/A	N/A

400 m

75 m
ACPV.35	Carbon Emission Savings	3	18.95%	2°	N/A	N/A
ACPV.36	Simple Payback Period (Years)	3	N/A (8.7%; FO: 1)	N/A (3°; FO: 1)	N/A	N/A
ACPV.37	Levelized Cost of Electricity ($/kWh)	3	N/A	N/A	N/A ($0.10–0.12kWh; FO: 1)	N/A ($0.30–0.35kWh; FO: 1)
ACPV.38	Climatic Conditions	2	15.93%	4 $°$

–5°	N/A	N/A
ACPV.39	Soil/Topographic Features	2	27.25% (FO: 1)	1° (FO: 1)	N/A	N/A
ACPV.40	Average Internal Rate of Return (IRR) (%)	2	N/A	N/A	N/A	N/A
ACPV.41	Impact on Wildlife and Habitats	2	8.42%	4 $°$

–5°	N/A	N/A
ACPV.42	Protection Laws and Regulatory Boundaries	2	4.8%	10 $°$

–11°	N/A	N/A
ACPV.43	Cost of Electricity Production ($/kWh)	2	N/A	N/A	N/A	N/A
ACPV.44	Land Availability	2	4.4%	8 $°$

–9°	N/A	N/A
ACPV.45	Policy Support	2	9.34%	4 $°$

–5°	N/A	N/A
ACPV.46	Average Solar Irradiance Exergy	2	N/A	N/A	N/A	N/A
ACPV.47	Average Exergy Efficiency (%)	2	N/A	N/A	N/A	N/A
ACPV.48	Distance from Unsuitable Areas (m)	2	26.2%	2°	N/A	N/A
ACPV.49	Annual Aerosol Optical Depth/Dust Risk	2	10% (FO: 1)	4° (FO: 1)	N/A	N/A
ACPV.50	Feeder Capacity of the Distribution Center	2	23% (FO: 1)	2° (FO: 1)	N/A	N/A
ACPV.51	Distance from Civil/Military Aviation Areas	2	4.23% (FO:1)	6° (FO:1)

4000 m

2250 m
ACPV.52	Distance from Archeological, Historical and Cultural Heritage Sites	2	6.5% (FO:1)	5° (FO:1)	>1609.344 m (FO:1)	<402.336 m (FO:1)
ACPV.53	Wind Velocity	2	5.23%	9°	N/A (>16 m/s; FO:1)	N/A (<16 m/s; FO:1)

Note: FO, frequency of occurrence.

a: Including land acquisition cost and/or panel cost and/or infrastructure cost and/or repair and maintenance cost and/or resettlement and rehabilitation cost and/or land cost and/or construction cost and/or equipment cost and/or operation cost and/or transmission cost and/or initial investment and/or outage cost.

The ACPV that are directly linked with the economic viability of the potential PV project, such as ACPV.1, ACPV.9, and ACPV.11, have high frequency of occurrence and/or importance (in terms of weight and priority position) in the assessment of the suitability of the sites. Additionally, ACPV.2, ACPV.3, ACPV.5, ACPV.12, and ACPV.14 used in a twofold way. In particular, some studies considered as preferable the sites that are as close as possible to these locations (e.g., necessity of proximity to water surfaces due to the water availability for cooling the PV systems) and some studies considered as preferable the sites that are as far as possible from these locations (e.g., preservation of distances from water surfaces in order to protect the environmental sustainability of these locations from unexcepted issues that could be arose from PV installations). It should be noted that ACPV.7, ACPV.10, and ACPV.22 were also used either as minimization or as maximization criteria. Specifically, studies defined and used these criteria differently in the siting applications (e.g., Georgiou and Skarlatos, 2016) defined ACPV.10 as minimization criterion due to the transportation costs are higher in high altitude areas, while Noorollahi et al. (2016) defined ACPV.10 as maximization criterion due to high altitude areas receive higher solar irradiance and therefore they have greater solar energy potential). Lastly, few studies conducted for off-grid PV siting applications and for that reason some ACPV, such as ACPV.2, ACPV.3, ACPV.14, and ACPV.22, used differently in order to properly determine the most suitable sites for these systems (e.g., the most suitable sites for off-grid PV should be as far as possible from the electricity grid).

3.3.2. AC for CSP siting

The AC in each CSP siting study varied also in number, type, assessment weights, priority position, and in their optimal and poor values. In total, 49 assessment criteria for concentrated solar power (ACCSP) systems siting were identified. Twenty-one ACCSP were used in more than one study, as it can be seen in Table 7. The remaining 28 AC were applied only once and include among others: elevation (Giamalaki and Tsoutsos, 2019), distance from shoreline (Giamalaki and Tsoutsos, 2019), proximity to railway network (Merrouni et al., 2018a), distance from environmental protected areas (Dawson and Schlyter, 2012), clear days (Dawson and Schlyter, 2012), cloudy days (Dawson and Schlyter, 2012), land ownership (Wu et al., 2019), agricultural productivity (Clifton and Boruff, 2010), local government support (Wu et al., 2014), land cost (Wu et al., 2014), water stress score (Deshmukh et al., 2019), co-location potential with other RES (Deshmukh et al., 2019), high wind load zones (Dawson and Schlyter, 2012), mean exergy efficiency of the CSP system (Boukelia et al., 2015), and proximity to auxiliary fuels (Dawson and Schlyter, 2012). The mean number of AC applied in the CSP siting studies was 3, and the mode was 0. Additionally, the maximal number of ACCSP applied in a study was 13 (Wu et al., 2019, Wu et al., 2014), while there were studies with no ACCSP used (e.g., Azoumah et al., 2010, Djebbar et al., 2014, Fluri, 2009, Martins et al., 2012, Merrouni et al., 2014, Omitaomu et al., 2015, Polo et al., 2015, Servert et al., 2014).

The five most important criteria based on their mean weight and their median priority position were: (1) ACCSP.1, (2) ACCSP.7, (3) ACCSP.2, (4) ACCSP.4, and (5) ACCSP.3. AC with frequency of occurrence less than five, excluded from the ranking due to very few studies used these criteria and assigned specific weights to them. ACCSP.1, ACCSP.2, ACCSP.3, and ACCSP.4 are four of the five most frequently used and important ACCSP in terms of weight and priority position. Although ACCSP.5 was frequently used, most studies either skipped to assign weight to this criterion or skipped to refer its weight.

Table 7. AC applied more than once for CSP siting in accordance with their frequency of occurrence, mean weight, median priority position, and the defined mean optimal and poor values.

No.	Description	FO	Mean weight	Median priority position	Mean optimal values	Mean poor values
ACCSP.1	Direct Normal Irradiance	15	35.87%	1°	>2222 kWh/m²/year	<1641 kWh/m²/year
ACCSP.2	Proximity to Electricity Grid	12	12.15%	4°	<3833 m	>27,000 m
ACCSP.3	Proximity to Road Network	11	6.33%	5°	<2100 m	>6700 m
ACCSP.4	Water Availability/Proximity to Water Surfaces or Supplies (FO: 7)	8	10.26%	4°	<4000 m from water covers, <10,000 m from dams, 0 m from underground water	>11,000 m from water covers, >25,000 m from dams, >5000 m from underground water
ACCSP.4	Distance from Water Surfaces (FO: 1)	8	12%	2°	>400 m	<200 m
ACCSP.5	Potential Electricity Generation (GWh/year)	8	N/A	N/A	N/A	N/A
ACCSP.6	Levelized Cost of Electricity	8	N/A (11.32%)^a	N/A (1°)^a	N/A ($0.10–0.12kWh)^a	N/A ($0.30–0.35kWh)^a
ACCSP.7	Slope of Terrain	6	17%	2 $°$

–3°	<3%	>11%
ACCSP.8	Proximity to Urban and Residential Areas (FO: 4)	5	2.72%	5°	<7250 m	>21,750 m
ACCSP.8	Distance from Urban and Residential Areas (FO: 1)	5	3.9%	Last	>5000 m	<1000 m
ACCSP.9	Land Covers/Uses (Class)	3	8.33%	3°	Bareland	Specific Agriculture Areas
ACCSP.10	Capacity Factor (%)	3	N/A	N/A	N/A	N/A
ACCSP.11	Average Air Temperature	2	16%	5 $°$

–6°	<15 °C^a	>37 °C^a
ACCSP.12	Visibility Degree/Visual Impact	2	5.18%	N/A	Invisible areas^a	Invisible areas only from archaeological sites^a
ACCSP.13	Mean Efficiency of CSP System (%)	2	N/A	N/A	N/A	N/A
ACCSP.14	Population Density	2	7.45%	Penultimate	¹0 Inhabitants/km²	¹ >500 Inhabitants/km²
ACCSP.15	Sunshine Time (Hours)	2	12%	4 $°$

–5°	N/A	N/A
ACCSP.16	Land Orientation	2	11%	3 $°$

–4°	South Aspect	North, Northeastern and Northwestern Aspects (N/A mean values)
ACCSP.17	Impact on the Surrounding Ecological Environment	2	3.62%	11 $°$

–12°	N/A	N/A
ACCSP.18	Pollutant Emission Reduction Benefits (t)	2	6.27%	7°	N/A	N/A
ACCSP.19	Impact on the Local Economy	2	5.64%	8 $°$

–9°	N/A	N/A
ACCSP.20	Public Support	2	6.58%	6 $°$

–7°	N/A	N/A
ACCSP.21	Soil Structure and the Geology	2	6.67%	8 $°$

–9°

N/A

Note: FO, frequency of occurrence.

a: Results are based on the availability of quantitative data by a single study.

The ACCSP.1 has the highest frequency of occurrence and greatest importance (in terms of weight and priority position) in the assessment of the suitable sites, since it has a major role to the expected electricity generation of the CSP projects in potential sites. Additionally, ACCSP.4 and ACCSP.8 used in a twofold way. In particular, in some studies the sites that are as close as possible to these locations are preferred (e.g., necessity of proximity to urban and residential areas for minimizing the distances of the potential CSP projects to the areas with high electricity demand), while in others the sites that are as far as possible from these locations are favored (e.g., preservation of distances from urban and residential areas to ensure the sustainability of these areas from unexcepted issues that could be arose from CSP installations). The ACCSP with the highest frequency of occurrence (higher than 5) are criteria of mainly economic and technical importance, while ACCSP of environmental or social importance have much lower frequency of occurrence. Lastly, the criterion of ‘distance from protected areas’, which is an essential environmental siting criterion is missing as an ACCSP from the international literature.

3.4. Thematic module 4 — Optimization modules and criteria

Optimization modules have been developed and applied only in the PV siting studies and specifically only in three studies (Dhunny et al., 2019, Gómez et al., 2010, Pillot et al., 2020). The optimization criteria (OC) used in each study varied in number, type, objectives, objective functions, and in the optimization methods that were employed for the fulfillment of their objectives. In total, 7 OC for optimizing the results and determining the optimal sites for PV installations were identified and presented in Table 8.

The maximal number of OC applied in a study was 4, while the minimum was 1. Few criteria were applied in the optimization processes due to the complexity of the procedure. Different optimization modules were developed in each PV siting study. Gómez et al. (2010) developed an improved version of Binary Particle Swarm Optimization Algorithm, which improves the accuracy of the results compared to the standard version of the algorithm, and even gives better accuracy than other optimization algorithms, such as genetic algorithms. The optimal location for PV siting was found based on profitability index. Pillot et al. (2020) developed a two-objective optimization module, to maximize energy generation, while minimizing the total costs of project investment. This optimization problem was solved by using the Pareto frontier, i.e., optimizing each function while constraining the other one, and reciprocally. In addition, knapsack constraints (e.g., constraints related to the size of PV system) were applied for enhancing the accuracy of the optimal set solutions. Optimization of the results was also applied by Dhunny et al. (2019), by integrating an optimization algorithm in MATLAB. The rule base of the OC was optimized by determining the optimum thresholds. This method optimizes both accuracy and interpretability of the fuzzy logic network. Four OC were used in this module as it can be seen in Table 8, in detail.

Table 8. Optimization criteria applied in the optimization modules of the PV siting applications.

No.	Criterion	FO	Objective(s)	Optimization method	Objective function(s)
OM.1	Profitability Index^a	1	Maximize the profitability of the project investment	Improved Version of Binary Particle Swarm Optimization Algorithm


OM.2	Energy Generation (GWh/year)^b	1	Maximize the total hourly energy demand production over the year	Robust Optimization, Knapsack Modeling with Pareto Frontier


Investment Costs (M€)^c	1	Minimize the total costs related to PV project investment


OM.3	Slope of Terrain	1	Minimize the slope of terrain	Optimization Algorithm of Fuzzy Logic System in MATLAB	N/A
	Presence of Settlements	1	Minimize the presence of settlements		N/A
	Solar Irradiance	1	Maximize solar irradiance		N/A
	Proximity to Electricity Grid	1	Minimize the distance from electricity grid lines		N/A

The equations needed for the estimation of the individual parameters of the objective functions as well as more detailed information needed for these functions and the knapsack constraints can be found in Pillot et al. (2020).

a

, profitability index; $N P V$ , net present value; $P V_{I N}$ , present value of cash inflows; $P V_{O U T}$ , present value of cash outflows; $P V I$ , present value of initial investment. The equations needed for the estimation of the individual parameters of the objective function of $P I$

can be found in Gómez et al. (2010).

, total energy; $i$ , site index; $h$ , hours per year; $S A$ , area of solar PV plant; $P_{p v_{h, i}}$ , estimated hourly production per PV unit (kWh/m $^{2}$

, total costs; $i$ , site index; $C_{c a p_{i}}$ , capital costs per new PV; $C_{o p_{i}}$ , annual operation costs per new PV; $C_{c o n_{i}}$ , connection costs per new PV, transmission lines; $C_{s t a_{i}}$

, capital costs for new substation.

3.5. Thematic module 5 — Geographic locations of the study areas

Regarding the PV siting, studies were conducted for numerous different geographic locations of 73 countries, excluding the countries that were investigated only on a very large spatial scale (i.e., global or Belt and Road Initiative scale). The majority of the studies were conducted for Asian countries (51 studies). In particular, the countries with the most frequency of occurrence were Iran (17 studies), China (7 studies), Turkey (6 studies), India (3 studies), Oman (3 studies), and South Korea (3 studies), as it can be seen in Fig. 6. In addition, many studies were conducted also for European countries (26 studies), such as Spain (9 studies), Italy (5 studies), United Kingdom (3 studies), Poland (3 studies) and Germany (3 studies), and for African countries (16 studies), such as Morocco (4 studies) and Mauritius (3 studies). North (8 studies) and South (3 studies) America were inadequately investigated, with most siting applications focusing on United States (6 studies) for the former. No studies were found for Oceania or Antarctica.

Regarding the CSP siting, studies were conducted for numerous different geographic locations of 50 countries, excluding the countries that were investigated only on a very large spatial scale (i.e., global scale). The majority of the studies were conducted also for Asian countries (17 studies), as it can be seen in Fig. 7. In particular, the countries with the most frequency of occurrence were China (3 studies), and Iran (3 studies). Many studies were carried out also for African countries (12 studies), such as Morocco (4 studies), Burkina Faso (3 studies), and Algeria (3 studies). North (5 studies) and South (4 studies) America, Europe (3 studies), and Oceania (2 studies) were inadequately investigated, with most siting applications focusing on United States (4 studies), Chile (3 studies), Spain (2 studies), and Australia (2 studies), respectively. No studies were found for Antarctica.

In the global map (Fig. 8) the countries that were investigated for PV or/and CSP siting are illustrated, except for the countries that were investigated only on a very large scale (global or Belt and Road Initiative scale). These countries were included in category “Need for further investigation for PV and CSP siting”. In addition, only a very small part of Russia was also investigated over the Black Sea catchment for PV siting and for that reason Russia was also included in the latter category. Yellow color in the global map reveals that almost all European countries are yet to be investigated for CSP siting, except for Spain and Greece. However, Greece could also be considered as an uninvestigated country for CSP siting, since only the Regional Unit of Rethymno in Crete was studied for the siting of this solar technology. Orange color in the global map reveals that African countries have been mostly investigated for CSP siting, while red color reveals that Asia and North America have been mostly investigated for the siting of both solar technologies (PV and CSP). The dark dusty color in the map reveals that a sufficient fraction of the world is needed to be further investigated regarding the proper siting of PV and CSP technologies; 73 (37.43%) and 50 (25.64%) of 195 countries were investigated for PV and CSP siting, respectively. The reviewed articles (except for the studies that conducted on global or Belt and Road Initiative scale) referred to only 16 (33.3%) and 12 (25%) of 48 Asian countries for PV and CSP siting investigation, respectively; even though the most frequently occurring studies included in this systematic review were conducted for Asian countries (see Fig. 6, Fig. 7).

Fig. 6. Frequency of occurrence of investigated geographic locations for PV siting per (a) continental and (b) national scale. Note ¹: Countries that were investigated only on a very large scale (global or Belt and Road Initiative scale) are not included in the chart (b). Note ²: Countries that have frequency of occurrence of one are not included in the chart (b), but they are shown in the global map of Fig. 8 (in total 37 countries).

3.6. Thematic module 6 — Spatial planning or reference scales

Very few studies were conducted on very large spatial reference scales, such as global (3 PV and 4 CSP siting studies), Belt and Road Initiative (1 PV siting study), European Union (1 PV siting study), and Black Sea Region Catchment (1 PV siting study) scale. Most of the studies was conducted to large spatial planning scales (national and regional scales) for PV (53 studies; 51%) and CSP (30 studies; 62.5%) siting (Fig. 9). However, several PV siting studies were carried out also on small planning or reference scales (local and site specific scales (30 studies; 28.85%)), and several CSP siting studies were applied also on small reference scale (site-specific (11 studies; 22.91%)). In very few studies, where a framework for the determination of the most suitable land sites is proposed, an application of their framework for PV (3 studies) or CSP (1 study) siting is missing.

According to the correlation analysis of TM.5 and TM.6, most studies that were conducted on national scale for PV (21 of 24) and CSP (15 of 18) siting were applied to Asian or African countries, while no PV and CSP siting applications can be found on this scale for North and South America and Oceania, and for Oceania, respectively (Fig. 10). On the regional scale, most studies that were carried out for PV siting were applied to Asian (15 of 29) or North American (6 of 29) countries, while no siting applications can be found for Oceania. Regarding the CSP siting on the regional scale, most studies were conducted for African or North American countries (8 of 12), while no siting applications can be found for South American countries on this scale. On the regional unit and local scales, most studies were investigated PV siting for European or Asian countries (10 of 13 and 15 of 18, respectively), while scant applications can be found for CSP siting on these scales (only 2 and 1 study, respectively). Lastly, most studies that were conducted on site-specific scale for PV (7 of 12) and CSP (5 of 11) siting were applied to Asian countries, while no PV and CSP siting applications can be found for North America and Oceania, and for Europe, North America and Oceania, respectively.

3.7. Thematic module 7 — Solar radiation data estimation and analysis

Solar radiation data analysis was conducted in 89 of 104 (85.58%) and 40 of 48 (83.33%) of PV and CSP siting studies, respectively. The main parameters of solar radiation data analysis included: (a) approach of estimating the solar radiation data, (b) methodology of processing the solar radiation data, (c) time-period analysis, (d) gap period (i.e., the period of time between the last year of solar radiation data estimated and the year of the siting study conducted), and (e) spatial resolution. The main identified approaches for estimating the solar radiation data in the study area can be categorized as follows: (a) satellite-based estimations (35 PV and 22 CSP siting studies), (b) ground-measured data (18 PV and 2 CSP siting studies), (c) correlation of satellite-based and ground-measured data estimations (2 PV and 3 CSP siting studies), and (d) utilization of solar radiation analyst tool of ArcGIS software with the use of Digital Elevation Model (DEM) of the study area (13 PV and 4 CSP siting studies). The solar radiation data that derived from the first three categories have been already estimated and are available from several international databases (Fig. 11; key examples of international radiation databases are presented); however there is the necessity of properly processing and analyzing these data in order to fully address the objectives of the siting studies. The most commonly used international radiation database for conducting a solar resources analysis was the SOLARGIS Radiation Database (including Global Solar Atlas) in both PV and CSP siting studies. It should be noted that all studies reported the methodologies used for processing the solar radiation data; however some studies (21 PV and 7 CSP siting studies) skipped to report the type of radiation data used and the relevant information (e.g., satellite data used, which derived from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) Radiation Database).

The most frequently used methodology for processing the satellite radiation data was GIS-based processing and analysis of spatial data, in both PV and CSP siting studies (Table 9). In ground-measured estimations, spatial interpolation techniques were a necessary tool for the calculation of solar radiation in a large geographic area. The most commonly used interpolation techniques were Inverse Distance Weighted (IDW) and Kriging. The most frequently employed methodologies (applied in more than one siting study) for processing the satellite-based and ground-measured radiation data (the first two most common estimation procedures of radiation data) are presented in Table 9.

In PV siting applications, the time-period of analysis and the gap period were reported in 40 and 35 studies (Table 10), respectively. In CSP siting applications, the respective parameters were reported in 24 and 16 studies, respectively. The time-period of solar radiation data analyses ranged from 0.5 to 30 years and from 1 to 22 years, in PV and CSP siting applications respectively. The most common time-period of analysis was 1 year in both PV and CSP siting applications. On the other hand, the most common gap period was 2 and 6 years in PV and CSP siting applications, respectively. Thirty-seven (37) PV and twenty-four (24) CSP siting studies reported the spatial resolution of solar radiation data used. Spatial resolutions of radiation data ranged from 0.005 to 40 km and from 0.09 to 111 km in PV and CSP siting applications, respectively. The most common spatial resolutions used were 1 and 10 km in PV and CSP siting applications, respectively.

Table 9. Most frequently methodologies used for processing the satellite-based and ground-measured solar radiation data.

Solar technology	Approach for solar radiation estimation	FO	Methodology for processing the data	FO
PV	Satellite-based	35	GIS Analysis, Processing and Presentation of Data	20
	Satellite-based	35	Numerical and prioritization analysis	5
			Spatial interpolation technique — Kriging and GIS analysis, processing and presentation of data	2
	Ground-measured	18	Spatial interpolation technique — IDW and GIS Analysis, processing and presentation of data	5
			Spatial interpolation technique — Kriging and GIS analysis, processing and presentation of data	4
			Numerical and prioritization analysis	4
			GIS Analysis, processing and presentation of data from WorldClim database	2
CSP	Satellite-based	22	GIS analysis, processing and presentation of data	15
	Satellite-based	22	Correlation Methods and Numerical Analysis	2
	Ground-measured^a	2	Spatial interpolation technique — IDW and GIS analysis, processing and presentation of data	1
	Ground-measured^a	2	Numerical and prioritization analysis	1

Note: FO, frequency of occurrence; IDW, inverse distance weighted.

a: All methodologies of processing the ground-measured data presented, since no most frequently methodologies revealed.

Table 10. Time-period of analysis, gap period and spatial resolution of solar radiation data.

Solar technology	Parameter of solar radiation analysis	FO	Min value	Max value	Mode value	Mean value
PV	Time-Period of Analysis (years)	40	0.5	30	1	11
	Gap Period (years)	35	0	28	2	6.5
	Spatial Resolution (km)	37	0.005	40	1	4.7
CSP	Time-Period of Analysis (years)	24	1	22	1	9
	Gap Period (years)	16	0	14	6	5.5
	Spatial Resolution (km)	24	0.09	111	10	19

Note: FO, frequency of occurrence.

3.8. Thematic module 8 — Sensitivity analysis related to site-selection procedure

Sensitivity analysis of the results of the site-selection procedure was conducted in 20 of 104 (19.23%) PV and 7 of 48 (14.58%) CSP siting studies (Table 11). In the PV siting applications, the sensitivity analysis focused mostly on modifying: (a) the original weights of AC, (b) the AC used for the evaluation process, and (c) the exclusion limits of the EC or/and the EC used. In the CSP siting applications, the sensitivity analysis focused on modifying: (a) the original weights of AC, and (b) the exclusion limits of the EC or/and the EC used. The main parameters of sensitivity analysis included: (a) siting stage that was conducted, (b) type of sensitivity analysis, (c) methodology employed, (d) number of scenarios, and (e) objectives of sensitivity analysis.

The main objectives of conducting a sensitivity analysis at the ES are the impact investigation of different exclusion criteria and limits on the determination of the final suitable sites and on the estimation of the total solar energy potential. On the other hand, the main objectives of conducting a sensitivity analysis at the assessment stages are: (a) the verification of the robustness of the results and of the methodology employed, (b) the impact investigation of different policy-oriented siting scenarios (e.g., socio-environmental scenarios focusing on the importance of the environmental and social criteria) on the evaluation results, (c) the impact investigation of different AC weights on the prioritization of the suitable sites and on their final suitability index, and (d) the impact investigation of each AC on the assessment of the suitable sites and on the final site suitability results. Sensitivity analysis mostly conducted on the assessment stages of the site-selection procedure in both PV and CSP siting studies. Lastly, the direct modification of the original weights of AC was the most commonly applied sensitivity analysis.

Table 11. Type of sensitivity analyses applied per siting stage and solar technology.

Solar technology	Stage	FO	Type	FO	Methodology	FO	Mean number of main scenarios
PV	AS	16	Modifying the original weights of AC	15	Direct modifying of weights	13	6
			Modifying the original weights of AC	15	AHP	3	6
			Modifying the selected AC	3	Direct modifying of the AC	3	2
			Applying other methods for the prioritization of the sites	1	Multi-Attribute Utility Theory	1	1
			Modifying the variable precision of the assessment methodology employed	1	Direct Modifying of the variable precision	1	8
	ES	4	Modifying the exclusion limits of the EC or/and the EC used	4	Direct Modifying of the Exclusion Limits or/and the EC	4	13
CSP	AS	4	Modifying the original weights of AC	4	Direct Modifying of Weights	3	5
	AS	4	Modifying the original weights of AC	4	AHP	1	5
	ES	3	Modifying the exclusion limits of the EC or/and the EC used	3	Direct Modifying of the Exclusion Limits or/and the EC	3	2

Note: FO, frequency of occurrence.

3.9. Thematic module 9 — Participatory planning

Twenty-five (25) PV and seven (7) CSP siting studies incorporated the opinion and preferences of different participatory groups in their site-selection framework. The parameters of participatory planning included: (a) participatory groups, (b) number of participants, (c) siting stages that participants are involved, (d) participants’ contributions, and (e) methodologies employed per participatory group and contribution.

Experts and stakeholders were involved in the site-selection procedure of PV and CSP systems, while public preferences and concerns were investigated and considered only in the PV siting studies. Thus, the public participation in the siting procedure of CSP systems is missing from the international literature as well as the impact investigation of their participation. In addition, public preferences and concerns were incorporated only in 3 PV siting studies and only at the ASPB stage of the siting procedure. As a result, the further investigation of public participation in PV siting is necessary and especially at the early stages (ES) of the procedure is missing.

The number of experts/stakeholders participants ranged from 1 to 33, the mean number of participants were 11, while the mode was 10 in the PV siting studies. In the CSP siting applications, few studies reported the actual number of experts/stakeholders participants; however, the maximal number of participants reported is 27 (Aly et al., 2017). The number of citizens participants was reported only in 1 PV siting study (Mostegl et al., 2017) (231 participants).

Experts/stakeholders were most frequently participated on the prioritization/assessment of AC and on the determination of AC, in both PV and CSP siting applications (Table 12). The most frequently used method was AHP for the former and the primary data-collection methods (i.e., interviews, Delphi method, interactive discussions and meetings, questionnaires) for the latter. Experts/stakeholders were least frequently participated on early stages of the siting procedure and especially on the determination of EC (3 PV and 1 CSP siting studies) and on their exclusion limits (1 PV siting study). Primary data-collection methods used for the incorporation of participants’ opinions at these steps of the siting procedure, such as in-depth interviews, and questionnaire surveys. A study (Dawson and Schlyter, 2012) also considered general suggestions from experts and stakeholders in the siting procedure of CSP systems. Lastly, it should be noted that public participated only on the ASPB stage, by taking part on questionnaire surveys and choice experiments.

Table 12. Frequency of occurrence of each involved participatory group, of each participatory contribution and of methodologies employed for each contribution and group, per siting stage and solar technology.

Solar technology	Participatory group	FO	Participatory contribution	FO	Methodology	FO	Siting stage	FO
PV	Experts/Stakeholders	22	Prioritization/Assessment of AC	18	AHP	11	ASPA	18
					Primary Data-Collection Methods	6
					Fuzzy AHP in Linguistic Terms	3
					SWARA	1
					MMCRC-CC	1
					ELECTRE-TRI/DSS IRIS Software	1
			Determination of AC	12	Primary Data-Collection Methods	12	ASPB	19
			Prioritization/Assessment of Suitable Sites based on the Selected AC	4	Primary Data-Collection Methods	2
					ELECTRE-TRI/DSS IRIS Software	1
					Fuzzy TOPSIS	1
					Decision Matrix of WASPAS	1
			Determination of Suitability Classes of each AC	3	Primary Data-Collection Methods	3
			Determination of Suitability Classes of each AC	3	Fuzzy Logic Membership	1
			Determination of EC	3	Primary Data-Collection Methods	3	ES	4
			Determination of the Exclusion Limits of the EC	1	Primary Data-Collection Methods	1	ES	4
			Discounting the AC and Confirming the Final AC	1	Primary Data-Collection Methods	1	ASPB	1
			Determination of PV technology for the siting analysis	1	Primary Data-Collection Methods	1	ES and ASPB	1
	Public/Citizens	3	Preferences in distances of PV systems to 5 specific land uses (AC Classes)	2	Questionnaire Survey	2	ASPB	3
Empty Cell	Public/Citizens	3	Preferences in suitable locations for siting, investment models, household savings, and in the promotion of RES	1	Questionnaire Survey and Choice Experiment	1	ASPB	3
CSP	Experts/Stakeholders	7	Prioritization/Assessment of AC	3	AHP	1	ASPA	3
			Prioritization/Assessment of AC	3	Revised Simos’ procedure	1	ASPA	3
					Fuzzy Measure	1
			Determination of AC	3	Primary Data-Collection Methods	3	ASPB	7
			Determination of Suitability Classes of each AC	2	Primary Data-Collection Methods	2	ASPB	7
			Prioritization/Assessment of Suitable Sites based on the Selected AC	2	Linguistic Variables	2
			Determination of EC	1	Primary Data-Collection Methods	1	ES	1
Empty Cell			Determination of CSP technology for the siting analysis	1	Primary Data-Collection Methods	1	ES and ASPB	1

Note: FO, frequency of occurrence; MMCRC-CC, method of multiple coefficient of rank correlation — coefficient of concordance; RES, renewable energy solutions.

3.10. Thematic module 10 — Laws, regulations and policies related to site-selection procedure

National and international legislations, regulations or policies related to the site-selection procedure of PV and CSP systems were reported and considered in 35 of 104 and in 2 of 48, respectively (Fig. 12). However, even much fewer studies reported that there are specific spatial planning regulations or national legislative frameworks for RES siting that they should consider in their approach (barely 19 studies). As a result, integrated legislative frameworks for solar technologies (especially for CSP) siting are missing for the majority of the geographic locations.

The majority of the studies applied recommendations of laws, regulations or policies for properly executing the ES of the siting procedure and avoid areas that they are prohibitive or unsuitable for PV and CSP installations. Studies that conducted for European countries most frequently report and consider related legislative frameworks in their siting procedure. It should be noted that in several PV (35; 33.65%) and CSP (26; 54.15%) siting studies, some EC (i.e., protected areas and restrictive sites) may be used for corresponding legislative purposes; however there is no reference or inclusion of specific legislative frameworks and policies in their siting framework. In addition, there are studies that were conducted for the same country, such as Calvert and Mabee (2015) and Nguyen and Pearce (2010); however the former reported and considered national RES regulations and acts and the latter omitted to include the recommendations of these regulations in the siting framework. As a result, some cases omitted to include RES siting regulations, due to the studies may be conducted in different time-periods or/and the proposed siting procedures have different aims. Nevertheless, well-developed legislative frameworks and policies for PV and especially for CSP siting are still missing in many countries globally and should be formed for the accelerated and sustainable solar technologies deployment as well as should be included as a mandatory step of the siting procedures.

3.11. Thematic module 11 — Suitability indexes and ranking of the suitable sites

Several different suitability indexes (SIs) were developed and applied for the proper determination of the suitability of the sites that considered eligible for PV and CSP siting. Fifty-two PV and nine CSP siting studies reported a SI (Fig. 13). The developed suitability indexes were either linguistic or/and numeric. The most frequently used numeric SI scales ranged from 0 to 1 (i.e., [0–1]) and from 0 to 100 (i.e., [0–100]) in PV and CSP siting studies, respectively (Table 13).

Most of the studies that used a SI for deriving the least and most suitable areas for PV or CSP siting, they categorized the index into specific suitability classes in order to correspond the numeric values of the index to a specific suitability and describe them in linguistic form (e.g., a value of 0.90 of SI corresponds to a site of excellent suitability). Classification systems of 4 or 5 suitability classes (17 and 14 studies respectively) were frequently employed in PV siting studies. In CSP siting studies, a classification system of 5 suitability classes (3 studies) was most frequently employed. Ranges from 3 to 10 and from 3 to 6 of suitability classes were found in PV and CSP siting applications, respectively. The linguistic terms that commonly used for the description of the suitability of the sites were: (a) from low or marginal to high suitable, (b) from least to most suitable, and (c) from poor to excellent. The remaining studies that developed a numeric SI, applied a continuous SI scale in which the higher the suitability value was, the higher the suitability of the site.

Table 13. Frequency of occurrence of numeric suitability indexes employed in the siting procedures.

Solar technology	Numeric suitability indexes	Frequency of occurrence
PV	[0–1]	22
	[0–100]	8
	[1–3]	2
	[1–9]	2
	[1–100]	1
	[1–10]	1
	[1–4]	1
	[0–3]	1
	[0–7]	1
	[1–5]	1
	[4.7–42.3]	1
	N/A	1
CSP	[0–100]	3
	[0–1]	2
	[1–4]	1
	N/A Specific upper and lower bounds	1

Several studies developed a prioritization process for deriving the least and most suitable areas for siting by commonly applying a multicriteria decision-making method (e.g., TOPSIS). Some studies applied a classification process for successfully revealing and presenting the hot spots for PV or CSP siting. In particular, they categorized the suitable sites into classes based on their characteristics in the selected AC. Lastly, a few studies presented the least and most suitable sites derived from the ASPB Stage without reporting if they used a specific SI or process for addressing this task.

4. Discussion, insights and trends

4.1. Insights and trends from systematic analysis of thematic module 1

ES, ASPA, ASPB and OS were identified as the main methodological stages of the siting procedure, and only OS is missing from CSP siting applications. The ES exists in almost all PV and CSP siting applications, while the OS appears as a methodological stage in really handful applications. This fact reveals the importance of the ES in the determination of the most suitable areas for PV or CSP deployment and the importance of the exclusion of the areas that are inappropriate for such installations.

At the ES, the importance of GIS-based methodologies for the identification of the suitable sites for PV or CSP installations is revealed from the tendency of their use. A variety of GIS-based methodologies were developed and applied based on the specific characteristics of each study area, the availability of geographic information data, the local legislative frameworks or policies related to solar technologies siting, and the different objectives of each study (e.g., a study may focus on the fulfillment of environmental or techno-economic objectives). At the ASPA, AHP was the most frequently applied methodology for the assessment of both ACPV and ACCSP weights. This can be explained from the fact that the consistency of the results of each step can be estimated in AHP method, and this is very important for the successful implementation of such a subjective stage, such as the estimation of AC weights. At the ASPB, the greatest variety of methodological approaches was introduced than any other stage of the siting procedure in both PV and CSP studies. GIS-based methodologies are also the most frequently applied for the evaluation of the suitable sites. The importance of GIS-based methodologies at the ES and ASPB stages can be explained from the fact that the geographic information data permits the spatial and precise determination of the suitable sites as well as the determination of their characteristics in accordance with essential EC and AC. At the OS, barely three different methodological techniques identified for the determination of the optimal sites and only for PV installations.

Therefore, GIS is an essential tool for the proper execution of ES and ASPB stages of the siting procedure and should be systematically used in future applications. AHP is an important tool for the proper determination of AC weights; however Entropy method is less subjective method than AHP by definition (Zhu et al., 2020). Therefore, it would be very beneficial and useful, the Entropy method to be applied in a greater extent at the ASPA of the future PV and CSP siting studies. Optimization methodologies should be developed based on the fulfillment of multi-objectives (technical and economic viability, environmental sustainability, and social acceptability of the solar energy projects) of PV and CSP siting. Thus, additional optimization functions should be formulated for a variety of OC. GIS should have also a crucial role at the OS, in order to be managed properly the spatial dimension of the optimal sites. Lastly, ES is a methodological stage that should not be omitted in the PV and CSP siting procedures and OS is a stage that should be incorporated more systematically in the respective procedures.

4.2. Insights and trends from systematic analysis of thematic module 2

The mode exclusion limits of ECPV of high environmental and/or social importance, such as ‘distance from protected areas’, ‘distance from urban and residential areas’, ‘distance from water surfaces’, ‘distance from agricultural land and croplands’, and ‘distance from bird habitats and wetlands’, that are frequently applied in the PV siting studies are quite low (i.e., 0 m). This insight could explain the fact that some citizens and environmental organizations are opposing to the PV deployment and/or to the selected land areas for PV siting. Thus, higher exclusion limits from these areas are proposed for the future PV siting applications. In addition, it is suggested that the needs and the opinion of local population should be incorporated into this stage of the site-selection procedure. Although, 83 different ECPV are identified in the international literature; the mean value of ECPV applied in a PV siting study was barely 8. Consequently, considering all the above, more restricted exclusion siting frameworks (i.e., higher exclusion limits in selected EC and higher number of EC) should be applied in future PV applications. This recommended direction could reduce the potential suitable areas for PV installations; however it could increase the environmental sustainability of the eligible sites as well as the social acceptance towards PV deployment. Lastly, it should be noted that the mean applied value of GHI exclusion limit is 1477 kWh/m²/year, which is almost equal to the recommended value of the lowest class of solar radiation potential by NREL (4 kWh/m²/day (i.e., 1461 kWh/m²/year)) (Sabo et al., 2016, NREL, 2022).

Similar insights are found from the systematic analysis of CSP siting studies regarding the mode exclusion limits of ECCSP of high environmental and/or social importance (e.g., ‘distance from protected areas’, ‘distance from archaeological, historical and cultural heritage sites’). Thus, the respective recommendations are also given. Additionally, the mode exclusion limit of ‘distance from road network’ is also quite low (i.e., 0 m) and in the case of CSP siting should be much higher in order to avoid any visual affect from potential glares of CSP systems to the local drivers. Higher exclusion limits should be also applied from civil and military aviation areas (i.e., mode exclusion limit is equal to 0 m) to avoid any potential issues (e.g., visual and radar interference concerns) to the regular operation of the airports. It should be noted that only two studies used GHI as an EC in the CSP siting analyses. However, it is suggested to use DNI instead of GHI criterion, since CSP systems can exploit the DNI and therefore the siting analysis can lead to more accurate siting results. Lastly, the mean and mode values of DNI exclusion limit (1830 and 1800 kWh/m²/year) that are frequently applied in the CSP siting studies are almost equal to the threshold that is proposed by NREL (5 kWh/m²/day (i.e., 1826 kWh/m²/year)) (Murphy et al., 2019).

4.3. Insights and trends from systematic analysis of thematic module 3

The most significant AC for the determination of the most suitable sites for PV installation are: (a) GHI, (b) proximity to electricity grid, and (c) slope of terrain. The most significant AC for the determination of the most suitable areas for CSP siting are: (a) DNI, (b) proximity to electricity grid, and (c) proximity to water surfaces or supplies. Slope of terrain is also one of the most important AC for CSP siting; however it has slightly less frequency of occurrence than the aforementioned AC. Proximity to water surfaces or supplies criterion is much less important in PV than in CSP siting, since CSP systems need much more water for cooling and cleaning purposes. The most frequently AC used are criteria of technical or/and economical importance, while the AC of environmental or/and social importance have much less frequency of occurrence, in both PV and CSP siting studies. In addition, ‘distance from protected areas’ criterion was applied only once as an ACCSP in the international literature. Thus, future studies should use more AC of environmental and social importance in order to determine sites of high suitability in regard to their environmental sustainability and social acceptability for solar technologies installations.

One-hundred five (105) different ACPV and forty-nine (49) different ACCSP identified in the international literature, from which the fifty-three (53) ACPV and the twenty-eight (28) ACCSP applied in more than one PV and CSP siting study, respectively. However, the mean value of ACPV and of ACCSP applied in a study was 5 and 3, respectively. Consequently, it would be beneficial and useful to apply more AC in the siting studies for the thorough evaluation of the potential suitable sites for solar technologies deployment as well as for the verification of their suitability on the basis of various important siting aspects, such as technical, economic, social, environmental, political, and cultural.

4.4. Insights and trends from systematic analysis of thematic module 4

The systematic absence of an optimization module within PV and CSP siting procedures is obvious from the results of the present systematic analysis. Barely three studies developed and applied a distinct optimization module by formulating objective functions and algorithms for the determination of the optimal sites for PV installations and for the optimization of the siting results. Few OC (2; mean value) were applied in the optimization processes, since the greater the number of the OC the greater the complexity of the optimization siting problem. Observing the OC used, their main objectives focused on maximizing the profitability and the energy produced from PV installations and simultaneously minimizing the relevant investment costs. Techno-economic viability of PV projects is of high importance; however, the environmental and social sustainability are also of great importance and should be examined and analyzed in the determination of the optimal sites. Thus, objective functions that are focusing on these dimensions of the siting problem should be formulated and presented in future applications. In addition, future siting studies should focus on developing multi-objective optimization modules (trade-off between the techno-economic viability, environmental sustainability and social acceptability of solar energy projects). Useful and interesting siting results as well as optimizations tools could be revealed and presented from such optimization siting studies. Optimization modules and functions for CSP siting are also missing from the international literature and should be developed in the near future for accelerating the CSP deployment in optimal sites.

4.5. Insights and trends from systematic analysis of thematic module 5

The insights revealed from the systematic analysis results of PV siting studies are quite relevant to the current trends in global solar PV deployment. In particular, at the end of 2021, Asia had globally the largest solar PV deployment with cumulative installed capacity of 484,930 MW, followed by Europe with 183,556 MW (IRENA, 2022). Asia and Europe are also the most investigated continents for PV siting in the international literature based on the results and insights of this systematic review. Results presented from the systematic analysis of CSP siting studies are quite relevant to the current trends in global CSP deployment per country of each continent. Specifically, the most CSP siting studies that conducted in Africa, investigated the siting of CSP systems in Morocco, which was the country with the largest CSP deployment in Africa at the end of 2021 (cumulative installed capacity of 540 MW IRENA, 2022). In addition, the most CSP siting studies that conducted in Europe, Asia, and North America, investigated the siting of CSP systems in Spain, USA, and China, which were the countries with the largest CSP deployment in their continents at the end of 2021 (cumulative installed capacity of 2304 MW, 1496 MW and 570 MW, respectively IRENA, 2022). All the above insights and trends reveal the necessity and the importance of conducting proper and thorough siting studies for the accelerated and efficient PV and CSP deployment globally.

4.6. Insights and trends from systematic analysis of thematic module 6

Most of the PV (51%) and CSP (62.5%) siting studies were conducted to large spatial planning scales (i.e., national and regional scales), since these studies reveal higher scientific and research interest, and have greater social and political impact. A few studies (6 for PV and 4 for CSP siting) were conducted on larger spatial reference scales, such as global, Belt and Road Initiative, European Union, and Black Sea Region Catchment scale; however, this number of siting studies is remarkable due to their geographical extent and their significance to the fulfillment of the objectives of the international energy roadmaps.

Nevertheless, the conduction of siting studies on local or site-specific scale is of high importance, since these studies can provide in much more detail the PV and CSP siting potential in a specific geographic area and reveal the local economic and energy benefits. In addition, some siting criteria can be analyzed and applied on the basis of the specific local characteristics, local climatic and political conditions, and not on the basis of the global or national characteristics. This fact can enhance the applicability of the siting results to local geographic areas. Thus, a significant number of studies have been conducted on these spatial scales for determining the PV (28.84% of the studies) and CSP (25% of the studies) siting potential.

4.7. Insights and trends from systematic analysis of thematic module 7

Solar radiation analysis was conducted almost in all PV (85.58%) and CSP (83.33%) siting studies. Satellite-based and ground-measured data as well as radiation data that were estimated by using the solar analyst tool of ArcGIS software were the most common applications for the solar radiation data estimation. SOLARGIS Radiation Database (including Global Solar Atlas) was the most commonly international radiation database used in both PV and CSP siting applications. GIS-based analyses of spatial information were the most frequently methodologies used for processing the satellite-based solar radiation data. In ground-measured data estimations, spatial interpolation techniques were useful tools for the calculation of solar radiation in large geographic areas, since these estimations provide solar irradiation information for specific geographic locations (i.e., vector point radiation data). IDW and Kriging were the most used interpolation techniques. The interpolation techniques were also applied in a GIS environment. Generally, for solar radiation analyses, GIS-based analyses were the most frequently applied methodologies, since they are widely used for the analysis and representation of various siting criteria.

The most common time-period of analysis (1 year) should be expanded to 10–15 years in both PV and CSP siting applications, in order to enhance the accuracy and the assurance of siting results. The most common gap period should be decreased to 2 years or less in CSP siting applications. The smaller the gap period the better the quality of siting results. Spatial information data in higher resolutions should be used, especially in CSP siting applications, in order to enhance the accuracy of siting results. Specifically, high-resolution solar radiation data produce more detailed spatial information than the low-resolution data, causing changes to the solar radiation estimations as it was verified by Kim et al. (2020).

4.8. Insights and trends from systematic analysis of thematic module 8

Sensitivity analysis of the results of the site-selection procedure was performed in the minority of PV (19.23%) and CSP (14.58%) siting studies. Sensitivity analysis was mostly applied at the results of the assessment stages by mainly modifying the original weights of AC. It was commonly conducted at this particular step due to the researchers’ subjectivity at assigning different weights to the AC and the great influence of these weights on final site suitability results. Thus, the verification of the robustness of the siting results and of the methodology employed is an important step that should be performed. At the ES, sensitivity analysis was conducted for the impact investigation of different exclusion limits and criteria on the determination of the final suitable sites and several times on the estimation of the actual total solar energy potential. Lastly, the sensitivity of ES results investigated at 25% of PV and at 42.86% of CSP siting studies that conducted a sensitivity analysis, by performing a mean number of 13 and 2 sensitivity scenarios respectively.

4.9. Insights and trends from systematic analysis of thematic module 9

Participatory planning was incorporated in the site-selection framework of the minority of PV (24.04%) and CSP (14.58%) siting studies. Solar energy planners prefer to incorporate the experts/stakeholders opinion than the public preferences in the siting procedure. In addition, public preferences were considered only in PV siting studies. Methodologies for incorporating the public opinion in CSP siting studies should be developed as well as the actual impact of public participation in the siting procedure of CSP systems should be investigated in the future applications. Regarding the PV siting, public opinion and preferences were considered only in 3 studies and only at the ASPB of the siting procedure. Consequently, the further investigation of public participation in PV siting and especially at the early stages of the procedure (i.e., ES) should be addressed in future studies. Experts/stakeholders were most frequently participated on the prioritization/assessment of AC and on the determination of AC, in both PV and CSP siting applications. AHP was the most frequently applied method for the former and primary data-collection methods (e.g., interviews) for the latter. Experts/stakeholders were also rarely participated on the early stages of the siting procedure and specifically on the determination of the EC and their exclusion limits. Studies that incorporate all participatory groups opinions from the early stages of the siting procedure and systematically involve them in the siting procedure based on the results of each stage, should be conducted and implemented.

4.10. Insights and trends from systematic analysis of thematic module 10

Most of the studies incorporated the recommendations of laws, regulations or policies in the ES of the siting procedure, in order to exclude areas from the analysis that are prohibitive or unsuitable for solar technologies installation. Siting studies that were conducted for European countries most frequently reported and considered legislative frameworks in their siting procedure, although most of the studies had been conducted for Asian countries. This insight may reveal that related legislative frameworks and policies are developed to a greater extent in European countries than in Asian or African countries. In North and South America and Oceania, much fewer siting studies have been conducted for revealing such an insight. Specific spatial planning regulations and policies that focus on PV or CSP siting are missing to a greater extent from the international literature, in many geographic regions globally. Specific and well-developed legislation frameworks should be formed and established for the accelerated and sustainable solar technologies deployment. These legislative frameworks for RES siting is recommended to be included as a mandatory step of the siting approaches, for bridging the gap between research and actual siting application.

4.11. Insights and trends from systematic analysis of thematic module 11

Most of the studies that developed a specific SI, used both a numeric and linguistic scale, since the precise determination of site suitability can be estimated by a numeric scale and the site suitability results can be easily descripted by a linguistic scale. The numeric scale from 0 to 1 (i.e., [0–1]) and its multiples (e.g., [0–100]) are mostly preferred, since they are quite user-friendly scales. Similar classification systems were identified to be frequently employed in PV and CSP siting studies. GIS is used as a handy tool for the illustration and estimation of the suitability index of the eligible sites. Prioritization of the suitable sites is also a frequently applied process that is used at the ASPB, for the determination of the least and most suitable areas for PV or CSP installation. Multicriteria decision-making methods (e.g., TOPSIS, ELECTRE, PROMETHEE, VIKOR) are the most frequently used methods for the successful ranking of the suitable sites.

5. Conclusions

Solar energy is one of the leading renewable energy sources in terms of installed power capacity on a global scale. Scientific research on the site-selection procedures of PV and CSP technologies is of significant importance, contributing to environmentally sustainable, technically and economically viable, and socially acceptable solar energy projects. Despite its significant importance, preceding reviews on site-selection topic of PV technologies are limited and focus on very few aspects of site-selection procedures, while no efforts have been previously made on the analysis and assessment of existing siting procedures of CSP technologies. This systematic review provides such a direct analysis and assessment, and addresses a gap in knowledge in the solar energy research, identifying insights and data trends in all thematic modules of site-selection issue. The present systematic review was driven by four main research questions: (1) Are there data trends in the site-selection procedures of PV and CSP systems? (2) Can these trends reveal valuable knowledge and provide a basis to inform and/or improve future studies and siting implementations? (3) Are there potential research gaps and shortages in the existing site-selection procedures? (4) Can these research gaps reveal valuable knowledge and insights for the deployment of new and innovative site-selection planning tools, methodologies, criteria, or policies and/or for the improvement of key aspects of the existing siting procedures and/or for the fulfillment of a sustainable land use allocation? All the above questions are fully addressed by the systematic analyses presented in this review article. Among a total of 10,121 scientific studies filtered and investigated, a total of 152 scientific studies were identified as eligible and reviewed in detail on the basis of the workflow proposed in this systematic review. Important insights and useful data trends are highlighted in the following 11 thematic modules: (1) site-selection methodologies; (2) type, number, and exclusion limits (min, max, mean and mode values) of EC; (3) type, number, importance, priority, and suitability classes (optimal and poor values) of AC; (4) optimization modules and criteria; (5) geographic locations of the study areas; (6) spatial planning or reference scales; (7) solar radiation data estimation and analysis; (8) sensitivity analysis related to site-selection procedure; (9) participatory planning approaches, groups, and contributions; (10) laws, regulations, and policies related to site-selection procedure; and (11) suitability indexes (linguistic and/or numeric) and ranking procedures. The identified insights and useful data trends could motivate the conduction of new updated site-selection analyses of PV and CSP technologies and globally improve siting implementations. In addition, the proposed workflow for conducting the systematic review and analysis as well as the identified insights and data trends could be used as a valuable basis for analyzing the site-selection procedures of current operational PV and CSP projects worldwide (e.g., analysis of the factors that the owning companies used for selecting the installation site of the solar energy projects).

Some important advantages of this systematic review are: (i) it focuses on both PV and CSP siting research; (ii) it investigates the existing site-selection procedures in an integrated manner, since it develops a workflow that examines qualitatively and quantitatively all key thematic modules of site-selection issue; (iii) it identifies insights and useful data trends in the site-selection procedures, contributing to the knowledge and improvement of future siting studies as well as global solar technologies siting implementations. Key concluding remarks are summarized as follows:

•
The ES is the most frequently applied methodological stage in both PV and CSP siting studies and the OS is the least frequently applied;
•
The importance of GIS-based methodologies and the high tendency of their use at the ES and ASPB stages are highlighted;
•
The importance of AHP method and the high tendency of its use at the ASPA is highlighted;
•
The Entropy method is suggested to be applied more frequently at the ASPA, since it is less subjective than AHP on the determination of AC weights by definition;
•
The identification of all employed EC in the current PV and CSP siting procedures and their related exclusion limits (min, max, mean, and mode values) can be used as a basis for the future siting applications;
•
More restricted exclusion siting frameworks (i.e., higher exclusion limits in environmental and social EC and higher number of EC) should be applied in the future PV and CSP siting applications in order to enhance the environmental sustainability of the eligible sites as well as the social acceptance towards PV and CSP deployment;
•
The identification of the optimal and poor values used for each ACPV and ACCSP can be used as a basis for future siting applications and contribute to the deployment of new and effective optimization stages in the future PV and CSP siting procedures;
•
‘GHI’, ‘proximity to electricity grid’, and ‘slope of terrain’ are the three most significant AC in PV siting procedures, while ‘DNI’, ‘proximity to electricity grid’, and ‘proximity to water surfaces or supplies’ are the three most significant AC in CSP siting procedures;
•
Optimization modules within PV and CSP siting procedures should be further developed for the effective solar technologies deployment in optimal sites;
•
Optimization criteria and functions that are focusing on environmental sustainability and social acceptability of solar energy projects should be formulated and applied;
•
The importance of conducting proper siting studies for the accelerated and efficient PV and CSP deployment globally is proven by the fact that the investigated geographic locations of the PV and CSP studies are quite identical with the current global PV and CSP deployment;
•
Accurate and thorough solar radiation estimation and analysis is of high importance for enhancing the quality of siting results and it depends on the resolution of radiation data as well as the time-period of analysis;
•
Methodologies that involve the citizens opinion and concerns in the CSP siting procedures and especially at the early stages of the siting should be formulated and applied;
•
Studies that incorporate all participatory groups opinions from the early stages of the siting procedure and systematically involve them in the procedure based on the results of each stage should be implemented;
•
Specific spatial planning regulations for PV and CSP siting should be formed and established to many geographic locations globally for the sustainable solar technologies deployment;
•
The creation of a linguistic and/or numeric SI and the application of a prioritization process are the two most frequent and useful techniques for the determination of the least and most suitable sites for PV or CSP installation at the ASPB.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was co-financed by Greece and the European Union (European Social Fund) through the Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the Act “Enhancing Human Resources Research Potential by undertaking a Doctoral Research”, Sub-action 2: IKY Scholarship Programme for Ph.D. candidates of Greek Universities.

Data availability

All References of the data used are provided in the References Section of the manuscript.

References

Aghbashlo et al., 2020

Aghbashlo M., Tabatabaei M., Rahnama E., Rosen M.A.

A new systematic decision support framework based on solar extended exergy accounting performance to prioritize photovoltaic sites

J. Clean. Prod., 256 (2020), Article 120356, 10.1016/j.jclepro.2020.120356

View PDF View article View in ScopusGoogle Scholar

Akkaş et al., 2017

Akkaş Ö.P., Arikan Y., Çam E.

Selection of a solar power plant location by using AHP method

Int. J. Energy Appl. Technol., 4 (2017), pp. 122-128

Google Scholar

Akkas et al., 2017

Akkas Ö.P., Erten M.Y., Cam E., Inanc N.

Optimal site selection for a solar power plant in the Central Anatolian Region of Turkey

Int. J. Photoenergy, 2017 (2017), Article e7452715, 10.1155/2017/7452715

Google Scholar

Al Garni and Awasthi, 2017

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, 206 (2017), pp. 1225-1240, 10.1016/j.apenergy.2017.10.024

View PDF View article View in ScopusGoogle ScholarAl Garni and Awasthi, 2018

Al Garni H.Z., Awasthi A.

Chapter 2 – solar PV power plants site selection: A review

Advances in Renewable Energies and Power Technologies, Elsevier (2018), pp. 57-75, 10.1016/B978-0-12-812959-3.00002-2

View PDF View article View in ScopusGoogle ScholarAl-Soud and Hrayshat, 2009

Al-Soud M.S., Hrayshat E.S.

A 50 MW concentrating solar power plant for Jordan

J. Clean. Prod., 17 (2009), pp. 625-635, 10.1016/j.jclepro.2008.11.002

View PDF View article View in ScopusGoogle ScholarAli et al., 2019

Ali S., Taweekun J., Techato K., Waewsak J., Gyawali S.

GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand

Renew. Energy, 132 (2019), pp. 1360-1372, 10.1016/j.renene.2018.09.035

View PDF View article View in ScopusGoogle ScholarAly et al., 2017

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, 113 (2017), pp. 159-175, 10.1016/j.renene.2017.05.077

View PDF View article View in ScopusGoogle ScholarAnwarzai and Nagasaka, 2017

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., 71 (2017), pp. 150-160, 10.1016/j.rser.2016.12.048

View PDF View article View in ScopusGoogle ScholarAqachmar et al., 2019

Aqachmar Z., Allouhi A., Jamil A., Gagouch B., Kousksou T.

Parabolic trough solar thermal power plant Noor I in Morocco

Energy, 178 (2019), pp. 572-584, 10.1016/j.energy.2019.04.160

View PDF View article View in ScopusGoogle ScholarArnette and Zobel, 2011

Arnette A.N., Zobel C.W.

Spatial analysis of renewable energy potential in the greater southern Appalachian mountains

Renew. Energy, 36 (2011), pp. 2785-2798, 10.1016/j.renene.2011.04.024

View PDF View article View in ScopusGoogle ScholarAsakereh et al., 2014

Asakereh A., Omid M., Alimardani R., Sarmadian F.

Developing a GIS-based fuzzy AHP model for selecting solar energy sites in Shodirwan region in Iran

Int. J. Adv. Sci., 68 (2014), pp. 37-48, 10.14257/ijast.2014.68.04

Google Scholar

Asakereh et al., 2017

Asakereh A., Soleymani M., Sheikhdavoodi M.J.

A GIS-based fuzzy-AHP method for the evaluation of solar farms locations: Case study in Khuzestan province, Iran

Sol. Energy, 155 (2017), pp. 342-353, 10.1016/j.solener.2017.05.075

View PDF View article View in ScopusGoogle ScholarAwan et al., 2018

Awan A.B., Zubair M., P P.R., Abokhalil A.G.

Solar energy resource analysis and evaluation of photovoltaic system performance in various regions of Saudi Arabia

Sustainability, 10 (1129) (2018), 10.3390/su10041129

Google Scholar

Azoumah et al., 2010

Azoumah Y., Ramde E.W., Tapsoba G., Thiam S.

Siting guidelines for concentrating solar power plants in the sahel: Case study of Burkina Faso

Sol. Energy, 84 (2010), pp. 1545-1553, 10.1016/j.solener.2010.05.019

View PDF View article View in ScopusGoogle ScholarBadran and Eck, 2006

Badran O., Eck M.

The application of parabolic trough technology under Jordanian climate

Renew. Energy, 31 (2006), pp. 791-802, 10.1016/j.renene.2005.05.005

View PDF View article View in ScopusGoogle ScholarBeltagy et al., 2015

Beltagy H., Semmar D., Said N.

Performance of medium-power fresnel concentrator solar plant in Algerian sites

Energy Procedia, 74 (2015), pp. 942-951, 10.1016/j.egypro.2015.07.725

View PDF View article View in ScopusGoogle ScholarBissiri et al., 2020

Bissiri M., Moura P., Figueiredo N.C., Pereira da Silva P.

A geospatial approach towards defining cost-optimal electrification pathways in West Africa

Energy, 200 (2020), Article 117471, 10.1016/j.energy.2020.117471

View PDF View article View in ScopusGoogle ScholarBoukelia et al., 2015

Boukelia T.E., Mecibah M.S., Kumar B.N., Reddy K.S.

Optimization, selection and feasibility study of solar parabolic trough power plants for Algerian conditions

Energy Convers. Manage., 101 (2015), pp. 450-459, 10.1016/j.enconman.2015.05.067

View PDF View article View in ScopusGoogle ScholarBrewer et al., 2015

Brewer J., Ames D.P., Solan D., Lee R., Carlisle J.

Using GIS analytics and social preference data to evaluate utility-scale solar power site suitability

Renew. Energy, 81 (2015), pp. 825-836, 10.1016/j.renene.2015.04.017

View PDF View article View in ScopusGoogle ScholarBroesamle et al., 2001

Broesamle H., Mannstein H., Schillings C., Trieb F.

Assessment of solar electricity potentials in North Africa based on satellite data and a geographic information system

Sol. Energy, 70 (2001), pp. 1-12, 10.1016/S0038-092X(00)00126-2

View PDF View article View in ScopusGoogle ScholarCai et al., 2020

Cai W., Li X., Maleki A., Pourfayaz F., Rosen M.A., Alhuyi Nazari M., Bui D.T.

Optimal sizing and location based on economic parameters for an off-grid application of a hybrid system with photovoltaic, battery and diesel technology

Energy, 201 (2020), Article 117480, 10.1016/j.energy.2020.117480

View PDF View article View in ScopusGoogle ScholarCalvert and Mabee, 2015

Calvert K., Mabee W.

More solar farms or more bioenergy crops? Mapping and assessing potential land-use conflicts among renewable energy technologies in eastern Ontario, Canada

Appl. Geogr., 56 (2015), pp. 209-221, 10.1016/j.apgeog.2014.11.028

View PDF View article View in ScopusGoogle ScholarCarrion et al., 2008

Carrion J.A., Estrella A.E., Dols F.A., Toro M.Z., Rodriguez M., Ridao A.R.

Environmental decision-support systems for evaluating the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants

Renew. Sustain. Energy Rev., 12 (2008), pp. 2358-2380, 10.1016/j.rser.2007.06.011

Google Scholar

Charabi and Gastli, 2010

Charabi Y., Gastli A.

GIS assessment of large CSP plant in Duqum, Oman

Renew. Sustain. Energy Rev., 14 (2010), pp. 835-841, 10.1016/j.rser.2009.08.019

View PDF View article View in ScopusGoogle ScholarCharabi and Gastli, 2011

Charabi Y., Gastli A.

PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation

Renew. Energy, 36 (2011), pp. 2554-2561, 10.1016/j.renene.2010.10.037

View PDF View article View in ScopusGoogle ScholarCharabi and Gastli, 2013

Charabi Y., Gastli A.

Integration of temperature and dust effects in siting large PV power plant in hot arid area

Renew. Energy, 57 (2013), pp. 635-644, 10.1016/j.renene.2013.02.031

View PDF View article View in ScopusGoogle ScholarChen et al., 2019

Chen S., Lu X., Miao Y., Deng Y., Nielsen C.P., Elbot N., Wang Y., Logan K.G., McElroy M.B., Hao J.

The potential of photovoltaics to power the belt and road initiative

Joule, 3 (2019), pp. 1895-1912, 10.1016/j.joule.2019.06.006

View PDF View article View in ScopusGoogle ScholarChu and Hawkes, 2020

Chu C.-T., Hawkes A.D.

A geographic information system-based global variable renewable potential assessment using spatially resolved simulation

Energy, 193 (2020), Article 116630, 10.1016/j.energy.2019.116630

View PDF View article View in ScopusGoogle ScholarClifton and Boruff, 2010

Clifton J., Boruff B.J.

Assessing the potential for concentrated solar power development in rural Australia

Energy Policy, 38 (2010), pp. 5272-5280, 10.1016/j.enpol.2010.05.036

View PDF View article View in ScopusGoogle ScholarColak et al., 2020

Colak H.E., Memisoglu T., Gercek Y.

Optimal site selection for solar photovoltaic (PV) power plants using GIS and AHP: A case study of Malatya province, Turkey

Renew. Energy, 149 (2020), pp. 565-576, 10.1016/j.renene.2019.12.078

View PDF View article View in ScopusGoogle ScholarDagdougui et al., 2011

Dagdougui H., Ouammi A., Sacile R.

A regional decision support system for onsite renewable hydrogen production from solar and wind energy sources

Int. J. Hydrog. Energy, 36 (2011), pp. 14324-14334, 10.1016/j.ijhydene.2011.08.050

View PDF View article View in ScopusGoogle ScholarDawson and Schlyter, 2012

Dawson L., Schlyter P.

Less is more: Strategic scale site suitability for concentrated solar thermal power in Western Australia

Energy Policy, 47 (2012), pp. 91-101, 10.1016/j.enpol.2012.04.025

View PDF View article View in ScopusGoogle ScholarDehghani et al., 2018

Dehghani E., Jabalameli M.S., Jabbarzadeh A.

Robust design and optimization of solar photovoltaic supply chain in an uncertain environment

Energy, 142 (2018), pp. 139-156, 10.1016/j.energy.2017.10.004

View PDF View article View in ScopusGoogle ScholarDeshmukh et al., 2019

Deshmukh R., Wu G.C., Callaway D.S., Phadke A.

Geospatial and techno–economic analysis of wind and solar resources in India

Renew. Energy, 134 (2019), pp. 947-960, 10.1016/j.renene.2018.11.073

View PDF View article View in ScopusGoogle ScholarDhunny et al., 2019

Dhunny A.Z., Doorga J.R.S., Allam Z., Lollchund M.R., Boojhawon R.

Identification of optimal wind, solar and hybrid wind-solar farming sites using fuzzy logic modelling

Energy, 188 (2019), Article 116056, 10.1016/j.energy.2019.116056

View PDF View article View in ScopusGoogle ScholarDias et al., 2019

Dias L., Gouveia J.P., Lourenço P., Seixas J.

Interplay between the potential of photovoltaic systems and agricultural land use

Land Use Policy, 81 (2019), pp. 725-735, 10.1016/j.landusepol.2018.11.036

View PDF View article View in ScopusGoogle ScholarDjebbar et al., 2014

Djebbar R., Belanger D., Boutin D., Weterings E., Poirier M.

Potential of concentrating solar power in Canada

Energy Procedia, 49 (2014), pp. 2303-2312, 10.1016/j.egypro.2014.03.244

View PDF View article Google ScholarDoljak and Stanojevic, 2017

Doljak D., Stanojevic G.

Evaluation of natural conditions for site selection of ground-mounted photovoltaic power plants in Serbia

Energy, 127 (2017), pp. 291-300, 10.1016/j.energy.2017.03.140

View PDF View article View in ScopusGoogle ScholarDomínguez Bravo et al., 2007

Domínguez Bravo J., García Casals X., Pinedo Pascua I.

GIS approach to the definition of capacity and generation ceilings of renewable energy technologies

Energy Policy, 35 (2007), pp. 4879-4892, 10.1016/j.enpol.2007.04.025

View PDF View article View in ScopusGoogle ScholarDoorga et al., 2019

Doorga J.R.S., Rughooputh S.D.D.V., Boojhawon R.

Multi-criteria GIS-based modelling technique for identifying potential solar farm sites: A case study in mauritius

Renew. Energy, 133 (2019), pp. 1201-1219, 10.1016/j.renene.2018.08.105

View PDF View article View in ScopusGoogle ScholarDupont et al., 2020

Dupont E., Koppelaar R., Jeanmart H.

Global available solar energy under physical and energy return on investment constraints

Appl. Energy, 257 (2020), Article 113968, 10.1016/j.apenergy.2019.113968

View PDF View article View in ScopusGoogle ScholarEnjavi-Arsanjani et al., 2015

Enjavi-Arsanjani M., Hirbodi K., Yaghoubi M.

Solar energy potential and performance assessment of CSP plants in different areas of Iran

Energy Procedia, 69 (2015), pp. 2039-2048, 10.1016/j.egypro.2015.03.216

View PDF View article View in ScopusGoogle ScholarFalter et al., 2020

Falter C., Scharfenberg N., Habersetzer A.

Geographical potential of solar thermochemical jet fuel production

Energies, 13 (802) (2020), 10.3390/en13040802

Google Scholar

Fang et al., 2018

Fang H., Li J., Song W.

Sustainable site selection for photovoltaic power plant: An integrated approach based on prospect theory

Energy Convers. Manage., 174 (2018), pp. 755-768, 10.1016/j.enconman.2018.08.092

View PDF View article View in ScopusGoogle ScholarFernández et al., 2019

Fernández A.G., Gomez-Vidal J., Oró E., Kruizenga A., Solé A., Cabeza L.F.

Mainstreaming commercial CSP systems: A technology review

Renew. Energy, 140 (2019), pp. 152-176, 10.1016/j.renene.2019.03.049

View PDF View article View in ScopusGoogle ScholarFernandez-Jimenez et al., 2015

Fernandez-Jimenez L.A., Mendoza-Villena M., Zorzano-Santamaria P., Garcia-Garrido E., Lara-Santillan P., Zorzano-Alba E., Falces A.

Site selection for new PV power plants based on their observability

Renew. Energy, 78 (2015), pp. 7-15, 10.1016/j.renene.2014.12.063

View PDF View article View in ScopusGoogle ScholarFirozjaei et al., 2019

Firozjaei M.K., Nematollahi O., Mijani N., Shorabeh S.N., Firozjaei H.K., Toomanian A.

An integrated GIS-based ordered weighted averaging analysis for solar energy evaluation in Iran: Current conditions and future planning

Renew. Energy, 136 (2019), pp. 1130-1146, 10.1016/j.renene.2018.09.090

View PDF View article View in ScopusGoogle ScholarFluri, 2009

Fluri T.P.

The potential of concentrating solar power in South Africa

Energy Policy, 37 (2009), pp. 5075-5080, 10.1016/j.enpol.2009.07.017

View PDF View article View in ScopusGoogle ScholarFreire et al., 2019

Freire M.M., Della Ceca L.S., Micheletti M.I., Novara I., García B., Mancilla A., Salum G.M., Crinó E., Piacentini R.D.

Site analysis in the argentinean andean region for the placement of astrophysical observatories and solar photovoltaic power plants. The case of the leoncito 2 site

Adv. Space Res., 64 (2019), pp. 551-566, 10.1016/j.asr.2019.04.023

View PDF View article View in ScopusGoogle ScholarGastli and Charabi, 2010

Gastli A., Charabi Y.

Siting of large PV farms in Al-Batinah region of Oman

Proceedings of the IEEE 2010 International Energy Conference, IEEE (2010), pp. 548-552, 10.1109/ENERGYCON.2010.5771742

View in ScopusGoogle ScholarGeorgiou and Skarlatos, 2016

Georgiou A., Skarlatos D.

Optimal site selection for sitting a solar park using multi-criteria decision analysis and geographical information systems

Geosci. Instrum. Methods, 5 (2016), pp. 321-332, 10.5194/gi-5-321-2016

View in ScopusGoogle ScholarGhasemi et al., 2019

Ghasemi G., Noorollahi Y., Alavi H., Marzband M., Shahbazi M.

Theoretical and technical potential evaluation of solar power generation in Iran

Renew. Energy, 138 (2019), pp. 1250-1261, 10.1016/j.renene.2019.02.068

View PDF View article View in ScopusGoogle ScholarGiamalaki and Tsoutsos, 2019

Giamalaki M., Tsoutsos T.

Sustainable siting of solar power installations in mediterranean using a GIS/AHP approach

Renew. Energy, 141 (2019), pp. 64-75, 10.1016/j.renene.2019.03.100

View PDF View article View in ScopusGoogle ScholarGómez et al., 2010

Gómez M., López A., Jurado F.

Optimal placement and sizing from standpoint of the investor of photovoltaics grid-connected systems using binary particle swarm optimization

Appl. Energy, 87 (2010), pp. 1911-1918, 10.1016/j.apenergy.2009.12.021

View PDF View article View in ScopusGoogle ScholarGunderson et al., 2015

Gunderson I., Goyette S., Gago-Silva A., Quiquerez L., Lehmann A.

Climate and land-use change impacts on potential solar photovoltaic power generation in the black sea region

Environ. Sci. Policy, 46 (2015), pp. 70-81, 10.1016/j.envsci.2014.04.013

View PDF View article View in ScopusGoogle ScholarHabib et al., 2020

Habib S.M., Emam Suliman A.E.-R., 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., 18 (2020), Article 100313, 10.1016/j.rsase.2020.100313

View PDF View article View in ScopusGoogle ScholarHafeznia et al., 2017

Hafeznia H., Yousefi H., Astaraei F.R.

A novel framework for the potential assessment of utility-scale photovoltaic solar energy, application to eastern Iran

Energy Convers. Manage., 151 (2017), pp. 240-258, 10.1016/j.enconman.2017.08.076

View PDF View article View in ScopusGoogle ScholarHanel and Escobar, 2013

Hanel M., Escobar R.

Influence of solar energy resource assessment uncertainty in the levelized electricity cost of concentrated solar power plants in Chile

Renew. Energy, 49 (2013), pp. 96-100, 10.1016/j.renene.2012.01.056

View PDF View article View in ScopusGoogle ScholarHang et al., 2008

Hang Q., Jun Z., Xiao Y., Junkui C.

Prospect of concentrating solar power in China—The sustainable future

Renew. Sustain. Energy Rev., 12 (2008), pp. 2505-2514, 10.1016/j.rser.2007.06.002

View PDF View article View in ScopusGoogle ScholarHaurant et al., 2011

Haurant P., Oberti P., Muselli M.

Multicriteria selection aiding related to photovoltaic plants on farming fields on Corsica island: A real case study using the ELECTRE outranking framework

Energy Policy, 39 (2011), pp. 676-688, 10.1016/j.enpol.2010.10.040

View PDF View article View in ScopusGoogle ScholarHazaymeh et al., 2018

Hazaymeh K., Zeitoun M., Al-Rawabdeh A., Al-Sababhah N.

A regional scale photovoltaic site selection based on geospatial techniques

Jordan J. Earth Environ. Sci., 9 (2018), pp. 47-56

http://jjees.hu.edu.jo/files/Vol9N1/JJEES_Vol9_N1_HQ_P6.pdf

View in ScopusGoogle Scholar

Hott et al., 2012

Hott R., Santini R., Brownson J.

GIS-based spatial analysis for large-scale solar power and transmission line issues: Case study of Wyoming, U.S.

Proceedings of the 2012-41st American Solar Energy Society Conference, American Solar Energy Society (2012)

Google Scholar

Huang et al., 2018

Huang T., Wang S., Yang Q., Li J.

A GIS-based assessment of large-scale PV potential in China

Energy Procedia, 152 (2018), pp. 1079-1084, 10.1016/j.egypro.2018.09.126

View PDF View article View in ScopusGoogle Scholar

IRENA, 2022

IRENA

Renewable Energy Statistics 2022

International Renewable Energy Agency, Abu Dhabi (2022)

Google Scholar

Islam et al., 2018

Islam M.T., Huda N., Abdullah A.B., Saidur R.

A comprehensive review of state-of-the-art concentrating solar power (CSP) technologies: Current status and research trends

Renew. Sustain. Energy Rev., 91 (2018), pp. 987-1018, 10.1016/j.rser.2018.04.097

View PDF View article View in ScopusGoogle ScholarJanjai et al., 2011

Janjai S., Laksanaboonsong J., Seesaard T.

Potential application of concentrating solar power systems for the generation of electricity in Thailand

Appl. Energy, 88 (2011), pp. 4960-4967, 10.1016/j.apenergy.2011.06.044

View PDF View article View in ScopusGoogle ScholarJanke, 2010

Janke J.R.

Multicriteria GIS modeling of wind and solar farms in colorado

Renew. Energy, 35 (2010), pp. 2228-2234, 10.1016/j.renene.2010.03.014

View PDF View article View in ScopusGoogle ScholarJung et al., 2019

Jung J., Han S., Kim B.

Digital numerical map-oriented estimation of solar energy potential for site selection of photovoltaic solar panels on national highway slopes

Appl. Energy, 242 (2019), pp. 57-68, 10.1016/j.apenergy.2019.03.101

View PDF View article View in ScopusGoogle ScholarKereush and Perovych, 2017

Kereush D., Perovych I.

Determining criteria for optimal site selection for solar power plants

Geomat. Land Manag. Landsc., 4 (2017), pp. 39-54, 10.15576/GLL/2017.4.39

Google Scholar

Khan and Rathi, 2014

Khan G., Rathi S.

Optimal site selection for solar PV power plant in an Indian state using geographical information system (GIS)

Int. J. Emerg. Eng. Res. Technol., 2 (2014), pp. 260-266

Google Scholar

Kim et al., 2020

Kim B., Han S., Heo J., Jung J.

Proof-of-concept of a two-stage approach for selecting suitable slopes on a highway network for solar photovoltaic systems: A case study in South Korea

Renew. Energy, 151 (2020), pp. 366-377, 10.1016/j.renene.2019.11.021

View PDF View article View in ScopusGoogle ScholarKoberle et al., 2015

Koberle A.C., Gernaat D.E.H.J., van Vuuren D.P.

Assessing current and future techno–economic potential of concentrated solar power and photovoltaic electricity generation

Energy, 89 (2015), pp. 739-756, 10.1016/j.energy.2015.05.145

View PDF View article View in ScopusGoogle ScholarKolendo et al., 2019

Kolendo Ł., Krawczyk D.A., Kobryń A.

The method of spatial suitability assessment for photovoltaic development at the municipality scale

Proceedings, 16 (2019), p. 54, 10.3390/proceedings2019016054

Google Scholar

Lara and García, 2021

Lara E.G., García F.S.

Review on viability and implementation of residential PV-battery systems: Considering the case of Dominican Republic

Energy Rep., 7 (2021), pp. 8868-8899, 10.1016/j.egyr.2021.11.208

Google Scholar

Larraín and Escobar, 2012

Larraín T., Escobar R.

Net energy analysis for concentrated solar power plants in northern Chile

Renew. Energy, 41 (2012), pp. 123-133, 10.1016/j.renene.2011.10.015

View PDF View article View in ScopusGoogle ScholarLarraín et al., 2010

Larraín T., Escobar R., Vergara J.

Performance model to assist solar thermal power plant siting in northern Chile based on backup fuel consumption

Renew. Energy, 35 (2010), pp. 1632-1643, 10.1016/j.renene.2010.01.008

View PDF View article View in ScopusGoogle ScholarLee et al., 2015

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, 7 (2015), pp. 13522-13541, 10.3390/su71013522

View in ScopusGoogle ScholarLiu et al., 2017

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., 148 (2017), pp. 386-397, 10.1016/j.jclepro.2017.02.012

View PDF View article View in ScopusGoogle ScholarMajumdar and Pasqualetti, 2019

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, 134 (2019), pp. 1213-1231, 10.1016/j.renene.2018.08.064

View PDF View article View in ScopusGoogle ScholarMaleki et al., 2020

Maleki A., Haghighi A., El Haj Assad M., Mahariq I., Alhuyi Nazari M.

A review on the approaches employed for cooling PV cells

Sol. Energy, 209 (2020), pp. 170-185, 10.1016/j.solener.2020.08.083

View PDF View article View in ScopusGoogle ScholarMalemnganbi and Shimray, 2020

Malemnganbi R., Shimray B.A.

Solar power plant site selection: A systematic literature review on MCDM techniques used, electronic systems and intelligent computing

Lecture Notes in Electrical Engineering, Springer, Singapore (2020), pp. 37-48

CrossRef

View in ScopusGoogle ScholarMarques-Perez et al., 2020

Marques-Perez I., Guaita-Pradas I., Gallego A., Segura B.

Territorial planning for photovoltaic power plants using an outranking approach and GIS

J. Clean. Prod., 257 (2020), Article 120602, 10.1016/j.jclepro.2020.120602

View PDF View article View in ScopusGoogle ScholarMartins et al., 2012

Martins F.R., Abreu S.L., Pereira E.B.

Scenarios for solar thermal energy applications in Brazil

Energy Policy, 48 (2012), pp. 640-649, 10.1016/j.enpol.2012.05.082

View PDF View article View in ScopusGoogle ScholarMehos and Owens, 2014

Mehos M., Owens B.

An analysis of sitting opportunities for concentrating solar power plants in the southwestern United States

Proceedings of the 8th World Renewable Energy Conference, World Renewable Energy Congress (2014)

https://www.nrel.gov/docs/gen/fy04/36703.pdf

Google Scholar

Mensour et al., 2019

Mensour O.N., Ghazzani B.E., Hlimi B., Ihlal A.

A geographical information system-based multi-criteria method for the evaluation of solar farms locations: A case study in Souss-Massa area, southern Morocco

Energy, 182 (2019), pp. 900-919, 10.1016/j.energy.2019.06.063

View PDF View article View in ScopusGoogle ScholarMerrouni et al., 2018a

Merrouni A.A., Elalaoui F.E., Ghennioui A., Mezrhab Ahmed, Mezrhab Abdelhamid

A GIS–AHP combination for the sites assessment of large-scale CSP plants with dry and wet cooling systems. Case study: Eastern Morocco

Sol. Energy, 166 (2018), pp. 2-12, 10.1016/j.solener.2018.03.038

Google Scholar

Merrouni et al., 2018b

Merrouni A.A., Elalaoui F.E., Mezrhab Ahmed, Mezrhab Abdelhamid, Ghennioui A.

Large scale PV sites selection by combining GIS and analytical hierarchy process. Case study: Eastern Morocco

Renew. Energy, 119 (2018), pp. 863-873, 10.1016/j.renene.2017.10.044

Google Scholar

Merrouni et al., 2014

Merrouni A.A., Mezrhab Ab, Mezrhab A.

CSP sites suitability analysis in the eastern region of Morocco

Energy Procedia, 49 (2014), pp. 2270-2279, 10.1016/j.egypro.2014.03.240

View PDF View article View in ScopusGoogle ScholarMerrouni et al., 2016

Merrouni A.A., Mezrhab Abdelhamid, Mezrhab Ahmed

PV sites suitability analysis in the eastern region of Morocco

Sustain. Energy Technol. Assess., 18 (2016), pp. 6-15, 10.1016/j.seta.2016.09.006

View in ScopusGoogle ScholarMessaoudi et al., 2019

Messaoudi D., Settou N., Negrou B., Settou B.

GIS based multi-criteria decision making for solar hydrogen production sites selection in Algeria

Int. J. Hydrog. Energy, 44 (2019), pp. 31808-31831, 10.1016/j.ijhydene.2019.10.099

View PDF View article View in ScopusGoogle ScholarMierzwiak and Calka, 2017

Mierzwiak M., Calka B.

Multi-criteria analysis for solar farm location suitability

Rep. Geodesy Geoinformatics, 104 (2017), pp. 20-32, 10.1515/rgg-2017-0012

Google Scholar

Mohammadi and Khorasanizadeh, 2019

Mohammadi K., Khorasanizadeh H.

The potential and deployment viability of concentrated solar power (CSP) in Iran

Energy Strateg. Rev., 24 (2019), pp. 358-369, 10.1016/j.esr.2019.04.008

View PDF View article View in ScopusGoogle ScholarMohammadi et al., 2019

Mohammadi K., Saghafifar M., Ellingwood K., Powell K.

Hybrid concentrated solar power (CSP)-desalination systems: A review

Desalination, 468 (2019), Article 114083, 10.1016/j.desal.2019.114083

View PDF View article View in ScopusGoogle ScholarMokarram et al., 2020

Mokarram M., Mokarram M.J., Gitizadeh M., Niknam T., Aghaei J.

A novel optimal placing of solar farms utilizing multi-criteria decision-making (MCDA) and feature selection

J. Clean. Prod., 261 (2020), Article 121098, 10.1016/j.jclepro.2020.121098

View PDF View article View in ScopusGoogle ScholarMondino et al., 2014

Mondino E.B., Fabrizio E., Chiabrando R.

A GIS tool for the land carrying capacity of large solar plants

Energy Procedia, 48 (2014), pp. 1576-1585, 10.1016/j.egypro.2014.02.178

View PDF View article View in ScopusGoogle ScholarMorelli et al., 2015

Morelli M., Masini A., Ruffini F., Potenza M.A.C.

Web tools concerning performance analysis and planning support for solar energy plants starting from remotely sensed optical images

Environ. Impact Assess. Rev., 52 (2015), pp. 18-23, 10.1016/j.eiar.2014.10.003

View PDF View article View in ScopusGoogle ScholarMostegl et al., 2017

Mostegl N.M., Pröbstl-Haider U., Haider W.

Spatial energy planning in Germany: Between high ambitions and communal hesitations

Landsc. Urban Plan, 167 (2017), pp. 451-462, 10.1016/j.landurbplan.2017.07.013

View PDF View article View in ScopusGoogle ScholarMurphy et al., 2019

Murphy C., Sun Y., Cole W.J., Maclaurin G.J., Mehos M.S., Turchi C.S.

The Potential Role of Concentrating Solar Power Within the Context of DOE’s 2030 Solar Cost Targets: (No. NREL/TP–6A20-71912, 1491726)

National Renewable Energy Laboratory (2019), 10.2172/1491726

Google Scholar

Nguyen and Pearce, 2010

Nguyen H.T., Pearce J.M.

Estimating potential photovoltaic yield with r.sun and the open source geographical resources analysis support system

Sol. Energy, 84 (2010), pp. 831-843, 10.1016/j.solener.2010.02.009

View PDF View article View in ScopusGoogle ScholarNoone et al., 2011

Noone C.J., Ghobeity A., Slocum A.H., Tzamtzis G., Mitsos A.

Site selection for hillside central receiver solar thermal plants

Sol. Energy, 85 (2011), pp. 839-848, 10.1016/j.solener.2011.01.017

View PDF View article View in ScopusGoogle ScholarNoorollahi et al., 2016

Noorollahi E., Fadai D., Shirazi M.A., Ghodsipour S.H.

Land suitability analysis for solar farms exploitation using GIS and fuzzy analytic hierarchy process (FAHP)—A case study of Iran

Energies, 9 (643) (2016), 10.3390/en9080643

Google Scholar

NREL, 2022

NREL

Solar resource maps and data

(2022)

https://www.nrel.gov/gis/solar-resource-maps.html

. (Accessed 28 January 2022)

Google Scholar

Ogbonnaya et al., 2020

Ogbonnaya C., Turan A., Abeykoon C.

Novel thermodynamic efficiency indices for choosing an optimal location for large-scale photovoltaic power generation

J. Clean. Prod., 249 (2020), Article 119405, 10.1016/j.jclepro.2019.119405

View PDF View article View in ScopusGoogle ScholarOmitaomu et al., 2015

Omitaomu O.A., Singh N., Bhaduri B.L.

Mapping suitability areas for concentrated solar power plants using remote sensing data

J. Appl. Remote Sens., 9 (2015), Article 097697, 10.1117/1.JRS.9.097697

Google Scholar

Ozdemir and Sahin, 2018

Ozdemir S., Sahin G.

Multi-criteria decision-making in the location selection for a solar PV power plant using AHP

Measurement, 129 (2018), pp. 218-226, 10.1016/j.measurement.2018.07.020

View PDF View article View in ScopusGoogle ScholarPalmer et al., 2019

Palmer D., Gottschalg R., Betts T.

The future scope of large-scale solar in the UK: Site suitability and target analysis

Renew. Energy, 133 (2019), pp. 1136-1146, 10.1016/j.renene.2018.08.109

View PDF View article View in ScopusGoogle ScholarPerpiña Castillo et al., 2016

Perpiña Castillo C., Batista e Silva F., Lavalle C.

An assessment of the regional potential for solar power generation in EU-28

Energy Policy, 88 (2016), pp. 86-99, 10.1016/j.enpol.2015.10.004

View PDF View article View in ScopusGoogle ScholarPillot et al., 2020

Pillot B., Al-Kurdi N., Gervet C., Linguet L.

An integrated GIS and robust optimization framework for solar PV plant planning scenarios at utility scale

Appl. Energy, 260 (2020), Article 114257, 10.1016/j.apenergy.2019.114257

View PDF View article View in ScopusGoogle ScholarPiyatadsananon, 2016

Piyatadsananon P.

Spatial factors consideration in site selection of ground-mounted PV power plants

Energy Procedia, 100 (2016), pp. 78-85, 10.1016/j.egypro.2016.10.135

View PDF View article View in ScopusGoogle ScholarPolo et al., 2015

Polo J., Bernardos A., Navarro A.A., Fernandez-Peruchena C.M., Ramírez L., Guisado M.V., Martínez S.

Solar resources and power potential mapping in Vietnam using satellite-derived and GIS-based information

Energy Convers. Manage., 98 (2015), pp. 348-358, 10.1016/j.enconman.2015.04.016

View PDF View article View in ScopusGoogle ScholarPowell et al., 2017

Powell K.M., Rashid K., Ellingwood K., Tuttle J., Iverson B.D.

Hybrid concentrated solar thermal power systems: A review

Renew. Sustain. Energy Rev., 80 (2017), pp. 215-237, 10.1016/j.rser.2017.05.067

View PDF View article View in ScopusGoogle ScholarPurohit et al., 2013

Purohit I., Purohit P., Shekhar S.

Evaluating the potential of concentrating solar power generation in northwestern India

Energy Policy, 62 (2013), pp. 157-175, 10.1016/j.enpol.2013.06.069

View PDF View article View in ScopusGoogle ScholarRahnama et al., 2019

Rahnama E., Aghbashlo M., Tabatabaei M., Khanali M., Rosen M.A.

Spatio-temporal solar exergoeconomic and exergoenvironmental maps for photovoltaic systems

Energy Convers. Manage., 195 (2019), pp. 701-711, 10.1016/j.enconman.2019.05.051

View PDF View article View in ScopusGoogle ScholarRediske et al., 2019

Rediske G., Siluk J.C.M., Gastaldo N.G., Rigo P.D., Rosa C.B.

Determinant factors in site selection for photovoltaic projects: A systematic review

Int. J. Energy Res., 43 (2019), pp. 1689-1701, 10.1002/er.4321

View in ScopusGoogle Scholar

REN21, 2021

REN21

Renewables 2021 Global Status Report

REN21 Secretariat, Paris (2021)

Google Scholar

Rijanto et al., 2013

Rijanto E., Aiman S., Santoso A.

Development of CSP plants in Wallacea region: Solar intensity resource assessment and CSP plant design specification

Energy Procedia, 32 (2013), pp. 232-241, 10.1016/j.egypro.2013.05.030

View PDF View article View in ScopusGoogle ScholarSabo et al., 2016

Sabo M.L., Mariun N., Hizam H., Mohd Radzi M.A., 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., 66 (2016), pp. 76-94, 10.1016/j.rser.2016.07.045

View in ScopusGoogle ScholarSabo et al., 2017

Sabo M.L., Mariun N., Hizam H., Mohd Radzi M.A., Zakaria A.

Spatial matching of large-scale grid-connected photovoltaic power generation with utility demand in Peninsular Malaysia

Appl. Energy, 191 (2017), pp. 663-688, 10.1016/j.apenergy.2017.01.087

View PDF View article View in ScopusGoogle ScholarSacchelli et al., 2016

Sacchelli S., Garegnani G., Geri F., Grilli G., Paletto A., Zambelli P., Ciolli M., Vettorato D.

Trade-off between photovoltaic systems installation and agricultural practices on arable lands: An environmental and socio-economic impact analysis for Italy

Land Use Policy, 56 (2016), pp. 90-99, 10.1016/j.landusepol.2016.04.024

View PDF View article View in ScopusGoogle ScholarSadeghi and Karimi, 2017

Sadeghi M., Karimi M.

GIS-based solar and wind turbine site selection using multi-criteria analysis: Case study Tehran, Iran

Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 42 (2017), pp. 469-476, 10.5194/isprs-archives-XLII-4-W4-469-2017

View in ScopusGoogle ScholarSánchez-Lozano et al., 2014

Sánchez-Lozano 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,

Renew. Energy, 66 (2014), pp. 478-494, 10.1016/j.renene.2013.12.038

View PDF View article View in ScopusGoogle ScholarSánchez-Lozano et al., 2016

Sánchez-Lozano J.M., 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., 127 (2016), pp. 387-398, 10.1016/j.jclepro.2016.04.005

View PDF View article View in ScopusGoogle ScholarSánchez-Lozano et al., 2013

Sánchez-Lozano 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., 24 (2013), pp. 544-556, 10.1016/j.rser.2013.03.019

View PDF View article View in ScopusGoogle ScholarSaracoglu, 2020

Saracoglu B.O.

Location selection factors of concentrated solar power plant investments

Sustain. Energy Grids Netw., 22 (2020), Article 100319, 10.1016/j.segan.2020.100319

View PDF View article View in ScopusGoogle ScholarSaracoglu et al., 2018

Saracoglu B.O., Ohunakin O.S., Adelekan D.S., Gill J., Atiba O.E., Okokpujie I.P., Atayero A.A.

A framework for selecting the location of very large photovoltaic solar power plants on a global/supergrid

Energy Rep., 4 (2018), pp. 586-602, 10.1016/j.egyr.2018.09.002

View PDF View article View in ScopusGoogle ScholarServert et al., 2014

Servert J., Tiangco C., López A., de Loizaga I., Castella D., Pérez R.

Roadmap for solar energy development in Uzbekistan

Energy Procedia, 49 (2014), pp. 1906-1915, 10.1016/j.egypro.2014.03.202

View PDF View article View in ScopusGoogle ScholarShiraishi et al., 2019

Shiraishi K., Shirley R.G., Kammen D.M.

Geospatial multi-criteria analysis for identifying high priority clean energy investment opportunities: A case study on land-use conflict in Bangladesh

Appl. Energy, 235 (2019), pp. 1457-1467, 10.1016/j.apenergy.2018.10.123

View PDF View article View in ScopusGoogle ScholarShorabeh et al., 2019

Shorabeh S.N., Firozjaei M.K., Nematollahi O., Firozjaei H.K., Jelokhani-Niaraki M.

A risk-based multi-criteria spatial decision analysis for solar power plant site selection in different climates: A case study in Iran

Renew. Energy, 143 (2019), pp. 958-973, 10.1016/j.renene.2019.05.063

View PDF View article View in ScopusGoogle ScholarSindhu et al., 2017

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., 73 (2017), pp. 496-511, 10.1016/j.rser.2017.01.135

View PDF View article View in ScopusGoogle ScholarSingh Doorga et al., 2019

Singh Doorga J.R., Rughooputh S.D.D.V., Boojhawon R.

High resolution spatio-temporal modelling of solar photovoltaic potential for tropical islands: Case of Mauritius

Energy, 169 (2019), pp. 972-987, 10.1016/j.energy.2018.12.072

View PDF View article View in ScopusGoogle ScholarSreenath et al., 2021

Sreenath S., Sudhakar K., Yusop A.F.

Solar PV in the airport environment: A review of glare assessment approaches & metrics

Sol. Energy, 216 (2021), pp. 439-451, 10.1016/j.solener.2021.01.023

View PDF View article View in ScopusGoogle ScholarSuh and Brownson, 2016

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, 9 (648) (2016), 10.3390/en9080648

Google Scholar

Sultan et al., 2018

Sultan H.M., Kuznetsov O.N., Diab A.A.Z.

Site selection of large-scale grid-connected solar PV system in Egypt

Proceedings of the 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, IEEE (2018), pp. 813-818, 10.1109/EIConRus.2018.8317214

View in ScopusGoogle ScholarSun et al., 2013

Sun Y., Hof A., Wang R., Liu J., Lin Y., Yang D.

GIS-based approach for potential analysis of solar PV generation at the regional scale: A case study of fujian province

Energy Policy, 58 (2013), pp. 248-259, 10.1016/j.enpol.2013.03.002

View PDF View article View in ScopusGoogle ScholarSward et al., 2019

Sward J.A., Siff J., Gu J., Zhang K.M.

Strategic planning for utility-scale solar photovoltaic development — Historical peak events revisited

Appl. Energy, 250 (2019), pp. 1292-1301, 10.1016/j.apenergy.2019.04.178

View PDF View article View in ScopusGoogle ScholarTahri et al., 2015

Tahri M., Hakdaoui M., Maanan M.

The evaluation of solar farm locations applying geographic information system and multi-criteria decision-making methods: Case study in southern Morocco

Renew. Sustain. Energy Rev., 51 (2015), pp. 1354-1362, 10.1016/j.rser.2015.07.054

View PDF View article View in ScopusGoogle ScholarTavana et al., 2017

Tavana M., Santos Arteaga F.J., Mohammadi S., Alimohammadi M.

A fuzzy multi-criteria spatial decision support system for solar farm location planning

Energy Strateg. Rev., 18 (2017), pp. 93-105, 10.1016/j.esr.2017.09.003

View PDF View article View in ScopusGoogle ScholarTlhalerwa and Mulalu, 2019

Tlhalerwa K., Mulalu M.

Assessment of the concentrated solar power potential in Botswana

Renew. Sustain. Energy Rev., 109 (2019), pp. 294-306, 10.1016/j.rser.2019.04.019

View PDF View article View in ScopusGoogle ScholarUyan, 2013

Uyan M.

GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey

Renew. Sustain. Energy Rev., 28 (2013), pp. 11-17, 10.1016/j.rser.2013.07.042

View PDF View article View in ScopusGoogle ScholarVafaeipour et al., 2014

Vafaeipour M., Hashemkhani Zolfani S., Morshed Varzandeh M.H., Derakhti A., Keshavarz Eshkalag M.

Assessment of regions priority for implementation of solar projects in Iran: New application of a hybrid multi-criteria decision making approach

Energy Convers. Manage., 86 (2014), pp. 653-663, 10.1016/j.enconman.2014.05.083

View PDF View article View in ScopusGoogle ScholarWanderer and Herle, 2015

Wanderer T., Herle S.

Creating a spatial multi-criteria decision support system for energy related integrated environmental impact assessment

Environ. Impact Assess. Rev., 52 (2015), pp. 2-8, 10.1016/j.eiar.2014.09.002

View PDF View article Google ScholarWang et al., 2016

Wang S., Zhang L., Fu D., Lu X., Wu T., Tong Q.

Selecting photovoltaic generation sites in tibet using remote sensing and geographic analysis

Sol. Energy, 133 (2016), pp. 85-93, 10.1016/j.solener.2016.03.069

View PDF View article View in ScopusGoogle ScholarWatson and Hudson, 2015

Watson J.J.W., Hudson M.D.

Regional scale wind farm and solar farm suitability assessment using GIS-assisted multi-criteria evaluation

Landsc. Urban Plan, 138 (2015), pp. 20-31, 10.1016/j.landurbplan.2015.02.001

View PDF View article View in ScopusGoogle ScholarWu et al., 2014

Wu Y., Geng S., Zhang H., Gao M.

Decision framework of solar thermal power plant site selection based on linguistic choquet operator

Appl. Energy, 136 (2014), pp. 303-311, 10.1016/j.apenergy.2014.09.032

View PDF View article Google ScholarWu et al., 2019

Wu Y., Zhang B., Wu C., Zhang T., Liu F.

Optimal site selection for parabolic trough concentrating solar power plant using extended PROMETHEE method: A case in China

Renew. Energy, 143 (2019), pp. 1910-1927, 10.1016/j.renene.2019.05.131

View PDF View article View in ScopusGoogle ScholarYang et al., 2019

Yang Q., Huang T., Wang S., Li J., Dai S., Wright S., Wang Y., Peng H.

A GIS-based high spatial resolution assessment of large-scale PV generation potential in China

Appl. Energy, 247 (2019), pp. 254-269, 10.1016/j.apenergy.2019.04.005

View PDF View article View in ScopusGoogle ScholarYimen and Dagbasi, 2019

Yimen N., Dagbasi M.

Multi-attribute decision-making: Applying a modified Brown–Gibson model and RETScreen software to the optimal location process of utility-scale photovoltaic plants

Processes, 7 (505) (2019), 10.3390/pr7080505

Google Scholar

Yushchenko et al., 2018

Yushchenko A., de Bono A., Chatenoux B., Kumar Patel M., Ray N.

GIS-based assessment of photovoltaic (PV) and concentrated solar power (CSP) generation potential in West Africa

Renew. Sustain. Energy Rev., 81 (2018), pp. 2088-2103, 10.1016/j.rser.2017.06.021

View PDF View article View in ScopusGoogle ScholarZhu et al., 2020

Zhu Y., Tian D., Yan F.

Effectiveness of entropy weight method in decision-making

Math. Probl. Eng., 2020 (2020), Article e3564835, 10.1155/2020/3564835

Google Scholar

Ziuku et al., 2014

Ziuku S., Seyitini L., Mapurisa B., Chikodzi D., van Kuijk K.

Potential of concentrated solar power (CSP) in Zimbabwe

Energy Sustain. Dev, 23 (2014), pp. 220-227, 10.1016/j.esd.2014.07.006

View PDF View article View in ScopusGoogle ScholarZoghi et al., 2017

Zoghi M., Ehsani A.H., Sadat M., javad Amiri M., Karimi S.

Optimization solar site selection by fuzzy logic model and weighted linear combination method in arid and semi-arid region: A case study Isfahan-IRAN

Renew. Sustain. Energy Rev., 68 (2017), pp. 986-996, 10.1016/j.rser.2015.07.014

View PDF View article View in ScopusGoogle Scholar

Spyridonidou, S., & Vagiona, D. G. (2023). A systematic review of site-selection procedures of PV and CSP technologies. Energy Reports, 9, 2947-2979. https://doi.org/10.1016/j.egyr.2023.01.132

Published in the December 2023 Issue of Energy Reports