Site Selection for Sustainable Wind-Solar Hybrid Energy Harvesting Plant

The world's energy landscape is undergoing rapid transformation as societies strive towards renewable and sustainable power sources in response to the need for energy security and climate change. Depletion of fossil fuels and increasing focus on environmental sustainability have been significant drivers in this global shift towards renewables. Among the promising solutions, solar-wind hybrid energy systems have gained attention, capitalizing on the complementary nature of solar and wind resources. However, selecting the optimal site for these hybrid plants is crucial for their successful implementation and efficiency. This research introduces a comprehensive methodology for site selection, seamlessly integrating the Delphi method and Analytic Hierarchy Process (AHP). The Delphi method harnesses expert consensus to distil essential criteria, while AHP offers a structured framework to evaluate and prioritize these criteria. The results showed successful utilization of the AHP and Delphi techniques for evaluating the optimal location selection among different cities for solar-wind hybrid energy harnessing in the northwest region of Pakistan. The key factors for selection of optimal location were identified through expert feedback and analysis. Among the selected key main criteria, resource availability and infrastructure and socio-economic factors emerged as the top-ranked criteria in this study. The results of this study hold significant relevance and should be duly considered for solar-wind power project site selection, while also offering opportunities for further enhancements in the future.


Introduction
The energy landscape at a global scale is experiencing a notable shift, driven by the growing awareness and recognition of transition towards sustainable and renewable energy sources.This transformation is being embraced by societies worldwide, reflecting a collective understanding of the pressing need to address environmental concerns and ensure a more sustainable future.The depletion of fossil fuels, growing environmental concerns, and the increasing need for energy security have prompted a global transition towards cleaner and more sustainable energy alternatives (World Energy Outlook, 2020).Renewable Energy (RE) resources, including solar and wind energy, have gained remarkable attention due to their abundance, scalability, and minimal environmental impact (Renewable Capacity Statistics, 2021).
As of 30 June 2022, Pakistan's power generation capacity stands at 43,775 MW, comprising various sources.The overall power capacity is derived from various sources.
Thermal power contributes 26,683 MW, hydroelectric power provides 10,635 MW, wind power contributes 1,838 MW, solar power stands at 630 MW, bagasse power contributes 369 MW, and nuclear power provides 3,620 MW (List of power stations in Pakistan, n.d.).These various sources contribute significantly to the country's electricity generation and act as a vital role in fulfilling power requirements.However, Pakistan possesses significant potential in RE sources that can help encounter its requirements of growing energy.
In Pakistan, where solar and wind energy exhibit complementary patterns in terms of time and region, the careful assessment of the most optimal locations for wind-solar hybrid energy harnessing becomes imperative.While solar and wind power offer numerous advantages individually, combining these two technologies in a hybrid system can provide enhanced energy generation and improved reliability (Vasant & Pawar, 2017).Specifically, in the north Punjab region, the monsoon climate leads to strong winter winds and low solar radiation intensity, while summers have weaker winds and high solar radiation intensity.The synergistic relationship between solar and wind resources presents an opportunity to maximize energy output and potential of these sources can be effectively harnessed.
However, the efficient output of solar-wind hybrid energy plants heavily depends on the selection of an optimal site.Although there are existing methods as employed by Asadi et al. (2023) for site selection in renewable energy projects, limited studies have focused specifically on the combination of Analytic Hierarchy Process (AHP) technique along with Delphi method for solar-wind hybrid energy projects.Hence, this research aims to utilize a framework by combination of Delphi and AHP in the context of site selection of hybrid energy harnessing.According to Solangi et al. (2019) and Marttunen et al. (2017), problem arises from the need to consider multiple criteria, expert opinions, and prioritize them effectively to identify optimal sites among different required sites in Pakistan that maximize energy production, minimize costs, and ensure long-term viability and sustainability.In this case, the integration of Delphi and AHP has promising potential.
The Delphi method is an approach utilized in qualitative research, where consensus is sought from experts group through a multiple questionnaires and iterative feedback rounds (Linstone, 1975).It facilitates the identification of essential criteria and the consolidation of expert perspectives, offering valuable insights into intricate issues.On the other hand, the AHP method is a systematic decision making process developed by Saaty in 1980.It is a technique utilized for making decisions by facilitating the systematic evaluation and prioritization of criteria through weightage gain from judgment of experts and finally prioritize/rank the different decision alternatives (Brunelli, 2014).
While previous research has extensively examined the site selection for renewable energy projects (i.e.Tafula et al., 2023;Khazael & Al-Bakri, 2021;Kocabaldır & Yücel, 2020;Wissing, 2013;Goh et al., 2022;Spyridonidou & Vagiona, 2023;Deveci et al., 2021;Hosseinzadeh et al., 2023;Soydan, 2021), there remains a notable gap in the literature concerning location selection for solar-wind hybrid energy projects using the Delphi approach and AHP methods.To address this gap, this study aims to develop a comprehensive approach by conducting a literature review to identify critical factors for site selection.Expert opinions were gathered through the Delphi method to refine and assess the importance and weightage of the obtained criteria/factors through literature review.By utilizing these criteria obtained from expert consensus into the structured decision-making technique of AHP, this research intends to create a robust framework for location selection among different cities in the northwest Punjab region of Pakistan, including Rawalpindi, Islamabad, Taxila, Wah Cantt, and Khanpur, facilitating informed decision-making and promoting the development of sustainable solar-wind hybrid energy harvesting plants.These locations are highlighted in figure 1.

Assessment of Criteria and sub-Criteria for Site Selection
Many countries endowed with substantial solar and wind power source actively pursuing the development of this environment friendly and pollutant-free energy source.Kollati and Debnath (2021), the techniques employed for site selection over the past few decades were systematically examined, providing a comprehensive analysis that has been widely utilized by the scientific community and policymakers.
Various academic researchers have focused on the selection of location for solar-wind hybrid energy harnessing.For example, Aydin et al. (2013) utilized fuzzy decision approach along with GIS to identify the most appropriate location for wind-solar hybrid energy harnessing.Yunna and Geng (2014) formulated a decision framework using AHP to prioritize optimal places for wind-solar hybrid potential harnessing.On the other hand, on the framework assigns priorities to alternatives based on their suitability, Yun-na et al. (2013)  ideal matter-factor model and ELECTRE II method to find the optimal macro-site for hybrid energy harnessing.When Lee et al. (2009) utilized AHP to examine the costs, risks, opportunities, and benefits of wind solar-PV systems, they suggested different criteria and an integrated process for evaluating power system projects.Hence, several studies used various techniques, methods, approach and process.For example, Van Haaren and Fthenakis (2011) investigated the selection process of sites for farms of wind using Spatial MCDM examination, impact, wind resources, proximity to the power station, land value and land cover (Tegou et al., 2010), financial, technical, ecological, and topographical factors (Noorollahi et al., 2016), land cover/use, approach from main communication distance, slope, grid access, wind speed average and orientation aspect (Pamučar et al., 2017) and security and quality, financial, policy, environment and ecosystem, and social impression (Yeh & Huang, 2014)  Resource Availability Average wind speed 2.
Wind patterns and variability 4.
Environmental factors Area of Flat Land & Without protected areas 8.
Threatened or endangered species 9.
Impact on Biodiversity and Habitat Preservation 11.
Noise and visual impact 12.
Distance to residential areas 13.
Noise restrictions and regulations 14.
Scenic or protected views 15.
Infrastructure & Connectivity Access to power grid 16.
Communication Network Coverage 18.
Road conditions and suitability 20.
Land Characteristics Size and Acquisition of the avail land 22.
Soil conditions and stability 26.
Lightning frequency and intensity 28.
Temperature range and variation 29.
Cooling requirements for solar panels 30.
Socio-Economic Factors Cost of land 31.
Operation and maintenance expenses 32.
Energy production demand 34.
-Renewable energy targets and subsidies

Renewable Energy resources in Pakistan
The Islamic Republic of Pakistan is situated in South Asia.The country is gifted with sufficient amount of RE resources.These resources comprise wind, hydro, geothermal, biomass and solar energy.These sources offer substantial potential for generating electricity.
Though the adoption and utilization of these sources have been sluggish in Pakistan, presently, they contribute only a minor portion to the country's total energy.In line with the RE Strategy of 2006, Pakistan has set an ambitious goal of incorporating 10,000 MW of power generated using renewable sources into its energy portfolio by the year 2030 (Farooqui, 2014).
Nevertheless, the actual estimated potential of renewable energy far exceeds this figure.With the ever-increasing energy demand, it is crucial to harness the maximum potential of renewable energy sources.Table 3 presents a comprehensive overview of the estimated capacity of these renewable energy sources and the existing energy generation potential derived from these sources in Pakistan.(Shaikh, Ji, & Fan, 2015) (Zafar, Ur Rashid, Khosa, Khalil, & Rahid, 2018) Solar Resource.Pakistan is rapidly evolving annually, increasing the requirement of energy demand.This necessitates exploration of alternative energy sources.Among these alternatives, solar power stands out as a promising option due to its distributed nature and holds great potential to address this energy shortage.The country is fortunate to receive ample sunlight throughout the year, making solar energy a viable and abundant renewable resource that can help address the prevailing energy shortage across the nation (Rauf et al., 2015).The country possesses a remarkable solar energy providing a significant opportunity to fulfill the nation's energy demand.However, despite having abundant solar potential, Pakistan has not fully utilized its capacity for solar power plant development.
Pakistan has a rich solar energy resource, with over 300 sunlight days and an average annual radiation of 1800-2200 kWh/m2 and average temperature of 26-28°C.This translates to a solar energy potential of 5.5-6 kilowatt hour per sq meter in one day (Wakeel et al., 2016) as shown in figure 2. Pakistan possesses solar energy capacity estimated to exceed 50,000 MW, with over 2500 hours of annual sunlight availability.Baluchistan and Sindh, in particular, have excellent potential for solar energy projects, receiving 7-8 hours of sunlight per day, approximately 2300-2700 hours annually (Rauf et al., 2015).Wind Energy Resource.In recent years, significant progress and development across various sectors have resulted in an increased reliance on technology, leading to a growing demand for energy and power generation in Pakistan.At present, conventional methods like, fossil fuels, coal and oil largely dominate the country's energy generation.However, as these finite resources are depleting, it becomes imperative to explore and adopt alternative energy sources to ensure a sustainable and secure energy future for Pakistan (Ullah et al., 2020).Pakistan possesses a substantial wind energy potential that can be harnessed for electricity generation.As early as 2003, wind projects with unit capacities ranging from 150 to 100 Mega Watts were established in the wind corridor of Sindh province.These projects collectively contributed to a cumulative capacity of 500 MW (Sheikh, 2010).The Renewable Energy Council of Pakistan (PCRET) built 26 wind projects, with a capability of 500W individually, in town named Gul Muhammad, marking Pakistan's first village that is wind power-electrified.The country is estimated to have the capacity to produce nearly 346,000 Mega Watt of power from wind (Solangi et al., 2018).Though the first wind energy harnessing project became operational in 2013 in Pakistan, a study indicated a significant wind energy source in the southern areas of province Baluchistan and Sindh estimated to be around 50,000 MW, while the Punjab province was estimated to carry a wind energy potential of 1000-1500 Mega Watt.The Pakistan Meteorological Department conducted measurements of speed of wind in the Baluchistan and Sindh coastal areas and identified steady wind speeds of 5-7 metre /second (Wind Energy Project, 2017).In particular, the Gharo and Keti Bandar regions in Sindh shown excellent wind potential for large-scale wind farm projects, an average wind velocity is recorded around 6.86 m/s.These figures indicate the area's economic feasibility and its suitability for wind energy harnessing.The capacity of wind energy in Pakistan is illustrated in Figure 3.

Methodology
The research methodology is the backbone of any study, guiding the entire process of data collection and analysis.The research begins by extensive review of existing literature to gain insights of previous studies and collection of criteria and sub-criteria related to the problem.Integrated Delphi-AHP Decision framework is presented in figure 4. The Delphi technique is employed to assess and refine key criteria and sub-criteria that influence site selection decisions.The research focuses on case studies of five cities in the northwest region of Pakistan, namely Islamabad, Taxila, Rawalpindi, Khanpur, and Wah Cantt.
The AHP is then employed and through pairwise comparisons, the weights of the criteria are obtained, providing a rational and transparent basis for the final evaluation and incorporates sensitivity analysis to explore any potential changes due to variations in criteria weights.

Delphi Method
This method is implemented to facilitate gathering of expert opinions, consensus building, and refinement of criteria and their importance.The process involves iterations, enabling the gradual convergence of opinions and the refinement of decision criteria.In this study, a panel of 8 professionals was engaged, including specialists from renewable energy field and industries and academia.However, the participation rate for the study survey was limited as only 5 experts consented to participate in the research.
Experts were presented with the list of criteria obtained through literature review.They ranked each criterion on a numerical scale based on their importance, considering their expertise and knowledge.The ratings provided by the experts is collected and examined to get the priority ranking of each factor.
Delphi method focus on achieving consensus among the expert panel regarding the final set of criteria and their relative importance.Feedback from the experts is shared with each other to facilitate the convergence of opinions.A feedback report summarizing the group's ratings, the range of opinions, and any points of disagreement is provided to the experts.They were given the opportunity to revise their ratings based on the feedback and engage in a structured discussion to address any divergent viewpoints.The process continues until a consensus is reached, indicating a high level of agreement among the experts regarding the final set of criteria and their priorities.

Analytic Hierarchy Process (AHP)
MCDM method is an effective tool in decision problems.One of the widely used MCDM technique pioneered in 1970s by Thomas L. Saaty is AHP.It offers a systematic and structured approach to tackle intricate decision-making challenges by decomposing them into manageable sub-problems.AHP involves pairwise comparisons of criteria and alternatives to determine numerical priority weights, which are then used to calculate overall priorities.The AHP process consists of several steps: Hierarchical Structure: Hierarchy formation is the first step of this technique which consist of a structure containing criteria linked with their sub-criteria for achieving goal.
Decision-makers then brainstorm and organize these elements in descending form to form a hierarchy as shown in Figure 5.

Figure 5
Derived Hierarchy Pairwise Comparisons: After establishing the hierarchy, decision-makers make pairwise comparisons matrix (table 4) based on judgements of experts between elements using scale (table 5) to express the relative significance of one factor over another.The comparisons are done for elements at the lower levels with respect to elements at the higher levels.

Calculation of Priority Weights:
The comparative weightage is derived for every level in the hierarchy through pairwise judgments.The number of matrices needed is determined by  Consistency index can be calculated by Formula mentioned below using Eigen value.
The consistency ratio is computed using Equation.

𝐶𝑜𝑛𝑠𝑖𝑠𝑡𝑒𝑛𝑐𝑦 𝑅𝑎𝑡𝑖𝑜 = 𝐶𝑜𝑛𝑠𝑖𝑠𝑡𝑒𝑛𝑐𝑦 𝐼𝑛𝑑𝑒𝑥 𝑅𝑎𝑛𝑑𝑜𝑚 𝐼𝑛𝑑𝑒𝑥
Overall Priority and Ranking: The overall priority for ranking alternatives is developed using composite weights derived from the relative weights of elements at different levels.A decision support system called "Expert Choice" is based on AHP and designed to facilitate more logical and unbiased decision-making processes.It organizes data in a hierarchical structure and performs computations on pairwise comparisons to help users solve complex problems involving multiple criteria and alternatives.
The questionnaire served as a tool to gather the valuable opinions and judgments of the The AHP approach is advantageous as it provides a structured framework for evaluating alternatives and enables decision-makers simultaneously examine both qualitative and quantitate data.It is particularly useful when dealing with large numbers of alternatives and selection factors.However, maintaining consistency in judgments can be challenging, and the AHP process involves several steps to ensure the validity of the results.Hence, AHP is a powerful decision-making method used in various domains.By following a structured approach of hierarchical decomposition and pairwise comparisons, AHP helps decisionmakers make informed and rational choices in complex decision-making scenarios.
Additionally, decision support systems like 'Expert Choice' based on AHP facilitate the implementation of this methodology and handle large amounts of data efficiently.

Delphi Results
Based from the literature review, considerable number of criteria were listed down and used for valid research as shown in Table 1.Due to the large number of criteria listed, it was impractical to utilize all of them for pairwise comparisons.Therefore, panel of specialists of 5 members related to renewable energy field were presented questionnaire in which they ranked out refined and key factors.In the end, 5 criteria and 18 sub-criteria were selected for further assessment.Table 6 presents the refined factors which are found vital for evaluation of location for harnessing hybrid energy.Impact on Biodiversity and Habitat Preservation 6.
Noise and visual impact 7.
Distance to residential areas 8.
Infrastructure & Connectivity Access to power grid 9.
Communication Network Coverage 11.
Land Characteristics Size and Acquisition of the avail land 13.
Socio-Economic Factors Cost of land 16.
Operation and maintenance expenses 17.
Energy production demand

AHP Results
To obtain the weights of factors in this method, a collaborative and systematic process of decision making was adopted.A total of 31 specialists participated and provided their judgments in the form of pairwise comparison matrices for criteria, sub-criteria, and alternatives.To consolidate the input from individual specialist, the method of taking geometric mean was utilized to compute their weights.The implementation of the AHP involved two distinct phases.Initially, the matrix main criteria were processed and their weights were measured, and similarly, the weights were determined for sub criteria.Table 7 presents the outcomes of this process.The next step is to perform comparison assessment of alternatives which included all the criteria.It is basically a question which asks experts to compare alternatives based on each criteria.This is done by numbering using the scale.When all the pairwise comparisons are performed, geometric mean is used to aggregate individual judgments (Saaty, 1990).These were analyzed by using the Expert Choice software.This software is used to calculate consistency ratio (CR) and weights of criteria and alternatives.The judgment is accepted if CR≤ 0.10.The questionnaire was presented back to expert if their values was greater than 0.10.These experts were explained the meaning of inconsistency in AHP.Many participants were able to achieve this value.The specialists were free to complete the questionnaire is their own way After completion of pairwise comparison of factors, the next step is to find pairwise comparison of alternatives using each criteria and sub criteria as shown in figure 6 by utilizing judgements recorded by the experts.

Figure 6
Pairwise comparison of Cities using each criteria/sub Criteria .
Finally, overall priorities of decision1alternatives are synthesized using the criteria priorities that have been calculated in previous steps as shown in table 8.

Sensitivity Analysis
In order to examine the potential impact on result due to change in expert's preferences, sensitivity analysis is executed in this research.The focus was to assess the significance of weights of criteria in relation to the priorities of cities as alternatives.Five variations were examined, and it was observed that the sequence of alternatives priorities remained consistent and did not vary.Table 9 shows the weightage of the criteria for the different variations and Table 10 shows the results and priorities calculated from the sensitivity analysis for these variations.
Variation 1 is the weights obtained in this research, while other variations are computed to analyze sensitivity aspect.Table 10 indicates that the alternatives ranking remained unchanged in variation 2 and 3, while the rankings were altered in variation 4 and 5.In conclusion, the sensitivity analysis demonstrates that the main findings of the study remain unchanged, indicating that altering the weights of the obtained results would have an insignificant impact.The highest-ranked alternative remains Islamabad, with Rawalpindi, Taxila, Wah Cantt, and Khanpur following in that order.Based on the conducted analysis and findings, the findings of the research are deemed valid and robust.The rigorous methodology employed and the careful consideration of various factors ensure the reliability and credibility of the study's outcomes.

Discussion
This study utilized a comprehensive research framework and implemented various methodologies, resulting in valuable insights and significant findings.As a result, energy specialists and policy creators can rely on the research framework and relevant findings to make informed decisions regarding selection of location for solar-wind harnessing.
The successful utilization of the Delphi and Analytic Hierarchy Process (AHP) methods has effectively addressed the process to make decision for selection of optimal city for solar-wind harnessing in the northwest area of Pakistan.The final rankings and results of this research are obtained by a robust methodology and appropriate utilization of expert opinions and related work.Among the selected key criteria identified in this research, namely resource availability, infrastructure and connectivity, land characteristics, environmental impact, and socio-economic factors, resource availability has been recognized as the most significant factor due to its direct impact on the energy generation potential and overall plant efficiency.Infrastructure and connectivity is considered the second most important factor in the process of selection of location.The prioritization of criteria in this research aligns with the perspective of a typical developing country, where the scarcity of funding resources poses a significant challenge for renewable energy project development.It is noteworthy that the respondents ranked the environmental aspect as the lowest priority.This lower ranking indicates that the local population is unaware regarding the significance of environmental ecological balance.
The results and rankings of the sub-criteria reveal that the quality of solar and wind resources, distance from grid, road accessibility, land availability, cost, and land acquisition are ranked as the top factors.These rankings reflect a robust outcome, emphasizing the critical significance of these criteria in the decision process of optimal city for solar-wind energy harnessing projects.
This ranking of different cities in the northwest region holds significant importance as it is based on a scientifically guided decision-making process and incorporates essential factors and a robust methodology.Islamabad, Rawalpindi, and Taxila are highly suggested as the optimal cities for installation and investments of solar-wind hybrid energy harnessing.
Islamabad, in particular, performs well across various criteria, including resource availability, infrastructure, land characteristics, environmental impact, and socio-economic factors.Its favorable access to the power grid, road accessibility, topography, elevation, and public acceptance contribute to its high ranking.These recommended locations have favorable conditions, including an adequate number of solar-windy days throughout the year and the presence of appropriate infrastructure.The complementary nature of solar and wind energy makes them an ideal combination for harnessing renewable energy potential.Solar-wind energy, in particular, shows promise and a valuable solution for addressing the current energy and power crises in the country.Hence, government authorities and decision makers can get valuable insights from this research in prioritizing optimal sites and implementing solar-wind harnessing projects not only in the northwest region but also in other regions of the country.

Conclusion
The comprehensive and systematic decision support framework was developed successfully in this research by utilization of the AHP and Delphi techniques for evaluating the optimal location selection among different cities for solar-wind hybrid energy harnessing in the northwest region of Pakistan.This research study presented a robust framework and a robust assessment of criteria often overlooked in site selection process that can be effectively employed by developers, policymakers and investors to select optimal decisions and maximize the potential of RE resources.By considering the specific criteria and involving relevant stakeholders, the implementation of sustainable and efficient solar and wind hybrid energy projects becomes feasible, thereby contributing to a greener and more sustainable future.
Solar and wind energy have significant potential offering numerous advantages like reducing dependence of energy mix on fossil fuels, stimulating both regional and national economic growth, employment opportunities and offers significant potential to drive sustainable development.Hence, prioritizing decision of selecting optimal and best locations to build solar-wind energy harnessing project becomes very crucial.Considering the absence of a systematic framework for decision of selecting solar-wind project location in Pakistan, this research aimed to bridge this gap by proposing a systematic decision framework specifically for the North West region of the country.The key factors for selection of optimal location were identified through expert feedback and analysis where experts from academia, industry, and the renewable and environmental field contributed their valuable insights and judgments to the implementation of the AHP decision model.
Among the selected key main criteria, resource availability and infrastructure and socio-economic factors emerged as the top-ranked criteria in this study.Based on the framework developed for supporting decision, the city of Islamabad was determined to be the optimal location for solar-wind harnessing, followed by Rawalpindi, Taxila, Wah Cantt, and Khanpur.It is important to emphasize that the outcomes and results of this research rely on the expert judgments collected at each assessment level, and therefore, engaging different experts and stakeholders, as well as exploring alternative MCDM methods, may slightly influence the results.Nonetheless, this study holds significant relevance and should be duly considered for solar-wind power project site selection, while also offering opportunities for further enhancements in the future.
The outcomes and findings of this research have several recommendations for practice and future research in the field of solar and wind hybrid energy site selection.The following recommendations/future research are put forth based on the conclusions drawn from this study:

Figure 3
Figure 3 Figure 4 expert panel.It is carefully structured in the form of pairwise comparison matrix to collect pertinent information regarding site selection criteria, their respective importance, and the decision making process.Participants assigned numerical values representing the relative importance or preference between each pair of criteria.The selection of an expert panel is crucial for obtaining diverse perspectives and domain-specific knowledge.The experts were invited to participate in the study and contribute their insights throughout the Delphi method and AHP analysis.Their involvement ensured a comprehensive and well-informed decisionmaking process, taking into account multiple viewpoints and expertise.In this study, the experts include professionals based on their experience and expertise from academia, renewable industry, and government departments like council of Pakistan for RE Technologies (PCRET) and local authorities from different sites.There were around 31 pairwise comparison questionnaires filled by these experts to prioritize the cities for solar wind hybrid energy harvesting plant.

Table 1
Cities/Alternatives for Present Study Positional Coordinates of Alternative Cities

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Wu et al. (2016) (2015)red the various factors while integrating other techniques.For instance,Baloch et al. (2019)put forward a proposal for generating off-grid energy from six wind places located in Pakistan,Wątróbski et al. (2015)conducted a feasibility study for selection of farms site for wind project located in Szczecin city, Poland using AHP and PROMETHEE,Wu et al. (2016)worked on decision process in which selection of location is carried out for offshore wind by utilizingELECTRE III, and Sánchez-Lozano et al. (2016)joint fuzzy methods from different MCDM approaches in order to dealt with problem in making decision for selection of location for building wind project onshore.The various factors generated from the literature are summarized in table 2.

Table 2
List of Criteria & sub-Criteria Obtained from literature

Table 3
The assessment of renewable energy potential and installed capacity across different sources in Pakistan

Table 4
Pair wise Comparison matrix Synthesis:In the synthesis, the values from the obtained matrix are summed and normalized.The mean of the values in rows of this matrix is computed to obtain the relative priorities of elements.Consistency Check: Decision-makers' consistency is crucial in the obtained responses of experts.The reliability of judgments is ensured using Consistency Ratio (CR) which can be obtained by taking ratio of Consistency and random Index (CI & RI), Consistency is considered satisfactory if this value is less than 10%.

Table 7
Results of pairwise comparison

Table 9
Criteria weights used in different Variations for sensitivity analysis