Application of Fuzzy AHP Approach for Evaluation of Sustainable Energy Sources in India


Sustainable energy is defined as “A safe, environmentally sound, and economically viable energy pathway that will sustain human progress into the distant future is clearly imperative (Klein and Whalley 2015; Mainali et al. 2014). Energy is an essential factor for the economic development of the country and an all-important factor in human life. Per capita energy consumption will show' the economic prosperity of the nation (Kahraman and Kaya 2010; Lee and Chang 2018). In during the 1970s, energy sustainability was analyzed with only a single criterion, especially economic or low cost was knowrn as a “single-pillar” analysis (Klein and Whalley 2015; Maxim 2014). However, in the 1980s, due to technical development, a technical factor was added as a secondary criterion. In the 1990s, a growing awareness about the environment and social issues lead to modifications in the above decision framework. The necessity of accompanying the technical, environmental and social factors in sustainability analysis gave way to implementing a multi-criteria decision making (MCMD) approach (Buyiikozkan and Giileryiiz 2016; Kaya and Kahraman 2010).

The MCDM approach is used for the selection of the best single option among a set of options by evaluating them with multiple decision criteria (Arce et al. 2015; Qolak and Kaya 2017). To prioritize or rank alternatives, first select the MCDM approach, suitable criteria, subcriteria and alternatives related to their goal. Second, collect the weights of experts about the importance of criteria, subcriteria and alternatives (Amer and Daim 2011). Commonly used MCDM approaches are AHP (Analytical Hierarchy Process), WSM (Weighted Sum Method), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations), SMART (Simple Multi-Attribute Rating Technique), ELECTRE (Elimination Choice Translating Reality), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ANP (Analytic Network Process) etc. Pohekar and Ramachandran (2004) reviewed more than 90 research articles and concluded that AHP, PROMEETHE and ELECTRE MCDM approaches are frequently used MCDM techniques in the sustainable energy planning sector. Amer and Daim (2011) found AHP is a widely used MCDM approach because of its flexibility and robustness. Kahraman, Kaya and Cebi (2009) wrote that AHP is the most outstanding MCDM tool to solve energy management problems. According to Qolak and Kaya (2017) fuzzy AHP is the most commonly used the approach in an energy field with a 40 percent share in overall fuzzy MCMD studies.

India is the second largest country in overall population, and it is the first largest country in the rural population in all over the world. IMF’s World Economic Outlook projected India’s growth rate will accelerate to 7.5 percent until 2026-2027 (Pappas and Chalvatzis 2017). Real GDP of the country raises with an increase in energy consumption, so energy acts as an essential factor for the economic development of every sector of the country (Kumar Shukla and Sharma 2017). At present, India is the third largest country in the world for both electricity generation and consumption, after China and the United States. Energy generation in India has been reached

2.5 times higher than the year 1997-98. India generates 344,689 MW of power, out of which thermal energy generates a major portion with 64.3 percent of overall generation. In the remaining portion, renewable energy covers 20.5 percent, followed by hydro energy (13.2 percent) and nuclear energy (2 percent) (Ministry of Power 2019). Energy demand is continuously increasing in India due to the development of industrialization and urbanization. Energy demand in India reached 915,123 million units in the years 2017-2018, which is double the energy requirement in 1997-1998. Figure 10.1 shows the increase in energy demand from 1997-1998 to 2017-2018. According to the government of India (Energy Statistics 2017), energy demand will rise to 551.8 trillion units until 2047. To fulfill current or future energy demand, India is mainly dependent on fossil fuels, which are a major source for greenhouse gas emission and climate change. Hydro energy is considered as clean and an ideal source for meeting India’s peak power demand, but due to social acceptance of large hydroelectric dams made their use limited (Hairat and Ghosh 2017). Nuclear energy is economic and clean, but the cost of import of nuclear fuel and public agitations against nuclear power will weak the future nuclear aspect. Renewable energy sources are the clean and economic source, but they have some drawbacks, such as their reliability and intermittent nature. So, there is a necessity to evaluate the most sustainable energy source in India.

Trend of increase in energy requirement

FIGURE 10.1 Trend of increase in energy requirement.

In this research work, fuzzy AHP approach is employed for evaluation of most sustainable energy source among renewable (solar, wind, biomass, hydro) and nonrenewable (thermal, gas power, nuclear) energy sources. This analysis includes six criteria: environmental, technical, economic, social, political and flexibility. In the best knowledge of the author, there is no such literature available in which seven energy sources as an alternative and six criteria are considered for the analysis.

The rest of chapter is organized as follows: In section 10.2, a collected recent literature review. In section 10.3, a methodology of fuzzy AHP is explained. In section 10.4, fuzzy AHP approach is employed for the selection of the most sustainable energy source and calculation and results of the analysis are presented. In section 10.5, the conclusions of the research work are presented.


The MCMD approach is an attractive tool in decision making since last two-three decades especially in energy planning and management problems. In last few years, the MCDM (AHP) approach has been widely used in sustainable energy planning, as presented in Table lO.l. The AHP approach is also widely used with the geographical information system (GIS) tool for selection of the most suitable location for the different energy sources. Uyan (2017) combined AHP with GIS software to select the most suitable locations for solar power installation in Turkey. He analyzed these locations by covering the economic as well as environmental factors. Baseer et al. (2017) analyzed for the Kingdom of Saudi Arabia to install wind power plants. By combining AHP with GIS, they highlighted near Ras Tanura on the coast in the eastern province, Al-Wajh on the coast in the western region. Turaif in Al-Jawf region at northern borders as a most suitable sites in Saudi Arabia. Aly, Jensen and Pedersen (2017) investigated spatial suitability for large-scale solar power plant in the Republic of Tanzania based on six exclusions and four decision criteria.

TABLE 10.1

Year-Wise Literature Survey of AHP MCDM Approach for Sustainable Energy Planning Decision Problems




Research Purpose



Chatzimouraddis and Pilavachi (2009)


To identify the most suitable power plant for future energy generation covering economic, sustainable and technical factors by using AHP MCDM approach

They identified hydro power plant as the most suitable power plant, order followed by geothermal and wind energy


Kahraman and Kaya(2010)


To determine the most appropriate energy source in Turkey using fuzzy multi-criteria decision-making approach

They obtained that wind and solar energy are the most appropriate energy sources in Turkey for power generation application


Daniel et al. (2010)


To prioritize the three most mature renewable energy sources in India using AHP MCDM approach

The prioritization order of renewable energy sources is wind, biomass and solar energy


Stein (2013)



To develop a multi criteria decision-making approach for decision makers to rank different renewable and nonrenewable energy technologies

The ranking order of different energy sources: wind, solar, hydro, geothermal, gas, oil, nuclear, coal, biomass


Ahmad and Tahar (2014)


To evaluate the most efficient renewable energy source in Malaysia for an application of electricity generation

They evaluated solar energy as the most efficient energy source in Malaysia for electricity generation


Al-Qudaimi and Kumar (2018)


To develop a new intuitionistic fuzzy-AHP (IF-AHP) decisionmaking approach for the analysis of sustainable energy planning problems

They concluded that nuclear and solar energy are the suitable energy sources for the sustainable energy planning


Al Garni et al. (2016)

Saudi Arabia

To prioritize renewable energy sources for sustainable electricity generation in the developed country of Saudi Arabia

The prioritization order follows; solar PV > solar thermal > wind > biomass > geothermal


Haddad. Liazid and Ferreira (2017)


To develop a multi-criteria decision-making approach to prioritize the renewable energy sources for the electricity generation system of Algeria

Solar energy is the most suitable option for the Algerian electricity system. Solar is followed by wind, geothermal, biomass and hydro power, respectively


Jlia and Puppala (2017)


To analyze the five available renewable energy sources and to rank them based on the Energy Index

The ranking order provides geothermal at the top followed by hydro, wind, biomass and solar energy


Mirjat et al. (2018)


To assess the energy combination factors for the fulfillment of the long-term electricity supply of Pakistan

Energy efficiency and conversion are the best energy combination factor for the Pakistan electricity system


Atabaki and Aryanpur(2018)


To propose a multi-criteria decision-making model for the identification of sustainable power source in Iran

Solar PV and combined cycle power plant are chosen as the sustainable power sources in Iran


Thomas Saaty developed the AHP approach in 1980. AHP is most frequently used for research because it prepares a hierarchical or network-based structure of the given problem. Afterwards, it breaks into many subproblems, which are separately analyzed or solved (Streimikiene. Sliogeriene and Turskis 2016). In the hierarchal structure, goal or objective represented is at the top level. Criteria and subcriteria are represented at the middle level and alternative represented at a lower level (A1 Garni et al. 2016; Stein 2013). In the AHP approach, criteria and subcriteria were pair-wise compared to obtain relative importance. The alternatives are also pair-wise compared for the considered criteria and obtained for the importance index. A product of relative importance (weights) of criteria and category weight of alternatives was given the final weights for the ranking of the alternatives. In AHP, experts have to give relative importance/weightage within the range of a 1-9 scale (crisp value). As if, object A is equally important with object В then weight of 1 is given or if object A is strongly more important than В that means A is given weightage of 7 or В is assigned reciprocal of that weightage means 1/7 (Kahraman and Kaya 2010). The weight allotted in Saaty crisp scale (1-9) always contains some kind of impreciseness and vagueness (Tasri and Susilawati 2014). Therefore, the fuzzy linguistic scale was adopted over the Saaty crisp scale to overcome impreciseness and vagueness. The flow diagram of the research work is shown in the Figure 10.2.

Flow diagram of the research work

FIGURE 10.2 Flow diagram of the research work.

Hierarchical structure of sustainable energy decision problem

FIGURE 10.3 Hierarchical structure of sustainable energy decision problem.

Fuzzy AHP approach has the following steps.

  • 1. Prepare the hierarchal network model to evaluate the most sustainable energy source in India as shown in Figure 10.3.
  • 2. The research work employed the fuzzy AHP approach, which is proposed by the Buckley in 1985.
  • 3. Perform pair-wise comparison among the considered criteria and subcriteria.
  • 4. The relative importance (weights) is obtained using geometric mean method.

5. Fuzzy weights are the product of fuzzy geometric mean (GM) and reciprocal of summation of that fuzzy geometric mean values as given in Eq. (2).

Where 1 = lower, m = middle and u = upper value

6. De-fuzzified crisp numeric value (DCNV) is the average of the fuzzy lower, middle and upper values as discussed in Eq. (3).

7. The consistency ratio has been checked to validate the consistency of the given judgements. Consistency ratio should be less than 0.1 for true criteria weight.

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