done with respect to the sustainability of electricity generation technologies at a global level [32–40] and also specifically of RE technologies [41–45], while some researchers have focused explicitly on the national level assessment of RE technologies, for example, for Turkey [44, 47], Irish city [46], Scotland [48], India [14, 15], Island of Sardinia [49], UK [50], Island of Crete in Greece [51], North Korea [52].
MCDM methods are the most common methods for assessing the sustainability of generation technologies; for example, analytic hierarchy process (AHP) [14, 44, 45, 52], MAVT [35, 37, 38], the weighted sum method (WSM) [34], the preference ranking organization method for enrichment evaluation (PROMETHEE) [48, 51], the elimination and choice translating reality (ELECTRE) [49], TOPSIS [41], (MULTIMORA, MACKBETH, NAIADE, CORPAS) [32, 41, 44, 46, 50].
The advantage of MCDM methods is that it allows considering all the objectives and indicators at the same time while making decisions [48, 53]. In the case of qualitative approaches, the value of indicators is expressed in the form of linguistic terms (e.g., low, high, very high), and due to the possibility of vagueness in a human decision, uncertainty is always associated with the result. Fuzzy combined with the MCDM method has proven to be useful in handling qualitative indicators with associated uncertainties [53, 54]. The fuzzy combined with MCDM methods have found vast application in the assessment of the energy system’s sustainability [43, 44, 47, 48, 55], and electricity distribution planning considering uncertainties [56, 57].
As far as sustainability assessment studies at a national level for India are considered there are majorly two studies [14, 15] done using MCDM applications. The study [14] accessed only three RE technologies i.e., wind power, solar power, and biomass and, did not consider small hydropower and large hydropower during the assessment. The assessment applied the AHP based on the Delphi technique and evaluated wind power as the most favourable technology. In another study [15], wind power, solar power, small hydropower, biomass, and geothermal were accessed using fuzzy-AHP using a wide range of sustainability indicators. However, the study [15] did not consider any social indicators.
The above-discussed limitations reviewed in the previous studies are addressed in the present study. Thus, the present study assessed all the RE technologies contributing to Indian grid-connected power using a range of technical, economic, environmental, and social indicators. The study also addressed the uncertainties associated with input data using fuzzy-TOPSIS and MCS.
Sustainability assessment of RE technologies required the following steps:
2.4.1 RE Technologies Selection
The potential of various RE technologies has been recognized in India such as onshore and offshore wind power, solar PV, CSP, large hydropower, small hydropower, tidal power, wave energy, bioenergy and, geothermal. The RE technologies which are contributing to grid-connected generation are assessed in the present study, i.e., large hydropower, small hydropower, onshore wind power, solar PV and, bioenergy (Table 2.1).
2.4.2 Sustainability Indicators Selection and Their Weightage
The social, environmental, and economic indicators are the three basic pillars of sustainability, although some technological and operational indicators have also been reviewed in the literature [36, 48, 58, 59]. The selection of sustainability indicators for the present study has been made with reference to studies [48, 53, 58]. Table 2.2 presents a summary of the selected indicators. Ten indicators have been selected comprised of three technological, two economic, two environmental, and three social types. The optimization preferences are assigned as maximum (max-m) or minimum (min-m). The indicator values of selected technologies with the range given in the brackets are presented in Table 2.3. The indicator’s values and range were estimated from substantial available literature [32, 35, 48, 60–83]. The present study assigned equal weightage to all indicators assuming that all indicators are equally important for sustainable development.
Table 2.2 Summary of selected indicators.
| Indicators | Type | Unit | Optimization preference |
| Efficiency (I1) | Technological | Percentage | max-m |
| Response to peak demand (I2) | Technological | Qualitative (1-5) | max-m |
| Capacity factor (I3) | Technological | Percentage | max-m |
| LCOE (I4) | Economic | USD/KWh | min-m |
| Service life (I5) | Economic | Years | max-m |
| Land use (I6) | Environmental | m2/MWh | min-m |
| GHG emissions (I7) | Environmental | g CO2-eq/KWh | min-m |
| Social acceptance (I8) | Social | Qualitative (1-5) | max-m |
| Social risks (I9) | Social | Qualitative (1-5) | min-m |
| Environmental risks (I10) | Social | Qualitative (1-5) | min-m |
2.4.3 Methodology
2.4.3.1 The TOPSIS Method
The TOPSIS is a well-known MCDM method. The concept on which this method is based is the distance of alternatives from best solution [84]. Accordingly, the most suitable alternative will be nearest from the best solution and far away from the worst solution [85, 86]. The best solution is one which maximizes the beneficial indicators and minimizes the non-beneficial indicators.
The steps of the TOPSIS method are described as follows:
a. A decision matrix wherein columns represent indicators (I1, I2, I3, …, In), (j = 1, 2, …, n) and rows represent alternatives (A1, A2, A3, . . . Am), (i = 1, 2, …, m) has to be established.(2.1)Table 2.3 Indicators value for each technology
