9.4407
Figure 2.3 Histograms obtained of the ranking for each of RE technologies from the 10,000 MCS.
2.6 Results and Discussion
Table 2.9 presents the ranking of RE technologies obtained from TOPSIS, fuzzy-TOPSIS, and TOPSIS run using MCS. Ranking from TOPSIS and TOPSIS run using MCS with equal weightage to all the indicators revealed that among all RE technologies small hydropower is highly preferred followed by large hydropower, onshore wind power, solar PV, and bioenergy while ranking from fuzzy-TOPSIS revealed that large hydropower technology is the most sustainable followed by onshore wind power, small hydropower, solar PV and bioenergy. Bioenergy has been ranked as the least favourable option in all three methods from a sustainability point of view.
Table 2.9 RE technologies ranking from TOPSIS and fuzzy-TOPSIS.
| RE technology | Ranking from TOPSIS | Ranking from fuzzy-TOPSIS |
| Large hydropower | 2 | 1 |
| Small hydropower | 1 | 3 |
| Solar PV | 4 | 4 |
| Onshore wind power | 3 | 2 |
| Bioenergy | 5 | 5 |
The results show that the ranking obtained from TOPSIS is highly uncertain in comparison with that obtained from fuzzy-TOPSIS and probabilistic ranking as uncertainties are addressed in these methods. The probabilistic ranking obtained shows that each of the RE technology is found to be highly preferred and also least preferred in some of the simulations. As there are possibilities of uncertainties involved in indicator values and even indicator weightage, hence conclusion on the unique ranking of RE technology should be drawn carefully. The present study addressed the possible associated uncertainties in the input data using two different approaches which shows its superiority over other studies on sustainability assessment in the context of India. Based on the above results, in the Indian context large hydropower, small hydropower, and onshore wind have come out to be the most sustainable RE technologies.
2.7 Conclusion
The study aims for ranking RE technologies in context to India under associated uncertainties considering various sustainability indicators. The qualitative data related to indicator values are associated with uncertainties and fuzzy-TOPSIS is used to address these uncertainties. Further, it is reviewed that indicator values also vary widely for each RE technology, and to address these possibilities of uncertainties associated with input data, TOPSIS is run using MCS to obtain probabilistic ranking. To understand the impact of these uncertainties both the fuzzy-TOPSIS and probabilistic ranking are compared with that obtained from the TOPSIS method. The ranking obtained by TOPSIS is found to be uncertain. Thus, the study concludes that for better decision-making and energy planning at the national level it is important to address the associated uncertainties while assessing the sustainability of RE technologies.
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