0.0807
Table 2.5 Normalized decision matrix for fuzzy-TOPSIS.
| RE technology | I1 | I2 | I3 | I4 | I5 | I6 | I7 | I8 | I9 | I10 |
| Large hydropower | 1.00 | 1.00 | 0.59 | 1.00 | 1.00 | 0.82 | 0.94 | 0.00 | 0.00 | 0.00 |
| Small hydropower | 1.00 | 0.00 | 0.59 | 0.13 | 0.50 | 1.00 | 1.00 | 0.50 | 0.50 | 0.50 |
| Solar PV | 0.00 | 0.00 | 0.00 | 0.00 | 0.17 | 0.98 | 0.81 | 1.00 | 1.00 | 0.75 |
| Onshore wind power | 0.31 | 0.00 | 0.15 | 0.80 | 0.17 | 0.89 | 0.97 | 1.00 | 0.75 | 1.00 |
| Bioenergy | 0.54 | 0.50 | 1.00 | 0.33 | 0.00 | 0.00 | 0.00 | 0.75 | 0.50 | 0.25 |
| Wj | 0.10 | 0.10 | 0.10 | 0.10 | 0.10 | 0.10 | 0.10 | 0.10 | 0.10 | 0.10 |
Figure 2.2 Fuzzy triangular membership function [88].
2.5.3 Monte Carlo Simulations–Based Probabilistic Ranking
The uncertainties related to a wide range in input values have been addressed by the TOPSIS method run using Monte Carlo simulation (MCS). For MCS each indicator value (Table 2.3) was randomly sampled with uniform distribution for 10,000 simulations. These randomly sampled variables are used as input to the TOPSIS method and probabilistic ranking was obtained. The histograms obtained of the ranking for each of RE technologies from the 10,000 MCS are presented in Figure 2.3. It can be seen from the histogram that small hydropower is on the top rank in more than 80% of simulations and bioenergy is on the bottom rank in more than 90% of simulations. While large hydropower has distributed ranking (from 1 to 4) in the range of 10% to 45% number of simulation cases.
Table 2.6 Decision matrix with fuzzy linguistic variable.
| RE technology | I1 | I2 | I3 | I4 | I5 | I6 | I7 | I8 | I9 | I10 |
| Large hydropower | Very high | Very high | Medium | Very high | Very high | High | Very high | Very low | Very low | Very low |
| Small hydropower | Very high |