Sensitivity analysis

The results of the sensitivity analysis focus on four main parameters: capital costs, variable O&M costs, fuel prices, and capacity factor (CF). These are presented in Tables 6.8, 6.9, 6.10, and 6.11. Parameter values of one of the technologies are changed to between -50%

and 50%, while values for other technologies are held constant. An exception is the sensitivity analysis of CF, which is changed from -10% to 10%. Sensitivity analysis of CF of renewable energy power plants is essential since many of the renewables’ production projections were overestimations (Al Irsyad et al., 2019b).

The resulting observations focus on changes in CO2e emissions from electricity generation, electricity supply costs, and renewable production shares. In general, coal PP is sensitive to capital cost increases in all scenarios, while geothermal PP, hydro PP, and wind PP are more sensitive to capital cost decreases in emission reduction scenarios, as shown in Table 6.8. For instance, in the RUPTL scenario, where the capital cost of coal-based power plants is increased by 50%, emissions are found to decrease by 1.43%, and generation costs increased by 2.46%. These effects are more substantial in the emission reduction scenarios, as seen in Table 6.8. Interestingly, both increased and decreased capital costs of coal power plants cause higher electricity supply costs in the emission reduction scenarios. The reduced capital cost means more investment in coal power plants, and consequently, in the restricted emission circumstance, it also means more investments in renewables. On the other hand, 50% reduction of capital costs of geothermal PP, hydro PP, and wind PP will reduce electricity supply costs by 6.9%, 6.16% and 5.28% respectively in the 11% scenario. Increased capital costs of these power plants will also reduce the electricity supply costs, since other low-cost renewables power plants will substitute them.

Table 6.8 Sensitivity analysis results for capital cost

Δ capital cost (%) RUPTL 11% Scenario 14% scenario

A B C A B C A B C

Coal PP -50 0.99 -2.91 -0.02 0.11 0.99 -0.41 0.38 1.55 1.92 +50 -1.43 2.46 0.01 -7.74 2.37 3.80 -6.75 3.54 5.69 CCGT -50 -0.23 -0.35 -0.01 0.38 1.90 -0.74 -0.13 -2.89 -4.34 +50 0.34 0.25 -0.04 0.41 2.03 -0.36 0.07 0.52 -0.23 OCGT -50 0.21 -0.08 -0.03 -0.21 -0.67 0.17 -0.16 0.48 0.17 +50 0.27 -0.13 -0.02 -0.55 -1.71 0.58 -1.24 0.02 -2.46 Geothermal PP -50 -1.17 -1.08 0.86 -0.83 -6.90 0.53 -4.51 -7.23 0.86 +50 0.70 0.85 -1.05 3.00 -0.83 -3.32 2.71 -0.03 -3.12 Hydro PP -50 -0.88 -0.78 0.90 -2.72 -6.16 2.16 -5.11 -0.75 3.36 +50 -0.05 0.74 0.01 -0.24 -1.73 0.14 -1.02 2.07 0.76 MHP -50 -0.93 -0.23 0.65 -0.07 -2.31 0.20 -1.51 -2.61 -2.46 +50 -0.16 0.21 0.03 0.55 -0.12 -0.87 0.62 0.26 -0.49 Wind PP -50 0.15 -0.17 -0.01 -0.50 -5.28 0.05 -0.23 -2.55 -0.34 +50 0.26 0.25 -0.03 0.71 -1.01 -0.85 -0.23 0.23 -3.94 WTE -50 -0.27 0.09 0.07 0.48 2.31 -0.69 -1.73 -1.42 -2.20 +50 0.01 -0.13 0.01 0.19 0.73 -0.23 -0.11 -0.91 0.05 Biomass PP -50 0.05 -0.10 -0.01 0.05 -1.31 0.21 -2.18 -3.27 -1.69 +50 -0.02 0.00 0.00 -0.17 -1.67 0.24 -0.34 -2.01 0.28 PV -50 0.44 -0.17 -0.07 0.23 0.18 -0.12 0.27 2.56 2.53 +50 0.20 0.12 -0.05 -0.01 0.61 -0.08 0.05 1.98 2.68 Oil PP -50 0.25 -0.44 -0.03 0.03 -0.15 -0.19 1.42 -0.10 -1.36 +50 -0.09 0.01 -0.01 0.11 1.22 -0.33 -1.37 0.65 -2.81 Note: A – Changes (%) on CO2e from electricity generation in 2025, B – Changes (%) on electricity supply costs in 2025, and C – Changes (%) on renewables production share in 2025.

Coal PP is also sensitive to changes in variable O&M costs, as presented in Table 6.9.

A 50% reduction of variable O&M costs in coal PP will increase emissions, reduce electricity generation costs and reduce the renewable energy share in all scenarios, and vice versa for higher variable O&M costs. The next most sensitive technology to variable O&M costs is OCGT, where a 50% increase in the variable O&M costs will increase emissions, electricity supply costs and renewables production shares by 1.97%, 6.27% and 1.82% respectively under the 14% scenario. Wind PP is also sensitive to both increases and decreases in O&M costs, resulting in each case in an escalation of the electricity supply cost in the 14% scenario. The decreased O&M costs naturally encourage more investments of wind PP, thus requiring more load follower power plants (i.e., OCGT).

Table 6.9 Sensitivity analysis results for variable O&M cost

Δ variable OM cost (%) RUPTL 11% scenario 14% scenario

A B C A B C A B C

Coal PP -50 3.45 -10.85 -0.34 2.71 -8.61 -2.00 4.81 -8.87 0.52 +50 -1.99 7.99 0.18 -1.88 7.02 0.38 -1.89 4.18 0.05 CCGT -50 -0.01 -1.12 -0.08 0.21 1.10 -0.93 0.07 1.13 2.51 +50 0.41 1.08 -0.03 0.00 2.76 0.11 -0.14 2.13 0.57 OCGT -50 0.14 -0.93 -0.06 0.00 -1.05 -0.58 -0.02 0.67 -0.30 +50 0.09 0.73 -0.02 0.21 0.95 -0.25 1.97 6.27 1.82 Geothermal PP -50 -0.96 -1.24 1.14 -0.21 -0.47 0.27 0.01 -1.49 -0.28 +50 0.42 0.36 -0.08 1.25 0.04 -0.88 1.60 -3.77 -4.28 Hydro PP -50 -0.34 -0.71 0.05 -0.20 -1.03 -0.13 3.30 0.06 1.44 +50 0.22 0.52 -0.20 0.10 0.89 -0.28 1.66 0.66 -0.53 MHP -50 -0.43 -0.13 0.23 0.50 -2.20 -0.39 2.80 -1.05 -1.37 +50 -0.20 0.08 0.02 0.33 -0.35 -0.40 2.32 2.30 1.50 Wind PP -50 0.00 0.13 0.03 0.17 -0.11 -0.05 0.26 4.26 2.40 +50 -0.34 0.27 0.04 -0.02 0.33 -0.21 -0.05 1.84 0.06 WTE -50 -0.17 0.11 0.02 -0.06 -0.49 0.36 0.39 1.98 -0.51 +50 -0.08 0.50 0.01 -0.18 -1.63 0.47 1.60 -1.84 -4.00 Biomass PP -50 -0.11 0.00 0.02 -0.19 -0.60 -0.07 -1.85 0.40 0.63 +50 -0.12 0.21 0.00 -0.15 -0.56 -0.09 -1.33 1.13 0.27 PV -50 0.23 -0.10 -0.01 0.20 0.09 -0.32 -1.50 -1.74 -2.59 +50 -0.04 0.01 0.03 0.44 0.68 -0.94 -0.28 -0.34 0.48 Oil PP -50 0.10 -0.95 -0.01 0.34 -0.52 -0.72 0.26 3.85 2.30 +50 0.16 0.79 -0.05 0.46 0.28 -0.68 -0.05 4.23 2.65 Note: A – Changes (%) on CO2e from electricity generation in 2025, B – Changes (%) on electricity supply costs in 2025, and C – Changes (%) on renewables production share in 2025.

Fossil-fuelled power plants are more sensitive to changes in fuel prices, as shown in Table 6.10. A 50% reduction of fuel price in coal PP, CCGT, OCGT, and oil PP will reduce average generation costs by 8.5% to 14.2%, 9.58% to 14.24%, 1.72% to 4.51%, and 3.54% to 6.88%, respectively. In all scenarios, the decreased fossil fuel price will reduce renewables shares, but its effect on emissions is different, depending on the power plant type. The decreased coal price certainly will increase electricity production from emission-intensive coal PP, but the decreased gas price will reduce the emissions, since OCGT and CCGT make it economical to displace the electricity production by coal PP.

Table 6.10 Sensitivity analysis results for fuel price

Δ Fuel price (%) RUPTL 11% scenario 14% scenario

A B C A B C A B C

Coal PP -50 1.84 -14.20 -0.37 2.09 -11.28 -1.75 3.96 -8.50 0.29 +50 -2.63 13.27 0.24 -10.44 9.34 6.61 -21.31 8.47 7.47 CCGT -50 -9.81 -9.58 -0.82 -12.89 -13.73 -14.44 -11.75 -14.24 -15.90 +50 2.59 4.91 0.49 -1.04 2.04 2.40 1.00 1.96 0.73 OCGT -50 -0.21 -1.72 0.01 -0.42 -4.51 0.08 -1.95 -4.08 0.88 +50 -0.09 1.98 0.00 -0.42 0.97 0.08 0.18 3.24 -0.31 Geothermal PP -50 -0.32 0.09 0.03 0.21 0.78 -0.39 0.14 4.16 2.69 +50 -0.58 0.18 0.07 0.00 1.55 0.05 1.47 1.66 2.06 Hydro PP -50 0.45 -0.29 -0.05 -0.44 -2.36 0.22 0.26 -1.57 -2.48 +50 -0.03 0.08 0.00 -0.36 -0.98 0.21 0.72 2.43 -0.69 MHP -50 0.24 -0.13 -0.04 0.48 -1.63 -0.10 0.52 -1.13 -0.37 +50 -0.05 -0.03 0.00 -0.43 1.16 0.70 0.15 -1.97 -3.09 Wind PP -50 -0.05 -0.02 0.01 -0.10 0.63 0.85 1.67 -1.35 -0.21 +50 0.04 -0.26 0.00 -0.35 0.19 0.39 2.07 1.23 -1.05 WTE -50 0.01 -0.16 0.00 -0.13 -0.38 -0.06 1.70 2.82 2.37 +50 -0.04 -0.08 0.01 -0.21 -1.36 0.56 2.98 0.95 1.71 Biomass PP -50 0.01 -0.35 0.00 -0.17 -0.24 0.18 -1.37 2.05 1.26 +50 0.00 0.55 -0.01 -0.46 -2.85 0.12 -3.00 -0.28 -0.92 PV -50 0.09 0.11 0.03 0.19 -0.02 -0.19 1.89 1.76 1.92 +50 -0.13 0.17 0.04 -0.11 -0.99 0.41 0.17 0.49 0.05 Oil PP -50 -0.32 -3.54 -0.03 0.15 -6.47 -0.24 0.00 -6.88 -0.16 +50 -0.34 3.78 0.04 0.09 5.41 -0.33 0.09 8.62 2.64 Note: A – Changes (%) on CO2e from electricity generation in 2025, B – Changes (%) on electricity supply costs in 2025, and C – Changes (%) on renewables production share in 2025.

In contrast, renewable energy, especially geothermal PP, hydro PP, and wind PP, is more sensitive to CF changes, as shown in Table 6.11. As an example, under the emission reduction scenarios, a 10% reduction in geothermal PP’s CF will increase emissions by 2.51%, to 2.96%, while a similar reduction in wind PP will increase emissions by 1.53%, to 2.28%.

The renewables share is also decreased from the CF reduction of geothermal PP and wind PP except for the CF reduction of wind PP under the 14% scenario. In this case, wind PP is substituted by other renewables, especially geothermal PP. In contrast, a 10% reduction of biomass PP’s CF will reduce the emissions and electricity supply cost due to higher electricity production of CCGT, geothermal PP and hydro PP; and, on the other hand, lower electricity productions of coal PP and oil PP.

Table 6.11 Sensitivity analysis results for capacity factor (CF)

ΔCF (%) RUPTL 11% scenario 14% scenario

A B C A B C A B C

Coal PP -10 -0.23 0.93 0.02 -0.09 1.06 0.10 -0.23 -2.27 -3.42 +10 0.32 -0.59 -0.01 -0.40 0.06 0.32 -0.41 -0.57 0.38 CCGT -10 -0.22 0.01 0.07 -0.03 1.54 -0.10 -0.77 -3.98 -2.54 +10 -0.49 0.04 0.00 0.11 1.86 -0.44 -0.06 -3.05 -2.34 OCGT -10 0.20 -0.05 -0.04 0.02 0.85 0.05 1.55 0.21 2.08 +10 0.28 -0.17 -0.04 0.18 -0.16 -0.39 -0.10 -1.42 -0.05 Geothermal PP -10 0.05 0.06 -0.30 2.51 0.18 -2.54 2.96 0.66 -2.23 +10 -0.52 -0.31 0.38 -0.30 -2.11 0.04 1.64 1.04 2.62 Hydro PP -10 0.63 0.08 -0.40 0.28 2.01 -0.37 -1.13 2.33 0.58 +10 -0.44 -0.13 0.42 1.20 0.78 -0.52 1.99 3.23 2.07 MHP -10 0.21 0.31 -0.10 0.90 1.68 -1.22 0.56 2.43 -1.28 +10 -0.55 0.20 0.20 0.65 -0.58 -0.67 2.02 -0.38 -0.86 Wind PP -10 0.08 0.02 -0.14 1.53 -0.22 -1.81 2.28 2.93 1.36 +10 0.22 -0.39 0.08 0.06 -1.24 -0.84 -0.29 0.66 0.45 WTE -10 -0.24 0.24 0.03 0.00 0.19 0.09 1.73 0.24 -1.00 +10 0.00 0.12 0.00 -0.12 -0.14 -0.11 0.26 0.91 2.49 Biomass PP -10 0.24 -0.07 -0.08 0.41 -0.47 -0.09 -3.35 -1.67 -0.84 +10 -0.14 0.16 0.02 0.43 -0.44 -0.22 -1.69 0.32 1.31 PV -10 0.26 0.07 -0.13 0.03 2.00 -0.22 -0.07 -2.37 -2.72 +10 -0.35 -0.25 0.14 -0.63 -0.92 1.09 -1.75 -1.43 -2.28 Oil PP -10 -0.23 0.01 0.04 -0.24 -0.45 0.20 -1.09 1.45 0.28 +10 -0.25 0.05 0.05 -0.11 -0.31 0.04 -2.95 0.78 -1.40 Note: A – Changes (%) on CO2e from electricity generation in 2025, B – Changes (%) on electricity supply costs in 2025, and C – Changes (%) on renewables production share in 2025.

6.5 Conclusions

The motivation for this study comes from the need to understand the penetration rate of renewable energy technologies in the future power plant expansion mix under three scenarios, i.e. official power plant expansion plan stated in the PLN’s electricity supply business plan (RUPTL) from 2019 to 2028, and the two alternative scenarios related to 11%

and 14% emission reduction targets of total emissions in the electricity sector. The study developed a linear optimisation problem enhanced with agent-based modelling (ABM), which has the flexibility to integrate economic input-output (IO) analysis and social behaviour analysis for the adoption of clean energy technologies.

The results of this study showed that the power plant expansions proposed in RUPTL from 2019 to 2028 cannot meet the targets for renewable energy production and emission

reductions by 2028. RUPTL expects the significant contribution of coal PP and CCGT, and consequently, emissions growth from 2018 to 2024 will be higher than current emissions growth. In contrast, optimal power plant expansions under the emission reduction scenarios will reach both renewable energy and emission reduction targets. To achieve the targets, the government should prepare a policy and research agenda to encourage rapid investment in wind energy, hydro-power plants, and geothermal energy. The emission reduction scenarios may cause lower regional costs of electricity productions in some regions, but eventually, the emission reduction scenarios have higher average costs of electricity productions.

This ABM-oriented study uses an algorithm combining linear optimisation and a heuristic approach. Linear optimisation aims to optimise yearly production costs of existing power plants and, if no optimal solution is found, then the heuristic approach adds new power plants into the linear optimisation problem. Though the algorithm may reflect a situation that maximises the economic returns of power plants investments, the algorithm may not produce a least-cost solution in the long term. Other issues to be addressed by future studies is utilising the feature of ABM as a social behaviour model. Our ABM simply assumes new power plants that would be constructed by IPP but, in reality, PLN determines the ownership of proposed new power plants by considering PLN’s investment portfolio. Therefore, future studies should revise our assumptions by modelling PLN’s investment behaviours. The behaviour modelling indeed should be extended to households that have a significant role in distributed renewable investments in developed countries. Another potential improvement is to add a sub-model that analyses seasonal correlations of renewable electricity production with other renewable energy production and electricity demand values to ensure the reliability of the recommended electricity production mix (Suomalainen et al., 2015). Moreover, changing our national IO table into regional IO tables proposed by IndoLab, an Indonesia’s Industrial Ecology Virtual Laboratory (IEALab) (Faturay et al., 2017), will greatly improve our IO analysis, especially to derive time-dynamic and higher resolution analysis. Future studies should also use different regional costs of investments, operations and maintenance of power plants.

CONCLUSIONS