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Monte Carlo Simulation in Project Finance Modeling – free demo model

December 26th, 2016 Posted in Monte Carlo Simulation

Monte Carlo Simulation in Project Finance Modeling – free demo model

Your energy technology selection and project finance modeling expert is pleased to announce that you can now download a free demo model for Stochastic analysis of the risks in a biomass direct combustion power plant. (Other MCS models are available for renewable energy, conventional, fossil, nuclear power generation technologies)

A free demo model linked to a Monte Carlo Simulation add-in that can be downloaded with this link: (Please use Google search to get a copy of the excel file below)

montecarlito_v1_10

to run the stochastic analysis of the project risks for the following variables:

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11.78 MW Configuration Monte Carlo Simulation inputs
0 1
Plant Variables 65,952 Deterministic Stochastic Used
Current Value Value Min Max Value In Model
Electricity Tariff 8.728 8.728 90.00% 110.00% 9.176 9.176
Plant Availability Factor 97.10% 94.52% 90.00% 110.00% 97.10% 97.10%
Fuel Heating Value 5,368 5,198 90.00% 110.00% 5,368 5,368
Debt Ratio 70% 70% 90.00% 110.00% 73% 73%
Plant Capacity per Unit 11.78 12.00 90.00% 110.00% 11.78 11.78
O&M Cost (Opex) – variable O&M 26.90 27.99 90.00% 110.00% 26.90 26.90
O&M Cost (Opex) – fixed O&M 5,159.29 5,094.28 90.00% 110.00% 5,159.29 5,159.29
O&M Cost (Opex) – fixed G&A 9.69 10.00 90.00% 110.00% 9.69 9.69
Cost of Fuel 1.307 1.299 90.00% 110.00% 1.307 1.307
Plant Heat Rate 12,739 12,186 90.00% 110.00% 12,739 12,739
Exchange Rate 40.41 43.00 90.00% 110.00% 40.41 40.41
Capital Cost (Capex) 1,966.60 1,935.98 90.00% 110.00% 1,966.60 1,966.60

======

To run the Monte Carlo simulation, you load up first the add-in link, and then run the project finance model:

adv-biomass-direct-combustion-model3_mcs

Then run the Macro 1 (press ctrl + d) to activate the Monte Carlo simulation add-in.

The results of the simulation is found in the Sensitivity worksheet:

=====

Stochastic Model Net Profit pre-Tax Feed-in
Equity Returns Project Returns After Tax WACC Tariff
press ctrl + W to run NPV IRR PAYBACK NPV IRR PAYBACK Million PhP % PhP/kWh
1,000 65,952 18.03% 6.32 (220,171) 13.92% 5.95 1,201 12.04% 8.728
Mean 6,853 16.72% 7.76 -301,632 13.16% 6.35 1,173 11.61% 8.728
Standard error 5,814 0.14% 0.08 6,348 0.07% 0.03 7 0.05% 0.000
Median -1,001 16.41% 7.22 -299,681 13.12% 6.26 1,183 11.51% 8.728
Standard deviation 183,855 4.29% 2.61 200,735 2.17% 0.93 222 1.43% 0.000
Variance 33,802,589,673 0.18% 6.81 40,294,436,447 0.05% 0.87 49,448 0.02% 0.000
Skewness 0.105 0.223 0.477 -0.045 -0.013 0.721 -0.266 0.223 -1.000
Kurtosis 2.410 2.603 2.030 2.533 2.563 3.639 2.704 2.603 1.000
Expected value = 6,853 16.72% 7.76 -301,632 13.16% 6.35 1,173 11.61% 8.728
The standard deviation*1.96 = 360,355 8.41% 5.12 393,440 4.26% 1.83 436 2.81% 0.000
95% of all outcomes, max = 367,208 25.13% 12.88 91,808 17.42% 8.18 1,609 14.41% 8.728
95% of all outcomes, min = -353,503 8.31% 2.65 -695,072 8.90% 4.52 737 8.80% 8.728

=====

The simulation  above shows the results after 1000 random trials (+/- 10% on the deterministic value), the mean, standard error, mean, standard deviation, variance, Skewness, Kurtosis,  expected value (mean),  standard deviation x 1.96, and the  maximum and minimum outcomes at 95% confidence level.

If the means of IRR, NPV, PAYBACK, net profit after tax, pre-tax WACC and first year tariff are lower than the deterministic value, then there is a significant project risk of not achieving that deterministic target. This would require extra effort to determine accurately this target (assumption) as having a poor estimate would introduce significant project risks.

When you set the number of trials to bold font, the add-in program will also show the distribution curve of each of the modeled variable so you can examine in more detail the attendant project risks.

Email me for more information and ordering details:

energydataexpert@gmail.com

Cheers

 

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