## Monte Carlo Simulation in Project Finance Modeling – free demo model

**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)

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 |
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0 |
1 |
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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 |

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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:

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Stochastic Model |
Net Profit |
pre-Tax |
Feed-in |
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Equity Returns |
Project Returns |
After Tax |
WACC |
Tariff |
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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 |

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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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