## Posts Tagged ‘Monte Carlo Simulation’

## Biomass Direct Combustion (steam boiler + turbine) Project Finance Models (Deterministic and Stochastic)

**Biomass Direct Combustion (steam boiler + turbine) Project Finance Models** **(Deterministic and Stochastic)**

Your energy technology selection expert is pleased to announce that deterministic (fixed inputs) and stochastic (random inputs from Monte Carlo Simulation) are now available for all power generation technologies (renewable energy such as biomass, solar PV and CSP, wind, mini-hydro, ocean thermal and ocean tidal/current, and conventional energy such as large hydro, geothermal, and fossil energy such as oil diesel and oil thermal, natural gas simple cycle and combined cycle, coal thermal and clean coal technologies, nuclear energy, and energy storage and waste heat recovery and combined heat and power technologies).

In the case of biomass direct combustion (steam boiler + turbine), the following samples may be purchased at 50% discount.

You may download the following samples to try the advanced features of using fixed inputs and random inputs in order to manage your project risks:

Deterministic (fixed inputs) model: (USD 700):

ADV Biomass Direct Combustion Model3 (demo) – in PHP

ADV Biomass Direct Combustion Model3 (demo) (USD)

Stochastic (random inputs from Monte Carlo Simulation) model (USD 1400):

ADV Biomass Direct Combustion Model3_MCS (demo) – in PHP

ADV Biomass Direct Combustion Model3_MCS (demo) (USD)

Before you can run the MCS model, you need to download first the Monte Carlo Simulation add-in and run it before running the above MCS model:

The model inputs consist of the fixed inputs (independent variables) plus a random component as shown below (based on +/- 10% range, which you can edit in the Sensitivity worksheet):

1) Plant availability factor (% of time) = 94.52% x ( 90% + (110% – 90%) * RAND() )

2) Fuel heating value (GHV) = 5,198 Btu/lb x ( 90% + (110% – 90%) * RAND() )

3) Plant capacity per unit = 12.00 MW/unit x ( 90% + (110% – 90%) * RAND() )

4) Variable O&M cost (at 5.26 $/MWh) = 30.05 $000/MW/year x ( 90% + (110% – 90%) * RAND() )

5) Fixed O&M cost (at 105.63 $/kW/year) = 1,227.64 $000/unit/year x ( 90% + (110% – 90%) * RAND() )

6) Fixed G&A cost = 10.00 $000/year x ( 90% + (110% – 90%) * RAND() )

7) Cost of fuel = 1.299 PHP/kg x ( 90% + (110% – 90%) * RAND() )

8) Plant heat rate = 12,186 Btu/kWh x ( 90% + (110% – 90%) * RAND() )

9) Exchange rate = 43.00 PHP/USD x ( 90% + (110% – 90%) * RAND() )

10) Capital cost = 1,935 $/kW x ( 90% + (110% – 90%) * RAND() )

The dependent variables that will be simulated using Monte Carlo Simulation and which a distribution curve (when you make bold font the number of random trials) may be generated are as follows:

1) Equity Returns (NPV, IRR, PAYBACK) at 30% equity, 70% debt

2) Project Returns (NPV, IRR, PAYBACK) at 100% equity, 0% debt

3) Net Profit After Tax

4) Pre-Tax WACC

5) Electricity Tariff (Feed-in-Tariff)

The models are in Philippine Pesos (PHP) and may be converted to any foreign currency by inputting the appropriate exchange rate (e.g. 1 USD = 1.0000 USD; 1 USD = 50.000 PHP, 1 USD = 3.800 MYR, etc.). Then do a global replacement in all worksheets of ‘PHP’ with ‘XXX’, where ‘XXX’ is the foreign currency of the model.

To purchase, email me at:

energydataexpert@gmail.com

You may pay using PayPal:

energydataexpert@gmail.com

or via bank/wire transfer:

====================

1) Name of Bank Branch & Address:

The Bank of the Philippine Islands (BPI)

Pasig Ortigas Branch

G/F Benpres Building, Exchange Road corner Meralco Avenue

Ortigas Center, PASIG CITY 1605

METRO MANILA, PHILIPPINES

2) Account Name:

Marcial T. Ocampo

3) Account Number:

Current Account = 0205-5062-41

4) SWIFT ID Number = BOPIPHMM

====================

Once I confirm with PayPal or with my BPI current account that the payment has been made, I will then email you the real (un-locked) model to replace the demo model you have downloaded.

Hurry and order now, this offer is only good until January 31, 2018.

Regards,

Your Energy Technology Selection and Project Finance Expert

## 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:

======

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 (demo)

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

## Wind Energy Financial Model with Stochastic (Probabilistic) Wind Turbine Simulator for Annual Power Output and Capacity Factor

**Wind Energy Financial Model with Stochastic (Probabilistic) Wind Turbine Simulator for Annual Power Output and Capacity Factor**

Wind Turbine Generator (WTG) is the current darling of the renewable energy power generation industry.

It is clean, generally available, and cost-effective. It’s power output, however, is very variable, ever changing by the hour with time.

Now with the instantaneous wind speed as a function of the average speed +/- the positive and negative deviation multiplied by a random fraction, the probable wind speed and thus the power output can be simulated, and when aggregated in 24 x 365 hours in a year, the annual energy output and annual capacity factor is determined.

And using statistical analysis, the expected value (mean), standard error, median, standard deviation, variance, skewness and Kurtosis are calculated for both the annual energy output and annual capacity factor. More »

Tags: Bergey, Bonus, capacity factor, Carter, Dewend, Fuhrlaender, Gamesa Eolica, GE Wind, Jacobs, Jacobs-additions, Lagerwey, MCS, Monte Carlo Simulation, NEG Micon, Nordex, Norwin, power output, Repower, simulator, Suzlon, TMA, Turbowinds Inland, Vestas, wind energy, wind power, wind turbine annual generation, wind turbine capacity factor, wind turbine simulator

## Integrated Wind Prospecting, Wind Resource Assessment, Annual Power Generation and Capacity Factor, and Wind Project Finance Model with Monte Carlo Simulation

**Integrated Wind Prospecting, Wind Resource Assessment, Annual Power Generation and Capacity Factor, and Wind Project Finance Model with Monte Carlo Simulation**

Yes, your energy technology expert has done it again.

Version 3 has been released that integrates all the steps needed in fully developing your wind energy project.

It combines the data entry of the wind velocity profile of the prospective wind farm site (from wind mast anemometer monitoring or from 3-TIER, NREL wind profile database), interpolation of daily and hourly wind speed (up to 15-minute pulse if needed by the TRANSCO / GRID operator), look-up tables for the various wind turbine generator manufacturers, calculation of annual power generation and capacity factor, overnight capital cost of wind turbine and its fixed and variable O&M costs, and project finance modeling (with option for both deterministic and stochastic modeling using Monte Carlo Simulation). More »

## Integrated Wind Prospecting, Wind Resource Assessment, Annual Power Generation and Capacity Factor, and Wind Project Finance Model with Monte Carlo Simulation (Ver. 4)

**Integrated Wind Prospecting, Wind Resource Assessment, Annual Power Generation and Capacity Factor, and Wind Project Finance Model with Monte Carlo Simulation (Ver. 4)**

Yes, your energy technology expert has done it again.

Version 4 has been released that integrates all the steps needed in fully developing your wind energy project.

It combines the data entry of the wind velocity profile of the prospective wind farm site (from wind mast anemometer monitoring or from 3-TIER, NREL wind profile database), interpolation of daily and hourly wind speed (even up to 15-minute pulse if needed by the TRANSCO / GRID operator), look-up tables for the various wind turbine generator manufacturers, calculation of annual power generation and capacity factor, overnight capital cost of wind turbine and its fixed and variable O&M costs, and project finance modeling (deterministic with option for both deterministic and stochastic modeling using Monte Carlo Simulation). More »

## Performance of Wind Turbine Generators from MONTE CARLO SIMULATION of Wind Speed

**Performance of Wind Turbine Generators from MONTE CARLO SIMULATION of Wind Speed**

Be a wind energy and power generation expert.

Follow the methodology and explanation below. Get hold of the advanced tools too, namely:

1) Wind energy resource monitoring (from wind anemometer measurements and data logger or wind map and resource providers such as 3-TIER, NREL, etc.)

2) Monte Carlo Simulation of wind speed (wind speed = average wind speed +/- standard deviation)

3) Annual power generation using power curve of wind turbine model and calculation of capacity factor

4) Overnight capital cost per kW of major wind turbine manufacturers

5) Project finance modeling (deterministic and stochastic with Monte Carlo Simulation) More »

## Stochastic (Probabilistic) Wind Turbine Simulator for Annual Power Output and Capacity Factor

**Stochastic (Probabilistic) Wind Turbine Simulator for Annual Power Output and Capacity Factor**

Wind Turbine Generator (WTG) is the current darling of the renewable energy power generation industry.

It is clean, generally available, and cost-effective. It’s power output, however, is very variable, ever changing by the hour with time.

Now with the instantaneous wind speed as a function of the average speed +/- the positive and negative deviation multiplied by a random fraction, the probable wind speed and thus the power output can be simulated, and when aggregated in 24 x 365 hours in a year, the annual energy output and annual capacity factor is determined.

And using statistical analysis, the expected value (mean), standard error, median, standard deviation, variance, skewness and Kurtosis are calculated for both the annual energy output and annual capacity factor. More »

Tags: Bergey, Bonus, capacity factor, Carter, Dewend, Fuhrlaender, Gamesa Eolica, GE Wind, Jacobs, Jacobs-additions, Lagerwey, MCS, Monte Carlo Simulation, NEG Micon, Nordex, Norwin, power output, Repower, simulator, Suzlon, TMA, Turbowinds Inland, Vestas, wind energy, wind power, wind turbine annual generation, wind turbine capacity factor, wind turbine simulator