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

January 4th, 2018 No Comments   Posted in financial models

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:

MonteCarlito_v1_10

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

December 26th, 2016 No Comments   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:

======

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

March 29th, 2016 No Comments   Posted in wind energy and power

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 »

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

March 29th, 2016 No Comments   Posted in wind energy and power

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)

February 11th, 2015 No Comments   Posted in wind energy and power

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

February 6th, 2015 No Comments   Posted in Monte Carlo Simulation

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

February 4th, 2015 No Comments   Posted in wind energy and power

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 »