Holiday Sale this December 2017 on Deterministic (fixed inputs) and Stochastic (random Monte Carlo Simulation) Power Plant Project Finance Models

December 4th, 2017 Posted in project finance models

Holiday Sale this December 2017 on Deterministic (fixed inputs) and Stochastic (random Monte Carlo Simulation) Power Plant Project Finance Models

Flash News:

There is great enthusiasm for the new models as 3 models have been purchased:

Dec 6 – Deterministic (fixed inputs) ADV NatGas Combined Cycle Gas Turbine (CCGT) – USD100

Dec 9 – Deterministic (fixed inputs) ADV NatGas Combined Cycle Gas Turbine (CCGT) – USD100

Dec 12 – Dual-capable Deterministic (fixed inputs) and Stochastic (Monte Carlo simulation random inputs) ADV Mini-hydro (run-of-river) – USD200 (buy one model to do both deterministic and stochastic modeling).

The client would use the mini-hydro model to study the following sensitivities:

1) 2 x 4.00 MW design capacity at 50% availability, 92% load factor and 2% parasitic load for a net capacity factor of 45.08%.  All-in capital cost is 3,523 $/kW while fixed O&M cost is 16.96 ‘000$/kW/year (excluding G&A of 20.00 ‘000$/year) and variable O&M cost is 2.40 $/MWh.

2) Vary the unit capacity of 4.00 MW to minimum and maximum value based on minimum river flow and maximum river flow (from hydrologic study) by using the power equation to predict power output given head (m net head) and river flow (m/s discharge rate).

3) Construction period of 2 years (24 months) with 0 year delay and 1 year (24 months) delay. This will increase construction cost (and thus all-in capital cost due to inflation) as well as first year tariff (feed-in-tariff) needed to meet equity and project returns.

Be part of my growing clientele of project finance modelers and develop your alternative cases or configuration and optimize your business concept and write your own project feasibility studies.

The following models may be downloaded for only USD100 for deterministic (uses fixed inputs) and USD200 for stochastic (uses random inputs using Monte Carlo Simulation) for the all clients this December 1-30, 2017.

To avail of the discount this December, please email me to confirm your order and model type and remit payment via PayPal using also the same email address and PayPal account:

You may also email me using the email address above to confirm your order and remit payment via bank/wire transfer to the account below:


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


2) Account Name:

Marcial T. Ocampo

3) Account Number:

Current Account = 0205-5062-41



Once I confirm receipt of payment, I will then email you the deterministic model (cost USD100) and the stochastic (Monte Carlo Simulation) model (cost USD200) should you desire it.

The models for renewable, conventional, fossil, nuclear, energy storage, and combined heat and power (CHP) project finance models are based on a single template so that you can prioritize which power generation technology to apply in a given application for more detailed design and economic study.

Our company (OMT Energy Enterprises) can also provide customization services to provide you with power plant project finance models with fixed inputs (deterministic models) as well as random inputs (stochastic models). If you have an existing model which you want to be audited or upgraded to have stochastic modeling capability, you may also avail of our services at an hourly rate of USD200 per hour for a maximum of 5 hours of charge for customization services.

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 below 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 order directly and immediately the deterministic models for USD100, please click the links below. But if you want the stochastic (Monte Carlo Simulation) models for USD200, you have to order it via email as explained above.


1) process heat (steam) and power

2) bagasse, rice husk or wood waste fired boiler steam turbine generator

3) gasification (thermal conversion in high temperature without oxygen or air)

4) integrated gasification combined cycle (IGCC) technology

5) waste-to-energy (WTE) technology for municipal solid waste (MSW) disposal and treatment

6) waste-to-energy (WTE) pyrolysis technology

7) run-of-river (mini-hydro) power plant

8) concentrating solar power (CSP) 400 MW

9) solar PV technology 1 MW Chinese

10) solar PV technology 25 MW European and Non-Chinese (Korean, Japanese, US)

11) includes 81 wind turbine power curves from onshore WTG manufacturers

12) includes 81 wind turbine power curves from offshore WTG manufacturers

13) ocean thermal energy conversion (OTEC) technology 10 MW

14) ocean thermal energy conversion (OTEC) technology 50 MW

14) ocean current and tidal technology (30 MW) – this is similar to an air wind turbine but under water with a turbine propeller (Taiwan has an operating prototype in Kuroshio and PNOC-EC is venturing into ocean current at the Tablas Strait).

(to follow – you can order it via email – new release)


1) geothermal power plant 100 MW

2) large hydro power plant 500 MW

3) subcritical circulating fluidized bed (CFB) technology 50 MW

4) subcritical circulating fluidized bed (CFB) technology 135 MW

5) subcritical pulverized coal (PC) technology 400 MW

6) supercritical pulverized coal (PC) technology 500 MW

7) ultra-supercritical pulverized coal (PC) technology 650 MW

8) diesel-fueled genset (compression ignition engine) technology 50 MW

9) fuel oil (bunker oil) fired genset (compression ignition engine) technology 100 MW

10) fuel oil (bunker oil) fired oil thermal technology 600 MW

11) natural gas combined cycle gas turbine (CCGT) 500 MW

12) natural gas simple cycle (open cycle) gas turbine (OCGT) 70 MW

13) natural gas thermal 200 MW

14) petroleum coke (petcoke) fired subcritical thermal 220 MW

15) nuclear (uranium) pressurized heavy water reactor (PHWR) technology 1330 MW


1) combined heat and power (CHP) circulating fluidized bed (CFB) technology 50 MW

2) diesel genset (diesel, gas oil) and waste heat recovery boiler 3 MW

3) fuel oil (bunker) genset and waste heat recovery boiler 3 MW

4) gasoline genset (gasoline, land fill gas) and waste heat recovery boiler 3 MW

5) simple cycle GT (propane, LPG) and waste heat recovery boiler 3 MW (e.g. Capstone)

6) simple cycle GT (natural gas, land fill gas) and waste heat recovery boiler 3 MW (e.g. Capstone)


Your energy technology selection and project finance modeling expert


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