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

March 29th, 2016 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.

 

Turbine Model and kW rating GE900 900 GE1.5 S 1500 GE1.5 SL 1500
MWH % CF MWH % CF MWH % CF
1000 2,108 26.74 3,878 29.51 4,408 33.55
Mean 2,126 26.96 3,911 29.76 4,446 33.83
Standard error 0 0.00 0 0.00 0 0.00
Median 2,126 26.97 3,911 29.77 4,446 33.83
Standard deviation 8 0.10 14 0.11 13 0.10
Variance 65 0.01 197 0.01 178 0.01
Skewness 0.0079 0.0079 0.0105 0.0105 0.0123 0.0123
Kurtosis 2.7407 2.7407 2.7155 2.7155 2.7468 2.7468
Expected value = 2,126 26.96 3,911 29.76 4,446 33.83
The standard deviation*1.96 = 16 0.20 27 0.21 26 0.20
95% of all outcomes, max = 2,142 27.16 3,938 29.97 4,472 34.03
95% of all outcomes, min = 2,110 26.76 3,883 29.55 4,420 33.63

 

To enable your technical team to optimize the selection of the equipment manufacturer (cost per kW, fixed and variable O&M, WTG power curve) and site selection (wind velocity profile given location and wind mast elevation), the following sets of excel models would be most helpful in your technical and economic analysis:

1) DETERMINISTIC  or PROBABILISTIC model that includes MONTE CARLO SIMULATION for equity and project returns (IRR, NPV and PAYBACK), net income after tax discounted at pre-tax WACC, pre-tax WACC, and electricity tariff (feed-in-tariff). A histogram may also be prepared by the MONTE CARLO simulation.

Here are two demo models for onshore wind and offshore wind farms:

ADV Wind Onshore Model3a_MCS-copy – Monte Carlo Simulation

14.62 MW Configuration Monte Carlo Simulation inputs
0
Plant Variables 11,612 Deterministic
Current Value Value Min Max
Electricity Tariff 5.207 5.335 90.00% 110.00%
Plant Availability Factor 38.04% 36.90% 90.00% 110.00%
Fuel Heating Value
Debt Ratio 70% 70% 90.00% 110.00%
Plant Capacity per Unit 1.46 1.50 90.00% 110.00%
O&M Cost (Opex) – variable O&M 5.20 4.85 90.00% 110.00%
O&M Cost (Opex) – fixed O&M 15.41 16.28 90.00% 110.00%
O&M Cost (Opex) – fixed G&A 205.86 200.00 90.00% 110.00%
Cost of Fuel
Plant Heat Rate
Exchange Rate 39.68 43.00 90.00% 110.00%
Capital Cost (Capex) 1,041.72 997.36 90.00% 110.00%

 

Stochastic Model Net Profit pre-Tax Feed-in
Equity Returns After Tax WACC Tariff
press ctrl + W to run NPV IRR PAYBACK Million PhP % PhP/kWh
1,000 (120,040) 13.00% 10.55 628 10.45% 5.388
Mean 2,992 16.58% 8.92 751 11.42% 5.082
Standard error 3,248 0.09% 0.05 4 0.03% 0.009
Median -2,466 16.34% 9.13 752 11.31% 5.084
Standard deviation 102,704 2.90% 1.55 116 1.01% 0.296
Variance 10,548,145,085 0.08% 2.40 13,364 0.01% 0.088
Skewness 0.292 0.418 -0.339 0.011 0.562 0.015
Kurtosis 2.864 2.969 2.434 2.680 3.093 1.780
Expected value = 2,992 16.58% 8.92 751 11.42% 5.082
The standard deviation*1.96 = 201,300 5.68% 3.04 227 1.97% 0.581
95% of all outcomes, max = 204,292 22.26% 11.96 977 13.39% 5.662
95% of all outcomes, min = -198,308 10.90% 5.88 524 9.45% 4.501

ADV Wind Offshore Model3a_MCS-copy – Monte Carlo Simulation

28.1 MW Configuration Monte Carlo Simulation inputs
0
Plant Variables (83,767) Deterministic
Current Value Value Min Max
Electricity Tariff 14.167 13.276 90.00% 110.00%
Plant Availability Factor 39.13% 40.15% 90.00% 110.00%
Fuel Heating Value
Debt Ratio 70% 70% 90.00% 110.00%
Plant Capacity per Unit 1.41 1.50 90.00% 110.00%
O&M Cost (Opex) – variable O&M 7.04 7.48 90.00% 110.00%
O&M Cost (Opex) – fixed O&M 53.08 58.65 90.00% 110.00%
O&M Cost (Opex) – fixed G&A 26.24 25.00 90.00% 110.00%
Cost of Fuel
Plant Heat Rate
Exchange Rate 41.29 43.00 90.00% 110.00%
Capital Cost (Capex) 3,563.79 3,281.78 90.00% 110.00%

 

Stochastic Model Net Profit pre-Tax Feed-in
Equity Returns After Tax WACC Tariff
press ctrl + W to run NPV IRR PAYBACK Million PhP % PhP/kWh
1,000 (83,767) 16.00% 8.84 5,010 11.18% 14.167
Mean -2,405 16.51% 8.73 5,203 11.35% 12.950
Standard error 15,883 0.08% 0.05 21 0.03% 0.024
Median -39,697 16.24% 8.96 5,201 11.26% 12.969
Standard deviation 502,278 2.50% 1.56 661 0.86% 0.749
Variance 252,283,550,966 0.06% 2.43 436,822 0.01% 0.560
Skewness 0.244 0.443 -0.257 -0.001 0.510 0.007
Kurtosis 2.835 2.965 2.323 2.634 3.073 1.786
Expected value = -2,405 16.51% 8.73 5,203 11.35% 12.950
The standard deviation*1.96 = 984,466 4.90% 3.05 1,295 1.69% 1.467
95% of all outcomes, max = 982,060 21.41% 11.79 6,499 13.04% 14.417
95% of all outcomes, min = -986,871 11.61% 5.68 3,908 9.65% 11.483

 

2) WIND SIMULATOR (hourly simulation of wind speed for a given location using the wind velocity profile from wind resource tools such as 3-TIER or NREL, power curves to calculate wind turbine power output, annual MWH generation, annual capacity factor CF).

Alternatively, a 15-minute simulation may be prepared if the local transmission/grid operator would require a grid impact study (system impact study) to identify the system/grid requirements to be able to handle the variable power output of the wind turbines.

Wind Energy Resource Assessment Tool_3TIER Prediction – Copy

3) Wind Energy Resource Assessment Tool Anemometer Measurement Log (allows you to enter hourly anemometer readings of wind speed vs. time, and use power curves to calculate wind turbine power output, annual MWH generation, and capacity factor CF).

Using the average wind speed as correction factor, hourly wind speed readings of a similar location may be synthesized if a reference location with complete data is known and may be used to extrapolate the anemometer readings of another location with limited wind data with a known average speed.

Wind Energy Resource Assessment Tool_Anemometer Measurement – Copy

4) Wind Turbine Price List (the overnight capital cost of a number of known wind turbines to get a feel of the relative costs of the various wind turbine models, and given the power curves, helps you optimize the selection of model size (MW output) and equipment manufacturer (turbine model).

Wind Turbine Price List – Copy

5) Wind Turbine Power Curves with customized MONTE CARLO SIMULATION of wind speed, annual power output and annual capacity factor for the following WTG models:

  • Bergey
  • Bonus
  • Carter
  • Dewind
  • Fuhrlaender
  • Gamesa Eolica
  • GE Wind
  • Jacobs
  • Jacobs-additions
  • Lagerwey
  • NEG Micon
  • Nordex
  • Norwin
  • Repower
  • Suzlon
  • TMA
  • Turbowinds Inland
  • Vestas

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To purchase any project finance model with Wind turbine GeneratorTemplate, please email me

energydataexpert@gmail.com

Purchase any model for USD 800 for the complete package (wind project finance model, wind turbine power curves, wind simulator, wind turbine costs and MCS add-in program) and remit via PayPal (use my gmail account above) or via bank / wire transfer (BPI current account which I will email you once you confirm your order).

If you want it customized with your own specific data and additional rows and columns for income, expense and balance sheet accounts, add another USD 500 for 2-hours of additional work.

Cheers,

Energy technology selection expert

 

 

 

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