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This page addresses issues that are specific to wind production, wind resource analysis and wind financing. Wind resource analysis deals with some tricky wind resource subjects that I think are difficult including understanding the difference between measurements of production using a P90 ten year estimate and a P90 one year (you could substitute P90, P95, P75 etc.). In the first section titled "Wind Resource Analysis" I have put together a case study from an old credit report that had one and ten year production estimates for different projects with different probabilities. I have also compiled an analysis of the variability in wind after projects are operating relative to before they are operating. My key theme is that standard deviations underlying the ten year P90 are very subjective where standard deviation in things like wake effect, availability, turbulence, correlation to historic site, wind shear, losses and other factors. One of the main tools in analysis of wind production with different probabilities is use the NORMINV function in excel to understand data in wind studies. After the wind resource analysis, the effects of wind probabilities on debt sizing are evaluated in the second lesson set. The Final lesson set involves computing a wind project finance model that includes a partnership with a flip structure and a DRO.


Lesson Set 1: Wind Resource Analysis


The first lesson set addresses computing wind capacity factors from wind data and power curves as well as causes of uncertainty that are not "mean revering". The videos and files also cover a subject that I find one of the most difficult issues to explain -- i.e. the difference between one year P90 and ten-year or twenty-year P90. I have tried to explain this with file named "Wind Study" listed below. This file uses a nice old financial analysis report that listed P50, P75, P90 and P95 for a series of different wind farms. It also reported the production statistics on an 1-year basis and on a 10-year basis. Using the P90 etc. production statistics you can back out the standard deviation that is related to wind variation only as well as the variation that is only related to permanent effects. I have also compiled an analysis of the variability in wind after projects are operating relative to before they are operating. My key theme is that standard deviations underlying the ten year P90 are subjective. I demonstrate how to use the NORMINV function in excel to understand data in wind studies.


Videos associated with Lesson Set 1: Wind Resource Analysis


Subject

Excel File

Video Link





Working with P50 and P90 One Year and Ten Year

Wind Analysis.xlsm

https://www.youtube.com/watch?v=WXP6x74QmHE
Isolating Permanent Effects and Wind Movements

Wind Analysis.xlsm

https://www.youtube.com/watch?v=hXwlTeSpjuw
Different Production Constraints and

P90, P99 DSCR Constraint

https://www.youtube.com/watch?v=UAMed97wCRk
Debt Sizing with P99, P90 and P50

P90, P99 Debt Sizing

https://www.youtube.com/watch?v=47XBFymVzCQ
Acquiring EIA Data on Wind Forecasts

EIA Database

https://www.youtube.com/watch?v=NUdYzd1rEOQ
Wind Data Analysis with EIA Data - Part 1

Wind Generation Database

https://www.youtube.com/watch?v=PoAvXzsrZqQ
Wind Data Analysis with EIA Data - Part 2

Wind Generation Database

https://www.youtube.com/watch?v=Hhx3trJMZck
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Files associated with Lesson Set 1: Wind Resource Analysis


In this section I describe how to compute the P90, P99 etc. from power curves and historic wind data. This involves compiling hourly wind data and matching the wind data against power curves. In addition, actual wind variation is evaluated for a number of wind farms using the Generation Database below. Computing the P90 or P99 etc. can be derived from hourly wind data and power curves. You can use the LOOKUP function to evaluate the amount of power at different wind speeds and the NORINV function to evaluate probability distributions. The hourly distribution of wind can also be computed from a Wiebull function as illustrated in one of the files below.






EIA Updating Instructions for Generation Databases that Evaluate the Variation in Production


For solar, wind and hydro I have included files that include historic monthly and more importantly annual production for U.S. plants. This is included so you can see the variation in year to year production. You can compare for example the P99 to the annual production to see how much of a DSCR buffer is necessary relative to the average production. This can then be compared to the P90, P75 etc. that is estimated by consultants. The analysis comes from a database named EIA 923 that records month by month production for electricity plants in the U.S. since 1970.

1. Find the file named EIA 923 database. This is an enormous file with data on the generation from each plant in the U.S. from 1970 to the current year. It contains month by month generation by unit. A lot of work was done on this file to get consistent fuel category names and plant names and codes accross years. I have copied the file into a google drive with the following link:
https://drive.google.com/open?id=0B5r4OP_dQLe8Y1JfaUlXZldveGM


2. Go to the EIA 923 database page in the EIA website and find the latest dataset (you can google EIA 923). Open the file and copy it into the EIA 923 database file. If the file is for a new year, then copy the prior year and create a new sheet (e.g. 2018) with ALT,E,M. Make sure the merged cells are removed from the EIA file when you copy a new file. Make sure that the formulas in columns A-C of the file are copied to the bottom of the sheet.

3. Go to the Summary Page of the EIA 923 database and update formulas. This involves copying formulas to the right. As the EIA 923 file is so big, you should do this with the calculation set to manual. After you have copied the formulas, press the macro titled "FULL CALCULATE"


4. Create an intermediate summary page that is not as giagantic as the the EIA 923 database. Copy and paste special the yellow area in the summary page of the EIA 923 file to a new file. Find the databases for solar, wind and hydro. In the google drive, these files are in Chapter 5, under the Renewable section and then in the Resource Studies Folder. Copy and paste special as values the Summary page to each of the files.


5. Find the databases for solar, wind and hydro. In the google drive, these files are in Chapter 5, under the Renewable section and then in the Resource Studies Folder. Copy and paste special as values the Summary page to each of the files after using the filter tool to select the appropriate prime-mover. After tyou have created the file you can sort the file by the number of months for which data is available.





Lesson Set 2: Debt Sizing with P50 and P99 etc.


It has become standard in the industry to apply different debt service coverage ratios to different wind production cases. A typical scenario is that a 1.35x coverage ratio is applied to the P50 case while either a 1.2x coverage is applied to a P90 ten-year case or a 1.0x coverage is applied to the P99 one year case. The modelling issues can be a little difficult as the debt may be sized on one scenario but the equity IRR is computed from a different scenario. The exercise below applies these concepts.




Computing P50 from Historic Wind Data and Power Curves







Wind Analysis