Developing a Database Because the EWITS files were not in a format that was helpful for our research, we chose to implement the data in the form of a Microsoft Access Database. The main metric we wanted to calculate from the wind speed estimates was the capacity factor. A capacity factor is the ratio of the average electrical output to the theoretical maximum and it is implications for the amount of electricity a power plant could generate as well as its reliability. To calculate the annual average capacity factor, we used the following equation:  where Pout,i is the power output for the selected wind
turbine model at sample i and Rated Capacity is the rated capacity of
the site. The numerator represents the average electrical
output of a wind turbine at the specific site and the denominator represents
the theoretical possible output of a wind turbine at the site. We added
conversion factors to change power output and rated capacity into units of
megawatt hours; the numerator contains a factor of 1/6 because power outputs
come in 1/6 hr samples and the denominator contains a factor of 8760, which
represents the number of hours in a standard year. We removed data for February 29, 2004 before calculating this value. We also calculated the monthly capacity factors, peak hour capacity factors, off-peak hour capacity factors, and summer peak capacity factors similarly. Peak hours occur between 7:00am and 11:00pm and do not include holidays or weekends while summer peak hours refer to any peak hours that occur from 1 June through 30 September. To automate these calculations, we used a Visual Basic program that opened every file within a folder, calculated these values, and pasted them into a new Excel file. Once the code terminated, the new Excel file contained all of the previously mentioned factors as well as site number and rated capacity. We manually added an extra column to indicate year, state, and whether or not the turbine was land-based. Originally, we had automated this process using MATLAB, which can communicate with Microsoft Excel and .CSV files using the xlsread, xlswrite, and csvread commands. All of the EWITS files have names formatted as SITE_XXXX_OutputData_V4, so we wrote a program that iterated through the site numbers, opened the file for the current site number, calculated the relevant values, and copied these values into an Excel spreadsheet. We originally preferred the MATLAB-based implementation because MATLAB does not display the file on the user interface when it extracts information from an Excel or .CSV file. Thus, we thought a MATLAB implementation would be more efficient that Visual Basic. In the long run, we chose not to use MATLAB because we would have to extract the site's rated capacity from a string and convert it into a number. We would also have to manually enter every site number instead of dumping all of the files into one folder and reading all of the files in the folder. Our database currently contains all of the relevant information for all of the sites in the EWITS study. It also has a few searches that we have used for our research purposes, such as searching for a specific site number or searching for all sites with a capacity factor greater than a specified threshold. Beginning the Analysis: Generalizing Capacity Factors to Zones Once we had our database, we started to group sites into zones and estimate a capacity factor for the zone based on the capacity factors for all sites within the zone. We wanted to find groups of sites that should experience similar wind conditions so that all sites within a zone would have similar capacity factors. Criteria we used to determine zones includes annual average calculated capacity factor, latitude and longitude, elevation, and other geographical features, such as water bodies or mountainous terrain. Below is an example of how we divided Illinois into zones.  After dividing states into capacity factor zones, we tried to calculate values that we thought would provide relevant information about the expected electrical output of a zone as well as the reliability of our calculations. Thus, we calculated the average annual capacity factor, the average monthly capacity factors, the average peak hour capacity factor, the average off-peak capacity factor, the average summer peak capacity factor, the average rated capacity factor, and the standard deviation of the average annual capacity factors for all sites within a zone. Below are samples of our calculations.   Calculating the average and standard deviation allowed us to create a normal distribution of capacity factor for each zone. This distribution will allow us to say with some confidence that the expected capacity factors of a wind turbine farm in that zone should be between two values. Below is an example of the normal distribution for Illinois.  |