Nguyen Homework3
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School
University of Texas, El Paso *
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Course
5312
Subject
Industrial Engineering
Date
Dec 6, 2023
Type
Pages
13
Uploaded by tuyennguyen0403
Electrical and Computer Engineering
Department, UTEP
ECE5312/ECE6312 Energy Sustainability
Instructor
: Dr. Eric Galvan
Homework 3. Wind Turbine Site Test
Tuyen Nguyen
10/19/2023
1.
Graph hourly power output of the wind turbine for the full year, for each year
2009
2010
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2011
2. Total Energy Production for each year.
(Calculation through Matlab)
3. The Capacity Factor for each year.
(Calculation through Matlab)
4. Create a table that includes the Total Energy Production and the Capacity Factors for each
year.
Year
Total Energy Production (MW)
Capacity Factor
(%)
2009
3545.18
20.23
2010
2907.27
16.59
2011
3158.98
18.03
5. Calculate the probability and the cumulative distribution function of the wind speeds from the
provided data set.
(Calculation on Excel using COUNTIF)
6. Create a table that includes the wind speeds, number of observations, probability of the wind
speed and the cumulative distribution function.
2009
2010
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2011
7. Compare the results for each year and discuss the differences observed for each year.
The total energy production in 2010 (2907.27 MW) was lower compared to 2009 (3545.18
MW) and 2011 (3158.98 MW). This indicates a decrease in energy production in 2010. In 2011,
the energy production slightly increased compared to 2010, but it was still lower than in 2009.
The capacity factor in 2010 (16.59%) was lower than in 2009 (20.23%), indicating that the
utilization of the power plant's capacity could have been more efficient in 2010. This may suggest
issues like maintenance or reduced operational hours. In 2011, the capacity factor increased to
18.03%, which is an improvement compared to 2010 but still below the level of 2009.
8. Generate a typical day with 24 hours of every month of the year, for each year and
provide the following:
•
Create a table that shows the values of the typical day of each month for each year.
•
Create a graph that contains the typical day of each month for each year.
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8.
Create a report that includes the program code, the power output graphs, typical day
monthly wind speed graphs, tables, and discussion of the results.
% Wind turbine specifications
clc;
rated_power = 2;
% kW
cutin_ws = 4;
% m/s
cutout_ws = 22;
% m/s
rated_ws = 15;
% m/s
r = 100;
% Vestas V90 rotor radius in meters
Area = pi * (r^2);
% Swiped area (m^2)
Coef= 0.5;
% coefficient
%windswpeedvalue = xlsread('WindData2009_2011.xlsx','Hourly 2009','E4:E8763');
%windswpeedvalue=xlsread('WindData2009_2011.xlsx','Hourly 2010','E4:E8763');
windswpeedvalue=xlsread(
'WindData2009_2011.xlsx'
,
'Hourly 2011'
,
'E4:E8763'
);
A= (1/(cutin_ws - rated_ws)^2)*(((cutin_ws*(cutin_ws + rated_ws))-
((4*cutin_ws*rated_ws)*((cutin_ws+rated_ws)/(2*rated_ws))^3)));
B= (1/(cutin_ws - rated_ws)^2)*(4*(cutin_ws +rated_ws)*(((cutin_ws +
rated_ws)/(2*rated_ws))^3)-((3*cutin_ws)+rated_ws));
C= (1/(cutin_ws - rated_ws)^2)*(2-4*(((cutin_ws + rated_ws)/(2*rated_ws))^3));
%
wind speeds from 0 m/s to 30 m/s in 0.5 m/s increments
%wind_speeds = 0:0.5:30;
wind_speeds =windswpeedvalue;
years = [2009, 2010, 2011];
% Add the years you want to analyze
% Initialize an array to store power output
power_output = zeros(size(wind_speeds));
% Calculate power output for each wind speed
for
i = 1:length(wind_speeds)
wind_speed = wind_speeds(i);
if
wind_speed < cutin_ws
power_output(i) = 0;
elseif
wind_speed >= cutin_ws && wind_speed < rated_ws
power_output(i) = rated_power*(A+(B*wind_speed)+(C*(wind_speed^2))) ;
elseif
wind_speed >=rated_ws && wind_speed<cutout_ws
power_output(i) = rated_power;
elseif
wind_speed > cutout_ws
power_output(i) = 0;
end
end
total_energy_production = sum(power_output) ;
capacity_factor = (total_energy_production / (8760 * 2))*100;
%xlswrite('WindData20092011.xlsx',power_output,'Hourly 2009','K4');
%xlswrite('WindData20092011.xlsx',power_output,'Hourly 2010','K4');
xlswrite(
'WindData2009_2011.xlsx'
,power_output,
'Hourly 2011'
,
'K4'
);
%xlswrite('WindData2009_2011.xlsx',total_energy_production,'Hourly 2009','L4');
%xlswrite('WindData2009_2011.xlsx',total_energy_production,'Hourly 2010','L4');
xlswrite(
'WindData2009_2011.xlsx'
,total_energy_production,
'Hourly 2011'
,
'L4'
);
%xlswrite('WindData2009_2011.xlsx',capacity_factor,'Hourly 2009','H4');
%xlswrite('WindData2009_2011.xlsx',capacity_factor,'Hourly 2010','H4');
xlswrite(
'WindData2009_2011.xlsx'
,capacity_factor,
'Hourly 2011'
,
'H4'
);
% Plot the wind power curve
figure;
plot( power_output,
'b-o'
,
'LineWidth'
, 2);
title(
'Wind Power Curve for Vestas V90-2.0 MW Wind Turbine 2011'
);
xlabel(
'Wind Speed (m/s)'
);
ylabel(
'Power Output (kW)'
);
grid
on
;
clc;
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Report
From the typical day of monthly windspeed, January, February, and March typically
represent the winter season. Wind turbine performance this month might be lower due to
less wind and extreme weather conditions. I see lower energy production during the first
three months in all three years.
In April, May, and March, the weather changes to warmer. Wind turbine performance is
likely to be better than in the winter months, with further energy production and capacity
factor increases.
It is summertime in July, August, and September; it has high and steady wind speeds. The
temperature is hugely different, so the wind turbine increases energy production and
capacity factor. September may continue to have good wind conditions, but it can mark
the transition towards the less windy fall season. Energy production and capacity factors
may decrease slightly.
Wind conditions typically decline in October, November, and December as fall
progresses. I observed reduced energy production and capacity factors compared to the
summer months. December represents the beginning of winter, and wind conditions can
become less favorable. Energy production and capacity factors are likely to decrease.
The time during the day
Night (12 AM - 6 AM): Wind speeds decrease during the night.
Energy production and capacity factors are expected to be at their lowest during these
hours throughout the year.
Morning (6 AM - 12 PM): Wind speeds tend to increase in the morning hours. The day
warms up, and surface heating creates wind patterns. Energy production and capacity
factors are relatively higher.
Evening (6 PM - 12 AM): In the evening, wind speeds may decrease as the day cools
down. Energy production and capacity factors are likely to decline from their afternoon
peaks but may remain higher than the morning hours.
In conclusion: Local geographical and climatic factors are crucial in wind patterns so the
specifics may vary depending on the location.
Changes in the capacity factor indicate how effectively the wind turbines utilize the
available wind resources during different times of the day.
Variations over the years can occur due to factors like maintenance, technological
improvements, or changing weather patterns. Analyzing year-to-year differences is
essential for identifying trends.