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Paper ID #8022
Educational Experiments in Renewable Energy Analysis, Forecasting, andManagement in Hybrid Power System
Mr. Tan Ma, Florida International University
Tan Ma received the M. Eng. degree in control theory and control engineering from Huazhong Universityof Science and Technology (HUST) in China in 2009 and the Bachelor of Eng. degree in automation fromHUST in China in 2007. He is currently pursuing his doctoral degree in electrical engineering at FloridaInternational University. His research interests include design of plug in electric vehicles (PEVs) smartcharging power management algorithms; vehicle to grid and vehicle to vehicle power flow controllerdesign; design of micro grid with renewable energy sources;power system control with high penetrationof sustainable energy;design, control and monitoring of hybrid energy storage system.
Dr. Osama A. Mohammed, Florida International University
Dr. Mohammed is a Professor of Electrical Engineering and is the Director of the Energy SystemsResearch Laboratory at Florida International University, Miami, Florida. He received his Master andDoctoral degrees in Electrical Engineering from Virginia Tech in 1981 and 1983, respectively. He hasperformed research on various topics in power and energy systems as well as computational electromag-netics and design optimization in electric machines, drive systems and other low frequency environments.He performed multiple research projects for ONR and NAVSEA since 1994 dealing with; power systemanalysis, physics based modeling, electromagnetic signature, sensorless control, electric machinery, highfrequency switching, electromagnetic Interference and shipboard power systems modeling and analysis.Professor Mohammed has currently active research programs in a number of these areas funded by DoD,the US Department of Energy and several industries. Professor Mohammed has published more than 350articles in refereed journals and other IEEE refereed International conference records. Professor Mo-hammed is an elected Fellow of IEEE and is an elected Fellow of the Applied Computational Electromag-netic Society. Professor Mohammed is the recipient of the prestigious IEEE Power and Energy SocietyCyril Veinott electromechanical energy conversion award. He is the author of book chapters including;Chapter 8 on direct current machines in the Standard Handbook for Electrical Engineers several in editionsincluding the 15th Edition, McGraw-Hill, 2007 He is also the author of a book Chapter entitled ” OptimalDesign of Magnetostatic Devices: the genetic Algorithm Approach and System Optimization Strategies,”in the Book entitled: Electromagnetic Optimization by Genetic Algorithms, John Wiley & Sons, 1999.Professor Mohammed Serves as Editor of several IEEE Transactions including the IEEE Transactionson Energy Conversion, the IEEE Transactions on Smart Grid, IEEE Transactions on Magnetics, COM-PEL and the IEEE Power Engineering Letters. Professor Mohammed serves as the International SteeringCommittee Chair for the IEEE International Electric Machines and Drives Conference (IEMDC) and theIEEE Biannual Conference on Electromagnetic Field Computation (CEFC). Professor Mohammed wasthe General Chair of the 2009 IEEE IEMDC conference held in Miami Florida, May 3-6 2009 and wasthe Editorial Board Chairman for the IEEE CEFC2010 held in Chicago, IL USA, May 9-12, 2010. Pro-fessor Mohammed was also the general chair of the IEEE CEFC 2006 held in Miami, Florida, April 30– May 3, 2006. He was also general chair of the 19th annual Conference of the Applied ComputationalElectromagnetic Society ACES-2006 held in Miami, Florida March 14-17, 2006. He was the GeneralChairman of the 1993 COMPUMAG International Conference and was also the General Chairman of the1996 IEEE International Conference on Intelligent Systems Applications to Power Systems (ISAP’96)Dr. Mohammed has chaired the Electric Machinery Committee for IEEE PES was the Vice Chair andTechnical Committee Program Chair for the IEEE PES Electric Machinery Committee for a number ofyears. He was a member of the IEEE/Power Engineering Society Governing Board (1992-1996) andwas the Chairman of the IEEE Power Engineering Society Constitution and Bylaws committee. He alsoserves as chairman, officer or as an active member on several IEEE PES committees, sub-committees andtechnical working groups.
Mr. Ahmed Taha Elsayed, Florida International University
c©American Society for Engineering Education, 2013
Paper ID #8022
Ahmed Taha Elsayed was born in Qaluobia, Egypt in 1984. He received his B.Sc. and M.Sc. degreesfrom the Shoubra Faculty of Engineering, Benha University, Egypt in 2006 and 2010 respectively. From2006 to 2012, he was a research/teaching assistant in the Faculty of Engineering, Benha University. Heis currently a research assistant in the Electrical and Computer Engineering Department, College of En-gineering and Computing, Florida International University, Miami, Florida, USA. His current researchinterests are Smart Grids, Renewable energy sources, Smart Operation and Energy Management of PowerSystems. Energy Systems Research Laboratory, Electrical and Computer Engineering Department, Col-lege of Electrical and Computer Engineering, Florida.
c©American Society for Engineering Education, 2013
Educational Experiments in Renewable Energy Analysis, Forecasting, and
Management in Hybrid Power System
Abstract
In this paper, analysis, forecasting and management of the power generated by a renewable
energy farm including both solar energy and wind energy in a hybrid power system will be
demonstrated. This renewable energy farm is connected to the utility grid. In order to properly
cooperate and balance power between the load and the distributed energy sources, a method of
building an accurate power forecasting and management model based on the analysis of the
existing data of the load, solar irradiance and wind speed by using neural network is given.
Considering realistic factors, a stochastic model of local load, available solar energy, and wind
energy is proposed. The detail of how to achieve the optimal size of the renewable energy farm
based on the analysis of the forecasting model and the cost function by using genetic algorithm is
discussed. Based on predicting the load and power generated by the optimal renewable energy
farm, the method to design a fuzzy logic power management controller to adjust the
charging/discharging ratio of the energy storage to keep the system voltage and frequency stable
is given. The model is built with Simulink and several other toolboxes from Mathworks Corp.,
such as neural network toolbox, optimization toolbox, and fuzzy logic toolbox.
Introduction
Wind and solar energy are gaining much popularity due to the global call for clean energy since
the exhaustion of the global energy reserves has already been a worldwide problem at
environmental, industrial, economic and societal levels. In 2011, more than 80% of the energy
consumed in the USA was generated by petroleum, natural gas and coal, meanwhile renewable
energy sources only supplied less than 8% of the total energy [1], [2]. Therefore it is urgent and
significant to teach the technologies related to development of utilization of renewable energy.
Meanwhile, as the concept of the smart grid is becoming popular, intelligent analysis, control
and optimization algorithms and tools are becoming essential topic to be taught to engineering
students [3]-[5].
There are three major obstacles in the utilization of renewable energy in our daily life. First, the
output power from the renewable energy resources is highly dependent on environmental factors
such as wind speed, solar irradiance and temperature. Second, the initial capital cost of building
the renewable energy farm is extremely high, how to find the optimal scale of the renewable
farm for a certain amount of load need to be studied. Third, the output power from the renewable
energy farm is varying with time, a method of keeping the power system with renewable energy
resources stable with energy storage equipment such as battery needs to be studied.
For the first obstacle, based on the historical data of a certain area’s wind speed, solar irradiance,
temperature and load, accurate forecasting models for estimating the next period output power
from renewable energy farm and local load can be built based on neural network (NN)[6]. With
the forecasting models, short term energy production and load for a certain area can be predicted.
For the second obstacle, based the historical data in this area, cost functions describing the gap
between the output power from the renewable energy farm and the load can be used to find the
optimal scale of the renewable energy farm through genetic algorithm (GA) [7]. With the
optimal scale of the renewable energy farm, the building cost will be greatly decreased. For the
third obstacle, with the forecasted next period output power from the renewable energy farm and
load, the gap between them can be estimated. Based on these values together with system
frequency regulation signal, a smart charging controller can be designed by using the fuzzy logic
to control the charging and discharging of the battery in the system, which will absorb or inject
power and finally make the system frequency and voltage stable [8].
In this educational paper, teaching the topic will start with the description of the hybrid system
with some general background about how the system works with solar energy, wind energy, load
and energy storage. This portion is theoretical and can be explained by the instructor in the class.
After that,three steps of planning and building the hybrid system involving renewable energy and
load forecasting, renewable energy farm scale optimization, power flow control will be studied
by using several artificial intelligent toolboxes in MATLAB. This portion would need the use of
a computer lab or it can be in the form of assignments to students depending on their knowledge
about power system and intelligent algorithm tools. Finally, students will combine the three steps
to design a hybrid power system in the MATLAB and implement it in hardware and test in real-
time operation.
Fig. 1 Abstract diagram of the hybrid AC/DC system
System description
An abstract diagram of the system under study is as shown in Fig. 1. This hybrid power system
contains both AC side and DC side. A renewable energy farm contains both solar energy and
AC-Bus
Alt
ern
ate
Res
ou
rces
&
En
erg
y S
tora
ge
DC-Bus
Generator-Rectifier
Subsystem
Inverter-Grid
Subsystem
Generator AC/DC
DC/AC Isolation
Transformer
Utility
Grid
DC/DC
PV
Battery
Wind
G
Utility
Loads
AC Grid
DC-Load
wind energy connected to the DC side, meanwhile, battery is also connected to the DC side to
play as energy storage. The utility grid is connected to the AC side. Between the AC side and DC
side there is a bidirectional AC/DC converter, which controls the power flow from both sides.
Also both sides have their local load. This hybrid power system can be viewed as a large area
power system with distributed renewable energy generators. In order to supply power for a
certain amount of loads on both sides by using renewable energy resources and limit the impact
of the environmental disturbance brought by renewable energy resources into the power system,
several artificial intelligence tools can be utilized in the forecasting, optimization, and control
aspects during the design process.
Load and renewable energy forecasting by using neural network
Artificial neural network (ANN) is an information processing tool that is inspired by the
biological nervous systems, in the same way as the human brain process information. It is
composed of a large number of highly interconnected processing elements (neurons) working in
unison to solve specific problems. Neural network has the flexibility to be configured in various
arrangements to perform a range of tasks including pattern recognition, data mining,
classification, forecasting and process modeling. In general neural networks can be expressed as
a mathematical model designed to accomplish a variety of tasks. Due to its high capability of
capturing non-linear trends, it lends itself as a very successful tool for load forecasting
applications and it was adopted widely in such applications during last two decades. The
building block of the neural network is the neuron, the mathematical model of the neuron is
given in Fig.2 (a). The mathematical expression of each single neuron can be given by:
][1
m
j
kjkjk bXWy (1)
The structure of an artificial neural network (ANN) consisting of 13 neurons is shown in Fig.2
(b). As shown in the figure, the ANN has four layers; one is the input layer, two hidden layers
and one output layer.
x1
x2
xm
bk
∑
wk1
wk2
wkm
Bias
ᵠk yk
Summing
Junction
Weights
Activation
Function
Output
Input
Signal
(a)
InputOutput
Hidden
Layer
Hidden
Layer
Input
LayerOutput
Layer
(b)
Fig.2 (a) Mathematical model of single artificial neuron (b) Structure of four layers ANN.
Untrained ANNs, like a newborn child, has to learn by example and does only what it’s trained
to do. So, the training process of the neural network should be carried out carefully because it
has a significant effect on the outputs and simulation accuracy. Training ANN can be done by
different ways through different computer software. In this work, explanation of training ANN
by using MATLAB will be provided. In MATLAB ANN can be trained by two methods, the first
one can be achieved by code in m-files which is relatively difficult for students as it requires a
certain level of knowledge of MATLAB commands. Another way is by NN toolbox, this method
is much easier because it involves using GUI (graphical user interface). For educational
purposes, the second method will be easier and more efficient for students’ level.
ANN is learning by extracting nonlinear patterns of input and target data sets. For further
illustration let’s consider the problem of short term load forecasting. It is found that the load
curve is depending on temperature and other weather factors, especially in areas characterized by
hot weather where consumers respond to the high temperature by turning on the power hungry
air conditioners. Hence, the independent variable is the temperature and the dependent variable is
the demand. The temperature is considered as the input data and load is the target data. Although
the given problem here is complicated and highly non- linear, ANN has a noticeable capability of
detecting such non-linear functions. Once the ANN is trained, it can forecast the demand for
futuristic time intervals by knowing the temperature for such intervals.
The load historical data for in one year duration is shown in Fig.3 (a), solar irridance and wind
speed historical data for one month is shown in Fig 3 (b) and (c).
(a) (b) (c)
Fig. 3 The historical data (a) load, (b) solar irradiance, (c) wind speed.
For load forecasting, it’s found that the behavior of consumers is highly correlated to the weather
conditions. This relation becomes stronger in hot areas, where the people respond to hot weather
by turning on power hungry AC conditions. A prepared study by EIA (US Energy Information
Administration) in 2001 showed that air conditions accounts for 21.4% of the total energy
consumption of households in south Atlantic states and 16% of the total household consumption
in the USA. The system under study is assumed to be in the southern coasts of Florida, USA,
where the weather is hot and humid. A previously prepared study for FPL (Florida Power and
Light) company shows that the most effective weather parameters in this area on load forecasting
are temperature and humidity. The hourly temperature and hourly humidity will be used as
training parameters. Also, it’s found that the dew point affects the load, not in a direct sense but
it is statistically correlated. Time series were created to let the network capture weather trends,
the first one is the hour of the day, the second one is the day of the month and the third one is the
05
1015
2025
0
50
100
150
2002000
4000
6000
8000
10000
12000
14000
Daily hourstotal summer days
valu
e
050
100150
200
0
10
20
30
400
200
400
600
800
1000
Time (0.1 Hours)
Auguest daily irradiances
Date (days)
Irra
dia
nce (
W/m
2)
0
8
16
24
05
1015
2025
3035
0
5
10
15
20
25
Time (hours)
Daily wind speed data in July
Date (days)
Win
d s
pee
d(m
/s)
month of the year. Load analysis shows that the load profile over the weekdays is not uniform,
the load is low at weekend days and higher at regular working days. Some researchers developed
seven separate networks for forecasting the load over the seven days of the week. This
methodology requires very long training time and huge computation capacity. A better
alternative is to add a vector to the training set that containing an index for each week day (1 for
Monday, 2 for Tuesday, etc.). ANN is capable of capturing the load characteristics for each day
and differentiate week days from their indices. The gathered load data is the hourly load for two
years (2008 & 2009) and it’s desire to forecast the load for the first week of 2010. After that, the
training matrix will be [7×17544].
The same procedure used for load forecasting , including the same neural network structure and
same preprocessing method will be used for predicting the wind speed for 168 hours ahead.
Definitely, the input variables must be manipulated. The training data for wind speed will only
contain time series of hour, day and month. The procedure used for wind speed forecasting will
be used for predicting the solar radiation. The same time series will be used. Analysis of
historical solar data covered revealed that each day has its own distinct characteristics. For
example, at 7:00 PM in the summer, the sun is still in the sky and there’s a value for solar
radiation but in winter it’s completely dark and the solar radiation will equal to zero. The same
case will be for the early morning hours, for this reason an alternative solution becomes
necessary. One AAN will be used to forecast the load for each hour (i.e. 18 neural networks will
be required for predicting the solar radiation.) There are six hours at night which have zero solar
radiation all the year, so they are excluded from the forecasting process. This alternative namely
“separate hour forecasting” will be compared to the first procedure, in which one ANN is
used.The comparison between real value and the forecasting results of load is shown in Fig.4.
Fig.4 Forecasted load versus actual load.
Creation of a new ANN can be summarized in the following steps:
1- Open MATLAB.exe and load historical data.
2- Type nntool to open the neural network toolbox. Give a name to the neural network.
0 20 40 60 80 100 120 140 1600
100
200
300
400
500
600
700
800
900
1000
Week Hours
Load
(K
W)
actual load
forecasted load
3- From the drop down menu select the network type, there are many types of the ANN. The
most commonly used types in forecasting applications are the feed-forward back-
propagation and radial basis function (RBF). In this study feed-forward network with
back-propagation algorithm is adopted.
4- Select the input data from the drop down menu, after importing the required input data in
NN-toolbox it will be available in the drop menu.
5- Select the target data from the drop down menu.
6- Select the training function which is responsible for updating weight and bias values.
There are many training functions such TRAINLM (Levenberg-Marquardt), TRAINOSS
(one step secant), TRAINGD (Gradient descent), etc. The default and most commonly
used one is “TRAINLM” because it is very fast.
7- Select learning function either “LEARNGDM” or “LEARNGD”, keep the default one
(LEARNGDM).
8- Select the performance function among MSE (mean squared error), MSEREG and SSE
(sum squared error). In this work, the used performance function is the MSE.
9- Select the number of layers, number of layers means the total number of hidden and
output layers. The default is two which indicate one hidden layer and on the output layer.
It was found in literature that 3 layers (input, hidden and output) are suitable for accurate
forecasting process. Increasing the number of layers will increase the time of training and
required memory significantly.
10- Specify the layer which properties are being set. Setting layer properties are done for
each layer independently. Set the number of neurons in the specified layer, Based on the
previous experience presented in the literature, The number of hidden neurons can be
chosen by trial and error method, it’s concluded empirically that the best point to start
trying is an integer close to the geometric mean (GM) of the number of inputs and
number of outputs.
NoNiW (2)
Where Ni is the number of inputs, No is number of output and W is the nearest integer to
the geometric mean. When the user tries to set the number of neurons in the second layer
which is the output layer, it will be inactive since number of neurons of output layer are
determined according to the number of output variables and it can’t be set manually.
11- Select the transfer function, the available selections are tansig, logsig and purelin. The
transfer function for the hidden layer is set to be “tansig” and for the output layer it is
“purelin”.
12- After setting all the properties of the network press create to create the network. The
network will appear in the NN-toolbox window.
Renewable energy farm optimization by using genetic algorithm
A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This
heuristic is routinely used to generate useful solutions to optimization and search problems. GA
belong to the larger class of evolutionary algorithms, which generate solutions to optimization
problems using techniques inspired by natural evolution, such as inheritance, mutation, selection,
and crossover.
Since the amount of daily energy consumption should be supplied by renewable energy sources,
in order to optimize the size of the renewable energy farm, historical solar irradiance and wind
speed data should be analyzed together with the daily energy consumption. To generate enough
power to feed the daily load energy consumption and at the same time minimize the cost of
building the renewable energy farm, an optimized solution should be found.
The daily energy generated by solar panels can be calculated by using equation (3). Where: A is
the solar panel size, β is the solar panel efficiency, in this model β = 22%.
tdttuAEt
tsloar
24
0)( (3)
On the other hand, for the output of the wind farm, General Electric 1.5-MW turbine model
1.5sle is used to determine the output power generated by the wind under different wind speed
situations. The total daily energy output from the wind farm is calculated from equation (4).
Where N is the number of wind turbines and S(t) is the wind speed at time t and fwind(s(t)) express
the single turbine output power of time t.
24
0)( )(
t
ttwindwind tdtsfNE (4)
To build a renewable energy farm that the output energy can match the load energy consumption
as much as possible, the cost function shown in equation (5) is used to optimize the size of the
renewable energy farm. The output value of equation (5) is the sum of the absolute daily energy
gap between the energy generated by renewable farm and load in this area. The best solution is
based on a proper A and N that can minimize the cost function (5).
365
1
24
0)(
24
0)()()(),(
d
d load
t
tt
t
tdEtdttdtsfNtdttuANAf (5)
In order to find the best A and N that can get the best result, a GA model is used to find the
optimal results. In this paper, the GA model is designed with population size set to 20, crossover
rate set to 0.8 and mutation rate set to 0.02. The optimization process is shown in Fig.5. In order
to do that, students need a computer with MATLAB 2010 or a newer version installed with
optimization Toolbox. The procedure to use the optimization toolbox to find the best scale of the
renewable energy farm is as follows,
1- Open MATLAB.exe, create a .m file and named cost function.m.
2- Load the wind speed data, solar irradiance data and load data into the workspace.
3- Program the function (5), (6) and (7), set the function (7) result as the output of the
costfunction.m.
4- Type optimtool in Command Window and choose ga-Genetic Algorithm in the Solver.
5- Name the fitness function as @costfunction, set the number of variables as 2. We can
adjust the boundary of A and N by setting some reasonable values in the constraints.
6- On the right hand, the detail of the GA model such as the population, fitness scaling,
selection, reproduction, mutation, crossover, stopping criteria and plot functions can be
adjusted. Set the right value and choose the best fitness from the plot functions, start the
optimization process.
Fig. 5 Optimization process by using GA
Power flow management by using fuzzy logic control
Fuzzy control is a powerful control method that can be applied on different systems. It is based
on the experience of the user on the system behavior rather than modeling the system under
control mathematically like in linear control theory. This makes fuzzy control a powerful control
technique especially with non-linear systems in which it is difficult to derive an accurate
approximated mathematical model of the system and expect its behavior. Fuzzy control is a rule
based control technique that is approached by linguistic fuzzy rules, which describe the output
desired out of the system under different operating conditions. Fuzzy rules are in the form of if-
then rules that the proficient should design such that they cover all the conditions the system is
expected to go through. Fuzzification, inference mechanism and defuzzification are three
important steps in designing a fuzzy logic controller. Different membership functions are used to
map the input variables in the fuzzification step, which are the next period load flow and
frequency regulation.
Since the output power of the renewable energy is varying based on the environment, it can’t
always keep balance with the load. Because of the unbalance between the renewable energy
generator and load, the system’s frequency will not be constantly at 60Hz. Therefore energy
storage is needed to help the system regulate the frequency. The battery is connected to the
system through a bidirectional converter, so based on the forecasting values of output power
from renewable energy farm, forecasting load value and system frequency regulation signal, a
power flow controller is needed to control the bidirectional converter to charge or discharge the
battery. Since the battery charging/discharging rate is not directly related to the system power
0 20 40 60 80 100 120 140 1600
2
4
6
8
10
12
14
16
18x 10
7
Generation
Co
stfu
nct
ion
val
ue
(MW
h)
Best: 2.07573e+06 Mean: 2.48171e+06
Best fitness
Mean fitness
flow and frequency regulation signal, a real time Mamdani-type fuzzy logic controller is
designed to connect them. In this fuzzy logic controller, system power flow and frequency
regulation signal are normalized as inputs of the controller, and the output is the signal used to
control the charging rate of the battery. The inputs to the controller are mapped into five fuzzy
subsets. After the fuzzification step, the control variables are converted into linguistics rules. The
fuzzified input variables are managed through putting certain linguistic rules in the inference
engine and rule based step. The fuzzy controller decides the proper control actions based on the
fuzzified input. In the defuzzification step, the output of the linguistic valuable is transformed to
a number that can be used to control the bidirectional converter to adjust the charging rate of the
battery. The five membership functions for power flow, frequency regulation signal and output
are shown in Fig.6 (a), (b) and (c). The surface of the rule set of the fuzzy logic controller is
shown in Fig.6 (d).
(a) (b)
(c)
(d)
Fig.6 Membership functions and the rules surface.
To design the fuzzy logic controller, students need a computer with MATLAB 2012a or a newer
version and Fuzzy Logic Toolbox. The procedure to build the fuzzy logic controller is as
follows.
1- Open MATLAB.exe and type fuzzy in the command window to open the fuzzy logic
toolbox.
2- File New FISC choose Mamdani model.
3- Edit add variable input.
4- Change the name of the two input variables and the output as normalized load flow,
frequency regulation signal and charging charging index respectively.
0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.2
0.4
0.6
0.8
1
Normalized load follow.
Mem
ber
ship
VL L N H VH
-10 -5 0 5 10
0
0.2
0.4
0.6
0.8
1
Frequency regulation signal.
Mem
ber
ship
NB N Z P PB
-1 -0.5 0 0.5 1
0
0.2
0.4
0.6
0.8
1
p
Mem
ber
ship
NB N Z P PB
0.4
0.6
0.8
1
-10
-5
0
5
10
-1
-0.667
-0.333
0
0.333
0.667
1
Normalized load flowFrequency regulation signal
Cha
rgin
g in
dex.
5- Double click each input and output, adjust the number of the membership functions to
five and modify each membership function’s shape.
6- Click the Mandani rule block to add rules for the controller.
7- View rules and surfaces. Check the rules and surface of the designed fuzzy logic
controller.
Conclusion
This paper describes the application of artificial intelligent tools to a hybrid AC/DC power
system with renewable energy resources. The curriculum is designed around Florida
International University energy systems research laboratory’s existing programs which are
already strong and proven. Introduction of neural network, genetic algorithm and fuzzy logic is
given. Three issues: load and renewable energy forecasting, renewable energy scale optimization
and power flow control are given as examples for students to apply those artificial intelligence
tools to solve power system problems. The detail of how to use toolboxes of neural network,
genetic algorithm and fuzzy logic in MATLAB is given. In this course, students learn to
understand how a hybrid AC/DC power system works, the characters of renewable energy
sources such as wind energy and solar energy, and how to utilize renewable energy sources
efficiently. Also, the knowledge of neural network, genetic algorithm and fuzzy logic can be
used in other courses for future study. Moreover, the simulation and experimental environments
used to develop and verify the developed hybrid system can be a very effective tool to increase
students’ knowledge and interests about those subjects. The practical classroom implementation
can be in the form of different experiments, for example an experiment to teach the students how
to create ANN and utilize it for data prediction. This can be done in well equipped computer
laboratory to provide the students with “hands on” experience.
References
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http://www.eia.gov/totalenergy/data/annual/pdf/aer.pdf
[2] National Renewable Energy Laboratory, “Learning about Renewable Energy”, http://www.nrel.gov/learning/,
Accessed: 19 January 2009.
[3] R.H Lasseter, “Smart Distribution: Coupled Microgrids,” Proceedings of the IEEE , vol.99, no.6, pp.1074-1082,
June 2011
[4] J.R. Ag ero , “Tools for Success,” Power and Energy Magazine, IEEE , vol.9, no.5, pp.82-93, Sept.-Oct. 2011
[5] P.J. Werbos, “Computational Intelligence for the Smart Grid-History, Challenges, and Opportunities,”
Computational Intelligence Magazine, IEEE , vol.6, no.3, pp.14-21, Aug. 2011
[6] A. Anvari Moghaddam, A.R. Seifi, “Study of forecasting renewable energies in smart grids using linear
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2011
[7] A. Arabali, M.Ghofrani, M. Etezadi-Amoli, M. S. Fadali, Y. Baghzouz, “Genetic-Algorithm-Based
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pp.162-170, Jan. 2013
[8] T. Ma, A. Mohamed, and O. Mohammed. “Optimal charging of plug-in electric vehicles for a car park
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