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

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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

[1] U.S. Energy Information Administration, “Annual Energy Review 2011” [Online]

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

predictive filters and neural networks,” Renewable Power Generation, IET , vol.5, no.6, pp.470-480, November

2011

[7] A. Arabali, M.Ghofrani, M. Etezadi-Amoli, M. S. Fadali, Y. Baghzouz, “Genetic-Algorithm-Based

Optimization Approach for Energy Management, ”Power Delivery, IEEE Transactions on , vol.28, no.1,

pp.162-170, Jan. 2013

[8] T. Ma, A. Mohamed, and O. Mohammed. “Optimal charging of plug-in electric vehicles for a car park

infrastructure.” Industry Applications Society Annual Meeting (IAS), IEEE, 2012.