Environmental

Communication

Biosci. Biotech. Res. Comm. 10(2): 330-340 (2017)

Environmental oriented optimal selection of renewable

and green energy sources using intelligent methods: A

case study of Bafgh – Iran

Roohollah Sadeghi Goughari,¹ Hadi Zayandehroodi*² and Mahdiyeh Eslami³

Department of Electrical Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran

ABSTRACT

The present work was formulated in order to obtain proper amount of load needed in Bafgh for 2017. The study

was performed in  ve sectors, i.e. industry, household, business, public, and agriculture. After obtaining the results

relative to load prediction for Bafgh, optimization of a compound generator system (wind and solar) independent of

the network was considered for this city. The results showed that Bafgh needs 368,976 (MW) per hour in 2017. The

results indicate the necessity of governmental support in private sector in establishment of such systems. The results

obtained from optimization indicated that use of renewable energies increase if subsidies are completely removed so

that generator diesel production will decrease as fuel price increases. The recommended system in the present work

can be the best solution for Bafgh in 2017.

KEY WORDS: WIND TURBINE – LOAD PREDICTION – SOLAR ARRAY-DIESEL GEN

330

ARTICLE INFORMATION:

*Corresponding Author: h.zayandehroodi@yahoo.com

Received 19

March, 2017

Accepted after revision 22

June, 2017

BBRC Print ISSN: 0974-6455

Online ISSN: 2321-4007 CODEN: USA BBRCBA

Thomson Reuters ISI ESC and Crossref Indexed Journal

NAAS Journal Score 2017: 4.31 Cosmos IF : 4.006

reserved.

Online Contents Available at: http//www.bbrc.in/

INTRODUCTION

In The study forecast load is performed once for the city

Bafgh in 2017. The forecast  ve trade tariffs, domestic,

industrial, agricultural and made public. This prediction

using neural network software SPSS Tool is available in

this software. Data used in this study include daily load

for the past  ve years. All economic and social activi-

ties of society are dependent on electric industry. With

regard to the fact that electric energy cannot be stored in

a large scale and that various projects of electric indus-

try demand long-term planning, a suitable estimation

for different parts of power systems is very important.

Optimal design of a power system is possible when eco-

nomic, technical, and environmental issues are accu-

rately considered. Therefore, the most important part of

a power system is prediction of load (Ghanbari, et al.

2009).

Goughari, Zayandehroodi and Eslami

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS ENVIRONMENTAL ORIENTED OPTIMAL SELECTION OF RENEWABLE AND GREEN ENERGY SOURCES 331

Planning for energy supply for next generations is

one of the most crucial concerns of governments. The

planning calls for suitable estimation of power systems’

demands in future. The planning is performed in three

parts, i.e. load prediction, production planning, and

transmission planning. The most important part of plan-

ning in a power system is load prediction. Due to its high

capability in learning and relationship between inputs

and outputs, neural network is one of the most ef cient

methods.

Neural networks are inspired from information pro-

cessing from biologic neural system and processes

information as brain does. Key element of the idea is

the novel structure of information processing system;

the system is comprised of several processing elements

which coordinately work for problem solving (Passino,

2002).Like humans, Arti cial Neural Networks (ANNs)

learn by examples. An ANN is adjusted for performing

a known task such as recognition of patterns and clas-

si cation of data during a learning process. In biologic

systems, learning is accompanied by adjustments in

synaptic connections between nerves (Lakshminarasim-

man and Subramanian 2008).

The present work was formulated in order to obtain

proper amount of load needed in Bafgh for 2017. The

study was performed in  ve sectors, i.e. industry, house-

hold, business, public, and agriculture. The estimation

was executed in order to prevent from occurrence of

troublesome conditions in network such as overload in

lines, network failure, etc. it is possible to increase con -

dence level of network in this city so that electrical sec-

tor’s authorities can have suitable programs in different

parts of electrical industry in Bafgh on the basis of the

results of the present work. After obtaining the results

relative to load prediction for Bafgh, optimization of a

compound generator system (wind and solar) independ-

ent of the network was considered for this city.

A technique for the optimal planning and design of

hybrid renewable energy systems for microgrid applica-

tions is presented in (Jung, et al. 2016); a novel approach

is used to determine the optimal size and type of dis-

tributed energy resources (DERs) and their operating

schedules for a sample utility distribution system. The 4

main aspects for energy ef ciency in a building consist

of  rst the nearly zero energy passive building design

before actual construction, secondly the employment of

low energy building materials during its construction,

thirdly use of energy ef cient equipment for low oper-

ational energy requirement while lastly integration of

renewable energy technologies for various applications.

These aspects along with their economics and environ-

mental impacts are discussed by Chel, et al. (2017).

Recently, Hong, et al. (2017) have investigated the

optimal sizing of renewable energy generation resources

in a community microgrid; the cost of renewables and

community welfare are optimized while the comfort

zone of indoor temperature in all houses is maintained

using air conditioning systems. Similarly, Tezer, et al.

(2017) have also with the aim of investigating optimiza-

tion techniques, developed from past to present to solve

the problems of stand-alone hybrid renewable energy

systems and especially have tried to determine the ef -

cacy of multi-objective optimization approaches.

MATERIALS AND METHODS

Neural network estimations in SPSS

SPSS Software (Statistical Package for the Social Sci-

ences) can be adopted to run prediction through neu-

ral network imbedded inside the software. Here, weight

coef cient and role of each independent variable are

determined in prediction of dependent variable by using

toolbar of neural network (Chen, 2000).

FIGURE 1. Neural network structure for choosing optimal plants of distributed

production

Goughari, Zayandehroodi and Eslami

332 ENVIRONMENTAL ORIENTED OPTIMAL SELECTION OF RENEWABLE AND GREEN ENERGY SOURCES BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Neural network architecture

Although neural networks do not need preconditions

and model structures very much, it is still effective to

grasp a whole de nition of network architecture. MLP

and RBF are functions of predictors minimizing pre-

diction error of target variables (Chen, 2000). Figure 1

depicts an example of a neural network used to choose

optimal plants of distributed production.

The above architecture is known as Feed Forward

Architecture because intra-network relationships are

directed forward from input layer toward output layer

without any return. In this  gure, input layer consists of

predictors; input layer consists of knots or invisible units.

Value of each hidden unit depends on predictors. Accu-

rate architecture of function is related to two dependent

factors, i.e. network type and controllable characteristics

by user; output layer consists of reactions.

MLP Network enables having two hidden layer; in

this case, each unit in the second hidden layer is a func-

tion of existing layers in the  rst hidden layer and each

response is a function of units of second hidden layer’s

units. MLP method provides a predicting model for one

or some dependent variable(s) (target) according to val-

ues of predictor variables (Ghanbari, et al. 2009).

DEPENDENT VARIABLES

The following scales can be considered here: (a) Nomi-

nal scale: Letters are used to measure variable; (b) Ordi-

nal scale: There is an order among variable levels, e.g.

5-choice scale (very low, low, average, high, and very

high) ; (c) Interval scale: Zero is conventional and the

interval between two consecutive units is a constant

value, e.g. Fahrenheit and Centigrade; (d) Ratio scale:

It is similar to interval scale, the only difference is

that Ratio scale’s zero is real, e.g. centimeter and inch.

In the above-mentioned steps, it is postulated that a

proper level of measurement is given to all dependent

variables. Nevertheless, measurement levels for a given

variable can be changed by right-clicking on the vari-

able in the list and choosing a measurement level from

the list. A pointer beside each variable in variable list

determines measurement level and type of data (Chen,

2000).

PREDICTOR VARIABLES

Predictors may be determined categorically or relatively.

Categorical variable coding of the process temporarily

recodes categorical predictor and dependent variables by

using one of “c” codes until the end of the steps. If there

are c categories of a given variable, they are saved as c

vectors. This model of coding increases synaptic weights

and slows down training. However, the more intensive

the coding methods are, the weaker conformity with

neural network will be achieved.

Creating an MLP neural network in SPSS Software

• Variables

Neural networks and then Multilayer perceptron are

chosen from Analyze menu in SPSS. Dependent vari-

ables and covariates are chosen.

On the Variable bar, rescaling of covariates is changed.

The choices are as follows:

 Standardized: Subtract the mean and divide it by

standard deviation

(1)

 Normalized: Subtract the minimum value and

divide it by range:

(2)

 Adjusted normalized: A sample is set from normal-

ized values between

(3)

 None : no rescaling of covariates.

• Partitions

The Partitions part is used to separate Training and Test

data. It is recommended to use 70% of data for network

training and 30% of them for network test to select opti-

mal plants of distributed production.

• Architecture

Network architecture is decided in this part. This menu

automatically assigns the best architecture.

 Activation function

The Activation function relates weighed sum of units in

one layer to unit values of next layer:

- Hyperbolic tangent : The function receives real

values and turns them in (-1,1) interval. The func-

tion is as follows:

(1)

- Sigmoid: The function receives values and turns

them in (0,1) interval. The function is as follows:

(2)

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BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS ENVIRONMENTAL ORIENTED OPTIMAL SELECTION OF RENEWABLE AND GREEN ENERGY SOURCES 333

The Output layer consists of dependent variable (tar-

get variable). Activator function makes a relationship

between weighed sum of units in one layer and values

of next units.

- Identity : The function receives real values and

returns them without any change.

- Soft max: This function receives real values and

turns them to a vector.

- Hyperbolic tangent: this function receives real val-

ues and turns them in (-1,1) interval.

- Sigmoid: This function receives real values and

turns them in (0,1) interval.

The Rescaling of scale dependent variables

• Standardized: Mean is subtracted and the result is

divided by standard deviation.

• Normalized: The minimum value is subtracted and

the result it is divided by range; the normalized

values are in (0,1) interval.

• Adjusted normalized: The minimum value is sub-

tracted and the result is divided by range. The

adjusted normalized value is in (-1,1) interval.

• None: No rescaling of scale dependent variables.

TRAINING

With regard to type of training, one of the following

types is selected (Elizabeth, et al. 1995, Mandal, et al.

2006).Table 1: The results of neural network analysis

Batch

The Batch brings synaptic weights up-to-date for train-

ing only after determination of all saved data. In other

words, training a batch of data related to all saved val-

ues in databank is preferred because it minimizes total

error. Batch training needs to be updated several times

to reach one of criteria of weights cease; therefore, the

databank should be checked several times. This method

is more suitable for smaller groups of data.

Online

The Online brings synaptic weights up-to-date after

each saved training data. In other words, online training

uses the information related to a save in a time. Online

training gets a save frequently and updates weights in

order to reach a criterion of cease. If all saves are used

once and no cease criterion is met, processing continues

with recovering information saves. Online training has

a better performance than batch method in bigger infor-

mation sets with related predictors. If there are several

saves and variables and their values are related to each

other, online training archives acceptable response in a

shorter period of time.

Mini-Batch

Test data are classi ed into almost same groups and

then updating is performed on synaptic weights after

passing a group. That is, the Mini-Batch method makes

use of the information from a saved group; then, the

process recovers group data if necessary. Mini-Batch

training is in fact an agreement between batch and

online training and it is considered the best method for

medium-sized information banks. In this method, the

program can assign the number of saved data automat-

ically for training and therefore, a value >1 or ≤max.

Number of cases is decided to be saved. Maximum

number of saved cases can be speci ed in the option

bar (Mandal, et al. 2006).

The data from electric power distribution companies

north of the province is taken. Data are divided into two

groups of training and test data for classi cation. For

this aim, 70% of data are used for network training and

30% of them are used for test. Multi-Layer Perceptron

(MLP) and Radial Basis Function (RBF) are used as neu-

ral networks. They compare the prediction results from

the model with target values, (Table 1) show the results

of neural network analysis. Neural network enables us

to adjust MLP and RBF networks and save model results

for scoring (Norusis, 2011).

Table 1. The results of neural network analysis

Characteristics Amount

Number of middle layers 1

Number of middle units 2

Trainig Data volume 70%

Sum of squared errors 31.131

Relative error 0.0988

Test Data volume 30%

Sum of squared errors 21.484

Relative error 0.996

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334 ENVIRONMENTAL ORIENTED OPTIMAL SELECTION OF RENEWABLE AND GREEN ENERGY SOURCES BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

OPTIMIZATION ALGORITHM

The Optimization algorithm is a method for estimation

of synaptic weights:

Scaled conjugate gradient: Hypotheses of this method

enable use of it only for batch training Gradient descent:

this method should be used for online and/or mini-batch

trainings and it may also be used for batch training

(Mandal, et al. 2006).

TRAINING OPTION

It enables us to adjust optimization algorithm carefully.

This part for scaled conjugate gradient consists of the

followings:

Initial lambda: Initial lambda in numerical algorithm

is assigned between 0< x <0.0000001.

Initial sigma: Initial sigma is assigned between 0< x

<0.00001

OUTPUT LAYER

The output layer contains the target variable (dependent)

and functions actively between units in a layer the sum

of the weighted values of the next relationship.

 Identity: The function of the real values and

returns them unchanged.

 Soft max: It function real values and converts

them to vector.

 Hyperbolic tangent: this function receives the actual

values to the values in the interval (1, -1) turns.

 Sigmoid: This function receives the actual values

and the values in the interval (1, 0) converts.

NETWORK STRUCTURE

• Description: It shows the information related to

neural network consisting of dependent variables,

number of input and output units, number of hid-

den layers and units, and activator functions.

• Diagram: It shows the network like an unchange-

able graph.

Synaptic weights: It shows the coef cients estimated

for showing the relationship between a layer and the

next one even if active information banks are assigned

to training data.

• Network performance:

It presents the results used for showing soundness of the

model.

• Model summary: It presents a summary of the

results on neural network in a complete and sepa-

rated manner.

• Classi cation results: It presents a classi ed table

for each dependent variable partially or fully.

• Roc curve: It presents roc dependent curve and a

table showing the value under the curve for each

dependent variable.

Cumulative gain chart: It shows a cumulative gain chart

for each dependent variable.

• Lift chart: It presents a lift chart for each de nite

dependent variable.

Predicted by observed chart: It present a predicted by

observed chart for each dependent variable.

• Residual by predicted chart: It presents residual by

predicted chart for each dependent variable.

• Case processing summary: It shows a table con-

sisting of case processing summary. Independent

variable importance analysis: It estimates effect of

each predictor in determination of neural network

and then, presents a table and a chart showing the

importance of each predictor (Mandal, et al. 2006).

THE SAVE MENU

• Save predicted value or category for each depend-

ent variable:

It saves predicted values of each dependent variables as

well as predicted category.

• Save predicted pseudo-probability or category for

each dependent variable:

It saves predicted pseudo- probability for each depend-

ent variable.

• Option:User-missing value: in order for factors to

enter analyses, they should have available values.

The controls enable us to decide about availability

of user-missing values in dependent variables and

factors. Maximum training time:

It assigns maximum training time which is >0.

• Maximum Training Epochs:

If maximum training epochs are exceeded, training will

stop

• Minimum Relative Change in training error:

Goughari, Zayandehroodi and Eslami

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS ENVIRONMENTAL ORIENTED OPTIMAL SELECTION OF RENEWABLE AND GREEN ENERGY SOURCES 335

If it is smaller than last step, a >0 value is assigned. If

test data are only used for estimation of error for online

and mini-batch training, the scale will be neglected.

• Minimum Relative Change in training error ratio:

If it is smaller than the given value, the training will

stop. After performing the mentioned steps, the results

obtained from prediction of neural network are inserted

in the main page of the software and other results and

characteristics are shown in another  le (Arrillaga, 1991).

MEAN SQUARED ERROR (MSE)

 MSE is the average of all of the squared errors.

 it magni es the errors by each of the error.

the forecast with the smallest MSE best  ts the data

(3)

THE RESOURCES TO BE USED IN THE

SOFTWARE HOMER

a. Solar resources

b. Water resources

c. Wind resources

d. Biomass resources

e. Fossil resources.

In the Homer, attribute is a part of power system which

is producer, presenter, converter, or saver of energy. There

are ten attributes in the Homer, three of which produces

electric power by renewable resources, i.e. wind, solar, and

water resources. Three types of generators are networks

and boilers which are predisposed to dispatch, that is,

system can control them if necessary. Two other types of

attributes, i.e. converters and electrolysis, convert electric

energy to another form of energy. Two more attributes

are batteries and energy saving tanks. The Homer enables

users to compare several design choices according to

technical and economic principles (Lippman, 2004).

RESULTS AND DISCUSSION

Bafgh is a city in and the capital of Bafgh County, Yazd

Province, Iran. At the 2006 census, its population was

30,867, in 7,919 families. It is very necessary to make use

of clean energies in this city in order to prevent from envi-

ronmental pollutions and economic considerations. Fig-

ures 2 and 3 depict wind speed pro le and solar horizontal

FIGURE 2. Windspeed in bafgh

FIGURE 3. Solar horizontal radiation in bafgh

Goughari, Zayandehroodi and Eslami

336 ENVIRONMENTAL ORIENTED OPTIMAL SELECTION OF RENEWABLE AND GREEN ENERGY SOURCES BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

radiation for Bafgh, respectively. This software is able to

analyze and model connected and/or separated systems to

mobile electric network with various blends of solar and

wind arrays, small water resources, biomass resources, die-

sel generators, and batteries. The main ability of this soft-

ware is to simulate long-term behavior of small power sys-

tems. In the Homer, equipment price, equipment longevity,

fuel price, and geographical and climatic characteristics.

Finally, the software selects optimal system according to all

prede ned conditions with the lowest process

The forecast  ve trade tariffs, domestic, industrial,

agricultural and made public. According to the above

charts. This prediction using neural network software

SPSS tool is available in this software. Data used in this

study include daily load for the past  ve years (Mandal,

et al. 2006).

Prediction results in different sectors show that effect

targeted subsidies were not the same on various sectors.

Also, it was found that effect of targeted subsidies in the

 rst year was higher on load pro le. However, such fac-

tors as appearance of electric agricultural wells have had

adverse effect on load pro le in agriculture sector. Require-

ment of Bafgh was found to be 368976 (mw/h) in 2017.

DEPICTS COST CHARTS FOR DIFFERENT

COMPONENTS OF THE SYSTEM STUDY FOR

THE CITY BAFGH

The studied system consisted of solar array, wind tur-

bines, diesel, battery, and converter that it cost charts

for different components of the system study for the city

Bafgh as independent of network. (Norusis, 2011). chrat

Goughari, Zayandehroodi and Eslami

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS ENVIRONMENTAL ORIENTED OPTIMAL SELECTION OF RENEWABLE AND GREEN ENERGY SOURCES 337

FIGURE 4.

of ( costs /kw) for different sources that the system for

the city Bafg. That’s according to type system cost pro-

duction and installation and replacement per 1 kwh is

different . That each case fully described.

Wind turbine

In this study, wind turbine model 5PGE11 / 3 is used. in

order to achieve the optimal size of the unit is consid-

ered 0,1 And height of the turbine 25m and turbine life

expectancy is 25 years (IBM, 2011).

Solar array

The cost of installing solar arrays depending on the

manufacturer and its ef ciency from $ 4 to $ 7 dollars

Goughari, Zayandehroodi and Eslami

338 ENVIRONMENTAL ORIENTED OPTIMAL SELECTION OF RENEWABLE AND GREEN ENERGY SOURCES BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

FIGURE 5. Windturbine

FIGURE 6. Solar array

FIGURE 7. Battery

per watt can be changed. 20 year lifetime of the solar

arrays for 1 ( k w ) system installation costs 7,000 $ and

5,000$ replacement cost is considered.

Figure 6 shows the Sun curved array. Systems of

different sizes between 1 and 5 have been used (IBM,

2011).

Battery

In order to energy save for battery model S6CS25P with

speci cations 6V, 1156Ah, 6.9kw, which is considered in

HOMER used. The useful life of the battery bank of bat-

teries 4 and 5 to 90 is consider. Figure7

Converter

In this hybrid system, be sure to communicate between

the consumer ac and the manufacturer dc is required to

converter power electronics. Power converter is intended

for system life expectancy of 15 years and 90% ef -

ciency with a size of (10,15,20,30.40) is considered.

FIGURE 8. Converter

Diesel Generator

The cost of commercial diesel generators may250 kW /

$ to 500 kW / $ change. The price per kW for smaller

units rather than larger units. Free fuel prices 35 (L / $)

is included. During the period of performance and value

0 and 35kW diesel 20000h to achieve optimal system is

intended. The cost per kw about 450 $ and maintenance

costs 400 $ for achieving optimal system are included.

The curve shows the cost of diesel (IBM, 2011).

FIGURE 9. Diesel Generator

OPTIMIZATION RESULTS

Homer according to the inputs given to it. And the sys-

tem may increase based on the  nal net cost of which

in a table called Table Optimization is arranged. As the

simulation is special arrangement of equipment mod-

Goughari, Zayandehroodi and Eslami

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS ENVIRONMENTAL ORIENTED OPTIMAL SELECTION OF RENEWABLE AND GREEN ENERGY SOURCES 339

FIGURE 10. Optimi-

zation Optimization

Results selected by

software Homer

FIGURE 11. The results in terms of fuel prices for Bafgh city

eling. Optimizing the best possible combination of

through them is selected. The best combination is a com-

bination that all of the constraints preset by the user

to satis ed with minimal NPC. The optimization process

homer all possible scenarios and impossible scenarios

to simulate that cannot provide load request Eliminates.

Then arrange them on the table and  nally realized

arrangement corresponds to the lowest npc as the opti-

mum arrangement is introduced. depending on the price

of fuel Various combinations are for the system based on

the NPC to the city Bafgh and the time is shown in (Fig-

ure 10), shows that optimal system for Bafgh consists of

solar array and diesel generator and battery.The results

show that the optimal system includes solar array and

diesel generator and battery is Bafgh city. The system is

optimized for the city Bafgh including wind turbine and

solar array and diesel generator .

Other states in  gure11 shown by the software.

The results in terms of fuel prices for the city Bafgh

shows the optimal system cost for delivering power to

the city for 2017 need to invest (10821000) dollars. This

article is in two parts, load forecasting and optimization

of hybrid systems has been presented. The  rst was how

to predict load and the results have been presented by

software SPSS.These results indicate that once required

Bafgh for the year 2017 is equal to 368,976 MWh (Cen-

trell, 2012, IBM, 2011, Margeta, et al. 2012, Weersooriya,

1992).

CONCLUSION

With regard to targeted subsidies, the results obtained

for different part were varying. It was shown that con-

sumption of agriculture sector was lower in the begin-

ning of execution of the targeted subsidies. Moreover,

after launching targeted subsidies, farmers decided to

have electric wells because of higher price of fuel and

consequently, effect of use of electric energy on load

pro le was obtained for 2017. Using electric wells has

had adverse effect on agriculture sector. The results

showed that Bafgh needs 368976 megawatt per hour in

2017.

With regard to low wind speed in Bafgh, optimal

system was found to be solar-diesel and independent

of network. Also, the results showed that with regard

to high potential of Bafgh in wind and solar energies,

Referring to[ Fig 11]government should invest in order

to make use of the resources. The results obtained from

optimization indicated that use of renewable energies

increase if subsidies are completely removed so that die-

sel generators will decrease as fuel price increases. With

regard to advantages of renewable plants in terms of

environment, it is recommended to make use of such

plants to reduce greenhouse gases.

The results indicate the necessity of governmental

support in private sector in establishment of such sys-

tems. The recommended system in the present work can

be the best solution for Bafgh in 2017.With regard to the

results obtained from the present work, the following

recommendations can be considered Modeling system

in RETSCREEN Software in order to obtain reduction of

greenhouse gases if the recommended system is adopted

Determination of effect of using such systems on net-

work parameters in connection to the system Use of

Goughari, Zayandehroodi and Eslami

340 ENVIRONMENTAL ORIENTED OPTIMAL SELECTION OF RENEWABLE AND GREEN ENERGY SOURCES BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

other renewable resources in order to provide load and

comparing the effect of costs and Modeling the systems

by Homer Software.

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