A comprehensive framework for solar panel technology selection: A BWM‐ MULTIMOOSRAL approach

Abstract

Concerning the overwhelming advantages of solar energy, controlling and exploiting solar energy by using solar panels is one of the main fields of research in the domain of renewable energy. The choice of solar panel technology is highly significant to exploit as much energy as possible. In this paper, the main goal is to select the best technology for solar panels by investigating nine technologies from the first, second, and third generations of solar panels. Moreover, five sustainable criteria of electrical, mechanical, economic, technical, and climate, and 20 subcriteria are given for making decision analysis. Then, the best–worst method is employed according to the experts' opinions for weighting the criteria and for comparing the ranking of the solar energy technologies a framework based on the MULTIMOOSRAL multiple criteria decision‐making method is proposed. Finally, sensitivity analysis will be conducted on the ranking of the technologies. By exploiting the proposed methodology, CIS/CIGS and Perovskite Solar cell are ranked 1 and 2 as the best solar panel technologies for the selected locations.

Full text

Received: 1 March 2022
|
Revised: 17 July 2022
|
Accepted: 3 August 2022
DOI: 10.1002/ese3.1292
ORIGINAL ARTICLE
A comprehensive framework for solar panel technology
selection: A BWM‐MULTIMOOSRAL approach
Pegah Shayani Mehr
1
|Ashkan Hafezalkotob
3
|Keyvan Fardi
2
|
Hamidreza Seiti
4
|Farzad Movahedi Sobhani
1
|Arian Hafezalkotob
5
1
Department of Industrial Engineering,
Science and Research Branch, Islamic
Azad University, Tehran, Iran
2
Faculty of Industrial Engineering, Urmia
University of Technology, Urmia, Iran
3
South Tehran Branch, College of
Industrial Engineering, Islamic Azad
University, Tehran, Iran
4
Department of Industrial Engineering,
Iran University of Science and
Technology, Tehran, Iran
5
Department of Computer Science and
Artificial Intelligence, Andalusian
Research Institute in Data Science and
Computational Intelligence (DaSCI),
University of Granada, Granada, Spain
Correspondence
Ashkan Hafezalkotob, College of
Industrial Engineering, South Tehran
Branch, Islamic Azad University, Tehran
1151863411, Iran.
Email: a_hafez@azad.ac.ir
Abstract
Concerning the overwhelming advantages of solar energy, controlling and
exploiting solar energy by using solar panels is one of the main fields of
research in the domain of renewable energy. The choice of solar panel
technology is highly significant to exploit as much energy as possible. In this
paper, the main goal is to select the best technology for solar panels by
investigating nine technologies from the first, second, and third generations of
solar panels. Moreover, five sustainable criteria of electrical, mechanical,
economic, technical, and climate, and 20 subcriteria are given for making
decision analysis. Then, the best–worst method is employed according to the
experts' opinions for weighting the criteria and for comparing the ranking of
the solar energy technologies a framework based on the MULTIMOOSRAL
multiple criteria decision‐making method is proposed. Finally, sensitivity
analysis will be conducted on the ranking of the technologies. By exploiting
the proposed methodology, CIS/CIGS and Perovskite Solar cell are ranked 1
and 2 as the best solar panel technologies for the selected locations.
KEYWORDS
best–worst method, MULTIMOOSRAL, multiple criteria decision making, solar panel,
technology selection
1|INTRODUCTION
Globally, energy has been recognized as an important
driver of economic development and its sources range
from fossil fuels like oil, gas, and coal to renewable
energy like wind, solar, geothermal, water, biomass, and
hydrogen.
1,2
The limited resources of fossil fuels and
their adverse effects on the earth and climate change
make it necessary to consider new energy sources. A
global energy transition by using renewable energy is
immediately needed to limit the average global surface
temperature rise below 2°C.
3
Indeed, technological
innovation enables us to replace fossil fuels with low‐
carbon solutions by exploiting renewable energies.
4
The
percentage of renewable energy targets in final energy
consumption is continuously increasing in numerous
countries for many reasons. The share of renewable
sources in the gross final consumption of energy in the
European Union was 18% in 2018. The Europe 2020
strategy aims to reach 20% of final energy consumption
based on renewable energies by 2020 and at least 32% by
2030.
5
Renewable energy resources supply 11% of the
Energy Sci Eng. 2022;10:4595–4625. wileyonlinelibrary.com/journal/ese3
|
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total energy demand and 17% of all electricity generation
in the United States based on the US Energy Information
Administration.
6
Earth receives more energy from the sun per hour
than the world consumes in a year, making the sun the
largest source of energy for life. Using solar energy is one
of the promising ways to power and generate electricity
compared to other energy sources.
7
Photovoltaics (PV)
may have some adverse impacts on the environment. For
instance, thin‐film technology uses toxic materials and
chemicals in manufacturing. Furthermore, solar panels
that reach the end of their useful life might become toxic
waste if not disposed of properly.
8–10
However, they are a
valuable source of energy and a large number of benefits
can be derived from solar power generation on a global
scale. In fact, the increasing competitiveness of solar PV
reinforces the capacity of using solar energy beyond that
of wind before 2025, past hydropower around 2030, and
past coal before 2040.
11
The geographical location of Iran,
with a latitude of 25–45 north, is one of the favorite
places with high reception of solar energy.
12
In many
parts of Iran, solar radiation energy is well above the
international average, and in some places, it has been
measured above 7–8 kWh. With 300 sunny days a year,
Iran can be a viable choice for studying solar energy and
utilizing it by choosing appropriate locations with high
solar reception.
13
One of the fundamental problems in the field of solar
energy is choosing appropriate solar panel technology for
the building of solar power plants to optimize the
utilization of solar energy and cost. With the rapid
progress in this field, in recent years, researchers have
achieved favorable results in the development of solar
cell technologies.
14–17
The properties of each technology
should be studied and appropriate related criteria and
subcriteria should be selected carefully to find the best
solar panel technology. In fact, one of the main
contributions of this study is considering the third‐
generation of solar panels technology in solar panel
selection by gathering the related data and information
and comparing it to the previous generation.
Multiple criteria decision‐making (MCDM) ap-
proaches refer to making decisions in the presence of
multiple criteria and can be employed for the selection of
the proper technology based on decision‐maker con-
straints. Various MCDM methods have been developed
in the literature to deal with complex problems. Each
method has its own philosophy and advantages. As one
of the latest attempts, MULTIMOOSRAL is developed by
Ulutaşet al.,
18
which involves five comprehensive
comparative steps, including logarithmic approximation,
which need to be utilized to rank alternatives. This
method can be considered as a reliable and robust
approach for decision‐makers to deal with contradictory
results generated by other MCDM methods.
In this study, several alternatives are evaluated based
on the selected criteria and subcriteria using the
MULTIMOOSRAL method. Besides, the best–worst
method (BWM) is employed for obtaining the weights
of the technology selection attributes. According to
BWM, the weights of criteria can be estimated by
comparisons with the best and the worst criteria.
19
To
this end, in this paper, we aim at selecting the best solar
energy technology for the two Iranian cities of Yazd and
Isfahan by using an integrated BWM‐MULTIMOOSRAL
multicriteria framework. Nine solar panel technologies
are chosen from the first, second, and third generation of
solar panels. The first generation includes PV technolo-
gies (crystalline silicon cells), polycrystalline silicon PV
panels, and monocrystalline silicon PV panels. The
second generation includes amorphous silicon (a–Si),
cadmium telluride (CdTe), copper indium gallium
selenium (CIGS) or copper indium diselenide (CIS),
and hybrid Si (a–Si/microcrystalline si) and third‐
generation such as concentrator photovoltaics (CPV),
perovskite solar cell, organic solar cell or plastic
solar cell.
Few studies have been conducted on choosing the
best solar panel technology for the third generation of
solar technology, and the research in this field has
mainly focused on the location of solar plants with a
focus on the first and second generations. We intend to
address the main gap in solar panel technology selection
studies by establishing a comprehensive decision‐making
matrix. Furthermore, the implementation of technologies
for different geographical locations for receiving solar
energy in the three generations has not been adequately
investigated. We use MCDM methods to select the best
solar panel technology as very few studies consider
MCDM methods when choosing the best solar panel
technology. The main novelty of this paper is as follows.
To determine the criteria and subcriteria, a thorough
investigation is done to collect the information on
electrical, mechanical, economic, and technical for three
generations of solar panel technologies. The BWM
method determines the criteria and subcriteria weights
to obtain reliable results. Then, a methodology is
developed for selecting the best solar panel technology
based on the MCDM MULTIMOOSRAL method. More-
over, the geographical information in choosing the
Isfahan and Yazd cities for a potential location to build
a solar farm is thoroughly reviewed and investigated with
the best solar panel technology. To this aim, these two
cities' geographical information is incorporated in creat-
ing a decision matrix to consider the effect on the
ranking of solar panel technologies.
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Sections of this article are as follows: Section 2 gives a
thorough review of the literature on applications of
MCDM methods to technology selection, and energy
decision‐making and identifies the research gap. In
Section 3, the existing solar technologies are briefly
discussed. Section 4 presents the proposed BWM‐
MULTIMOOSRAL algorithm for the problem of selection
of the best solar panel technology. Section 5 provides the
results and conclusions, and finally, in Section 6, we
present the conclusion and outline suggestions for future
research.
2|LITERATURE REVIEW
In this section, first, the current research in MCDM
methods is reviewed in Section 2.1 and then the
application of MCDM methods for technology selection
is studied in Section 2.2.
2.1 |MCDM methods in the energy
industry
MCDM methods have many advantages in the energy
industry, selection, and allocation of energy resources
and policies.
20,21
In fact, MCDM has become popular in
the energy industry because it helps the decision‐maker
to consider all the criteria, such as technical or economic
factors that are available, and make an appropriate
decision as per the priority. Kumar et al.
22
In Akash
et al.,
23
Jordan's different electricity power production
options are compared using the AHP methodology. By
emphasizing renewable energy sources' specific role,
24
aims to contain all the positive and negative effects of
electricity generation technologies. Georgopoulou et al.
25
examined the contribution of a multicriteria decision aid
method, ELECTRE III, to power generation using a case
study on a Greek island as a case study. By usage of the
GIS and MCDM approaches for analysis, the spatial
suitability of India's solar and wind farm locations was
investigated in Saraswat et al.
26
based on the technical,
economic, and socio‐environmental perspectives. Liu
et al.
27
used BWM for verification of feasibility and
consistency of their approach to calculating the overall
risk levels of clean energy power generation—energy
storage using virtual enterprise with fuzzy analytic
hierarchy process a MCDM framework is presented in
Balezentis et al.
28
to promote heating systems based on
renewable energy sources. The BWM and WASPAS
method are integrated for eliciting the criteria weights
during the expert survey and for the multicriteria
ranking of the heating technologies. Noorollahi et al.
29
aim to assess utilizing PV solar power plant in Khuzestan
province in Iran, using Fuzzy‐Boolean logic and AHP
decision analysis based on GIS. The climatic, economic,
orography, and environment are investigated as the main
criteria for optimal location selection.
A comprehensive review of the recent advance in
designing standalone PV systems based on multiobjective
optimization and MCDM methodologies is presented in
Ridha et al.,
30
considering the mathematical models
utilized in calculating the PV module output power and
storage battery. Yücenur et al.
31
suggested an appropriate
approach for selection of a city for a biogas facility in
Turkey. After giving criteria weights with the SWARA
method, the COPRAS method is applied to select a
suitable city to build a biogas facility. In Babatunde
et al.,
32
the HOMER robust capability and criteria‐
COPRAS are utilized to investigate the possibility of a
renewable energy system selection to power a residential
load in Lagos, Nigeria. Dapkus and Streimikiene
33
presented an MCDM framework for selecting the best
sustainable electricity generation technology by employ-
ing the MULTIMOORA method. Zavadskas et al.
34
implemented the MULTIMOORA and SWARA methods
in the analysis of the ecological energy parameters of the
experimental internal combustion engine. They implied
application of MCDM methods can help to establish the
best alternatives which provide the best energy ecological
parameters for the internal combustion engine.
Siksnelyte et al.
35
evaluated Baltic Sea Region countries'
achievements in sustainable energy development and
present an original framework for sustainable energy
development indicators. The aggregate measures of
energy sustainability were created by utilizing the
MULTIMOORA method. Asante et al.
36
recognized and
listed the limitations to renewable energy expansion
using Ghana's renewable energy. Twenty‐three restric-
tions were finalized and categorized with six titles. Then,
an integrated MULTIMOORA approach was employed
for listing the barriers. Further research has been
discussed in the appendix (Table A.1).
2.2 |Application of MCDM methods in
technology selection
In recent years, many researchers have been conducting
their research on selecting technologies by MCDM
methods.
37–39
; Balo and Şağbanşua
40
investigated the
best solar panel technology that has been investigated for
the design of PV systems using the analytic hierarchy
process of MCDM methods. An MCDM algorithm based
on the concept of Rank Weight‐Rank is studied in
El‐Bayeh et al.
41
to determine the best solar panel by
SHAYANI MEHR ET AL.
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regarding many criteria. The method is compared to
TOPSIS, and it exhibits advantages, especially regarding
the simulation time and the accuracy of the selection
when the criteria and alternatives number increase.
42
method that uses linguistic neutrosophic numbers for
multicriteria decision making. Using Iranian electricity
industry case studies as a case study, an efficient method
for selecting renewable energy resources as proposed in
opinions
43
based on simultaneous evaluation of criteria
and alternatives (SECA). To identify the best renewable
energy technologies for Rohingya refugees in Bangla-
desh, Ali et al.
44
proposed the AHP integrated compen-
satory distance‐based assessment hybrid MCDM method.
Helbig et al.
14
discussed that crystal silicon and thin‐film
PV had made a significant contribution to future global
electricity generation because of lower production costs
and increased efficiency. The AHP method has been
implemented to determine the specific amount of
elements for supply risk. A novel two‐stage MCDM
model integrating AHP and MULTIMOORA methods is
presented in Seker and Kahramann
45
to select the most
appropriate solar PV panel manufacturer for solar power
plants in the Southeastern Anatolia Region of Turkey
based on qualitative and quantitative factors. Lamata and
Sánchez‐lozano
46
investigated the best PV cell among
monocrystalline, polycrystalline, thin‐film (CdTe, a‐Si,
CIS/CIGS) technologies, and organic solar cells. They
presented the relevant information using the TOPSIS
method to collect all the combined information using
fuzzy sets simulated. Ijadi Maghsoodi et al.
47
studied a
selection of renewable energy technology issues by
suggesting a hybrid MADM approach based on the
SWARA approach and the full form of the MULTI-
MOORA method. An innovative approach is presented in
Bączkiewicz et al.
48
based on two newly developed
MCDM methods, namely COMET combined with
TOPSIS and SPOTIS, which could be the basis for a
decision support system in the problem of selecting solar
panels.
2.3 |Research gap
We have investigated and studied the capabilities of
MCDM methods in the energy industry and the selection
of technology. The following research gaps have been
identified.
Solar systems have not been fully explored in terms of
the first, especially the third generations of solar
technology, and there are a few studies on choosing the
best solar panel technology.
49
Research in this field
shows that in most studies, the third generation of solar
panel technologies and the criteria affecting this
technology has not been thoroughly investigated. In fact,
the conducted research in this field has focused more on
the field of location by focusing on the first and second
generation. Numerous studies have been conducted in
other areas, such as locating and use of solar energy in
buildings, water heaters, and power plants. The main gap
among the studies related to solar panel technology
selection is the lack of a comprehensive approach to
building a decision‐making matrix, which we intend to
address in this study. Also, the implementation of
technologies in the three generations for geographical
locations regarding reception solar energy has not been
thoroughly investigated. Moreover, there are a few
studies that consider MCDM methods for choosing the
best solar panel technology. In this regard, we use
MCDM methods to select the best solar panel technology.
To fill the gaps, our main contributions in this study
are as follows: (i) the information on electrical, mechan-
ical, economic, and technical for three generations of
solar panel technologies are thoroughly investigated
concerning the location of the solar panel site, (ii) the
method is enhanced with the BWM method for
determining the criteria and subcriteria weights to obtain
reliable results, (iii) the proposed methodology is
developed for selecting the best solar panel technology
on the basis of the MCDM MULTIMOOSRAL method,
(iv) the geographical information to choose the Isfahan
and Yazd cities for a potential location to build a solar
farm is thoroughly reviewed and investigated with the
best solar panel technology. To this aim, the geographical
information is incorporated of these two cities in creating
a decision matrix to consider the effect on the ranking of
solar panel technologies.
3|SOLAR PANEL
TECHNOLOGIES
One of the fundamental issues in the field of solar energy
is specifying the type of solar panel technology to
optimally exploit solar energy. In recent years, with the
advancement of science in this field, researchers have
achieved favorable results in improving solar cell
technologies.
2
In addition to exploiting solar energy,
there are a lot of advantages of using PV systems. Long
service life (approx. 20 years), ability to easily install and
operate in specific geographical conditions such as in
mountainous and impassable areas, usable in mobile
systems, easy maintenance, remote network dependency,
and usability grid‐connected are all advantages of PV
systems. PV modules convert solar energy into electricity
without pollution, noise, and fluctuations. It has a low
energy density, so PV modules must have a high surface
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area to produce low energy. Industrial developments and
the evolution of technologies used in the production of
PV cells will result in higher productivity and utilization
of these systems.
1
We have thoroughly studied the solar
panel technologies as alternatives in this study that
include first, second, and third generations. In Figure 1,
three generations of solar panel technology including,
crystalline silicon from the first‐generation, thin‐film
from the second generation, and nano cell from the third
generation have been represented.
3.1 |First‐generation PV technologies
The most common type of solar panels available on
today's market is silicon solar cells. Solar panels are a
series of parallel and serial solar cells generally made
of silicon. The first generation includes Mono-
crystalline silicon solar cells, called single crystals,
and are easily identifiable by an even visible coloring
and regular shape, showing excellent pure silicon.
Another type of polycrystalline silicon solar cell also
determined as polysilicon (P–Si) and multicrystalline
silicon (mc–Si), is composed of numerous regular
crystals. Monocrystalline solar cells and polycrystalline
silicon solar cells are shown in Figure 2.Asisshownin
this Figure 2, monocrystalline solar cells that are
cylindrical in shape are built of silicon ingots. To build
silicon wafers, four sides are cut out of the cylindrical
ingots. This gives monocrystalline solar panels their
distinctive shape and reduces costs, and improves the
performance of a single monocrystalline solar cell.
Polycrystalline solar cells look rectangular with no
rounded edges, which is an appropriate way to
distinguish monocrystalline and polycrystalline solar
panels from each other.
FIGURE 1 Comparison between photovoltaic cells
FIGURE 2 Monocrystalline cells versus polycrystalline cells
solar panel
50
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3.2 |Second‐generation PV
technologies: Thin‐film solar cells (TFSC)
Placing one or more thin layers of PV material with
irregular crystals made on a thin piece of semiconductor
material. The thin layer can be in the range of a few
nanometers to tens of micrometers, and this irregularity
of the crystals affects this low thickness. Different types
of thin‐film solar cells are illustrated in Figure 3and
classified according to the PV material that is coated on
the substrate:
1. Cadmium telluride (CdTe) (Figure 3A).
2. Amorphous silicon (a‐Si) (Figure 3B).
3. Copper indium diselenide (CIS)/copper indium gal-
lium selenium PV cells (CIGS) (Figure 3C).
There are several benefits of exploiting the second
generation of solar panels. First, solar panels of this
generation are easier for mass production and they are
cheaper for manufacturing than crystalline solar cells.
Also, their homogeneous appearance makes them more
attractive. Finally, shading and high temperature can
have less effect on solar panel performance in this
generation.
3.3 |Third‐generation PV cell
technologies
Other thin‐film technologies are still in the early periods
of research and development, or with limited access to
commercialization, often classified as new‐generation or
third‐generation PV cells. These technologies can be
classified into the organic solar cell or plastic solar cell,
CPV in (Figure 4A) (dye‐sensitized solar cell (DSSC,
DSC, or DYSC), quantum dot solar cell, perovskite solar
cell (Figure 4B), copper zinc tin sulfide (CZTS) in
(Figure 4C), and multijunction solar cells. In this respect,
the advantages of this generation are that organic
materials can develop long‐term technology that is
economically promising on a large scale of electricity
FIGURE 3 Second‐generation solar panel.
51
(A) Cadmium Telluride (CdTe) solar cells, (B) Amorphous silicon (a‐Si) solar cells, (C)
CIGS solar cells.
FIGURE 4 Third‐generation solar panel.
53
(A) Concentrator photovoltaics, (B) perovskite solar cells, and (C) organic solar cells.
4600
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production based on environmentally friendly materials
with unlimited access. Their many benefits can be
justified for international investment and scientific
research to increase efficiency and achieve low cost and
production on a large scale. Silicon solar cells have high
costs that show a lack of raw material supply, which
leads to a delay in project implementation, but on the
other hand, organic solar cell materials reduce produc-
tion costs through new processes.
52
A comparison
between the advantages and disadvantages of organic
and inorganic cells have been shown in Table 1.
4|RESEARCH METHODOLOGY
The purpose of this study is to develop a comprehensive
model to consider more effective criteria and decision
tools for properly selecting solar panel technologies
especially by focusing on the third‐generation of solar
panel technologies that have not previously been studied
by other researchers.
In the theory of MCDM, there are several alternatives
and criteria, and it always starts with a decision matrix
that shows the scores of the alternative in each criterion.
To this aim, after identifying the discussed alternatives,
criteria, and subcriteria, we proceed to form the decision‐
making matrix. Then, we select the best solar panel
technology using the proposed BWM‐MULTIMOOSRAL
method. In this framework, in the first phase, the weights
of the criteria are obtained by the BWM by identifying
the best and worst criteria and the comparison between
each of the two best and worst and the other criteria. In
the next phase, we obtain the best solar technology using
the MULTIMOOSRAL MCDM method. Figure 5demon-
strates the steps and derivation of the proposed decision‐
making methodology illustrated as a solution algorithm.
In Sections 4.1 and 4.2 the preliminaries of the BWM and
MULTIMOOSRAL are briefly discussed.
4.1 |BWM method
There are several factors involved in choosing the best
solar panel technology. BWM can be employed to
determine the weight of decision criteria effectively.
The algorithm of this method is based on the decision
maker's opinion.
19
Here we explain a summary of the
BWM steps for obtaining weights.
Step 1. Determine a set of decision criteria.
At this point, the decision‐maker defines ncriteria
CC C{, ,…, }
n12 for making a decision.
Step 2. Determine the best (the most desirable and
the most important) and the worst (least desirable and
least important) criteria.
Step 3. Prioritize the best criterion for all other
criteria using a number between 1 and 9:
Aaa a=( , ,…, )
,
BBB Bn12
Where aBindicates the priority of the best criterion B to
criterion j. It stands to reason that a=1
.
BB
Step 4. Prioritize all the criteria to the worst, using a
number between 1 and 9:
Aaa a=( , ,…, )
,
WWWnW
T
12
where
a
iW indicates the priority of criterion j to the
worst criterion of W. It is certain that
.
a=1
WW
Step 5. Obtain the optimal weights
(
)ww w
*,*,…, *
n12 .
The purpose of determining the optimal weights of a
criterion is to define the absolute difference of maximum
a−
w
wBj
B
jand
a−
w
wjw
j
wfor all jis minimized.
wawwawminmax {| −|, | −|}
jBBjjjjWW (1)


st w w j.. =1, 0, for all
j
jj
ξ
min
(2)



s
tw
waξw
waξww
j
.. −−=1,
0,
B
j
Bj
j
w
jw
j
jj
Model (1) is equivalent to model (2). For each value
of
ξ
, the first set of model constraints (2) is multiplied by
w
j
, and the second set of constraints is multiplied by
W
w
,
TABLE 1 The advantages and disadvantages of organic and inorganic cells
Type Advantages Disadvantages
Organic solar cell Low cost, low weight, high flexibility, environmentally friendly, high
production method, high transparency, and high productivity.
Low stability and efficiency weakening
of cells in the light.
Inorganic
solar cell
High stability and high efficiency. High cost and difficult production
process.
SHAYANI MEHR ET AL.
|
4601

where the model solution space (2) is an intersection
(
n
4
−
5
) of linear constraints. As an alternative
to minimizing the maximum value of the set
{}
aa−,−
w
wBj
w
wjW
B
J
j
J
, we aim to minimize the maxi-
mum value of the set
wawwaw{| −|, | −|}
BBjjjjWW
,
according to the following problem equation:
wawwawminmax {| −|, | −|}
jBBjjjjWW (3)


st w w j.. =1, 0,
j
jj
Problem (3) is interpreted as the problem of linear
programming as follows:
ξ
min
(4)




st
wawξw
waw ξw
ww j
..
|−|
|−|
=1, 0,
BBjj j
jjWW w
jjj
Solving the optimization problem (4) obtains
ww w
(
*,*,…, *)
n12
that are optimal weights.
FIGURE 5 The algorithm of the proposed methods
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4.2 |The MULTIMOOSRAL method
By integrating advantages of three well‐known MCDM
methods: the MOORA, the MOOSRA, and the MULTI-
MOORA,
18
developed the MULTIMOOSRAL method,
which compares alternatives based on five comparison
approaches, namely, ratio system (RS), reference point (RP),
full multiplicative form (FMF), addition form (AF), and
logarithmic approach (LA). Considering
X
as the decision
matrix and
x
ij,
as the
i
th alternative of
j
th objective, the
equation below calculates the normalized value of
x
ij,
.

xx
xjn
*=,=1,2,…,
ij
ij
i
mij
,
,
=1 ,
2(5)
Step 1. RS approach
Calculation of the overall importance of alternatives
is the first substep of RS approach which can be
accomplished by below equation

ywx wx
*=*−*
,
i
j
g
jij
jg
n
jij
=1
,
=+1
,
(6)
In the above equation,
g
denotes beneficial attributes,
which need to be maximized. The overall utility of
alternatives p
(
)
ias the second substep can be calculated
by formulation below:













p
yy
yy
yy
=
*,max
*>0
*+1, max *=0
−1/ *,max
*<0
i
iii
iii
iii
(7)
Finally, the overall utility of alternatives needs to be
normalized by below equation
ppp
pp
′=−min ( )
max( ) −min ( )
i
ii
ii (8)
Step 2. RP approach
First, Equation (9) is applied for calculating the
reference point.
∈
∈
{
}
{}
()
yyyy yj g
yj g n
** =**,**,…, ** =max*{1, 2, …, },
min *+1,…,
niij
iij
12
(9)
Then, the maximum distance between alternatives and
the reference point is calculated by the equation below:
()
t
wy y=max ** −*
.
ijj
jij (10)
Finally, utilizing Equation (11), the normalized value
for the RP approach can be obtained:
t
tt
tt
′=max( ) −
max( ) −min( )
.
iii
ii (11)
Step 3. FMF approach
After calculating the overall utility by Equation (12),
the results need to be normalized by Equation (13).


uwy
wy
=
*
*
,
i
j
gjij
jg
njij
=1
=+1 (12)
uuu
uu
′=−min ( )
max( ) −min ( )
.
iii
ii
(13)
Step 4. AF approach
Equation (14) is applied for calculating the utility,
and the results are normalized by Equation (15).


vwy
wy
=
*
*
,
i
j
gjij
jg
njij
=1
=+1 (14)
vvv
vv
′=−min( )
max( ) −min ( )
.
iii
ii (15)
Step 5. LA approach
Equations (16) and (17), respectively are used for
calculating the utility and normalized values.

() ()
hwy
wy
=ln1+ *+1
ln 1 + *
,
i
j
g
jij
jg
njij
=1 =
(16)
hhh
hh
′=−min( )
max( ) −min( ) .
ii
ii (17)
Step 6. The aggregated utility calculation and ranking
Having the normalized values of the five steps, the
final ranking of alternatives can be calculated by the
equation below:
Z
ptuvh=′+′+′+′+′.
iiiiii (18)
5|REAL‐WORLD CASE STUDY
The main target of this section is to discuss some
preliminary information related to finding suitable
locations in Iran for using solar panels. The information
about the locations is used to select the best solar panel
technology. The regions of the country according to
SHAYANI MEHR ET AL.
|
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sunshine hours are shown, and the average monthly
sunshine is illustrated in Figure 6A,B, respectively. The
cities of Isfahan and Yazd belong to the yellow region of
Figure 6A where they are the places with high average
monthly sunshine (highlighted in Figure 6B). Therefore,
these two cities are selected as the ideal areas for locating
solar panels with appropriate technologies. The subcri-
terion of climate characteristics of solar hours, solar
energy potential, solar energy intensity, and geographical
locations for these cities are provided in Table 2. Figure 7
describes the main criteria, subcriteria, and solar panel
technologies that can be utilized for two cities.
5.1 |The criteria and subcriteria
selection for solar panel technologies
The explanation of the criteria and their related
subcriteria are provided in Table A.2 in the appendix.
The presented criteria and subcriteria in this study have
been selected on the basis of a comprehensive literature
review from the research studies related to solar panel
technologies selection methods and the consultant with
the experts in a related field of solar energy. The criteria
and subcriteria with some symbols are shown in Tables 3
and 4.
In this study, weights for criteria and subcriteria are
obtained by the BWM method. First, we evaluate the
preference of the best criterion over all the other criteria
utilizing a number between 1 and 9 based on expert or
decision‐maker opinion. The economic criterion (C3)is
assigned a weight of 1. Therefore, this feature is
considered one of the most important criteria, and then
the technical (
C
4
) and electrical (C1) weight are assigned
to 3. The following climate (
C
5
) and mechanical criteria
(
C
2
) are assigned to 7, respectively. For the determination
of subcriterion preference, panel cost subcriteria (
S
C
16
)
and module maximum power (
S
C
4
) are considered as the
most important factors. The maximum power of the
module, according to which results in the most power
from the solar panels, is more important than the panel
cost and is considered as the best subcriterion. The
subcriterion of weight (
S
C
15
), which belongs to the
mechanical criterion group, is selected as the worst
subcriterion. In the next step, the optimal weights are
obtained based on the preference weights.
5.2 |Computation results
In this section, first, we exploit the gathered information
about the criteria and subcriteria of the solar panel
technologies as well as the information about the two
cities of Isfahan and Yazd to build the decision matrices
of the MCDM methods. We utilize the BWM method to
determine the optimal weights of criteria and subcriteria
based on the discussion in the previous section and
Equation (4). Then, with discussed MCDM methods in
the research methodology section, we conclude the final
ranking for each method to select the best solar panel
FIGURE 6 Information about sunshine hours in Iran.
54
(A) Regions of the country in terms of sunshine hours and (B) average monthly
sunshine.
4604
|
SHAYANI MEHR ET AL.

technology. Finally, two analyses have been conducted to
investigate the effect of variation in subcriteria weight on
the ranking of panel technologies. During the presenta-
tion of the results, although the information for Isfahan
city has been provided for sake of brevity, we only show
the information for Yazd city.
After identifying alternatives, criteria, and subcriteria, the
final decision matrix for the two cities of Isfahan and Yazd is
presented in Table 5. In this table, information is provided
about electrical, mechanical, economical, technical, and
climate criteria and related subcriteria for selecting the solar
panel. The presented information in this table is gathered
TABLE 2 Measuring radiation hours, intensity, solar energy potential, and geographical location of Isfahan and Yazd
City
Solar Energy Intensity
(MJ/m
2
year)
Measured sunshine
(h/year)
Solar Irradiation
(kwh/m
2
day) Longitude (
°
E
)
Latitude
(
°N
) Elevation (m)
Yazd 7787 3270 2.4–5.5 51.87 32.67 1600.7
Isfahan 6242.15 3277.2 4.5–5.2 54.40 31.90 1230.2
FIGURE 7 Algorithm for finding the best solar panel technology for two cities of Isfahan and Yazd
SHAYANI MEHR ET AL.
|
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from a commercialized solar panel that is provided by the
supplier. Now, we should specify the weight of each criterion
and subcriteria. Decision‐makers may have a different
perspective on the importance of different criteria and
subcriteria that can have different effects on the ultimate
result of the selection of solar panel technology. Table 6
shows the weights determined by an expert separately for the
two cities of Isfahan, and Yazd, each subcriterion weight is
multiplied by its own weight and the final weight is obtained
using the BWM method.
Next, the weights of the criteria and subcriteria for
the two cities of Isfahan and Yazd are obtained based on
the BWM method which is presented in Table 6.
In the next stage, using data in Table 5as the inputs
of Equation (5), the dimensionless normalized decision
matrix is obtained. Having the weight matrix (Table 6),
we ranked alternatives based on five comparing ap-
proaches of the MULTIMOOSRAL method; p′
iis the
result of Equations (6)–(8),
t
′
i
is calculated by Equations
(9)–(11), u′
i
is obtained from the utilization of Equation
(12) and (13),
v′
i
is acquired from the application of
Equations (14) and (15), h′
iis generated by Equation (16)
and (17), and the cumulative value is calculated by
Equation (18), which are displayed in Table 7.
To evaluate the reliability of the results of the
MULTIMOOSRAL method, the results are recalculated
by other MCDM methods such as MULTIMOORA,
55
Borda,
56
COPRAS,
84
, and WASPAS
58
in comparison with
the MULTIMOOSRAL method and solar panel technol-
ogies were ranked, which the results are displayed in
Table 8and Figure 9.
TABLE 3 Introducing the symbol for the selected alternatives
Symbol Alternative Panel technology
A1 a‐Si Second‐generation: Thin‐
film solar cells (TFSC)
A2 CdTe
A3 Hybrid si(a‐Si/
microcrystalline sí)
A4 CIS/CIGS
A5 Organic solar cell Third‐generation
Photovoltaic
A6 Perovskite solar cell
A7 CPV
A8 p‐Si/multi‐si First‐generation
Photovoltaic
A9 m‐Si/s‐Si
TABLE 4 Introducing the symbol for
the selected subcriteria
Symbol Criteria Selection subcriteria
SC1 C1 (Electrical) Power tolerance (%)
SC2 Series fuel rating (A)
SC3 Temperature coefficient of VOC
(
)
V
°C
SC4 Maximum power (
P
max
) (wp)
SC5 Maximum system voltage (V)
SC6 Temperature coefficient of ISC
(
)
A
°C
SC7 Temperature coefficient of
P
max
(
)
%
°C
SC8 Product warranty (Years)
SC9 Max power voltage (
V
mp
) (V) (STC)
SC10 Max power current (
I
mp ) (A) (STC)
SC11 Open circuit voltage (VOC) (V) (STC)
SC12 Short circuit current (ISC) (A) (STC)
SC13 Panel efficiency (%)
SC14 C2 (Mechanical) L × W × H (cm
3
)
SC15 Weight (kg)
SC16 C3 (Economic) Panel cost ($)
SC17 C4 (Technical) Compute output manufacturer error (
f
man )
SC18 Calculate the effect of pollution and dust (
f
dirt )
SC19 AVG temperature effect of Isfahan/Yazd (°
C
)
SC20 C5(Climate) AVG temperature of Isfahan/Yazd (°
C
)
4606
|
SHAYANI MEHR ET AL.

TABLE 5 The decision‐making matrix
Yazd City Panel SC
1
SC
2
SC
3
SC
4
SC
5
SC
6
SC
7
SC
8
SC
9
SC
10
A1 2.50 8.00 0.29 105.00 800.00 0.01 0.31 10.00 71.50 1.47
A2 5.00 10.00 0.24 85.00 1000.00 0.02 0.25 10.00 45.20 1.89
A3 5.00 10.00 0.39 120.00 600.00 0.06 0.35 25.00 55.00 2.18
A4 1.50 8.00 0.27 360.00 1000.00 0.01 0.28 25.00 60.00 5.99
A5 5.00 8.00 0.14 200.00 600.00 0.02 0.30 25.00 45.00 4.10
A6 2.50 9.00 0.22 380.00 800.00 0.03 0.31 28.00 47.00 8.46
A7 3.00 10.00 0.07 360.00 1000.00 0.03 0.20 10.00 32.84 10.96
A8 3.00 16.00 0.31 335.00 1000.00 0.04 0.40 12.00 37.20 9.01
A9 3.00 20.00 0.28 310.00 1000.00 0.03 0.38 12.00 32.80 9.35
Panel SC
11
SC
12
SC
13
SC
14
SC
15
SC
16
SC
17
SC
18
SC
19
SC
20
A1 93.10 1.63 13.80 6.80 20.00 0.37 5.25 94.78 87.96 7.17
A2 60.00 2.17 12.00 4.90 12.00 0.35 4.25 76.71 72.27 5.79
A3 71.00 2.60 9.80 48.70 18.00 0.28 6.00 108.30 99.52 8.10
A4 76.60 6.44 15.30 105.50 33.00 0.31 18.00 324.90 303.82 6.48
A5 70.60 9.13 18.00 53.10 15.00 0.48 10.00 180.50 167.96 6.94
A6 58.00 8.97 19.00 82.00 30.00 0.29 19.00 342.95 318.32 7.17
A7 36.79 11.97 28.00 113.90 44.00 0.50 18.00 324.90 309.85 4.63
A8 45.50 9.51 17.30 77.60 23.00 0.29 16.75 302.33 274.32 9.26
A9 40.40 9.96 18.90 65.60 17.00 0.35 15.50 279.77 255.15 8.80
Isfahan City Panel SC
1
SC
2
SC
3
SC
4
SC
5
SC
6
SC
7
SC
8
SC
9
SC
10
A1 2.50 8.00 −0.29 105.00 800.00 0.01 −0.31 10.00 71.50 1.47
A2 10.00 10.00 −0.24 85.00 1000.00 0.02 −0.25 10.00 45.20 1.89
A3 10.00 10.00 −0.39 120.00 600.00 0.056 −0.35 25.00 55.00 2.18
A4 1.50 8.00 −0.27 360.00 1000.00 0.01 −0.28 25.00 60.00 5.99
A5 5.00 8.00 −0.13 200.00 600.00 0.02 −0.30 25.00 45.00 4.10
A6 2.50 9.00 −0.22 380.00 800.00 0.03 −0.31 28.00 47.00 8.46
A7 3.00 10.00 −0.06 360.00 1000.00 0.03 −0.20 10.00 32.84 10.96
A8 3.00 16.00 −0.31 335.00 1000.00 0.04 −0.40 12.00 37.20 9.01
A9 3.00 20.00 −0.28 310.00 1000.00 0.03 −0.38 12.00 32.80 9.35
Panel SC
11
SC
12
SC
13
SC
14
SC
15
SC
16
SC
17
SC
18
SC
19
SC
20
A1 93.10 1.63 13.80 6,862,730 20.00 0.378 5.25 94.76 88.35 6.76
A2 60.00 2.17 12.00 4,968,000 12.00 0.353 4.25 76.71 72.18 5.45
A3 71.00 2.60 9.80 48,787,200 18.30 0.284 6.00 108.30 100.02 7.64
A4 76.60 6.44 15.30 1.06E+08 33.30 0.315 18.00 324.90 305.04 6.11
A5 70.60 9.13 18.00 53,171,200 15.00 0.48 10.00 180.50 168.68 6.54
A6 58.00 8.97 19.00 82,008,000 30.00 0.29 19.00 342.95 319.74 6.76
A7 36.79 11.97 28.00 1.14E+08 44.00 0.50 18.00 324.90 310.71 4.36
A8 45.50 9.51 17.30 77,614,080 23.00 0.29 16.75 302.33 275.93 8.73
A9 40.40 9.96 18.90 65,600,000 17.00 0.352 15.50 279.77 256.56 8.29
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|
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5.3 |Sensitivity analysis
In this section, we design some sensitivity analyses to
investigate the effect of changing the preference value
determined by the expert or decision‐maker on the final
ranking of solar panel technologies. The overall changes
in the ranking of different solar panel technologies are
based on the weight changes of different subcriteria.
Taking 20 subcriteria into account makes the evaluation
of all weight variations an unpractical task. Therefore,
four possible alterations in the weights of subcriteria are
examined, and their impact on the aggregated utility
value of the MULTIMOOSRAL method is investigated.
Considering Table 9as the basic weight matrix of
alternatives, we proposed the following assumptions for
a better explanation of Figures 10 and 11.
•An identical approach is used for the same subplots in
both Fig.s. In other words, a similar weight matrix is
applied for plotting Figures 10A and 11A. The
following steps are done for the sensitivity analysis:
•Consider
W
ww w
=
(
,,…,
)
Uuu u
12 20
and
W
ww w
=
(
,,…,
)
Lll l
12 20
,
as weight vectors based on which upper bound and
lower bound of the Perturbation Bound (Figures 10
and 11) are obtained.
•For Figures 10A and 11A, we have:
w
= (1 + 0.25) ×
iu

w
i,1 5
ito satisfy
∈

w=
1
iiu
{1,…,20}
, the weights of
some alternatives need to be reduced. Therefore,
∆
is
defined to capture entire addition or subtraction, and
is calculated by 
w
Δ
=0.25 ii
15. Afterward, an equal
amount (
Δ
/
5
) is subtracted from five other alternatives
to maintain equality (

w
wi=−Δ/5, 6 10
iui
), and
the weights of other alternatives remain unchanged
(
w
wi= , 11 20
iui
). Almost a similar approach is
utilized for the calculation of
W
L; that is,

w
wi=(1−0.25) × , 1 5
ili,(

w
w=+Δ/5, 6
ili

i
10
), and (

w
wi= , 11 20
ili
). In other words,
Column 1 of Table 9is altered
±25%
which led to
Δ
/
5
variation in Column 2 (Table 9) and Columns 3 and 4
(Table 9) remained unchanged.
•The same methods of addition and subtraction are
applied to calculate the Perturbation Bond in sub-
figures (b), (c), and (d):
o For Figures 10B and 11B: Column 2 is altered
±25%
which led to
Δ
/
5
variation in Column 3, and
Columns 1 and 4 remained unchanged.
TABLE 6 Final weight for the cities
of Yazd and Isfahan
Weights SC
1
SC
2
SC
3
SC
4
SC
5
SC
6
SC
7
SC
8
SC
9
SC
10
General 0.220
Local 0.040 0.022 0.053 0.119 0.080 0.040 0.032 0.053 0.053 0.053
Final 0.008 0.005 0.011 0.026 0.017 0.008 0.007 0.011 0.011 0.011
Weights SC
11
SC
12
SC
13
SC
14
SC
15
SC
16
SC
17
SC
18
SC
19
SC
20
General 0.094 0.331 0.220 0.132
Local 0.040 0.032 0.080 0.02 0.011 0.080 0.026 0.053 0.053 0.053
Final 0.008 0.007 0.017 0.001 0.001 0.026 0.005 0.011 0.011 0.007
TABLE 7 Results of the MULTIMOOSRAL method
Panel
Isfahan Yazd
p′
i
t′
iu′
i
v
′
i
h
′
i
Z
i
Rank
p′
i
t′
iu′
i
v
′
i
h
′
i
Z
i
Rank
A1 0.353 0.105 0.483 0.571 1.000 2.512 5 0.336 0.105 0.239 0.238 0.746 1.664 8
A2 0.154 0.000 1.000 0.230 0.773 2.158 8 0.425 0.000 1.000 0.315 1.000 2.740 3
A3 0.000 0.183 0.000 0.000 0.474 0.657 9 0.000 0.183 0.000 0.000 0.181 0.364 9
A4 0.875 0.845 0.225 0.804 0.268 3.018 2 0.778 0.845 0.109 0.780 0.166 2.678 5
A5 0.400 0.602 0.475 0.378 0.312 2.167 7 0.586 0.602 0.235 0.574 0.407 2.404 6
A6 0.923 0.853 0.146 0.779 0.177 2.878 4 0.848 0.853 0.069 0.846 0.095 2.712 4
A7 0.675 0.740 0.367 0.458 0.000 2.241 6 1.000 0.881 0.180 1.000 0.183 3.244 1
A8 0.876 0.955 0.065 0.823 0.293 3.013 3 0.600 0.938 0.029 0.614 0.000 2.181 7
A9 1.000 1.000 0.266 1.000 0.363 3.629 1 0.751 1.000 0.130 0.754 0.138 2.772 2
4608
|
SHAYANI MEHR ET AL.

o For Figures 10C and 11C: Column 3 is altered
±25%
which led to
Δ
/
5
variation in Column 1, and
Columns 2 and 4 remained unchanged.
o For Figures 10D and 11D: Column 2 is altered
±25%
which led to
Δ
/
5
variation in Column 4, and
Columns 1 and 3 remained unchanged.
5.4 |Results and managerial insights
•According to Figure 8,
AA,
94
, and
A
8
are the most
suitable panels for Isfahan city, and
AA,
79
, and A2are
the best alternatives for Yaz city. These results indicate
that all three types of technology are applicable in
Yazd. However, only first and second‐generation
technologies are among the highest suitable planes in
Isfahan. Another significant insight from Table 7and
Figure 8indicates that policymakers can develop the
usage of A9as a type of panel which has the almost
same level of desirability for both cities. The final
insight which is indicated in Figure 8is the impact of
the logarithmic approach on the ranking of panels by
the MULTIMOOSRAL method. Although the patterns
of ptv′,′,′
,
, and u
′
are relatively similar for the two
cities, the patterns of h
′
for Isfahan and Yazd are
significantly different from each other. As it is
demonstrated by Ulutas et al.,
18
the most essential
element which distinguishes the MULTIMOOSRAL
method from the four MCDM approaches is the
logarithmic approach consideration in the process of
ranking the alternatives.
•According to the results demonstrated in Table 8and
Figure 9, the COPRAS and the WASPAS methods
seem to produce more similar results to the MULTI-
MOOSRAL method. Although the WASPAS shows
better performance than COPRAS for Isfahan's case,
the overall performance of COPRAS is better than
other methods.
•The following results are inferred by juxtaposing
similar subplots in Figures 10 and 11:
1. According to Figures 10A and 11A, altering weights of
subcriteria belonging to Electrical criteria does not
TABLE 8 Ranks of panels by different methods for Isfahan and Yazd
Panel
Isfahan Yazd
MULTIMOORA Borda COPRAS WASPAS MULTIMOOSRAL MULTIMOORA Borda COPRAS WASPAS MULTIMOOSRAL
A1 7 7 8 7 5 7 7 8 7 8
A2 8 8 7 8 8 8 8 7 8 3
A3 9 9 9 9 9 9 9 9 9 9
A4 1 1 3 1 2 1 1 3 1 5
A5 6 6 6 6 7 6 6 6 6 6
A6 2 2 2 3 4 2 2 2 3 4
A7 3 3 1 2 6 3 3 1 2 1
A8 5 5 5 4 3 5 5 5 4 7
A9 4 4 4 5 1 4 4 4 5 2
TABLE 9 Basic weights of alternatives
Column 1 Column 2 Column 3 Column 4
w
1
0.04
w
60.040
w
11
0.040
w
16 0.080
w
2
0.023
w
70.032
w
12
0.032
w
17 0.027
w
3
0.053
w
8
0.053
w
13
0.080
w
18 0.053
w
4
0.120
w
9
0.053
w
14 0.020
w
19 0.053
w
5
0.080
w
10 0.053
w
15 0.011
w
20 0.053
SHAYANI MEHR ET AL.
|
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significantly affect the ranking of alternatives. Addi-
tionally, Yazd shows more stability against tolerances
of subcriteria's desirability in comparison with Isfahan
city. Although Isfahan shows a slight tendency of
changes in ranking order of Solar Panel Technologies,
even these tolerances cannot make investors or
policymakers change the generation of solar panels
in this city.
2. Figures 10B and 11B indicate that decreasing/increas-
ing weights of Electrical factors (
S
CSC,…,
510
) and
increasing/decreasing weights of Mechanical factors
significantly change the ranking of alternatives and
even change the desirability of technology generation
in both cities. Additionally, it seems that Yazd is more
sensitive to this type of change in comparison with
Isfahan city.
3. Considering Figures 10C and 11C, decreasing/increas-
ing weights of Electrical factors (
S
CSC,…,
15
) and
increasing/decreasing weights of Mechanical factors
does not dramatically affect the rank of alternatives.
In other words, managers and policymakers may
imply their desirability without being worried about
drastic consequences.
4. Figures 10D and 11D demonstrate that adding/
subtracting weights of Electrical factors and subtract-
ing/adding weights of Economical,Technical, and
Climate factors lead to significantly different results
for the cities. While Isfahan is more reliable against
these weight alterations, Yazd city shows a noticeable
tendency to switch between generations of solar
panels. A possible explanation for this dissimilarity
is the existence of infrastructure in Isfahan city. In
FIGURE 8 Radar graph of rankings of MULTIMOOSRAL for solar panel technologies
FIGURE 9 Solar panel technology ranking by different MCDM methods. (A) Isfahan and (B) Yazd.
4610
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SHAYANI MEHR ET AL.

fact, Isfahan city second to Tehran, the capital city of
Iran, is the most developed state in the country.
5. In general, regarding Figures 10 and 11, Isfahan city is
a more reliable state for solar panel technology
investment rather than Yazd, which makes Isfahan
the best region for utilizing solar energy in Iran.
•This study provides managers and investors in the
renewable energy sector with a decision‐making
methodology for selecting appropriate solar panel
technologies. The following managerial insights are
derived from the research:
1. The results of all MCDM methods show that none of
the first‐generation solar panel technologies has been
ranked in the top three technologies for both Isfahan
and Yazd cities, respectively. CIS/CIGS, CPV, and
Perovskite Solar cells are suggested as the top 3
technologies from the second and third‐generation
technologies.
2. The same results of the best solar panel technology for
Yazd and Isfahan cities explain that changing the
location does not significantly affect the best solar
panel technology. This finding can be justifiable since
these two cities have similar geographical
characteristics.
3. The sensitivity analysis can help the decision‐maker to
evaluate the significance of subcriteria and their
effects on the ranking of solar panel technology. In
our case study, top‐ranked technologies CIS/CIGS and
CPV are robust to the small changes of subcriteria
significance, but the rank of the organic solar cell is
highly sensitive to the subcriteria significance.
4. Private sector investors may find Isfahan city more
reliable for solar panel utilization caused by its result
from relatively sufficient infrastructures compared to
other cities in Iran.
5. Policymakers need to develop urban infrastructures to
have private sections invest in solar energy sectors in
Yazd to reduce the risk of private investments. The
same approach needs to be established in other states
to attract essential capital.
FIGURE 10 Sensitivity analysis on columns of Table 9 for Isfahan city
SHAYANI MEHR ET AL.
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4611

It should be noted that to the best of our
knowledge, there is no studytoconsiderthethree
solar panel technologies and the location with the
provided criteria and subcriteria. In Helbig et al.,
14
based on the political and economic criteria for the
element material of the solar panels, two first‐
generation and second‐generation technologies of solar
panels, including crystalline silicon thin‐film PV
(CdTe, CIGS), are assessed for supply risk. According
to the result, CdTe exhibits lower supply risk values for
all aggregation options. In Lamata and Sánchez‐
lozano
46
based on economic and environmental crite-
ria, including the manufacturing cost, efficiency in
energy conversion, market share, emissions of green-
house gases, and energy pay‐back time the solar panel
technologies of monocrystalline and polycrystalline,
amorphous silicon, CdTe, and CIGS, photovoltaic cells
with advanced III–V thin layer and organic and hybrid
cells are investigated and by using TOPSIS method the
best solar panel technology has been selected as
photovoltaic cells with advanced III–Vthinlayer.
5.5 |Implication
The proposed methodology in this study aims to rank the
best solar panel technology for two cities of Isfahan and
Yazd. One implication of the proposed methodology is
using the approach for ranking the solar panel technol-
ogy for other geographical locations based on the new
location information. Therefore, the method can be
implicated in ranking solar panel technology for new
locations based on the experts' or decision makers'
opinions. Moreover, the procedure can be considered
new solar panel technology, which means upon the
availability of new solar panel technologies information,
they can be considered in selecting the best solar panel
technology based on the proposed methodology.
One of the applications of the proposed method is the
selection of solar panels based on the technology for
industry, such as building solar saline water reverse
osmosis (RO) desalination plants. The solar desalination
plant utilizes solar energy to desalinate seawater. The
proposed method can be used for the selection of the best
FIGURE 11 Sensitivity analysis columns of Table 9 for Yazd city
4612
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SHAYANI MEHR ET AL.

solar panel technology considering new solar technology
based on the opinion of the decision‐makers of experts
for the building of the solar desalination plant and based
on the information about the new location.
6|CONCLUSIONS
Solar energy can improve many of today's problems,
including the use of clean energy for factory, home and
organization uses, as well as the implementation of
energy source technologies for livelihoods in remote
areas. In this study, solar panel technologies are
classified and identified as the first, second, and third
generations. Nine technologies are considered in the
selection process, in addition, the criteria and subcriteria
on the solar panels, are classified into five criteria and 20
subcriteria. To select the best solar panel technology, two
cities of Yazd and Isfahan have been selected after
extensive studies of characteristics, and related factors
such as solar energy reception and appropriate radiation
angle. Subsequently, the decision matrix was established
based on the opinion of several experts. Then, they
determined the weights of the criteria and subcriteria of
the matrix with the BWM method MULTIMOOSRAL
method has been suggested for evaluation of the
technologies for two cities. The obtained results of solar
panel technologies rankings by implementing MCDM
methods established that three of the best solar panel
technologies were ranked as CPV (Second Generation),
perovskite solar cell (Third Generation), and CIS/CIGS
(Third Generation) for two cities. Moreover, sensitivity
analysis explained that a small variation in the signifi-
cance of subcriteria does not affect the rank for the
technologies like CIS/CIGS and CPV and these technol-
ogies remained the best solar panel technology in the
ranking list. The following outcomes can be presented
from the research (i) for both Isfahan and Yazd cities,
none of the first‐generation solar panel technologies
ranked within the top three technologies. As for second
and third‐generation technologies, the most promising
technologies are CIS/CIGS, CPV, and Perovskite solar
Cells. (ii) as can be understood from the same results for
Yazd and Isfahan, varying the location does not
significantly impact solar panel technology. Considering
the similar geographical characteristics of these two
cities, this finding can be justified. (iii) in a sensitivity
analysis, it is shown how the decision‐maker can assess
the way that subcriteria affect solar panel technology
rankings and their significance. As shown in the case
study, top‐ranked technologies such as CIS/CIGS and
CPV are resilient to small changes in subcriteria
significance, while organic solar cells have a high
sensitivity to such changes. (iv) due to its relatively
sufficient infrastructure compared to other Iranian cities,
Isfahan may be more attractive to private sector investors
for solar panel installation. (v) policymakers need to
develop urban infrastructures to have private sections
invest in solar energy sectors in Yazd to reduce the risk of
private investments. The same approach needs to be
established in other states to attract essential capital.
During the research, there are some limitations that
can be considered for future works as follows. First, some
subcriteria are avoided to reduce the dimension of
decision‐making matrices for brevity and lack of
information. The information about third‐generation
solar panels is limited because it is not commercialized
as much as first and second generations. Moreover,
future researchers may regard the following points to
develop this study.
1. The implemented procedure MCDM approaches in
this study can be considered in technology selection
ranking for other forms of renewable energy exploita-
tion for example in the wind and geothermal energies
with other criteria and subcriteria.
2. Other MCDM methods such as VIKOR, TOPSIS
SWARA, and PROMETHEE can be utilized in the
proposed methodology for selection of the best solar
panel technology.
3. This methodology can be implemented in other
geographical areas and compare the solutions of solar
panel technology ranking with the results of this
study.
4. This approach can be modified and implemented for
selecting the best solar technology for use in indus-
tries with a specific location such as desalination
seawater. Then, the best solar panel technology can be
selected by considering the geographical information.
5. When using an MCDM method, determining the
criteria's weight is one of the most critical steps. In
this study, the BWM method has been used to
determine the criteria weights. New techniques such
as PIvot Pairwise RElative Criteria Importance Assess-
ment (PIPRECIA) can be used for specifying the
criteria weights.
ORCID
Hamidreza Seiti http://orcid.org/0000-0002-4892-6975
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How to cite this article: Shayani Mehr P,
Hafezalkotob A, Fardi K, Seiti H, Movahedi
Sobhani F, Hafezalkotob A. A comprehensive
framework for solar panel technology selection: a
BWM‐MULTIMOOSRAL approach. Energy Sci
Eng. 2022;10:4595‐4625. doi:10.1002/ese3.1292
4616
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SHAYANI MEHR ET AL.

T A B L E A.1 Comparison among studies about solar panel technologies, criteria, and methods
Reference
Energy Industry Generation of solar Technology
selection Criteria
Case
study Location DM techniques
Literature
reviewSolar Other I II III
Asante et al.
6
** Technical,
economic,
social, political,
institutional,
geographical
* * MULTIMOORA‐
EDAS
Bagher
10
* Organic
Baležentis,
Baležentis
11
** MOORA,
MULTIMOORA
*
Balo, Şağbanşua
13
* Monocrystalline,
poly-
crystalline
* Electrical,
mechanical,
economic,
environmental,
customer
related
* AHP
Yilmaz et al.
81
* Monocrystalline
and
poly-
crystalline
Amorphous Si
Thin Film
* Economic * *
Cañete et al.
17
* Polycrystalline
silicon
a‐Si/mc‐Si,
CdTe, a‐Si,
Electrical, climate * *
Chen et al.
21
* Monocrystalline
silicon
(mono‐Si)
Electrical,
environmental,
climatic
* * Life cycle
assessment
(LCA)
Brauers,
Zavadskas
84
* MOORA,
MULTIMOORA,
Ameliorated
Nominal Group
and Delphi
(Continues)
APPENDIX
SHAYANI MEHR ET AL.
|
4617

TABLE A.1 (Continued)
Reference
Energy Industry Generation of solar Technology
selection Criteria
Case
study Location DM techniques
Literature
reviewSolar Other I II III
Cavallaro
18
* Monocrystalline,
poly-
crystalline,
Thin‐film
(CdTe, a‐Si,
a‐S/m‐
Si, CIS)
* Mechanical,
economic,
social
ELECTRE III
Dapkus,
Streimikiene
25
* * * Economic,
environmental
* MULTIMOORA
Chatterjee, Bose
20
* Climate, economic,
location, social,
technical,
environmental
* * COPRAS‐F
Gupta
31
* CdTe, CIGS/
CIS, a‐Si
* Mechanical * TOPSIS
Ijadi Maghsoodi
et al.
37
* * * Social, technical,
economic
* * Hierarchical
SWARA,
MULTIMOORA
Hafezalkotob
et al.
32
* Technical,
environmental,
financial
* BWM, Interval
Borda rule,
Interval
MULTIMOORA
Helbig et al.
34
* Crystalline
silicon
thin‐film
photovolta-
ics
(CdTe,
CIGS)
Political, economic * AHP
Hsu et al.
36
* Technology,
economy,
environmental
* Fuzzy Delphi
Method, AHP
Ijadi Maghsoodi
et al.
38
* Aesthetic design,
practical design,
technical,
economic
* Fuzzy Best‐Worst
Method
(FBWM), Fuzzy
Axiomatic
Design (FAD)
4618
|
SHAYANI MEHR ET AL.

TABLE A.1 (Continued)
Reference
Energy Industry Generation of solar Technology
selection Criteria
Case
study Location DM techniques
Literature
reviewSolar Other I II III
Ijadi Maghsoodi
et al.
37
* Technicality and
sustainability,
health, safety
and
environmental,
economic
* fuzzy axiomatic
design (FAD)
Siksnelyte‐
Butkiene
et al.
67
* * * Economic, social,
technological,
environmental
*
Kumar et al.
44
** Climate, economic,
location, social,
technical,
environmental
*
Lamata, Sánchez‐
lozano
46
* Monocrystalline,
poly-
crystalline,
amorphous
silicon, CdTe
and CIGS,)
Organic and
hybrid
cells
* Economic,
environmental
* TOPSIS
Li et al.
47
* Technical,
economic
*
Liu et al.
48
** Market, social
politics,
environmental,
economic
* * Fuzzy AHP,
BWM,Stochastic
multi‐criteria
acceptability
analysis (SMAA)
Ma et al.
49
* CIGS, CdTe, μc‐
Si, a‐Si
Dye‐
sensitized
solar cells
(DSSCs),
polymer
solar cells
* Electrical,
mechanical
Economic,
Environmental
* * Fuzzy AHP,
Delphi
Siksnelyte et al.
68
** Economic,
environmental,
social
* * MULTIMOORA
(Continues)
SHAYANI MEHR ET AL.
|
4619

TABLE A.1 (Continued)
Reference
Energy Industry Generation of solar Technology
selection Criteria
Case
study Location DM techniques
Literature
reviewSolar Other I II III
Noorollahi et al.
54
* Economic,
environmental,
geographical,
technical
* * GIS
Rahimzadeh
et al.
59
* Geographical * *
Rahimi et al.
58
Economic,
environmental,
social
* * Group fuzzy
(MULTI-
MOORA, BWM)
Renn, Marshall
62
** **
Strantzali,
Aravossis
70
** Economic,
environmental,
social, technical
**
Streimikiene
et al.
71
* * * Economic, social,
environmental
* MULTIMOORA,
TOPSIS
Vafaeipour et al.
75
* Economic,
environmental,
technical,
social, risk
* * SWARA,WASPAS,
Delphi
Yazdani‐Chamzini
et al.
79
** Social, economic,
technological,
environmental
* TOPSIS,VIKOR,
SAW, MOORA,
ARAS(Additive
Ratio
Assessment),
AHP‐COPRAS
Noorollahi et al.
53
* * Orography,
climatic,
economic,
environment
* * Fuzzy‐Boolean
logic, AHP
Seker,
Kahraman
66
* Energy,
environment,
economy, social
* * Interval
Valued Pythagorean
Fuzzy (IVPF),
4620
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SHAYANI MEHR ET AL.

TABLE A.1 (Continued)
Reference
Energy Industry Generation of solar Technology
selection Criteria
Case
study Location DM techniques
Literature
reviewSolar Other I II III
MULTIMOORA,
AHP
Zoghi et al.
85
* Environmental,
geo-
morphological,
location,
climatic
* * AHP, FUZZY
Wang et al.
76
* Economic, social,
environmental,
technical
*
Yücenur et al.
82
* Location, cost, risk,
raw material
* * SWARA, COPRAS
Wang et al.
78
** Economic, socio‐
political,
environmental,
technical
* * Fuzzy AHP
Yazdani et al.
80
* * * Economic, social,
environmental
DEMATEL‐ANP,
COPRAS,
WASPAS
Çolak, Kaya
23
** Quality of energy
source,
environmental,
technical,
economic,
technological,
socio‐political
* * AHP, hesitant fuzzy
TOPSIS
Büyüközkan
et al.
16
* * * Environmental,
technical,
economic,
socio‐political
* * Hesitant Fuzzy
Linguistic AHP,
Hesitant Fuzzy
Linguistic
COPRAS
Zlaugotne et al.
84
* * * Electrical,
economic,
social
* COPRAS,
MULTIMOORA;
PROMETHEE‐
(Continues)
SHAYANI MEHR ET AL.
|
4621

TABLE A.1 (Continued)
Reference
Energy Industry Generation of solar Technology
selection Criteria
Case
study Location DM techniques
Literature
reviewSolar Other I II III
GAIA, TOPSIS,
VIKOR, AHP
Büyüközkan,
Güleryüz
15
** Environmental,
technical,
economic,
social, political
* * Decision Making
Trial and
Evaluation
Laboratory
Model
(DEMATEL),
ANP
Rani et al.
60
* * * Environmental,
economic risks,
policy, technical
* * VIKOR,
Pythagorean
Fuzzy Numbers
(PFNs) (PF‐
VIKOR)
Cavallaro et al.
19
* * Technical,
economic,
environmental
* Fuzzy TOPSIS
Nazari et al.
52
* Technical, climate,
location, social
* * TOPSIS
Wang et al.
77
* R&D value,
technology,
economic‐
social, technical
* * ANP, Entropy‐
weighted
Akhundzadeh,
Shirazi
83
* Technical,
economical,
environmental,
risk
* * Fuzzy AHP
Kheybari et al.
43
* * Environmental,
economic,
social
* * BWM, AHP
Onar et al.
55
* * Technical,
economic,
location, social
* * IVIF (interval‐
valued
intuitionistic
fuzzy, AHP
4622
|
SHAYANI MEHR ET AL.

TABLE A.1 (Continued)
Reference
Energy Industry Generation of solar Technology
selection Criteria
Case
study Location DM techniques
Literature
reviewSolar Other I II III
Abdullah, Najib
82
* * * Technical,
economic,
environmental,
social
* * intuitionistic fuzzy
analytic
hierarchy
process (IF‐AHP)
This paper * Monocrystalline
cell,
poly-
crystalline
cells
a‐si, CdTe,
hybrid si(a‐
si/
micro-
crystalline
si),
CIS/CIGS
Organic solar
cell,
perov-
skite solar
cell, CPV
* Electrical,
mechanical,
economic,
technical,
climate
* * BWM‐
MULTIMOOS-
RAL,
MULTIMOORA,
WASPAS,
COPRAS,
Borda rule
SHAYANI MEHR ET AL.
|
4623

T A B L E A.2 The criteria and subcriteria discussed in this research Zavadskas and others,
34,75
and Chen et al.
49
Criteria ID Criteria Subcriteria ID Subcriteria Description/definition
C1 Electrical characteristics SC1 Power tolerance Measure how much power a solar panel can have at any time above or below
capacity.
SC2 Series fues rating (A) Displays a proper amount of fuse (circuit breaker) for each solar panel. Its unit is
an ampere.
SC3 Temperature coefficient Of VOC (
%
/°C
) The open‐circuit voltage values of the solar panel module when the temperature
decreases or increase.
SC4 Maximum power (
P
max
) (wp) Photovoltaic base on Watt, which was obtained under standard conditions of
temperature and sunlight.
SC5 Maximum system voltage A parameter to be used to determine the number of solar panels that can be put
in series together. The unit is (
V
DC
) (DC).
SC6 Temperature coefficient Of ISC
(
%
/°C
)(
A/°C
)
The values of the short‐circuit current of the solar panel module when the
temperature of the solar cell increases or decreases.
SC7 Temperature coefficient of
P
max
The temperature of the solar panel directly affects the maximum output power.
As the panel temperature increases, its output current increases as long as the
voltage output decreases linearly.
SC8 Product warranty The lifetime of the solar panel warranty is estimated by the company for the
number of years of operation.
SC9 Max power voltage (
V
)
pm (STC) This voltage is in the solar panels when the maximum rated power is removed. In
other words, the maximum power voltage (
V
Vor )
pm mp occurs when the
module is connected once and operates at its maximum output under
standard test conditions (STC).
SC10 Max power current (
ISTC)( )
pm The output current from the solar panels when it connects to the Maximum
Power Point Tracker (MPPT) under standard conditions. The unit
is (
IIor
pm mp ).
SC11 Open circuit voltage (V)(STC) This parameter represents the maximum voltage that the solar panel can produce
under standard conditions. This attribute is used to determine the number of
solar panels allowed in a series to connect to the inverter or to the controller
charge. The unit is the Volt
SC12 Short circuit current (A)(STC) This parameter represents the maximum current that the solar panel can produce
under standard conditions. During the short circuit, the maximum amount of
current solar panel can produce and its unit is Amperes.
SC13 Panel efficiency (%) This solar panel efficiency criterion (expressed as a percentage) determines the
ability to convert solar energy into electricity. The more efficient panel
produces more electricity than the less efficient panel.
4624
|
SHAYANI MEHR ET AL.

TABLE A.2 (Continued)
Criteria ID Criteria Subcriteria ID Subcriteria Description/definition
C2 Mechanical
characteristics
SC14 Length × Width × Height(L × W × H) (mm) The dimensions of the solar panels include length, width, and height, and are
calculated in millimeters.
SC15 Weight (kg) The weight of the solar panel is in kilograms.
C3 Economic characteristics SC16 Panel Cost ($) The cost of solar panels is an economic criterion in this discussion, the unit of
which is in dollars ($).
C4 Technical characteristics SC17 Compute output manufacturer error (
f
man ) The output power of the photovoltaic modules in watts is expressed as an error of
approximately 5% based on the temperature of 25°C for the cells. Therefore,
for the 265W photovoltaic module, the maximum reduction in output power
is about 13.25 W.
SC18 Calculate the effect of pollution and
dust (
f
dirt )
The output power of a photovoltaic module may be reduced due to
contamination on the module surface, and this decrease is calculated by the
reduction factor due to air pollution.
SC19 AVG temperature effect (°C) The average cell temperature inside the photovoltaic module.
C5 Climate characteristics SC20 AVG Temperature (°C) This criterion shows us that the efficiency and maximum PV output temperature
of a solar panel. With raising the temperature of the panel, the less power it
produces.
SHAYANI MEHR ET AL.
|
4625