Content uploaded by Muhammad Adnan
Author content
All content in this area was uploaded by Muhammad Adnan on Dec 12, 2024
Content may be subject to copyright.
VOLUME XX, 2017 1
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2017.Doi Number
Hybrid Multi-Criteria Decision Framework for
Prosumers Energy Storage Systems in Smart
Grids
Marriam Liaqat1, Member IEEE, Muhammad Adnan1, Member IEEE, Mohaira Ahmad2,
Member IEEE, Muhammad Sajid Iqbal1, Member IEEE, Faizan Ahmad3, Member IEEE, and
Sami ud Din3, Member IEEE
1Department of Electrical Engineering, National University of Computer and Emerging Sciences (FAST), Chiniot-Faisalabad Campus, Pakistan
(emails: mariam.liaqat@yahoo.com, m.adnan@nu.edu.pk, iqbal.sajid@nu.edu.pk).
2
School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, Pakistan
(e-mail: mohaira.ahmad@seecs.edu.pk)
3Department of Electrical Engineering, Namal University Mianwali, Mianwali 42250, Pakistan (e-mails: faizan.ahmadd54@gmail.com,
sami.uddin@namal.edu.pk)
Corresponding authors: Muhammad Adnan, Mohaira Ahmad (e-mails: m.adnan@nu.edu.pk, mohaira.ahmad@seecs.edu.pk).
ABSTRACT Modern power systems encompass multiple prosumers, smart grid technologies, and
renewable energy resources (RERs). These prosumer-based smart grids are facing the reliability issues that
can be mitigated through the adoption of competitive storage technologies. A range of competitive storage
technologies have been developed by scientists. However, the systematic selection of best storage
technologies is still one of the main challenging research issues in the current literature. To fill this
literature gap, this paper proposes a multi-criteria decision framework for energy storage selection in
prosumer-based networks. First, a decision-making hierarchy was developed based on the three main
criteria including energy flow management for prosumers, technical features, and sustainability. Under
these criteria, various sub-criteria were identified. Second, multi-criteria decision making (MCDM)
problem was solved for two cases using Analytic Hierarchy Process (AHP) and Preference Ranking
Organization Method for Enrichment of Evaluations (PROMETHEE) methods separately. Third, a hybrid
AHP and PROMETHEE method was proposed for the selection of an efficient storage system for
prosumers based smart grid. Finally, a comprehensive outlook has been provided for prosumer energy
storage evaluation. Findings revealed several implications for more accurate storage evaluation decision
making. For instance, sensitivity analysis reveals that the lithium ion battery (LIB) has the first preference
in AHP method, and the lead acid battery (LAB) has the first preference in PROMETHEE method.
However, the second preference for AHP method was the LAB, and the second preference for
PROMETHEE method was the LIB. Hence, separate application AHP and PROMETHEE offered a slightly
different ranking. According to the proposed hybrid procedure, LIB was ranked as the first option by both
AHP and PROMETHEE methods. As a result, the hybridization of AHP and PROMETHEE methods
offered a more robust and unique solution to the storage selection problem as compared to the individual
method. Storage evaluation decision making has several implications for prosumer-based power systems. In
future, only a few storage alternatives would not meet the needs of the large scale emerging power systems,
such as peer-to-peer networks. In this situation, the rapid and accurate ranking of various competitive
storage alternatives would be a challenging problem. This study is valuable because it offers a
comprehensive and flexible decision making tool for storage evaluation for prosumer-based smart grids.
For instance, the proposed model can be evaluated with addition or removal of criteria and storage
alternatives under different situations.
INDEX TERMS AHP, energy management, multi-criteria methods, PROMETHEE, prosumer, renewable energy, smart
grid.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
I. INTRODUCTION
Prosumers in nano-grids represent an integral part of
modern smart grids [1]. Similarly, energy storage systems
represent an integral part of prosumers [2]. A range of
competitive energy storage systems are available, and the
storage selection is a challenging problem for prosumers
[3]. Usually, management decisions become inefficient and
ineffective by improper selection of different resources [4].
In the same way, prosumer based smart grid becomes
incompetent due to improper energy storage systems.
Storage selection problems are challenging due to the
consideration of multiple criteria such as cost, technical
features, compatibility, social aspects, and environmental
aspects [5-6]. The best way to solve multi-criteria problems
is the use of multi-criteria decision making (MCDM)
methods in operations research. Mostly used MCDM
methods include AHP (Analytic Hierarchy Process),
PROMETHEE (Preference Ranking Organization Method
for Enrichment of Evaluations), TOPSIS (Technique for
Order of Preference by Similarity to Ideal Solution), multi-
objective optimization, fuzzy AHP, fuzzy TOPSIS,
weighted sum method, ELECTRE (Elimination and Choice
Translating Reality), weighted product method, and
VIKOR (Visekriterijumsko Kompromisno Rangiranje) [7-
11]. It has been argued that no MCDM method is better
than the other due to advantages and disadvantages of every
method [12].
Decision maker can use any MCDM method in
operations research. Also, decision makers can obtain more
robust solution through the use of more than one method.
First, this paper applied the AHP method for prosumers
energy storage selection. AHP is a leading MCDM method
that has been used to solve hundreds of sensitive MCDM
problems [13]. Any other MCDM method can be used for
the validation of AHP solution. In this regard, this paper
selected PROMETHEE method which is also a prominent
MCDM method in the decision sciences [14]. AHP method
shows the compensatory decision-making behavior. AHP
performs complete aggregation of criteria in which positive
scores compensate the negative scores. PROMETHEE
method is considered the non-compensatory or partially
compensatory. In PROMETHEE method, positive
outranking scores do not compensate the negative
outranking scores. Hence, prosumer-based energy storage
selection problem was validated by two different methods.
However, the result might be slightly different for the two
methods. In order to reach a unique conclusion, decision
maker will need a hybrid MCDM storage selection method
which should result in a unique, robust, and more reliable
solution. Hence, this paper proposed a novel storage
selection method based on hybrid AHP and PROMETHEE
method.
In the following subsections, we present the storage
need, storage benefits, storage alternatives, storage
selection criteria, significance of MCDM methods,
significance of hybrid AHP and PROMETHEE methods,
research novelty, and objectives focusing on prosumers
storage evaluation.
A. ENERGY STORAGE NEED FOR PROSUMERS
Futuristic power systems will facilitate the integration of
multiple prosumers, multiple renewable energy resources,
and a range of smart grids technologies [15]. A large
number of electricity consumers will turn into prosumers
(i.e. consumers as well as producers) due to fossil fuels
shortage, expensive electricity, and access to substantial
clean energy [16]. Ultimately, Sustainable Development
Goal-7 of the United Nations (i.e. affordable and clean
energy) would be supported through P2P energy networks
[17]. However, the emergence of multiple prosumers would
cause severe variability, instability, and demand variations
in smart grids [18]. For example, the higher amounts of
photovoltaics (PV) integration would result in overvoltage
and overloading [19]. A competitive strategy to resolve
such problems is the integration of competitive storage
technologies into these challenging power systems [20].
B. STORAGE BENEFITS FOR PROSUMERS
Many studies reported the effectiveness of different storage
technologies for the prosumer-based smart grids [21-22].
Many papers reported the cost savings for these systems.
For instance, Reference [23] reported 35% cost savings for
a grid-connected university campus using PV system, diesel
generator, and battery. Also, Reference [24] reported
significant cost savings for a system with residential
prosumer, commercial prosumer, and pumped hydro
storage system. Similarly, Reference [25] developed an
optimal power sharing model for grid-connected residential
and commercial prosumers with PV systems and battery
storage. The proposed system offered 69% cost saving for a
residential prosumer and 81% cost saving for a commercial
prosumer. Many other papers have reported the cost savings
from the prosumer-based smart grids with storage [26-28].
In addition to the cost savings, many papers reported the
significant self-consumption for the storage systems under
different settings. For example, Reference [29] performed
techno-economic analysis of PV system with shared storage
for a prosumer community. As a result, the self-
consumption of the prosumer community was increased up
to 11%. Also, Reference [30] optimized a prosumer-based
power system using PV system with heat pump, batteries,
and heat storage. It was concluded that the batteries and
heat storage significantly increased the self-consumption of
the prosumers. Similarly, Reference [31] used thermal
energy storage for the storage of excess PV power in the
form of heat in a prosumer-based power system. It was
reported that the PV self-consumption and the renewable
energy integration were increased. Also, the different
storage systems have been integrated with the prosumers
with the help of electric vehicles [32]. Reference [33]
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
reported the use of power to gas technology for the storage
of excess PV in the form of gas for the peer-to-peer
prosumers. It was reported that the use of power to gas
technology would decrease the reliance on the main grid.
Further, Reference [34] analyzed the hydrogen storage with
PV systems for the grid-connected residential prosumers
and identified that the hydrogen storage is the only feasible
option if power demand is high or highly seasonal, or the
system operates in off-grid mode. Also, Reference [35]
used hybrid battery and supercapacitor storage for a
residential PV prosumer.
C. RECOMMENDED STORAGE SYSTEMS FOR
PROSUMERS
Recently, most of the literature reported the use of batteries
for the prosumer-based smart grids. However, many studies
have reported the effectiveness of the other storage options.
It can be concluded that a range of storage technologies
would be integrated into the prosumer-based smart grids,
such as batteries, pumped hydro storage, superconducting
magnetic energy storage, flywheel energy storage,
supercapacitors [36], solar thermal storage [37], hydrogen
fuel cells [38], and compressed air energy storage [39].
Hence, this paper included these storage technologies into
the multi-criteria decision making (MCDM) models.
D. STORAGE SELECTION CRITERIA FOR PROSUMERS
Most of the studies have recommended storage
technologies for prosumers only on the basis of cost
criteria. In fact, a storage technology should be judged
based on all the important criteria related to the prosumer-
based smart grids, specifically the criteria related to the
technical characteristics, compatibility with prosumers, and
the achievement of the sustainable development goals [40].
In this context, this paper integrates these main criteria into
the storage selection decision making process. More
specifically, this paper integrates three main criteria
including the energy flow management for prosumers,
technical features, and sustainability. Under these criteria,
the many relevant sub-criteria have been identified in this
paper. Besides, the storage selection is a MCDM problem
that should be solved through the MCDM methods.
E. SIGNIFICANCE OF MCDM METHODS FOR
PROSUMERS STORAGE SELECTION
Decision making methods in operations research and
management sciences are greatly regarded by practitioners
for very sensitive decision making. Amongst these
methods, MCDM methods are considered more suitable for
complex problems with multiple criteria and many
alternatives. The exact optimization methods do not offer
the integration of all the possible criteria or objectives, and
they do not offer efficient solutions to such problems. In
contrast, MCDM methods are very comprehensive and
simple for efficient solution to a complex problem. There
are many well established and proven MCDM methods.
Each of these methods needs different information and
work on different mathematical models. There is a
possibility that different MCDM methods would offer
different solutions. Comparison and combination of more
than one MCDM method would offer more in-depth
insights into the application problem as well as behavior of
MCDM methods [41]. Hence, this paper performs the
storage selection for prosumers in smart grids using two
different MCDM methods, namely Analytic Hierarchy
Process (AHP) and Preference Ranking Organization
Method for Enrichment of Evaluations (PROMETHEE).
AHP and PROMETHEE methods have been used in
numerous MCDM problems for sensitive decision making
[42-44]. The proposed storage selection problem contains
the qualitative and quantitative criteria, and the proposed
MCDM methods support the inclusion of both types of
criteria.
F. SIGNIFICANCE OF HYBRID AHP AND PROMETHEE
METHODS
Existing literature used a range of MCDM methods for a
range of problems. For instance, Reference [45] applied
three MCDM methods on aircraft selection problem,
including hybrid AHP and TOPSIS, THOR 2, and Gaussian
AHP-TOPSIS. This reference compared the solutions
obtained by different methods and offered several insights
and benefits of the results integration. Reference [46]
proposed a hybrid MCDM method based on PROMETHEE
and SAPEVO (Simple Aggregation of Preferences
Expressed by Ordinal method) for helicopter selection for
military needs in Brazil. The results were compared with
classical PROMETHEE method. Comparative analysis of
both methods exhibited the different rankings by each
method. Reference [47] ranked the different renewable
energy alternatives for investment in Brazil using the
SWARA-MOORA-3NAG method. They compared the
results with other two methods and found that the solar
energy is the best option for investment in Brazil. It was
found that all the three methods ranked solar energy as the
first option, and the different rankings were obtained for
wind, biomass, and hydropower. Reference [48] applied
AHP Gaussian method for the evaluation of smart sensors.
In this method, Gaussian analysis is used for the pairwise
comparison. Despite the availability of a large number of
different MCDM methods, this study used AHP and
PROMETHEE methods for storage evaluation in prosumer-
based smart grids.
AHP was selected because it is widely used MCDM
method in the world due to its more flexibility, user-
friendly behavior, and precision. Also, it is helpful in the
determination of weights for the other MCDM methods.
PROMETHEE was used due to its simplicity, visualization,
and easy computation. In addition, AHP and PROMETHEE
have availability of sophisticated open-source software (i.e.
SuperDecisions and Visual PROMETHEE) from the
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
inventors of these methods. The proposed hybrid approach
was more effective as compared to the application of
individual method. AHP and PROMETHEE involve
different information and mathematical background, which
would result in different outcomes for individual methods.
To enhance solution uniqueness, main idea of the proposed
approach is to apply two different MCDM methods on a
same problem, integrate results, and make more reliable
decision. The decision maker should have thorough
knowledge related to the application field and MCDM
methods, so that the removal of any potential viable
alternative can be avoided.
Some papers combined the AHP and PROMETHEE
methods for different problems. For instance, Reference
[49] used hybrid AHP-PROMETHEE for the service
provider selection for the hair care manufacturing company.
They calculated criteria weights with AHP and used the
PROMETHEE method for the ranking of alternative service
providers. Similarly, Reference [50] evaluated the
resilience of the four blocks in some drainage areas using
hybrid AHP-PROMETHEE. They calculated the criteria
weights from AHP and used them in the PROMETHEE for
the ranking of four blocks. Reference [51] used hybrid
AHP-PROMETHEE for the selection of fruit crops. They
obtained criteria weights from AHP and used them in
PROMETHEE for the ranking of fruit crops. These recent
papers used hybrid AHP-PROMETHEE method in such a
way that AHP was used for the finding of criteria weights
and the PROMETHEE was used to rank the different types
of alternatives. The above papers do not validate the results
with a comparative selection method. In the present paper,
the PROMETHEE method was applied using the weights
from AHP method. Additionally, AHP was also used for
the ranking of storage systems. Hence, this paper compares
the storage alternatives from both methods. In addition, the
present paper reports a novel procedure for the storage
selection based on the hybrid solution from both methods,
which further increases the reliability of the results.
Until now, the existing literature has not reported a
systematic storage selection model for prosumers in smart
grids. Majority of papers have focused only on the cost
saving criteria. Furthermore, the storage selection literature
on prosumers did not use scientifically established decision-
making methods. For instance, Reference [39] compared
lead acid, lithium ion, and redox flow batteries for the
prosumer-based microgrids. It was found that the lead acid
batteries would be least preferable for the small-scale
prosumer-based microgrids. However, that comparison was
based on the literature review of the different storage
technologies. In contrast, the present paper includes all the
relevant criteria and storage alternatives in the decision-
making models. In addition, this paper performs the storage
selection for prosumers in smart grids using the two leading
MCDM methods, namely AHP and PROMETHEE. These
methods have not been used for prosumers storage
selection. However, a few comprehensive research used the
AHP method for the storage selection in different
applications. For instance, Reference [52] developed a
storage selection model for grid-connected PV systems
using AHP method. Also, Reference [53] applied AHP
model for the storage selection for electric vehicles.
However, these two papers focused only on the PV systems
or electric vehicles. Furthermore, PROMETHEE was not
used in these problems. Also, the present study includes the
effect of prosumers in the storage selection problem.
G. NOVELTY AND OBJECTIVES
Prosumer based large scale power systems will emerge in
the near future, and substantial storage capacity will be
required in order to prevent the failure of the whole power
system. Some batteries are toxic (e.g. lead-acid) and some
batteries will face materials scarcity (e.g. lithium-ion). This
will cause the integration of alternative batteries or other
storage systems. In such situation, the storage selection and
ranking will be a significant trend in these futuristic power
systems. Prosumers will use all the required criteria in the
storage selection problems, and the MCDM methods are the
best candidates for multi-criteria selection and ranking
problems. Hence, the problem and the proposed methods of
this research are significant as well as sufficiently novel.
AHP and PROMETHEE combination is highly regarded in
the literature. In this regard, weights are obtained from
AHP, and the final ranking is achieved by PROMETHEE
method. Although, this paper used a similar approach for
PROMETHEE solution, the separate AHP solution has also
been used for the final ranking. In addition, a novel hybrid
method has been proposed using AHP and PROMETHEE
solutions. Moreover, the integration of prosumers is an
additional novelty which has not been achieved in
literature. In the existing literature, some papers have used
fuzzy set theory. For instance, Reference [54] applied fuzzy
logic and AHP method for storage selection amongst
flywheel, supercapacitors, pumped hydro, compressed air,
and hydrogen storage alternatives. In that work, the
individual application of fuzzy logic and AHP method
resulted in exactly the same ranking. This shows the
authenticity of AHP results. However, the integration of
fuzzy set theory is suggested as more reliable, but still no
work has proposed the storage selection model for
prosumers using hybrid MCDM and fuzzy sets. We leave
this investigation for future research and focus on the AHP
and PROMETHEE methods. Still, no research has proposed
the storage selection and ranking model using hybrid AHP
and PROMETHEE methods. Even the separate application
of these two methods has not been reported in the literature
for prosumer-based storage systems. In contrast, this work
reports hybrid application as well as separate application of
both methods. Based on the research gap, this paper has the
following key objectives.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
• Identification of the criteria, sub-criteria, and
alternatives for the MCDM problem focusing on the
storage selection for prosumers in smart grids.
• Solution of the storage selection problem using hybrid
AHP and PROMETHEE methods.
• Application of a novel storage selection procedure
based on hybrid AHP and PROMETHEE methods.
• Establishment of a future outlook for proposed
prosumers-based storage selection problem.
II. METHODOLOGY
This section proposes a systematic storage selection
methodology for the prosumers in smart grids. This
methodology has been presented in Figure 1. The proposed
methodology contains 10 steps which have been explained
in the following.
A. IDENTIFICATION OF STORAGE SELECTION
CRITERIA
In the first step, storage selection criteria were identified for
prosumer-based smart grids as shown in Figure 2. There are
three main criteria including the energy flow management
for prosumers, technical features, and sustainability.
First criterion “energy flow management for prosumers”
is a central element of the energy management in prosumer-
based smart grids. Under this criterion, there are four sub-
criteria at level 1 and 14 sub-criteria at level 2. A brief
description of these fourteen sub-criteria is given below.
1) SUPPORT IN BIDIRECTIONAL ENERGY SHARING
(BISH)
Bidirectional energy sharing capability is the most important
feature of the future smart grids, and prosumers are the key
elements of such smart grids. A prosumer-friendly storage
system should support the efficient bidirectional power flow
[55]. Hence, the bidirectional energy sharing capability is an
integral part of the storage selection criteria for the prosumer-
based smart grids.
2) TRANSMISSION STABILITY AND CONGESTION
MANAGEMENT (TRCON)
This criterion ensures the efficient operations of the
transmission line parameters, resulting in congestion
reduction and good power quality. The integration of
multiple renewable energy resources, prosumers, and smart
grid requires the effective strategies for the transmission
stability and congestion management. For this purpose, the
integration of a competitive storage system is a key
supportive strategy.
3) TELECOMMUNICATIONS BACKUP (TELB)
Smart grids require a highly reliable and efficient
communication flow. The prosumers would store the back-up
power for the telecommunications in smart grids. For this
purpose, a compatible storage system would supply reliable
back-up power when required.
4) FLUCTUATION SUPPRESSION (FSUP)
This criterion ensures the mitigation of the fluctuations due
to the renewable energy-based prosumers. The prosumer-
based smart grids require the energy storage for the
fluctuation suppression [56].
5) VARIABILITY REDUCTION (VRED)
The variability reduction is a key requirement of the
renewable energy based smart grids, and prosumers are the
key consumers and producers of the multiple renewable
energy-based resources. A storage system should be capable
of variability reduction for the prosumer-based power
systems [57].
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Figure 1. Storage selection methodology
6) TIME SHIFTING (TSHI)
Prosumers store the energy during the low prices or over-
production and use it during the high prices or under-
production. A storage system should be responsive during
these times. Especially, the environmentally responsible
prosumers would implement the time-shifting strategy
according to their own production and the requirements of
the utility grid [58].
7) PEAK SHAVING (PSHA)
Peak shaving allows a prosumer to store the energy during
the peak times and use it during the off-peak times.
According to this criterion, a good storage system should be
able to store the power during peak generation [59]. Peak
shaving would become an integral part of the prosumer-
based smart grids [60].
8) LOAD LEVELLING (LLEV)
This feature ensures that a good storage system should store
energy during the low demand and use it during the peak
loads. Usually, the prosumers fulfill a major part of the
demand from the renewable energy or utility grid.
Sometimes, the renewable energy resource or the utility grid
may not fulfill the demand. In such situations, the storage
system should manage the large load variations.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Figure 2. Storage selection criteria for prosumers in smart grids
9) SPINNING RESERVE (SPIR)
Prosumers would face a sudden increase in production during
the peak generation hours or the power demand from utility
grid or local loads may decrease. In such situations, a storage
system should offer the quick spinning reserve.
10) STANDING RESERVE (STR)
Sometimes, a prosumer may lose the generation capacity or
grid connection. In such situations, a storage system would
work as a power generation unit.
11) PV/WIND BACKUP ABILITY (PWBA)
This criterion ensures that storage technology should be able
to store energy for the times when the wind is not flowing or
the sun is not shining, especially during the night times or the
absence of sun or wind for the longer times.
12) EMERGENCY POWER SUPPLY (EMPS)
A storage system should be able to supply the power during
failures.
13) DISTURBANCE MANAGEMENT (DISM)
A storage system should be capable to manage a disturbance
(e.g. short circuit) in the power system.
14) BLACK-START ABILITY (BLACA)
This criterion ensures that a storage system should be able to
reconnect the components of the power system after a
blackout.
The second main criterion “technical features” contains
the fundamental characteristics of a storage system. The third
main criterion “sustainability” contains the three pillars of
sustainability which should be integrated into the storage
selection criteria due to their strong support towards the
achievement of sustainable development goals. Hence,
storage selection criteria of this paper include prosumer-
related criteria, fundamental features, and criteria related to
sustainable development.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Figure 3. Storage alternative for prosumers
B. IDENTIFICATION OF STORAGE ALTERNATIVES
In this step, the fifteen most important storage alternatives
were identified based on the existing literature. These
alternatives have been presented in Figure 3.
C. QUANTITATIVE/QUALITATIVE INPUT DATA
In this step, the qualitative and quantitative input data was
collected (Table 1). The abbreviations for all the criteria
have been presented in Figure 2, and the abbreviations for
all the storage alternatives have been presented in Figure 3.
The input data for the MCDM model was obtained using
the secondary data sources [52-53, 61-62].
In Table 1, there are eight quantitative criteria.
Quantitative criteria may be entered into software without
any changes. However, the values of the six quantitative
criteria were not appropriate. For instance, energy density
(EneD) for PHS storage is 2 Wh/L and it is 770 for
hydrogen fuel cells (HFC), which can be interpreted as
“HFC is 385 times better than PHS”. However, AHP
method encourages the nine times better or worse
preferences. Moreover, Visual PROMETHEE software
accepts the quantitative data in integer form, but some
values in the quantitative data were significantly less than
1.00 (in fraction form). Hence, these criteria were converted
to the qualitative scale (Table 2). Only two criteria,
including round-trip efficiency (RouT) and lifetime (LT),
were entered into the software as original quantitative
values.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Table 1. Input data for multi-criteria storage selection problem
Main/
Level-1
criteria
Level-2
criteria
Level-3
criteria
No.
Storage alternatives
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
PHS
CAS
FS
SCS
STS
SMES
HFC
LIB
LAB
VRB
SSB
NCB
ZBB
NMH
SNC
Energy flow management (EFM)
Esha
TrCon
1
L-
L-
H+
M+
L-
H+
M+
H+
M+
M+
L
L
M
L
L
BiSh
2
L
L
H+
H+
L
M
M
H+
H+
M
H+
H+
M
H
H
TelB
3
L
L
M+
M+
L
L+
M+
M+
H+
M
M+
M+
M
M+
M+
IntmR
VRed
4
L
L
H+
H+
L
M+
M+
H+
H+
M+
H+
H+
M+
H+
H+
FSup
5
L
L
H+
H+
L
H+
L
H+
H+
M+
M+
M+
M+
M+
H+
DemM
TShi
6
H+
H+
L
L
M+
L
M+
H+
H+
M+
M+
H+
M+
L
L
PSha
7
H+
H+
M+
M+
M+
M+
M+
H+
H+
M+
H+
H+
M+
H+
H+
LLev
8
H+
H+
M+
M+
M+
M+
M+
H+
H+
M+
H+
H+
M+
L
M+
SpiR
9
L-
M+
M+
L-
L+
M+
M+
H+
H+
M+
M+
H+
M+
H+
M+
StR
10
M+
M+
L
L
L+
L
M+
M+
M+
M+
M+
M+
M+
L+
L+
PWBA
11
M+
H-
L
L
L
L
M+
H+
H+
M+
H+
H+
H+
H+
H+
FaiM
EmPS
12
L-
H-
H+
L
L
L
M+
H+
H+
H+
H+
H+
H+
H+
H+
DisM
13
L-
L-
M+
H+
L-
M+
L-
H+
M+
M+
H+
M+
M+
M+
H+
BlacA
14
L
H+
L-
L-
M
L-
M+
M+
H+
H+
H+
H+
H+
H+
M+
Technical aspects (Tech)
-
EneD
(Wh/L)
15
2
20
80
35
250
13.8
770
693
100
90
345
300
70
300
160
-
ChaT
16
H+
H+
H+
H+
H
H+
H+
M+
L-
H
L-
M
M
L+
L
-
DisD
(hours)
17
8
5
0.25
0.17
18
0.008
24
5
5
10
7
8
8
4
8
-
SDis
18
H+
H
L-
L+
H+
M
H+
H
H
H+
M-
M-
H
M-
L+
-
RouT (%)
19
85
89
95
98
72
95
47
97
90
85
92
90
75
85
90
-
PDen
(kW/m3)
20
1.5
2
2000
4500
30
4000
35
800
400
33
50
141
25
588
300
-
PRat (MW)
21
1000
400
20
0.1
300
10
50
100
40
100
34
50
10
3
3
Sustainability (Sust)
EcoS
PCos
(US$/kW)
22
4300
1000
700
480
400
489
10200
4000
900
9444
3300
1500
2500
530
10000
ECos (US
$/kWh)
23
100
120
14000
2000
60
10854
13000
4000
1100
2000
900
3500
1000
5529
345
EnvS
ToxM
24
H+
H+
H+
H
H+
H+
H
M+
L+
M+
H-
L+
M
M+
M+
Recy
25
H+
H+
H
H
H+
H
M
H
H+
H+
H
H
H+
H
M
LT (years)
26
60
40
20
10
30
30
20
16
15
20
20
20
20
15
11
EnvI
27
H+
H+
H+
H
H+
H
H
M
L+
H-
H-
L+
M
L+
H-
SocS
PeoS
28
L
M
H+
H+
M+
H+
L+
L+
M+
H+
L+
L+
L
L+
L+
AccP
29
L
L
H+
H+
L
H+
H+
H-
M+
H
H+
M-
M
M
H
EaUs
30
L
L
H+
H+
H+
H+
H+
H+
H+
H+
H+
H+
H+
H+
H+
Table 2. Conversion of inappropriate quantitative data into qualitative scale
Level-2 criteria
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
PHS
CAS
FS
SCS
STS
SMES
HFC
LIB
LAB
VRB
SSB
NCB
ZBB
NMH
SNC
EneD (Wh/L)
15
L-
L-
L
L-
L+
L-
H+
H
L
L
M-
M-
L-
M-
L
DisD (hours)
17
L+
L
L-
L-
H-
L-
H+
L
L
M-
L+
L+
L+
L
L+
PDen (kW/m3)
20
L-
L-
M-
H+
L-
H
L-
L
L
L-
L-
L-
L-
L
L
PRat (MW)
21
H+
M-
L-
L-
L+
L-
L-
L-
L-
L-
L-
L-
L-
L-
L-
PCos (US$/kW)
22
M+
H+
H+
H+
H+
H+
L-
M+
H+
L-
H-
H
H
H+
L-
ECos (US $/kWh)
23
H+
H+
L-
H
H+
L
L-
L+
H+
H
H+
H-
H+
M+
H+
D. PROBLEM SOLUTION USING AHP
The AHP method involves complex mathematics, which
triggered the development of the sophisticated software for
the efficient solution. This paper uses SuperDecisions
software for the implementation of AHP method. The
implementation of AHP method involves the following
steps. Equations (1) to (14) show the methodology of the
AHP technique. These equations have been well addressed
in the literature [63-66].
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Let
d
Deviation between two alternatives
n
Number of elements (i.e. criteria and alternatives)
to be compared
max
Maximum eigenvalue
v
The eigenvector
i
X
Non-zero vector
r
Random pairwise comparisons
CR
Consistency ratio
CI
Consistency index
RI
Randomness index
A
Comparison matrix
i
Line in the matrix
j
Column in the matrix
The first stage in the implementation of AHP method is
the construction of the problem hierarchy. The hierarchy of
the storage selection model has been presented and
explained above (Figure 2). After the hierarchy
development, the pairwise comparison is performed. The
AHP method works on the principle of the pairwise
comparison between criteria as well as between
alternatives. Each alternative and each criterion are pairwise
compared with respect to the immediate upper-level criteria
or goal. The AHP method uses a 9-point rating scale for
comparison [67]. This paper uses a 9-point scale based on
Reference [53]. This scale includes nine levels including
H+, H, H-, M+, M, M-, L+, L, and L-. In this scale, H+
means “Highly Promising'' and `” L-'' means the “Least
Promising’. For further details, the exiting literature may be
referred [52-53].
In the pairwise comparisons, the
nn
dimensional
square matrix is obtained, which indicates the significance
of each criterion or alternative. This matrix contains the
criterion/alternative comparison values in the colums and
rows. Equation (1) presents the property of
nn
matrix A.
ij
a
, where,
, 1,2,3,...,i j n=
(1)
Equation (2) shows that the two identical criteria or
alternatives cannot be compared, and all the values on the
diagonal of the matrix are equal to 1.
1
ij
a=
for
ij=
(2)
Equation (3) shows that the preferences will be reciprocal.
1
ij
ji
aa
=
for
ij
(3)
Equation (4) shows that the total number of comparisons in
a pairwise comparison will be as follows.
( )
1
2
nn−
(4)
Matrix
A
can also be represented as follows.
11 12 1
21 22 2
12
..
..
. . . . .
. . . . .
..
n
n
n n nn
a a a
a a a
A
a a a
=
(5)
The characteristic function of the above matrix is used
to calculate the eigenvector. The eigenvector should match
the maximum eigenvalue of the matrix A. The
characteristic function of the matrix A is given as follows.
11 12 1
21 22 2
12
..
..
( ) . . . . .
. . . . .
..
n
n
n n nn
a a a
a a a
fA
a a a
−
−
= − =
−
(6)
Eigenvectors of the matrix A are each column and the non-
zero vector
i
X
.
( )
0
ii
AX
−=
(7)
If
i
Xv=
for
max
, the eigenvector is the solution to the
following equation.
max
Av v
=
(8)
In the following, the eigenvector corresponding to
max
has been derived using the normalised arithmetic averages.
First, the normalization of matrix A is performed so that the
very large or very small values can be avoided [68].
Consequently, matrix A is transformed to matrix B.
ij
Bb
=
(9)
The elements of normalized pairwise comparison matrix are
obtained using the following equation.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
1
ij
ij n
ij
i
a
b
a
=
=
(10)
Then, the preferences between the different criteria or
alternatives are calculated using the eigenvector
i
vv=
.
For this purpose, the arithmetic average is calculated as
follows.
1
n
ij
j
i
b
vn
=
=
(11)
Maximum eigenvalue
max
is calculated as follows.
( )
max 1
1ni
ii
Av
nv
=
=
(12)
After the pairwise comparisons, the consistency of the
AHP model is determined. The AHP method works well if
the pairwise comparison is the highly consistent.
Consequently, the consistency test is performed. For this
purpose, the consistency index (CI) is calculated as follows.
max 1
n
CI n
−
=−
(13)
The consistency ratio of the comparison matrix is given
below.
( )
max 100%
1Random avera
n
CI CI
CR RI rge CI n
−
= = = −
(14)
The consistency ratio should be maximum 10%. If it is
greater than 10%, the decision maker should review the
pairwise comparison data until the inconsistency is
acceptable [69]. In the present study, inconsistency remained
within this threshold in all cases.
E. PROBLEM SOLUTION USING PROMETHEE
PROMETHEE is based on the pairwise comparison of the
alternatives. In this paper, the alternatives were evaluated
based on the PROMETHEE-I (partial ranking) and
PROMETHEE-II (complete ranking). This paper uses Visual
PROMETHEE software (academic edition) for the
implementation of PROMETHEE method. The
implementation of PROMETHEE method involves the
following steps. Equations (15) to (23) show the
methodology of the PROMETHEE technique [70-72].
Let
y
Index of the evaluation criteria, where
1,2,3,...yY=
Z
Set of finite alternatives, where
1 2 3
, , ,...,Z a a a N=
,
,,a b x Z
y
w
Weights associated with criterion
y
()
y
ga
Value of criterion
y
for alternative
a
()
y
gb
Value of criterion
y
for alternative
b
The first stage in the implementation of PROMETHEE
method is to define the criteria and alternatives. In this
paper, the criteria and alternatives for PROMETHEE
method were same as in AHP method. Then, the weight
y
w
is assigned to each criterion
y
. In this paper, relative
weights of the criteria were taken from AHP model. Hence,
the relative importance of all the criteria was set same in
AHP and PROMETHEE methods. In PROMETHEE, the
weights for all the criteria should satisfy the following
condition.
1
1
Y
y
y
w
=
=
(15)
After the problem definition, the next step is to compute
the deviations between the different alternatives through
pairwise comparisons. The deviations between the values of
the two alternatives
a
and
b
for each criterion can be
calculated as follows.
( )
, ( ) ( )
y y y
d a b g a g b=−
,a b Z
(16)
The next step is to define the preference function. This
method requires the preference functions, which are used to
define the deviations between different alternatives for each
criterion. The preference function can be defined as
follows.
( ) ( )
,,
y y y
P a b F d a b
=
Where,
( )
0 , 1
y
P a b
(17)
The preference function
( )
,
y
P a b
is the function of the
difference between evaluations of alternative
a
regarding
the alternative
b
for each criterion. The different types of
preference functions can be used in PROMETHEE method.
This paper used the two preference functions including
“Usual” and “Linear” types. The preference function
“Usual” was used for the qualitative scale and preference
function “Linear” was used for the quantitative values. The
preference function “Usual” can be defined as follows.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
( )
0, 0
1, 0
for d
Pd for d
=
(18)
And the preference function “Linear” can be defined as
follows.
( )
0, 0
, 0 0
1,
d
d
P d d
m
dm
=
(19)
In this function, linearity of preference function
increases until point m. This point is arbitrary, and the
decision maker should fix it. In the next step, the
aggregated preference indices are calculated in the
PROMETHEE method. For this purpose, the overall
preference index can be calculated as follows.
( )
( )
1
1
( , ) , ,
( , ) , ,
Y
y
y
Y
y
y
a b P a b w
b a P b a w
=
=
=
=
,a b Z
(20)
The preference index
( , )ab
is the degree to which
a
is preferred to
b
over all the criteria. Similarly,
( , )ba
is
the degree to which
b
is preferred to
a
over all the criteria.
In the next step, the ranking is made using the
PROMETHEE-I and PROMETHEE-II. The PROMETHEE
I or the partial ranking can be obtained through the positive
and negative outranking flows. An alternative with the
highest value of the positive outranking flow is the best
alternative. The positive outranking flow can be computed
as follows.
1
( ) ( , )
1xZ
a a x
N
+
=−
xZ
(21)
Each alternative
a
is compared with
1N−
other
alternatives in Z. Similary, an alternative with the lowest
value of the negative outranking flow is the best alternative.
The negative outranking flow can be computed as follows.
1
( ) ( , )
1xZ
a x a
N
−
=−
xZ
(22)
Usually, all the alternatives may not be comparable. In
this situation, the complete ranking or PROMETHEE-II is
performed through the computation of the net outranking
flow. The net outranking flow can be computed as follows.
( ) ( ) ( )a a a
+−
=−
(23)
F. HYBRID STORAGE SELECTION BASED ON AHP AND
PROMETHEE SOLUTIONS
Once the storage rankings were received from both methods,
an iterative novel procedure was implemented for the
selection of the best storage technology. Firstly, the common
alternatives were discarded from the specified number of the
least ranked alternatives in both methods. For this purpose,
this paper selected the five least ranked alternatives from the
individual solution of AHP and PROMETHEE. From these
five least ranked alternatives, the common alternatives were
excluded. Then, the AHP and PROMETHEE models were
solved again using the remaining alternatives. From the new
solutions by each method, the five least ranked alternatives
were selected, and the common alternatives were excluded
again. This procedure was repeated until the best alternative
was achieved. For the purpose of discarding the common
alternatives, this paper selected five least ranked alternatives
from the solution. However, when the number of alternatives
in the problem became less than or equal to five, then the
number of discarded alternatives were less than five. The
decision makers may change the number of discarded
alternatives according to the suitability of the problem.
III. RESULTS AND DISCUSSION
In the following, the proposed methodology has been applied
on the two different cases, and the key results have been
presented.
A. CASE 1. ALL THE THREE MAIN CRITERIA ARE
EQUALLY PREFERABLE
In this case, it was assumed that the energy flow
management for prosumers, technical aspects, and
sustainability should be given equal importance in the
decision making. This case has been evaluated in the
following based on the AHP and PROMETHEE methods.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Figure 4. AHP storage selection model structure in Super Decisions software
1) AHP MODEL EVALUATION FOR THE CASE 1
In AHP model, the preferences for the different criteria and
alternatives were set as follows. In Case 1, the level 1 criteria
in the hierarchy (i.e. energy flow management for prosumers,
technical aspects, and sustainability) were kept equally (i.e.
one time) preferably with respect to goal. Similarly, the level
2 criteria and level 3 criteria were kept equally preferable
with respect to immediate upper-level criteria. Finally, the
preferences of the storage alternatives were set based on the
actual data in Table 1 and Table 2. Figure 4 presents the AHP
model structure in the SuperDecisions software.
Figure 5. Case 1. AHP model solution
Figure 5 presents the AHP model solution based on Case
1. AHP results contain three columns. The column “Raw”
contains the model solution extracted from the Limit
Supermatrix. The columns “Ideals” and “Normals” were
derived from the column “Raw” for the purpose of easy
understanding. The column “Normals” was derived by
dividing an individual value with sum of all the values within
“Raw” column. The column “Ideals” was derived by
dividing an individual value with the largest value within
“Raw” column. According to Figure 5, the lithium-ion
battery (LIB) was found as a best storage alternative for the
prosumers in smart grids. However, the supercapacitors
(SCS) and lead acid battery (LAB) were found as the 2nd and
3rd options. Hence, the supercapacitors may be used with
lithium-ion batteries. The supercapacitors would not offer
some of the major requirements in the prosumer-based smart
grids. In this situation, the lead acid battery would be the next
option. Several other storage technologies have received a
good ranking, but lithium-ion battery has been selected as a
best alternative in the Case 1.
In the AHP model solutions, the inconsistency was
ensured negligible. For instance, Figure 6 presents the data
entry window for the criteria “transmission stability and
congestion management (TrCon)”. For this criterion, the
pairwise comparison of all the 15 storage technologies was
performed as shown in the left side of the window. In the
right side, the inconsistency value is displaying at the top of
results. The inconsistency of the pairwise comparisons for
this criterion is zero. Similarly, all the other criteria exhibited
zero or negligible inconsistency, which shows the high
reliability of the pairwise comparisons.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Figure 6. Data entry and inconsistency test for the pairwise comparison
2) PROMETHEE MODEL EVALUATION FOR THE CASE 1
In PROMETHEE model, the preferences weights for the
level 1 criteria, level 2 criteria, and level-3 criteria were
obtained from AHP model. The preferences of the storage
alternatives were set based on the actual data in Table 1 and
Table 2. Figure 7 presents the upper left part of the
PROMETHEE model structure in the Visual PROMETHEE
software.
Figure 7. Case 1. PROMETHEE model structure in Visual PROMETHEE
software
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
(a) PROMETHEE I Partial Ranking
(b) PROMETHEE II Complete Ranking
Figure 8. Case 1. PROMETHEE model solution
Figure 8 presents the PROMETHEE model solution based
on Case 1. This method suggested the lead acid battery
(LAB) as first choice for both PROMETHEE I and
PROMETHEE II. The storage alternatives are overlapped in
Figure 8 due to very close scores. Figure 9 presents these
values in a clearer form. According to PROMETHEE I, the
lithium-ion battery (LIB) has the 2nd ranking based on the
better Phi+ scores, but it has worse scores on Phi-. Hence, the
priority of LIB is not clear, but it can be confirmed with
PROMETHEE II. According to PROMETHEE II complete
ranking, LIB has the second priority and LAB has a priority.
In contrast, AHP model offered the first priority for LIB and
the third priority for LAB. Hence, the AHP and
PROMETHEE methods offered somewhat different
solutions.
Visual PROMETHEE software contains the criteria names
along the horizontal direction in rows, and the alternatives
names (i.e. evaluations) along the vertical direction in
columns. In the evaluations window, the qualitative and
numerical data can be entered directly. The software allows
the hierarchy development using the “Criteria Hierarchy
Assistant” window. Also, the weights can be assigned using
the “Weighing Assist” window. For instance, Figure 10
presents the upper part of the “Weighing Assistant”
windows.
Figure 9. Case 1. PROMETHEE model solution
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Figure 10. Weighing Assistant window in Visual PROMETHEE software (Case 1)
C. CASE 2. ENERGY FLOW MANAGEMENT FOR
PROSUMERS IS EXTREMELY MORE PREFERABLE
In the Case 1, the decision-making process assumes equal
importance among energy flow management, technical
aspects, and sustainability. However, the priorities of
prosumers may vary significantly based on their specific
contexts and requirements. In this situation, the equal
weighting cannot be justified for the potential trade-offs.
Hence, the proposed model would be evaluated for several
potential changes in the preferences. In this study, Case 2
has been included, which assumes that the main criteria
“energy flow management for prosumers” is extremely
more preferable than the remaining two main criteria. This
case has been evaluated in the following based on the AHP
and PROMETHEE methods.
1) AHP MODEL EVALUATION FOR THE CASE 2
In AHP model, the preferences for different criteria and
alternatives were set as follows. In Case 2, the level 1 criteria
“energy flow management for prosumers” was set extremely
(i.e. nine times) more preferable than the remaining two main
criteria “technical aspects” and “sustainability” with respect
to goal. The preferences for the level 2 criteria, level 3
criteria, and alternatives were set same as in Case 1.
However, the preferences for the level 2 criteria and level 3
criteria were affected, resulting in the entirely different
values of the preferences weights compared with Case 1.
Figure 11 presents the AHP model solution based on Case 2.
The lithium-ion battery (LIB) was found as the first storage
alternative for the prosumers in smart grids. However, the
lead acid battery (LAB) and sodium sulfur battery (SSB)
were found as the 2nd and 3rd options, respectively. In the
absence of the lithium ion (LIB), LAB and SSB would be the
next option. Several other storage technologies have received
a good ranking, but LIB has been selected as the best
alternative in Case 2.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Figure 11. Case 2. AHP model solution
(a) PROMETHEE I Partial Ranking (Case
2)
(b) PROMETHEE II Complete Ranking (Case
2)
Figure 12. Case 2. PROMETHEE model solution
2) PROMETHEE MODEL EVALUATION FOR THE CASE 2
In PROMETHEE model, the preferences for the criteria and
alternatives were same as in Case 2 for the AHP model.
Figure 12 presents the PROMETHEE model solution based
on the Case 2. This method suggested the lead acid battery
(LAB) as the first choice for both PROMETHEE I and
PROMETHEE II. However, the lithium-ion battery (LIB)
and sodium sulfur battery (SSB) were found as the 2nd and
3rd options, respectively. In the absence of the LAB, the LIB
and SSB would be the next option. It can be observed that the
AHP and PROMETHEE resulted in more comparable
ranking than the Case 1. It can also be observed that the
storage alternatives are less overlapping compared with Case
1, which resulted in more clear rankings of the storage
alternatives for both PROMETHEE I and PROMETHEE II.
As anticipated, the change in priority of a criterion
changed the final ranking. For instance, the different
priorities were assigned to three main criteria in Case 1 and
Case 2. In the Case 1, prosumer required all the three main
criteria as equally preferable. In this case, supercapacitors
were ranked higher than most of the other alternatives in the
Case 1. In the Case 2, prosumer preferred main criteria
“energy flow management for prosumers” on the remaining
two main criteria. In this case, the ranking of supercapacitors
decreased significantly.
C. SENSITIVITY ANALYSIS
In the following, the sensitivity analysis has been
performed for the AHP and PROMETHEE solutions. In
AHP and PROMETHEE models, the sensitivity analysis
was performed by changing the weights of the criteria. Both
software (i.e. SuperDecisions and Visual PROMETHEE)
offer sensitivity analysis based on the changes in the
weights or priorities for a criterion. The results confirmed
that the variations in the priorities for the different criteria
did not significantly affect the model solutions.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
We consider a sub-criterion “support in bidirectional
energy sharing (BiSh)” in Case 2 for the sensitivity
analysis. The criterion “Energy Sharing (Esh)” contains the
three sub-criteria including support in bidirectional energy
sharing (BiSh), transmission stability and congestion
management (TrCon), and telecommunications backup
(TelB). In Case 2, these criteria are equally preferable. In
AHP method, these three sub-criteria correspond to the
approximate priority 0.33 out of 1.00. However,
PROMETHEE software allowed the direct entry of criteria
weight in percentage (i.e. 0-100%). In PROMETHEE
method, a maximum 20.45% weight was allowed to the
criteria “Energy Sharing (Esh)”. Consequently, 20.45% in
PROMETHEE method is exactly equal to 1.00 in AHP
method.
6.82% priority
15% priority
20% priority
Figure 13. Sensitivity analysis of AHP solution for Case 2
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Table 3. Comparison of sensitivity analysis results
Ranking
AHP model
PROMETHEE model
6.82%
priority
15% priority
20% priority
6.82% priority
15% priority
20% priority
1
LIB
LIB
LIB
LAB
LAB
LAB
2
LAB
LAB
LAB
LIB
LIB
LIB
3
SSB
SSB
SSB
SSB
SSB
SSB
4
NCB
NCB
NCB
NCB
NCB
NCB
5
SNC
SNC
SNC
SNC
FS
FS
6
FS
NMH
NMH
FS
SNC
SNC
7
VRB
FS
FS
NMH
SCS
SCS
8
NMH
SCS
SCS
SCS
NMH
NMH
9
ZBB
VRB
VRB
VRB
VRB
VRB
10
SCS
ZBB
ZBB
ZBB
ZBB
ZBB
11
SMES
SMES
SMES
SMES
SMES
SMES
12
HFC
HFC
HFC
HFC
HFC
HFC
13
CAS
CAS
CAS
CAS
CAS
CAS
14
PHS
PHS
PHS
PHS
PHS
PHS
15
STS
STS
STS
STS
STS
STS
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
(a) 6.82% priority
(b) 15% priority
(c) 20% priority
Figure 14. Sensitivity analysis of PROMETHEE solution for Case 2
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
In the Case 2, the criterion “support in bidirectional
energy sharing (BiSh)” has 6.82% weight out of 20.45%.
Consequently, 6.82% in PROMETHEE method is exactly
equal to 0.33 in AHP method. In the sensitivity analysis, the
weight of this criterion was steadily increased from 6.82%
to 20% for PROMETHEE method and 0.33 to 1.00 for
AHP method. In this section, only three priority levels (i.e.
6.82%, 15%, and 20%) have been presented. In AHP
method, 6.82%, 15%, and 20% were equivalent to 0.33,
0.74, and 0.98, respectively. In summary, the sensitivity
analysis was performed with similar priorities for AHP and
PROMETHEE methods.
Figure 13 presents the sensitivity analysis of AHP
solution for the three different priority levels. The
horizontal axis shows the priority of a criterion, and the
vertical axis shows the storage rankings. It can be observed
that most of the storage alternatives have occupied similar
rank for each priority level of the criterion “support in
bidirectional energy sharing (BiSh)”. The remaining
alternatives exhibited only slightly different ranks (i.e. 1 or
2 ranks more or less) for the three priority levels. Similar
trends can be observed for the sensitivity analysis of
PROMETHEE solution (Figure 14). Table 3 presents the
comparison of the sensitivity analysis results based on the
criterion “support in bidirectional energy sharing (BiSh)”. It
can be observed that most of the rankings are same or
slightly different for the priority levels of the criterion
“support in bidirectional energy sharing (BiSh)”. Similar
trends were observed for the remaining criteria in both
methods. It was found that the results were very stable for
the AHP and PROMETHEE solutions.
D. HYBRID DECISION MAKING BASED ON AHP AND
PROMETHEE
In the above sections, AHP and PROMETHEE methods
offered somewhat different solutions. This difference in the
problem solutions is due to the fact that both methods have
different mathematical backgrounds and calculations,
resulting in different solutions. There should be some ways
to combine the AHP and PROMETHEE solutions, so that
the best most reliable storage technology can be identified.
In the following, a collective decision-making approach has
been proposed based on the solutions of both AHP and
PROMETHEE methods. Figure 15 presents the systematic
detection of the best storage alternatives from AHP and
PROMETHEE solutions for Case 1. In Case 1, all the three
main criteria were given equal priority as discussed in
Section 3.1.
Please refer to Section 2.6 for the hybrid storage selection
procedure adopted in this section. In the first step, all the 15
storage technologies were included. In this step, the
problem and solution were exactly same as the individual
models of AHP and PROMETHEE. In this step, the AHP
(left side) and PROMETHEE (right side) solutions are
given in Figure 15(a). Within the last five storage
alternatives in both methods, three storage technologies (i.e.
ZBB, NMH, and SNC) were found common in both
solutions. These three storage technologies were excluded,
and the models were solved again with the remaining 12
storage options (Figure 15(b)). Amongst these 12 storage
technologies, three storage technologies (i.e. NCB, PHS,
and STS) were found to be common within the last five
storage alternatives. These three storage technologies were
excluded, and the models were solved again with the
remaining nine storage options (Figure 15(c)).
This procedure was repeated again, and the HFC, CAS,
SMES, VRB were excluded in the next step, and the
models were solved for the remaining five alternatives
(Figure 15(d)). Here, the storage alternatives were exactly
five. Here, the common alternatives were excluded from the
last four alternatives (Figure 15(e)). In this way, SSB, SCS,
and FS were excluded from the analysis, and the models
were solved for the remaining two storage alternatives. As a
result, the lithium-ion battery (LIB) was ranked first, and
the lead acid battery (LAB) was ranked second by both
AHP and PROMETHEE methods. These results were
obtained based on Case 1. This procedure was applied on
the Case 2 as shown in Figure 16. In the Case 2, the main
criteria “energy flow management for prosumers” was
given extremely more priority (please refer Section 3.2).
Again, the LIB was ranked first, and the LAB was ranked
second by both methods. It can be observed that the Case 2
ranked the storage alternatives more clearly compared with
the Case 1. It can be noted that the best solution is reached
in four steps for Case 2 (i.e. Figure 16), while the best
solution reached in five steps for Case 1 (i.e. Figure 15).
The exclusion of the least ranked storage alternatives in a
few steps interprets that the solutions offered by the
MCDM methods are significantly identical. This fact
proves the validity and reliability of the solutions offered by
the two entirely different methods within the MCDM
domain of operations research and management sciences.
Exclusion of storage technologies throughout the
iterative process should not lead to the loss of potentially
viable alternatives. Several strategies could be applied to
prevent the loss of a better alternative. A careful
comparison of different solutions may be helpful. For
instance, the number of alternatives to be excluded can be
increased or decreased as required by decision maker.
Another important way to prevent the loss of a potentially
viable alternative is the sensitivity analysis of the individual
solution based on a specific criterion.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
(a) Solve AHP (leftside) and PROMETHEE (right side) models with all the 15 storage alternatives. In the next
step, exclude ZBB, NMH, and SNC and solve.
(b) Solve models with remaining 12 alternatives. In the next step, exclude NCB, PHS, and STS and solve.
(c) Solve models with remaining 9 alternatives. In the next step, exclude HFC, CAS, SMES, VRB and solve
.
(d) Solve models with remaining 5 alternatives. In the next step, exclude SSB, SCS, and FS and solve.
(e) Solve models with remaining two alternatives.
Figure 15. Determination of the best storage based on AHP and PROMETHEE solutions for Case 1.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
(a) Solve AHP and PROMETHEE models with all the 15 storage alternatives. In the next step, exclude SMES,
HFC, CAS, PHS, and STS and solve.
(b) Solve models with remaining 10 alternatives. In the next step, exclude SNC, ZBB, NMH, and SCS and solve.
(c) Solve models with remaining 6 alternatives. In the next step, exclude SSB, NCB, VRB, and FS and solve.
(d) Solve models with remaining two alternatives.
Figure 16. Determination of the best storage based on AHP and PROMETHEE solutions for Case 2.
E. FUTURE OUTLOOK
Until 2050, the world is committed towards significant
elimination of unsustainable generation resources from the
power systems. As a result, the share of renewable energy-
based power generation will rapidly increase across the
whole world. In an effort to support this wonderful
revolution, a large number of prosumers will actively
participate in the power generation for self-consumption or
sharing with other prosumers or grid [73-74]. Large scale
participation of prosumers in the electricity market is an
emerging trend in the developed world. However,
prosumer-based power systems would fail without
renewable energy storage systems [75].
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Most of the recent research has focused on the utilization
of batteries in prosumer-based power systems [76-82].
Specially, PV and battery system combination is more
common in the modern power systems [83]. Moreover, it
has been reported that Europe will install 57 GW battery
storage systems till 2030 [84]. Batteries are easily available
in markets and are more compatible with prosumer-based
systems. However, they have various strategic
disadvantages (e.g. high cost, toxicity, short term storage,
and materials scarcity) which encourage improving these
batteries or searching for alternative storage technologies.
This paper suggests that the batteries are the most favorable
selection for prosumers.
In this paper, the lead acid and lithium-ion batteries have
been selected for prosumer-based systems. The toxicity of
lead acid batteries will restrict its application in the long
term. Recent research is focusing on lead-free dielectric
ceramics. Still, the lead-free ceramics lack practical
applicability due to their lower efficiency and energy
density compared with lead-based ceramics [85]. Also,
scarce lithium resources cannot meet the growing storage
needs of multiple prosumers. Hence, there will be several
alternatives for lithium-ion batteries. For example, aqueous
sodium-ion batteries are more cost effective, safe, and
abundantly available compared with lithium-ion and lead-
acid batteries. On the other hand, the aqueous sodium-ion
batteries have some drawbacks including lower energy
density and flammability. However, recent research is
focusing on improving these aspects of aqueous sodium-ion
batteries [86]. Despite, the aqueous aluminium-ion batteries
have good energy density, good power density, more safety,
and abundant availability. Recent research is working
actively on the improvement of aqueous aluminium-ion
batteries. In addition, magnesium, potassium, and calcium
are abundantly available, and they can be considered as
metal anode materials [87].
Although, the batteries seem more compatible with
prosumers, they incur high investment cost and longer
payback period. This fact would limit the use of batteries in
the very large-scale prosumer-based power systems [88]. In
the recent literature, evaluation of community storage
systems is emerging for multiple prosumers. In such
systems, a common storage is managed by an independent
operator [79]. These community storage systems offer a
common storage capacity to multiple houses [90]. A
significant cost reduction and power saving have been
reported for peer-to-peer energy trading systems based on
community storage systems. In addition, such energy
storage offers a more resilient energy future for prosumers
communities [91]. These systems require battery packs
containing a large number of similar or different batteries.
Even a same type of battery involves different materials,
manufacturing techniques, and structures, which influences
the consistent operation of the large-scale battery systems.
The inconsistency of battery pack would result in more
faults, reduction in lifetime, and more maintenance. To
overcome this issue, it has been recommended to trace the
inconsistency of batteries through advanced battery
management technologies [92]. A more competitive
solution will be the utilization of long duration energy
storage systems for the large-scale prosumer communities.
This paper has identified least preference for the large-scale
non-battery storage systems including pumped hydro
storage, hydrogen storage, compressed air storage, and
solar thermal storage. Pumped hydro is more suitable when
enough water is available at a specific place. Pumped hydro
storage will be more attractive where the water storage can
be ensured for a long duration. Small scale pump hydro
storage will be more feasible for some prosumers.
Hydrogen storage is another sustainable option. However,
the application of hydrogen storage is less accepted for
prosumer-based systems due to less availability in market
and more overall cost [93]. Each storage technology offers
unique strengths and weaknesses. Strengths of different
storage technologies can be combined through the
development of hybrid storage systems [94-95]. Recently,
hybrid hydrogen and battery storage has been
recommended for prosumer-based systems. Battery can
manage the rapid and frequent power storage needs and
hydrogen storage can be used for large scale storage
capacity [96]. Recent research concludes that the
integration of batteries with hydrogen storage can decrease
the operational cost of prosumer-based power systems.
However, the higher infrastructure cost of hydrogen storage
results in a significant increase in overall cost. Hydrogen
storage offers up to several months of energy storage
compared with batteries that offer short term storage. Major
cost factors in the integration of hydrogen storage into
prosumer-based power systems include the additional
complexity and new infrastructure requirement. The
infrastructure cost of hydrogen storage techniques should
be decreased through advanced technologies. In addition,
innovative electrolysers should be developed which should
be compatible with hybrid storage systems. Hydrogen
production should be done using renewable energy
resources, which will result in the reduction of air pollution,
resulting in decrease of climate change risk [97].
Compressed air energy storage is another large-scale
storage alternative. CAES has several major disadvantages
including less energy density, less efficiency, and location
dependency. Several cost-effective advancements to
compressed air energy storage have been proposed, such as
the use CO2 as an alternative to compressed air [98].
Prosumer based power systems will require an ideal storage
system owning a range of related characteristics.
Unfortunately, there is no storage system with all the
required characteristics for these modern systems. Future
work is required in various directions in order to enhance
the storage selection decisions for prosumer-based power
systems. Literature has extensively combined two different
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
MCDM methods for different types of problems. In the
literature, AHP and PROMETHEE combination is a
prominent combination for multi-criteria decisions. Some
papers have combined fuzzy sets with MCDM methods to
increase the robustness of MCDM methods. Future work
would combine MCDM with different extensions of fuzzy
sets such as type-2, intuitionistic, spherical, hesitant, and
Pythagorean fuzzy sets. Fuzzy sets support human
judgment by decreasing vagueness and imprecision of
human judgments. Recently, spherical fuzzy sets have been
combined with AHP method to select the most appropriate
energy storage in Egypt. The ideal storage was selected as
the pumped hydro storage [99]. Future research would use
this hybrid method for selecting the ideal storage system for
prosumer-based power systems at various locations around
the world. Reference [45] applied fuzzy logic and AHP
method for storage selection amongst flywheel,
supercapacitor, pumped hydro, compressed air, and
hydrogen storage alternatives. In that work, the individual
application of fuzzy logic and AHP method resulted in
exactly the same ranking. This shows the authenticity of
AHP results. However, fuzzy sets have been suggested as
more reliable methods compared with AHP. Future work is
required to confirm this suggestion for prosumer-based
power systems. Some other methods can be used for
prosumer-based power systems, such as ELECTRE,
DEMATEL, TOPSIS, SAW, and ANP. Consequently, the
limitations of different storage selection methods should be
identified through practical implementation of different
methods. Reference [100] performed energy storage
selection based on fuzzy AHP and fuzzy VIKOR.
Electromagnetic storage was selected as the top alternative.
Fuzzy set theory has been integrated with MCDM methods
for different applications. Fuzzy sets offer the integration of
incomplete or inaccurate information into decision making.
Reference [101] selected the compressed air storage based
on the type-2 fuzzy AHP and type-2 fuzzy TOPSIS.
Moreover, type-2 fuzzy AHP was used to determine the
weights of criteria and the type-2 fuzzy TOPSIS was used
for the selection of alternatives. Hence, a range of
alternative MCDM methods can be adopted for storage
selection for prosumer-based power systems.
In future, energy storage selection will remain a critical
decision for efficient energy management in the prosumer-
based power systems. There will be a plethora of
competitive storage alternatives, and a careful selection of
an ideal storage system will be an essential decision for
prosumer-based power systems [102]. The inclusiveness
and simplicity of a storage selection method will be the
more attractive selection criteria. Prosumers have a limited
time to select a comparatively more efficient technology.
To select competitive storage technologies, scientific
decision-making methods are very important. In this
context, this paper is a substantial effort towards the
implementation of two highly regarded MCDM methods,
specifically AHP and PROMETHEE, for the storage
selection for prosumer based smart grids. This research
shows how the hybrid application of these two MCDM
methods improves the reliability of results. Moreover, their
robustness allows the reliable solution to a complex
problem.
IV. CONCLUSION
This study successfully enhanced the storage selection
decision making for prosumers in smart grids with the help
of two leading MCDM methods, namely AHP and
PROMETHEE. Lithium-ion battery (LIB) was found to be
the best storage alternative for prosumer-based smart grids.
However, lead acid battery (LAB) remained very
competitive. In addition, a few other storage technologies
remained competitive, which suggests that scientists would
improve the specific features of these batteries. Due to the
emergence of large-scale modern power systems, only LIB
and LAB would not meet the substantial storage demand. In
this situation, a few more storage technologies should be the
focus of improvement. Which storage technologies, except
LIB and LAB, would be the most promising for large scale
prosumer-based complex power systems? Obviously,
MCDM methods offer fair ranking based on each important
criterion. At least, these MCDM methods assist us towards
reliable decision making. It can be concluded that the
hybridization of two different MCDM methods can tackle the
storage selection problem in more systematic ways. It was
found that the ranking of storage technologies was slightly
different for AHP and PROMETHEE. The systematic
removal of the least ranked storage technologies from both
methods offered an effective and innovative decision-making
method. It was also found that the combination of two
methods helped in correct data entry into the respective
software. It also increased the solution reliability and
confidence in the decision making. Sensitivity analysis
exhibited that the model solutions for AHP and
PROMETHEE were very stable and robust. This work
evaluated the proposed storage selection framework only in
two cases due to space constraint. The proposed model is
very comprehensive and flexible, which can be evaluated in
numerous ways under different situations such as giving
different preferences to the different main criteria and sub-
criteria. The model can be evaluated with addition or
removal of criteria and storage alternatives. In summary, the
proposed model would substantially assist decision makers in
the storage selection for prosumer-based smart grids. Future
work may focus on adding a number of other MCDM
methods for storage selection problems. Scientists would find
more innovative ways for getting more valid and reliable
results based on the hybridization of different MCDM
methods. Furthermore, this paper provides future outlook on
the storage selection problem, which offers future trends and
research directions for the prosumer’s storage selection.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
REFERENCES
[1] S. Yousaf, A. Mughees, M. G. Khan, A. A. Amin, and M. Adnan, “A
comparative analysis of various controller techniques for optimal
control of smart nano-grid using GA and PSO algorithms,” IEEE
Access, vol. 8, pp. 205696–205711, 2020.
[2] Ahmed, M. Adnan, M. Ali, and G. Kaddoum, “Supertwisting sliding
mode controller for grid-to-vehicle and vehicle-to-grid battery electric
vehicle charger,” Journal of Energy Storage, vol. 70, p. 107914, 2023.
[3] N. U. Huda, I. Ahmed, M. Adnan, M. Ali, and F. Naeem, “Experts
and intelligent systems for smart homes’ Transformation to
Sustainable Smart Cities: A comprehensive review,” Expert Systems
with Applications, vol. 238, p. 122380, 2024.
[4] E. Aksakal, M. Dağdeviren, E. Eraslan, and İ. Yüksel, “Personel
selection based on talent management,” Procedia-Social and
Behavioral Sciences, vol. 73, pp. 68–72, 2013.
[5] T. Balezentis, D. Streimikiene, and I. Siksnelyte-Butkiene, “Energy
storage selection for sustainable energy development: The multi-
criteria utility analysis based on the ideal solutions and integer
geometric programming for coordination degree,” Environmental
Impact Assessment Review, vol. 91, p. 106675, 2021.
[6] S. Hosouli, J. Elvins, J. Searle, S. Boudjabeur, J. Bowyer, and E.
Jewell, “A Multi-Criteria decision making (MCDM) methodology for
high temperature thermochemical storage material selection using
graph theory and matrix approach,” Materials & Design, vol. 227, p.
111685, 2023.
[7] N. Mahdavi, P. Mojaver, and S. Khalilarya, “Multi-objective
optimization of power, CO2 emission and exergy efficiency of a novel
solar-assisted CCHP system using RSM and TOPSIS coupled
method,” Renewable Energy, vol. 185, pp. 506–524, 2022.
[8] L. Turcksin, A. Bernardini, and C. Macharis, “A combined AHP-
PROMETHEE approach for selecting the most appropriate policy
scenario to stimulate a clean vehicle fleet,” Procedia-Social and
Behavioral Sciences, vol. 20, pp. 954–965, 2011.
[9] R. Hasanzadeh, P. Mojaver, T. Azdast, S. Khalilarya, A. Chitsaz, and
M. A. Rosen, “Decision analysis for plastic waste gasification
considering energy, exergy, and environmental criteria using TOPSIS
and grey relational analysis,” Process Safety and Environmental
Protection, vol. 174, pp. 414–423, 2023.
[10] R. Hasanzadeh, T. Azdast, M. Mojaver, and C. B. Park, “High-
efficiency and low-pollutant waste polystyrene and waste polystyrene
foam gasification: Comprehensive comparison analysis, multi-
objective optimization and multi-criteria decision analysis,” Fuel, vol.
316, p. 123362, 2022.
[11] N. Rane, A. Achari, and S. Choudhary, “Multi-Criteria Decision-
Making (MCDM) as a powerful tool for sustainable development:
Effective applications of AHP, FAHP, TOPSIS, ELECTRE, and
VIKOR in sustainability,” International Research Journal of
Modernization in Engineering Technology and Science, vol. 5, no. 4,
pp. 2654–2670, 2023.
[12] H.-C. Lee and C.-T. Chang, “Comparative analysis of MCDM
methods for ranking renewable energy sources in Taiwan,” Renewable
and sustainable energy reviews, vol. 92, pp. 883–896, 2018.
[13] B. Singh, “Analytical hierarchical process (AHP) and fuzzy AHP
applications-A review paper,” International Journal of Pharmacy and
Technology, vol. 8, no. 4, pp. 4925–4946, 2016.
[14] A. Ishak and V. Akmaliah, “Analytical hierarchy process and
PROMETHEE as decision making tool: a review,” in IOP Conference
Series: Materials Science and Engineering, IOP Publishing, 2019, p.
012085. Accessed: Oct. 25, 2024. [Online]. Available:
https://iopscience.iop.org/article/10.1088/1757-
899X/505/1/012085/meta.
[15] S. Howell, Y. Rezgui, J.-L. Hippolyte, B. Jayan, and H. Li, “Towards
the next generation of smart grids: Semantic and holonic multi-agent
management of distributed energy resources,” Renewable and
Sustainable Energy Reviews, vol. 77, pp. 193–214, 2017.
[16] Raza, L. Jingzhao, M. Adnan, and M. S. Iqbal, “Transforming smart
homes via P2P energy trading using robust forecasting and scheduling
framework,” Results in Engineering, vol. 23, p. 102766, 2024.
[17] Raza, L. Jingzhao, M. Adnan, and I. Ahmad, “Optimal load
forecasting and scheduling strategies for smart homes peer-to-peer
energy networks: A comprehensive survey with critical simulation
analysis,” Results in Engineering, vol. 22, p. 102188, 2024.
[18] Raza, L. Jingzhao, Y. Ghadi, M. Adnan, and M. Ali, “Smart home
energy management systems: Research challenges and survey,”
Alexandria Engineering Journal, vol. 92, pp. 117–170, 2024.
[19] M. H. Shahbaz et al., “A streamlined 17-level cascaded H-bridge
multilevel converter with solar PV integration,” Bulletin of Electrical
Engineering and Informatics, vol. 10, no. 2, pp. 598–608, 2021.
[20] J. Hjalmarsson, K. Thomas, and C. Boström, “Service stacking using
energy storage systems for grid applications–A review,” Journal of
Energy Storage, vol. 60, p. 106639, 2023.
[21] H. A. Muqeet, A. Ahmad, I. A. Sajjad, R. Liaqat, A. Raza, and M. M.
Iqbal, “Benefits of distributed energy and storage system in prosumer
based electricity market,” in 2019 IEEE International Conference on
Environment and Electrical Engineering and 2019 IEEE Industrial
and Commercial Power Systems Europe (EEEIC/I&CPS Europe),
IEEE, 2019, pp. 1–6. Accessed: Oct. 25, 2024. [Online]. Available:
https://ieeexplore.ieee.org/abstract/document/8783636/.
[22] A. Mahmood, A. R. Butt, U. Mussadiq, R. Nawaz, R. Zafar, and S.
Razzaq, “Energy sharing and management for prosumers in smart grid
with integration of storage system,” in 2017 5th International Istanbul
Smart Grid and Cities Congress and Fair (ICSG), IEEE, 2017, pp.
153–156. Accessed: Oct. 25, 2024. [Online]. Available:
https://ieeexplore.ieee.org/abstract/document/7947623/
[23] H. A. U. Muqeet and A. Ahmad, “Optimal scheduling for campus
prosumer microgrid considering price based demand response,” IEEE
Access, vol. 8, pp. 71378–71394, 2020.
[24] K. Kusakana, “Optimal Peer-to-Peer energy sharing between
prosumers using hydrokinetic, diesel generator and pumped hydro
storage,” Journal of Energy Storage, vol. 26, p. 101048, 2019.
[25] K. Kusakana, “Optimal peer-to-peer energy management between
grid-connected prosumers with battery storage and photovoltaic
systems,” Journal of Energy Storage, vol. 32, p. 101717, 2020.
[26] M. C. Soini, D. Parra, and M. K. Patel, “Impact of prosumer battery
operation on the cost of power supply,” Journal of Energy Storage,
vol. 29, p. 101323, 2020.
[27] F. Berglund, S. Zaferanlouei, M. Korpås, and K. Uhlen, “Optimal
operation of battery storage for a subscribed capacity-based power
tariff prosumer—A Norwegian case study,” Energies, vol. 12, no. 23,
p. 4450, 2019.
[28] D. Keiner, M. Ram, L. D. S. N. S. Barbosa, D. Bogdanov, and C.
Breyer, “Cost optimal self-consumption of PV prosumers with
stationary batteries, heat pumps, thermal energy storage and electric
vehicles across the world up to 2050,” Solar Energy, vol. 185, pp.
406–423, 2019.
[29] S. M. Tercan, A. Demirci, E. Gokalp, and U. Cali, “Maximizing self-
consumption rates and power quality towards two-stage evaluation for
solar energy and shared energy storage empowered microgrids,”
Journal of Energy Storage, vol. 51, p. 104561, 2022.
[30] A. Pena-Bello, P. Schuetz, M. Berger, J. Worlitschek, M. K. Patel,
and D. Parra, “Decarbonizing heat with PV-coupled heat pumps
supported by electricity and heat storage: Impacts and trade-offs for
prosumers and the grid,” Energy Conversion and Management, vol.
240, p. 114220, 2021.
[31] P. Huang et al., “Transforming a residential building cluster into
electricity prosumers in Sweden: Optimal design of a coupled PV-heat
pump-thermal storage-electric vehicle system,” Applied Energy, vol.
255, p. 113864, 2019.
[32] S. Habib, A. Ahmarinejad, and Y. Jia, “A stochastic model for
microgrids planning considering smart prosumers, electric vehicles
and energy storages,” Journal of Energy Storage, vol. 70, p. 107962,
2023.
[33] Basnet and J. Zhong, “Integrating gas energy storage system in a peer-
to-peer community energy market for enhanced operation,”
International Journal of Electrical Power & Energy Systems, vol.
118, p. 105789, 2020.
[34] D. Keiner, C. Thoma, D. Bogdanov, and C. Breyer, “Seasonal
hydrogen storage for residential on-and off-grid solar photovoltaics
prosumer applications: Revolutionary solution or niche market for the
energy transition until 2050?,” Applied Energy, vol. 340, p. 121009,
2023.
[35] M. Ruiz-Cortés, E. Romero-Cadaval, C. Roncero-Clemente, F.
Barrero-González, and E. González-Romera, “Energy management
strategy to coordinate batteries and ultracapacitors of a hybrid energy
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
storage system in a residential prosumer installation,” in 2017
International Young Engineers Forum (YEF-ECE), IEEE, 2017, pp.
30–35. Accessed: Oct. 25, 2024. [Online]. Available:
https://ieeexplore.ieee.org/abstract/document/7935636/
[36] C.-T. Pham and D. Månsson, “Optimal energy storage sizing using
equivalent circuit modelling for prosumer applications (Part II),”
Journal of Energy Storage, vol. 18, pp. 1–15, 2018.
[37] D. Wang, J. Carmeliet, and K. Orehounig, “Design and assessment of
district heating systems with solar thermal prosumers and thermal
storage,” Energies, vol. 14, no. 4, p. 1184, 2021.
[38] A. O. Gbadegesin, Y. Sun, and N. I. Nwulu, “Stacked value streams
of hybrid energy storage systems in prosumer microgrids,” Journal of
Engineering, Design and Technology, vol. 19, no. 5, pp. 1063–1079,
2021.
[39] M. Jarnut, S. Wermiński, and B. Waśkowicz, “Comparative analysis
of selected energy storage technologies for prosumer-owned
microgrids,” Renewable and Sustainable Energy Reviews, vol. 74, pp.
925–937, 2017.
[40] M. Adnan, I. Ahmed, S. Iqbal, M. R. Fazal, S. J. Siddiqi, and M.
Tariq, “Exploring the convergence of Metaverse, Blockchain,
Artificial Intelligence, and digital twin for pioneering the digitization
in the envision smart grid 3.0,” Computers and Electrical
Engineering, vol. 120, p. 109709, 2024.
[41] J.-P. Brans and Y. De Smet, “PROMETHEE Methods,” in Multiple
Criteria Decision Analysis, vol. 233, S. Greco, M. Ehrgott, and J. R.
Figueira, Eds., in International Series in Operations Research &
Management Science, vol. 233. , New York, NY: Springer New York,
2016, pp. 187–219. doi: 10.1007/978-1-4939-3094-4_6.
[42] M. Çolak and İ. Kaya, “Multi-criteria evaluation of energy storage
technologies based on hesitant fuzzy information: A case study for
Turkey,” Journal of Energy Storage, vol. 28, p. 101211, 2020.
[43] Z. Wang, S. Chen, X. He, C. Wang, and D. Zhao, “A multi-factor
evaluation method for the thermal runaway risk of lithium-ion
batteries,” Journal of Energy Storage, vol. 45, p. 103767, 2022.
[44] A. Dwivedi, A. Kumar, and V. Goel, “A consolidated decision-
making framework for nano-additives selection in battery thermal
management applications,” Journal of Energy Storage, vol. 59, p.
106565, 2023.
[45] M. V. G. Rodrigues, M. Dos Santos, and C. F. S. Gomes, “Selection
of helicopters for offshore service using three multi-criteria decision
analysis methods: AHP-TOPSIS-2N, THOR 2 and Gaussian AHP-
TOPSIS-2N,” Journal of Control and Decision, pp. 1–15, Jan. 2024,
doi: 10.1080/23307706.2024.2302491.
[46] R. C. A. Pereira et al., “Evaluation of smart sensors for subway
electric motor escalators through AHP-Gaussian method,” Sensors,
vol. 23, no. 8, p. 4131, 2023.
[47] V. V. A. Almeida, C. F. S. Gomes, L. H. C. Pinochet, and M. dos
Santos, “Prioritization of investment order in Brazil among renewable
energy sources: an analysis using the SWARA-MOORA-3NAG
method,” Journal of Modelling in Management, 2024, Accessed: Nov.
18, 2024. [Online]. Available:
https://www.emerald.com/insight/content/doi/10.1108/jm2-09-2023-
0208/full/html
[48] M. Â. L. Moreira, I. P. de Araújo Costa, M. T. Pereira, M. Dos
Santos, C. F. S. Gomes, and F. M. Muradas, “PROMETHEE-
SAPEVO-M1 a Hybrid approach based on ordinal and cardinal inputs:
Multi-Criteria evaluation of helicopters to support brazilian navy
operations,” Algorithms, vol. 14, no. 5, p. 140, 2021.
[49] A. Siagian and R. Gwynne, “An integrated model for selecting and
evaluating logistics outsourcing decisions using AHP and
PROMETHEE II,” Journal of Industrial Engineering and Education,
vol. 1, no. 1, pp. 30–45, 2023.
[50] C. Zhang, S. Kwon, J. Oh, and K. Park, “An integrated AHP-
PROMETHEE II ranking method to evaluate the resilience of sewer
networks considering urban flood and ground collapse risks,” Water
Science & Technology, vol. 87, no. 6, pp. 1438–1453, 2023.
[51] M. H. Mohammadi, Z. Hejazi, M. A. Saeedi, and E. Giordani, “Using
AHP and PROMETHEE Multicriteria Decision-Making Approaches
to Rank Available Fruit Crops for Orchard Expansion in Nangarhar,
Afghanistan,” Erwerbs-Obstbau, vol. 65, no. 5, pp. 1837–1847, Oct.
2023, doi: 10.1007/s10341-023-00902-y.
[52] M. Liaqat, M. G. Khan, M. R. Fazal, Y. Ghadi, and M. Adnan,
“Multi-criteria storage selection model for grid-connected
photovoltaics systems,” IEEE Access, vol. 9, pp. 115506–115522,
2021.
[53] M. Liaqat, Y. Y. Ghadi, M. Adnan, and M. R. Fazal, “Multicriteria
evaluation of portable energy storage technologies for electric
vehicles,” IEEE Access, vol. 10, pp. 64890–64903, 2022.
[54] A. Barin, L. N. Canha, A. da Rosa Abaide, and K. F. Magnago,
“Selection of storage energy technologies in a power quality
scenario—the AHP and the fuzzy logic,” in 2009 35th Annual
Conference of IEEE Industrial Electronics, IEEE, 2009, pp. 3615–
3620. Accessed: Oct. 25, 2024. [Online]. Available:
https://ieeexplore.ieee.org/abstract/document/5415150/
[55] R. Zafar, A. Mahmood, S. Razzaq, W. Ali, U. Naeem, and K.
Shehzad, “Prosumer based energy management and sharing in smart
grid,” Renewable and Sustainable Energy Reviews, vol. 82, pp. 1675–
1684, 2018.
[56] U. G. Mulleriyawage and W. Shen, “A Review of Battery Energy
Storage Systems for Residential DC Microgrids and Their Economical
Comparisons,” DEStech Trans. Environ. Energy Earth Sci, 2019,
Accessed: Oct. 25, 2024. [Online]. Available:
https://www.academia.edu/download/74240226/6da05f66b63b65550d
1dfd4b529b72e4c1e0.pdf
[57] M. Ourahou, W. Ayrir, B. E. Hassouni, and A. Haddi, “Review on
smart grid control and reliability in presence of renewable energies:
Challenges and prospects,” Mathematics and computers in simulation,
vol. 167, pp. 19–31, 2020.
[58] A. R. Hansen, M. H. Jacobsen, K. Gram-Hanssen, and F. Friis, “Three
forms of energy prosumer engagement and their impact on time-
shifting electricity consumption,” ECEEE 2019 Summer Study
Proceedings. Presented at the ECEEE Summer Study, 2019,
Accessed: Oct. 25, 2024. [Online]. Available:
https://www.researchgate.net/profile/Anders-Rhiger-Hansen-
2/publication/339846195_Three_forms_of_energy_prosumer_engage
ment_and_their_impact_on_time-
shifting_electricity_consumption/links/5e68de4b92851c7ce05b8f3e/T
hree-forms-of-energy-prosumer-engagement-and-their-impact-on-
time-shifting-electricity-consumption.pdf.
[59] A. I. Nousdilis, A. A. Nikolaou, E. O. Kontis, G. C. Kryonidis, G. C.
Christoforidis, and G. K. Papagiannis, “A Sizing Method for
Decentralized Energy Storage Systems Operating Under a Peak
Shaving Control Strategy,” in 2018 53rd International Universities
Power Engineering Conference (UPEC), IEEE, 2018, pp. 1–6.
Accessed: Oct. 25, 2024. [Online]. Available:
https://ieeexplore.ieee.org/abstract/document/8541966/
[60] G. Karmiris and T. Tengnér, “Peak shaving control method for energy
storage,” Corporate Research Center, Vasterås, Sweden, 2013,
Accessed: Oct. 25, 2024. [Online]. Available:
https://www.sandia.gov/files/ess/EESAT/2013_papers/Peak_Shaving_
Control_Method_for_Energy_Storage.pdf
[61] E. Hossain, H. M. R. Faruque, M. S. H. Sunny, N. Mohammad, and
N. Nawar, “A comprehensive review on energy storage systems:
Types, comparison, current scenario, applications, barriers, and
potential solutions, policies, and future prospects,” Energies, vol. 13,
no. 14, p. 3651, 2020.
[62] F. B. Wu, B. Yang, and J. L. Ye, “Chapter 2—Technologies of energy
storage systems,” Grid-Scale Energy Storage Systems and
Applications; Academic Press: Cambridge, MA, USA, pp. 17–56,
2019.
[63] S. Unver and I. Ergenc, “Safety risk identification and prioritize of
forest logging activities using analytic hierarchy process (AHP),”
Alexandria Engineering Journal, vol. 60, no. 1, pp. 1591–1599, 2021.
[64] P. Cabała, “Using the analytic hierarchy process in evaluating
decision alternatives,” Operations research and decisions, vol. 20, no.
1, pp. 5–23, 2010.
[65] M. Mojaver, R. Hasanzadeh, T. Azdast, and C. B. Park, “Comparative
study on air gasification of plastic waste and conventional biomass
based on coupling of AHP/TOPSIS multi-criteria decision analysis,”
Chemosphere, vol. 286, p. 131867, 2022.
[66] P. Mojaver, S. Khalilarya, A. Chitsaz, and M. Assadi, “Multi-
objective optimization of a power generation system based SOFC
using Taguchi/AHP/TOPSIS triple method,” Sustainable Energy
Technologies and Assessments, vol. 38, p. 100674, 2020.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
[67] S. Hammami et al., “Application of the GIS based multi-criteria
decision analysis and analytical hierarchy process (AHP) in the flood
susceptibility mapping (Tunisia),” Arab J Geosci, vol. 12, no. 21, p.
653, Nov. 2019, doi: 10.1007/s12517-019-4754-9.
[68] H. Veisi, R. Deihimfard, A. Shahmohammadi, and Y. Hydarzadeh,
“Application of the analytic hierarchy process (AHP) in a multi-
criteria selection of agricultural irrigation systems,” Agricultural
Water Management, vol. 267, p. 107619, 2022.
[69] S. Panchal and A. K. Shrivastava, “Landslide hazard assessment using
analytic hierarchy process (AHP): A case study of National Highway
5 in India,” Ain Shams Engineering Journal, vol. 13, no. 3, p. 101626,
2022.
[70] L. Abdullah, W. Chan, and A. Afshari, “Application of PROMETHEE
method for green supplier selection: a comparative result based on
preference functions,” J Ind Eng Int, vol. 15, no. 2, pp. 271–285, Jun.
2019, doi: 10.1007/s40092-018-0289-z.
[71] J. J. Thakkar, Multi-Criteria Decision Making, vol. 336. in Studies in
Systems, Decision and Control, vol. 336. Singapore: Springer
Singapore, 2021. doi: 10.1007/978-981-33-4745-8.
[72] M. Behzadian, R. B. Kazemzadeh, A. Albadvi, and M. Aghdasi,
“PROMETHEE: A comprehensive literature review on methodologies
and applications,” European journal of Operational research, vol.
200, no. 1, pp. 198–215, 2010.
[73] I. López-Forniés and L. Asión-Suñer, “Analysing the prosumer
opportunity. Prosumer products’ success or failure,” Journal of
Engineering Design, vol. 35, no. 1, pp. 1–21, Jan. 2024, doi:
10.1080/09544828.2023.2290914.
[74] W. Leal Filho et al., “Prosumers and sustainable development: An
international assessment in the field of renewable energy,”
Sustainable Futures, vol. 7, p. 100158, 2024.
[75] R. Rodríguez-Vilches, F. Martín-Martínez, Á. Sánchez-Miralles, J. R.
G. de la Cámara, and S. M. Delgado, “Methodology to assess
prosumer participation in European electricity markets,” Renewable
and Sustainable Energy Reviews, vol. 191, p. 114179, 2024.
[76] S. Tongsopit, S. Junlakarn, A. Chaianong, I. Overland, and R.
Vakulchuk, “Prosumer solar power and energy storage forecasting in
countries with limited data: The case of Thailand,” Heliyon, vol. 10,
no. 2, 2024, Accessed: Oct. 25, 2024. [Online]. Available:
https://www.cell.com/heliyon/fulltext/S2405-8440(24)00028-8.
[77] . Zhang et al., “Modelling the formation of peer-to-peer trading
coalitions and prosumer participation incentives in transactive energy
communities,” Applied Energy, vol. 355, p. 122173, 2024.
[78] M. Ali et al., “Integration of Data Driven Technologies in Smart Grids
for Resilient and Sustainable Smart Cities: A Comprehensive
Review,” Aug. 03, 2023, arXiv: arXiv:2301.08814. Accessed: Nov.
19, 2024. [Online]. Available: http://arxiv.org/abs/2301.08814
[79] A. Najafi-Ghalelou, M. Khorasany, and R. Razzaghi, “Maximizing
social welfare of prosumers in neighborhood battery-enabled
distribution networks,” Applied Energy, vol. 359, p. 122622, 2024.
[80] M. Adnan, “Smart Grid 3.0: Navigating the Future-Unleashing the
Power of Metaverse, Blockchain, and Digital Twins in the Evolution
of Smart Grids,” Blockchain, and Digital Twins in the Evolution of
Smart Grids (Janauary 20, 2024), Accessed: Nov. 19, 2024. [Online].
Available:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4801458
[81] X. Nie, S. A. Mansouri, A. R. Jordehi, M. Tostado-Véliz, and Y. Z.
Alharthi, “Emerging renewable-based electricity grids under high
penetration of cleaner prosumers: unraveling the flexibility issues
using a four-layer decentralized mechanism,” Journal of Cleaner
Production, vol. 443, p. 141107, 2024.
[82] K. Fida, U. Abbasi, M. Adnan, S. Iqbal, and S. E. G. Mohamed, “A
comprehensive survey on load forecasting hybrid models: Navigating
the Futuristic demand response patterns through experts and
intelligent systems,” Results in Engineering, p. 102773, 2024.
[83] H. Kang, S. Jung, H. Kim, J. Jeoung, and T. Hong, “Reinforcement
learning-based optimal scheduling model of battery energy storage
system at the building level,” Renewable and Sustainable Energy
Reviews, vol. 190, p. 114054, 2024.
[84] S. O. Rey et al., “Powering the future: a comprehensive review of
battery energy storage systems,” Energies, vol. 16, no. 17, p. 6344,
2023.
[85] F. Yan, J. Qian, S. Wang, and J. Zhai, “Progress and outlook on lead-
free ceramics for energy storage applications,” Nano Energy, p.
109394, 2024.
[86] H. Wu et al., “Alkaline-based aqueous sodium-ion batteries for large-
scale energy storage,” Nature Communications, vol. 15, no. 1, p. 575,
2024.
[87] X. Zheng, C. Han, C. Lee, W. Yao, C. Zhi, and Y. Tang, “Materials
challenges for aluminum ion based aqueous energy storage devices:
Progress and prospects,” Progress in Materials Science, p. 101253,
2024.
[88] P. Du, B. Huang, Z. Liu, C. Yang, and Q. Sun, “Real-Time Energy
Management for Net-Zero Power Systems Based on Shared Energy
Storage,” Journal of Modern Power Systems and Clean Energy, 2024,
Accessed: Oct. 25, 2024. [Online]. Available:
https://ieeexplore.ieee.org/abstract/document/10419164/
[89] S. Cui, R. Zhu, and J. Wu, “A double layer energy cooperation
framework for prosumer groups in high altitude areas,” Renewable
Energy, vol. 224, p. 120090, 2024.
[90] Y. Z. Gerdroodbari, M. Khorasany, R. Razzaghi, and R. Heidari,
“Management of prosumers using dynamic export limits and shared
Community Energy Storage,” Applied Energy, vol. 355, p. 122222,
2024.
[91] H. Ghasemnejad, M. Rashidinejad, A. Abdollahi, and S. Dorahaki,
“Energy management in citizen energy communities: A flexibility-
constrained robust optimization approach considering prosumers
comfort,” Applied Energy, vol. 356, p. 122456, 2024.
[92] J. Tian, Y. Fan, T. Pan, X. Zhang, J. Yin, and Q. Zhang, “A critical
review on inconsistency mechanism, evaluation methods and
improvement measures for lithium-ion battery energy storage
systems,” Renewable and Sustainable Energy Reviews, vol. 189, p.
113978, 2024.
[93] A. Boretti, “A market opportunity in power generation for hydrogen
energy storage systems,” International Journal of Hydrogen Energy,
vol. 49, pp. 166–172, 2024.
[94] R. K. R. Karduri and C. Ananth, “Decarbonizing the Grid: Pathways
to Sustainable Energy Storage,” This paper has been published in
International Journal of Advanced Research In Basic Engineering
Sciences and Technology (IJARBEST) DOI, vol. 10, 2023, Accessed:
Oct. 25, 2024. [Online]. Available:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4637767
[95] Z. M. Ali, M. Calasan, S. H. A. Aleem, F. Jurado, and F. H.
Gandoman, “Applications of energy storage systems in enhancing
energy management and access in microgrids: A review,” Energies,
vol. 16, no. 16, p. 5930, 2023.
[96] Y. Yang et al., “An overview of application-oriented multifunctional
large-scale stationary battery and hydrogen hybrid energy storage
system,” Energy Reviews, p. 100068, 2024.
[97] L. Abdolmaleki and U. Berardi, “Hybrid solar energy systems with
hydrogen and electrical energy storage for a single house and a
midrise apartment in North America,” International Journal of
Hydrogen Energy, vol. 52, pp. 1381–1394, 2024.
[98] F. Dewevre, C. Lacroix, K. Loubar, and S. Poncet, “Carbon dioxide
energy storage systems: Current researches and perspectives,”
Renewable Energy, p. 120030, 2024.
[99] A. Gamal, M. Abdel-Basset, I. M. Hezam, K. M. Sallam, A. M.
Alshamrani, and I. A. Hameed, “A computational sustainable
approach for energy storage systems performance evaluation based on
spherical-fuzzy MCDM with considering uncertainty,” Energy
Reports, vol. 11, pp. 1319–1341, 2024.
[100] D. Nagaraju et al., “Semantic Approach for Evaluation of Energy
Storage Technologies under Fuzzy Environment,” Advances in Fuzzy
Systems, vol. 2022, pp. 1–11, Aug. 2022, doi: 10.1155/2022/1149503.
[101] B. Ozkan, İ. Kaya, U. Cebeci, and H. Başlıgil, “A Hybrid
Multicriteria Decision Making Methodology Based on Type-2 Fuzzy
Sets For Selection Among Energy Storage Alternatives:,” IJCIS, vol.
8, no. 5, p. 914, 2015, doi: 10.1080/18756891.2015.1084715.
[102] A. G. Olabi, C. Onumaegbu, T. Wilberforce, M. Ramadan, M. A.
Abdelkareem, and A. H. Al–Alami, “Critical review of energy storage
systems,” Energy, vol. 214, p. 118987, 2021.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
VOLUME XX, 2017 9
Marriam Liaqat received the M.S. electrical engineering degree at
National University of Computer and Emerging Sciences (FAST), Chiniot-
Faisalabad Campus, Pakistan. She is currently pursuing the PhD electrical
engineering at National University of Computer and Emerging Sciences
(FAST), Chiniot-Faisalabad Campus, Pakistan. Her research interests
include power system modeling, renewable energy in power systems, and
smart grids.
Muhammad Adnan received the B.S.
degree in Electrical Engineering from the
National University of Computer and
Emerging Sciences, Peshawar, Pakistan, in
2013, and the M.S. degree in Electrical
Engineering from the COMSATS Institute of
Information and Technology, Islamabad,
Pakistan, in 2015. He had recently
completed his Ph.D. degree in Electrical
Engineering from National University of
Computer and Emerging Sciences, Peshawar, Pakistan, where he served as
a Research Fellow with the Department of Electrical Power Engineering,
from Jan, 2017 to Dec, 2019. Currently, he is working as an Assistant
Professor in the Department of Electrical Engineering in FAST NUCES,
CFD Campus. His research interests include energy management systems,
load flow balancing, load forecasting, power systems dynamic analysis,
protection, stability, and intelligent control in renewable energy resources
using a fuzzy controller and unified power flow controller.
Mohaira Ahmad obtained her B.E. degree
in Electrical (Telecom) Engineering from
National University of Sciences and
Technology, Islamabad, Pakistan, in 2010
and Ph.D degree in Computer Application
Technology with specialization in
computational electromagnetics from
Jiangsu University, China in 2019. She is
currently an Assistant Professor at the
School of Electrical Engineering and
Computer Science, National University of
Sciences and Technology, Islamabad, Pakistan. She worked as an
Assistant Professor at the Electrical Engineering Department, University
of Lahore, Pakistan from 2014 to 2015. She worked as an R&D engineer
at National Radio and Telecommunication Corporation from 2010-2012.
Her research interest includes reconfigurable antennas and filter designing,
MIMO antennas, computational electromagnetics and machine learning.
Muhammad Sajid Iqbal received the B.S. degree
in telecommunication engineering from NUCES-
FAST Karachi, Pakistan, in 2011, and the M.S.
degree in electrical engineering from the King Fahd
University of Petroleum and Minerals, Saudi
Arabia. He is currently a Lecturer with the
Electrical Engineering Department, NUCESFAST
Chiniot–Faisalabad. His research interests include
power electronic devices, fault-tolerant control for
industrial application, antenna array design, and machine learning.
Engr. Faizan Ahmad received his B.Sc and
M.Sc degrees in Electrical Engineering with
specialization in Electronics and Fault
Tolerant Control Systems from the National
University of Computer and Emerging
Sciences, Faisalabad, Pakistan. He possesses
4 years of academic experience with 03
years served as a Lab Engineer at FAST
(NUCES) and currently serving as a Lab
engineer at Namal University, Mianwali
form the previous 01 year. He has achieved various academic distinctions
such as winner of IEEE Student Tech Talks, being three times on the
Dean's List, and getting three bronze medals which show his excellent
performance in academics. He has various international impact factor
journal publications. His research interests include control systems and
automation.
Sami ud Din earned his B.S. in Electrical
Engineering from the Federal Urdu University
of Arts, Science and Technology, Islamabad,
Pakistan, in 2009. He subsequently completed
his M.S. in Electronics Engineering with a
focus on Control Systems at Muhammad Ali
Jinnah University, Islamabad, in 2012. In
2019, he received his Ph.D. in Electrical
Engineering, specializing in Control Systems,
from the Capital University of Science and
Technology, Islamabad.
Before joining Namal University Mianwali, Pakistan, he was with the
Department of Electrical Engineering at The University of Lahore,
Islamabad Campus, until October 2019. His research interests include non-
linear control, sliding mode control, under-actuated systems, chaotic
systems, and robotics.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3514698
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
