Energy Conversion and Management 235 (2021) 113962
Contents lists available at ScienceDirect
Energy Conversion and Management
journal homepage: www.elsevier.com/locate/enconman
A fuzzy decision-making model for optimal design of solar, wind,
diesel-based RO desalination integrating flow-battery and pumped-hydro
storage: Case study in Baltim, Egypt
Kotb M. Kotb a, b, *, M.R. Elkadeem a, c, Ahmed Khalil d, Sherif M. Imam e, Mohamed A. Hamada f,
Swellam W. Sharshir f, András Dán b
a
Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta 31521, Egypt
Department of Electrical Engineering, Budapest University of Technology and Economics, Budapest 1111, Hungary
c
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
d
Civil Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
e
Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
f
Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Hybrid renewable energy systems
Multicriteria decision making
Fuzzy AHP
Fuzzy VIKOR
Flow batteries
Pumped hydro energy storage
This paper aims to propose a conceptual design model for sustainable hybrid renewable stand-alone energy
system (HRSES) to meet the electricity demand of a large-scale reverse osmosis desalination plant in Baltim,
Egypt. The model investigates the feasibility of different HRSES alternatives and develop a fuzzy-based multi­
criteria decision-making model for meticulously selecting the optimal energy solution. Both zinc-bromine flow
battery and turbine-pumped hydro energy storage technologies are integrated independently with wind, solar,
and diesel power sources. Firstly, the proposed model uses HOMER software to execute an energy-economicecological optimization analysis for studying the practicability and components sizing of nine HRSES alterna­
tives. Second, an integration between Fuzzy-AHP and Fuzzy-VIKOR decision-making methods is executed to
choose the best design considering ten performance criteria. In the second stage, the fuzzy environment is
engaged to expedite decision-makers to express their ratings in linguistic terms and to achieve more sensible and
accurate results. Among ten feasible alternatives, the results reveal that the optimal system consists of 5 × 20-kW
wind turbines, 328-kW photovoltaic array, 100-kW diesel generator, 112 batteries and 235-kW converter. This
system has the best economic performance among all alternatives with least NPC, COE, and payback-period of
$1,048,046, 0.101$/kWh and 1.1 yr, respectively. Besides, it has a treasured share of renewable of 95.55%;
hence, it produces a realistic CO2 of 25,426.46 kg/year. Lastly, the sensitivity analysis illuminates that the future
load growth and low-interest rate hurt upcoming investments while the projected reduction in the cost of energy
storages has an encouraging influence on financing decisions.
1. Introduction
Sustainable progress of countries is mainly aiming to provide reli­
able, affordable, sustainable energy and freshwater for all [1]. However,
the demand for water and energy is increasing annually in parallel with
the increase of the agricultural area, civilization, economic develop­
ment, and population growth [2]. Globally, there are about 1.06 billion
people do not have access to electricity and about 2.1 billion people do
not have access to sufficient clean water [3,4]. Besides, based on the
United Nations (UN) world assessment report, up to 40% of the global
inhabitants will be influenced by water shortage by 2030 due to climate
change and greenhouse gasses [5]. The key reasons of insufficient freshwater resources are climate change and greenhouse gasses [6]. With
around 97% of the entire existing water resources being saline, the
evident possibility to find sustainable freshwater is through desalination
and water recycle technologies [7]. According to the Sustainable
* Corresponding author at: Department of Electrical Engineering, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and
Economics, Budapest 1111, Hungary.
E-mail addresses: kotb.mohamed@f-eng.tanta.edu.eg (K.M. Kotb), mohammad.elkadim@f-eng.tanta.edu.eg (M.R. Elkadeem), sharshir@eng.kfs.edu.eg
(S.W. Sharshir), dan.andras@vet.bme.hu (A. Dán).
https://doi.org/10.1016/j.enconman.2021.113962
Received 13 December 2020; Accepted 15 February 2021
Available online 18 March 2021
0196-8904/© 2021 The Author(s).
Published by
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Elsevier
Ltd.
This
is
an
open
access
article
under
the
CC
BY-NC-ND
license
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
140
Solar, PV
Onshore wind
Offshore wind
Biomass & waste
Geothermal
(a)
100
Power (GW)
460
Geothermal
Biomass & Waste
Offshore wind
Onshore wind
Solar PV
(b)
120
80
60
40
143
20
Brazil
Turkey
Belgium
Nigeria
Mexico
Spain
Taiwan
Japan
Algeria
Netherlands
France
Italy
South Africa
U.A.E
U.K.
South Korea
China
Saudi Arabia
0
India
2
Germany
35
80
Fig. 1. Renewable energy targets by 2030 in GW [11] (a) by type (b) top 20 countries.
Fig. 1(a) indicates that 721 GW of wind, solar, biomass, and geothermal
power plants would need to be established around the upcoming 10
years to fulfill the 2030 goals. Besides, Fig. 1(b) arranges the top 20
countries, excluding U.S. since it has no national renewables deploy­
ment targets, by the size of their pursued renewable power additions
between 2020 and 2030. On the research level, the research interest
towards the utilization of hybrid renewable energy systems (HRESs) fed
desalination plants (DPs) increased significantly over the last decade as
displayed in Fig. 2 based on the science direct database.2
350
Number of articles
300
250
200
150
1.1. National and international policies on renewable energy
100
Accelerating the deployment of renewable energy (RE) requires
policies that contribute to creating an enabling environment for
attracting investments. As the implementation of renewables has grown
and technology has matured, renewable energy policies are increasingly
integrated into overall energy sector planning. To inform policy-making,
the International Renewable Energy Agency (IRENA) provided a stateof-the-art analysis of enabling policy frameworks and spanning the
entire RE development cycle. This includes identifying best practice and
trends in policy design and evaluating support mechanisms and their
adaptation to changing market conditions [12]. IRENA, the Interna­
tional Energy Agency (IEA), and the Renewable Energy Policy Network
for the 21st Century identified key barriers and highlighted policy op­
tions to boost RE deployment considering the current world context and
the UN goals. These policies examine sector-specific policies for heating
and cooling, transport, and power, as well as measures for integrating
hybrid and variable renewables. The key findings of international
renewable energy policies can be found in [13,14]. Nationally, high
income European countries were early in embracing advanced national
RE development policies [15,16]. Recently, various countries such as
China, India, South-Africa, and Egypt endorsed similar policies to sup­
port RE deployment [16]. Taking Egypt as an example, Egypt’s Vision
2030, the country’s sustainable development plan, considers energy as
the second most crucial post amongst ten posts for national sustainable
development [17]. The Egyptian vision emphasizes both optimal and
household utilization of hybrid energy resources to integrate renewable
energy for energy production. Moreover, since the energy sector is
accountable for 41% of global CO2 emissions [17], the Egyptian vision
50
0
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Year
Fig. 2. Published articles on the field of HRESs fed DPs over the last decade.
Development Goals (SDGs) adopted by the UN1 in 2015 to end poverty,
protect the planet and ensure that all people enjoy peace and prosperity
by 2030, the world context towards sustainability and cleaner energy
production becomes a crucial part of countries’ sustainable development
plans worldwide. Therefore, the transition towards sustainable energy
systems via the accurate integration among renewable energy sources
(RESs) has become a must along with the rising energy and clean water
needs and problems that arise within each country [8]. In accordance
with the SDGs, the 6th goal aims to ensure the availability and sustain­
able management of water and sanitation while goal-7 focuses on
scaling up the access to affordable, reliable, sustainable, and modern
energy for all. In this context, integration of RESs such as photovoltaic
(PV) and wind turbines (WTs) with modern water desalination tech­
nologies, particularly reverse osmosis desalination plants (RODPs), is
recognized as an efficient, highly desirable, economical, and ecofriendly solution to achieve the SDGs [9,10]. Currently, official policy
goals have been written by policymakers around the world to maximize
the amount of clean energy resources built by 2030 as shown in Fig. 1.
1
2
From https://sdgs.un.org/goals
2
https://www.sciencedirect.com
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
Table 1
Summary of most recent works for RODPs based HRSESs.
Ref-year
Study type
Plant capacity & demand
(m3/d)
(kWh/d)
Feeder
Location
System structure
Studied aspects
Sensitivity
analysis
[26]2020
[27]2020
[28]2020
[29]2018
[30]2019
[31]
− 2019
[32]2020
[33]2021
[34]2020
[35]2021
[50]2020
Theoretical
4800
106,600
Seawater
Isolated island
WT/PV/BS/DG
Economic
No
Theoretical
10
60.3
Seawater
Al Batinah,Oman
PV/BS/DG
Economic andEcological
No
Theoretical
150
522
Seawater
Neom city, KSA
PV/FC/BS
Economic
No
Theoretical
24
105.12
Seawater
WT/PV/BS/DG
Economic
No
Theoretical
150
126
PV/FC/Electrolyzer
Economic andEcological
No
Theoretical
50
250
Brackish
water
Seawater
Bozcaada Island,
Turkey
Minya, Egypt
WT/PV/BS/DG
Economic
Yes
Theoretical
100
557.22
WT/PV/BS/DG
850
903.6
Cairo, Egypt
Theoretical &
Practical
Theoretical
1500
4393
WT/PV/BS/DG/CHPGen/TLC/BLR
WT/PV/BS/DG
39.75–102.15
33–84.78
Seawater
5 villages, Iran
Theoretical
700–4600
Varied
Seawater
Astypalaia Island,
Greece
Economic,Energy,
andEcological
Economic,Energy,
andEcological
Economic,Energy,
andEcological
Economic,Energy,
andEcological
Economic
No
Theoretical
Brackish
water
Brackish
water
Seawater
Canary Islands,
Spain
Sinai, Egypt
Abo Ramad, Egypt
emphasizes mitigation of CO2 emissions produced by its energy sector.
More details about Egypt’s sustainable development strategy and its
energy and environmental policies can be found in [18,19].
PV/WT/Hydro/BioGas/
DG/BS
WT/CSP/BS
No
No
Yes
Yes
develop a robust post-optimality decision-making model to evaluate and
analyze the feasibility and design optimization of an integrated largescale RODP fed from HRSESs incorporating two different energy stor­
age technologies and considering multiple design aspects.
1.2. Research motivation and main objective
1.3. Literature review
On the national level of Egypt, the country is currently suffering a
reduction of water extent in the Nile River due to the crisis of the Grand
Ethiopian Renaissance Dam which challenges Egypt’s historical hege­
monic position on the Nile basin [20]. With an annual water content/
capita less than 665 m3, the stability of agriculture implementations and
human beings become in risk. Even though the Egyptian government’s
remarkable attempts to address the electricity sector bottlenecks, about
250 communities are still deprived of electricity access and clean water.
Hence, drawn from the SDGs 6 and 7, the Egyptian Vision 2030 reflects
the country’s long-term strategic plan to accomplish sustainable devel­
opment standards and objectives in all fields (e.g., energy access and
water quality). A part from that vision is embarking to augment the
share of renewables to be 49.5-GW and 62.6-GW in years 2029/30 and
2034/35, respectively and to increase the amount of freshwater through
establishing 47 desalination plants (DPs) by 2025 [21]. These ambitious
goals can be effectively accomplished by integrating hybrid renewable
stand-alone energy systems (HRSESs) with DPs fed from either seawater
or brackish water. However, the optimal planning of such systems is
influenced by various interconnected and overlapped aspects (e.g.,
technological, economic, ecological, topographical, and socio-political)
which make the design further complicated [22]. Besides, since the RESs
are daily and seasonally climate-dependent sources, their output power
is naturally unpredictable (for example; hail, snow, dirt, and tempera­
ture fluctuations) [23]. Thus, integrating suitable and efficient energy
storage devices is of great benefit so that the energy system can better
fulfill the load demand and system oversizing can be prevented [24,25].
This spotted issue still necessitates a great deal of work to be accom­
plished in this area. Therefore, the primary objective of this study is to
So far, innumerable efforts are directed to examine the viability and
optimal design of HRSESs to energize different load categories in various
locations worldwide, as reviewed in [14]. A significant concern of
integrating HRSESs with RODPs to fulfill the ever-growing need for
water, particularly in water-scarce sites, is currently of high interest as
reviewed in [7]. A joint water and energy supply system for a remote
island using HRSES was modelled and examined in [26]. The author
used various desalination processes, to produce fresh-water, which are
supplied by a hybrid solar-wind renewable system with the support of
batteries as a storage system, and diesel generator (DGen) to enhance
reliability level of the supply system. Another feasibility study of using
HRSESs for small DPs based on its life cycle cost was presented in [27].
Due to the low oil cost and high potential of the solar index in Oman, the
study showed that the hybrid PV/battery storage (BS)/DGen system is
considered the most economical HRSES for RODPs. The feasibility and
techno-economic evaluation of using three scenarios of HRSES for
seawater RODP in Saudi Arabia was studied and compared with the gridconnected option [28]. The three scenarios studied the combination
among PV, BS, and fuel cells (FCs) in which obtained results proved that
the best configuration for powering 150 m3 RODP is the hybrid PV/FC/
BS even with the grid connection. Another study investigated the
techno-economic feasibility of wind/PV/DGen/BS system driving an
RODP system of 24 m3/day capacity in Turkey [29]. The resulted cost of
energy (COE) and cost of water were found $0.308/kWh and $2.20/m3,
respectively. Also, the study highlighted the economic effectiveness of
combining HRSESs and RODPs in remote areas of good solar and wind
energy potential. The feasibility of using FCs with both stand-alone PV
3
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
Fig. 3. Methodical description of the research method and sections relations of the manuscript.
system and grid-connected RO system was inspected in [30]. The
resulted COE ($0.062/kWh) was found lower than the stand-alone DGen
system by 70%. Also, authors claimed that using FCs is an efficient way
in resolving the intermittence problems of RESs. A real-data technoeconomic and sensitivity investigation of PV/WT/DGen/BS fed RODP
for two islands in Spain was presented in [31]. However, the resulted
COE values ($0.478/kWh and 0.404/kWh) were found high compared
with the global trend. The sizing and techno-economic optimization of
PV/WT/DGen/BS systems fed 100 m3/day RODP was investigated in
[32]. The resulted COE of the optimal plan was found $0.1074/kWh and
the high renewable penetration effectively reduced emissions by 94%.
Unlike the common applications of HRSESs, the feasibility and optimi­
zation of an energy-water-heat nexus serving up the facilities of an
airport in Egypt was accomplished in [33]. The addressed system
included a large-scale RODP of 850 m3/day and had a COE of $0.089/
kWh which was decreased by 56.4% due to the utilization of thermal
load controller. A methodical techno-enviro-economic framework to
recognize the optimal HRSES design to supply a large-scale DP in a
remote seaside village in Egypt was addressed in [34]. The winning
system (WT/PV/DGen/BS) showed a superior reduction in both the net
present cost (NCP) and COE by 60.7% and 60.6%, respectively
compared to the stand-alone DGen system. The feasible solutions
between HRSES and grid-extension to supply electrical power and freshwater for remote villages of different climate zones was examined in
[35]. The results showed that for the selected villages, the HRSESs are
the best option for fulfilling the required electricity over the gridextension option. A summary of the most recent case-studies on
RODPs fed from HRSESs is shown in Table 1.
Since the design of HRSESs is influenced by several interrelated as­
pects along with the economic performance, multicriteria decisionmaking (MCDM) analysis is then employed for selecting the most
appropriate system among pre-determined alternatives by assessing
them regarding many criteria. MCDM methods are generally classified
to traditional and fuzzy-based [39]. The fuzzy-based methods are pro­
posed to handle imprecision issues and achieve more tangible solutions
and facilitate decision-makers (DMs) to convey their views using lin­
guistic terms. Therefore, more realistic, and precise results can be ach­
ieved. Traditional analytic hierarchy process (AHP) and technique for
order preference by similarity to ideal solution (TOPSIS) approaches can
be implemented in fuzzy environment. Alternatively, fuzzy elimination
and choice expressing the reality (ELECTRE) and fuzzy multicriteria
optimization and compromise solution (VIKOR) achieve more delicate
results. Also, integrating two or more fuzzy-based methods for solving
the RE decision-making problem is possible and is seen to achieve higher
4
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
Start
Read resources data of load profile solar radiation and wind speed
PV
FBAT
Define components cost and technical specs
TPH
Precision
condition is
meet
WT
DGen
Systems without ESTs
Classify HRSES
No
Re-design
the HRSES
Systems with FBAT storage
Systems with PHT storage
Create a number K hybridization cases
All feasible cases
are checked
No
Set m=1
Perform hourly energy balance calculations for each case over 8760 hrs
Yes
Print the optimized results of feasible
HRSES
m=m+1
No
Check system
viability
Use Fuzzy-AHP to determine the optimal
weights
Use Fuzzy-VIKOR for final ranking of
HRSES
Yes
The system with 1st rank is identified as
the optimal system
Consider feasible cases
Search optimal design using proprietary derivative-free algorithm of HOMER
End
Determine the values of different KPC
(NPC, COE, OPEX, etc)
Fig. 4. Flowchart of the proposed post-optimality MCDM model.
precision [42]. Different attempts have been carried out on the appli­
cation of MCDM with RE as reviewed in [40]. Also, the fuzzy-based
MCDM methods have been widely addressed in the energy field for
several objectives such as plant site selection [43], energy investing
[44], appraising RE alternatives [4245,46], establishing energy strategy
[47,48], and quality function deployment of electric vehicles [49].
• To propose PV/ WT/DG/ FBAT(or TPH)/ BDC HRSES integrated
with large-scale RO desalination plant for electricity and freshwater
production, with a case-study on beach resort in Baltim city, Egypt.
• To perform an energy-economy-ecology (3E) feasibility and optimi­
zation analysis of nine HRSES-RO alternatives. The uniqueness of the
study is that a thorough investigation and comparison between FBAT
and TPH storage options are conducted.
• To develop a novel hybrid decision-making model based on FuzzyAHP and Fuzzy-VIKOR methods for choosing the final optimal
HRSES-RO alternative. The model features consideration, at the
same time, ten sustainability performance criteria.
• To explore the functionalities of the optimal system against the
variation of key input parameters (cost of ESTs, load growth and
interest rate) through sensitivity analysis.
1.4. Literature gaps and study contributions
A closer look to the literature, reveals a number of observation and
research gaps, which include: (i) Studies on the feasibility of integrated
HRSES-RO for electricity and freshwater production within the Egyptian
context in coastal regions have not been explored, (ii) The comparative
analysis of multiple types ESTs has received limited attention in litera­
ture and most of previous works have analyzed HRSES performance with
single EST (iii) Most of prior works typically only investigated the issue
of HRSES design considering single objective, where the literature offers
no clear methodology for the design optimization of HRES considering
various sustainability aspects related to technical, cost, emission, and
socio-political objectives, and lately (iv) As far as the authors know,
some studies utilized MCDM while designing HRSESs, however studies
on the application of Fuzzy-AHP and Fuzzy-VIKOR decision making
methods are seldom presented in the main literature. To overcome the
shortcomings outlined above, the following are the major contributions
of the current study:
1.5. Manuscript organization
To outline the structure of the paper: Section 2 describes the research
methodology of the proposed 3E fuzzy decision-making optimization
model. Section 3 depicts the assessment of renewable resources and load
demand of the investigated site. The developed HRSES-RO specifications
and mathematical modelling are illustrated in Section 4. Section 5 re­
cords the main results and discussion. The most important conclusions
and recommendations of the study are condensed in Section 6.
5
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
Table 2
Selected attributes for assessing the HRSES configurations.
Aspect
Economic
Ecological
Energy
KPC
C1
Type
Net Present Cost (NPC)
C2
Cost of Energy (COE)
C3
C4
Operating and Maintenance
Expenses (OPEX)
Payback Period (PbP)
C5
Carbon Emission Impact (CEI)
C6
C7
Renewable Energy Fraction
(REF)
Land Requirement (LR)
C8
Capacity Shortage Ratio (CSR)
C9
C10
Surplus Energy (SE)
Installation easiness (IE)
Objective
Nonbeneficial
Nonbeneficial
Nonbeneficial
Nonbeneficial
Nonbeneficial
Beneficial
Nonbeneficial
Nonbeneficial
Beneficial
Beneficial
Minimize
Minimize
Minimize
Minimize
Minimize
Maximize
Minimize
Minimize
Maximize
Maximize
2. Methodology
The main objective of this work is to develop an optimal HRSES
alternative plan among different hybridization scenarios of solar, wind,
diesel, converter, battery storage, and pumped hydro storage technol­
ogies. The HRSES is established to deliver the necessary electricity to a
large-scale RO plant which in turn provide the daily freshwater demand
of Pharma peach resort facility located in Baltim city, Egypt. Hence, a
procedural approach of 3E feasibility and optimization is first developed
to search out the feasible HRSES alternatives using HOMER software
(see Section 2.1). In this step, the performance of each alternative is
assessed concerning its net present cost (NPC), cost of energy (COE),
capacity shortage ratio (CSR), carbon emission impact (CEI), payback
period (PbP), and internal rate of return (IRR) (see Section 2.2)
considering design constraints of load balance and storage safe limits
(see Section 2.3). For compressive multicriteria optimization, a combi­
nation of Fuzzy-AHP and Fuzzy-VIKOR based MCDM methods is sec­
ondly applied (see Section 2.4). The fuzzy-AHP is used for weighting the
different key performance criteria (KPC), while fuzzy-VIKOR is utilized
for final alternative ranking. Fig. 3 illustrates the general flow of the
research method applied in this study. Descriptive details of the
employed techniques are illustrated in the following subsections.
Fig. 5. Flowchart of the Fuzzy-AHP method.
modelled and simulated considering diverse constraints. HOMER starts
constructing a number of HRSES alternatives. Then validating each
alternative viability by investigating whether the load is satisfied or not
throughout hourly-based energy balance computation over a year. For
each alternative, 3E simulation and optimization analysis are performed
to obtain the optimal capacities of each element. Once the accuracy
convergence is fulfilled, the optimization process stops, and the feasible
alternatives are obtained. Lastly, in the 3rd layer, each feasible solution
is evaluated based on its economic, energy, and ecological features
utilizing the values of NPC, COE, CSR, PbP, CEI, SE evaluation criteria.
The definations and mathmetical formulas of these criteria, as well as
the model constriants are given in the Appendix. It is worth to mention
that, as the best of the authors knowledge, most of the previous re­
searchers settle for introducing the HRESES with least NPC or COE as the
winner configuration without assessing the impact of the other inter­
connected aspects. Therefore, the current study features a postoptimality analysis using combined fuzzy-AHP and fuzzy-VIKOR deci­
sion-making analysis to identifiy the winning HRSES plan as a final and
single solution cosidering a total of ten evaluation criteria (Subsection
2.4).
2.1. The 3E optimization analysis using HOMER
The hybrid optimization of multiple energy resources (HOMER)
optimizer is an investigative software which globally employed as a
guideline for optimal planning and assessment of grid-connected
[51,52] and stand-alone [34–53] energy systems. Thanks to its reli­
ability, cohesive programming formation, and friendly user-interface,
HOMER has been implemented in numerous energy studies as
reviewed in [54,55]. Therefore, In this work, the 3E optimization, per­
formed based on HOMER, involves three layers of implementation, as
illustrated in Fig. 4 [38]. First, a primary evaluation based on the
planned strategy is accomplished through assessments of RERs activity,
load demand, components costs, and climate data. Also, identifying the
distinct design constraints. This layer leads to primarily pick out the
appropriate energy technologies and developing a formidable mixing
amongst them to satisfy the load demand. The 2nd layer is considered
the optimization core where the predetermined energy technologies are
2.2. Multicriteria decision-making analysis
Generally, the MCDM analysis examines deciding the best solution
6
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
methods for resolving multicriteria dilemma including incommensu­
rable and contradictory criteria, and it has been utilized in several
research works according to the review done by Mardani et al. [67]. It
principally focuses on the alternatives ranking and selecting an alter­
native with contradictory attributes and provide a compromise justifi­
cation assisting the DM in selecting the best solution [68,69]. The theory
of both fuzzy set and VIKOR method has been merged to develop the
Fuzzy-VIKOR technique to discover the most satisfactory solution of
multi-person MCDM dilemmas [70]. Fig. 6 shows the flow chart applied
Fuzzy-VIKOR technique while the detailed description of each step is
provided in the supplementary materials (Section S2).
3. Case-study description
The developed HRSES is used to power large-scale RODP located in
Baltim coastal city, Kafrelsheikh Governorate, Egypt (latitude of
31◦ 34′ 30′ ’ N and longitude of 31◦ 12′ 0′ ’ E). The RODP is installed to
provide the necessary daily freshwater to Pharma Beach Resort in the
city of Baltim by desalinating the raw-water. The site which demon­
strated in Fig. 7(a) has an inspiring renewable resource potential which
justifies being exploited for energizing the RODP, particularly when its
location is far from the main gird as seen in Fig. 7(b). A detailed
demonstration of the resource data of the investigated site is given in the
following subsections.
3.1. Resources assessment
The examination of RESs performance requires collecting the
renewable resources data in the investigated site. Therefore, meteoro­
logical data, including the solar and wind activities for the studied
location, are collected from the National Aeronautics and Space
Administration (NASA) database [71].
3.1.1. Wind energy data
Baltim coastal city have promising wind resource potential with an
average WS of 5.8 m/s. Fig. 8(a) shows the heat map of the WS at the
resort area in which it can be seen that the highest average WS is
detected in February with 6.63 m/s while the lowest WS is observed in
September (5.13 m/s). Remarkably, it can be recognized from the figure
that the WS is astonishing during the months when the solar radiation
low. This harmonizing nature of these REs is considered beneficial to
fulfil the load demand over the year.
Fig. 6. The Fuzzy-VIKOR flowchart.
amongst several alternatives considering different viable attributes [65].
Since individual judgments thorough evaluation may be inaccurate,
fuzzy sets have been merged with MCDM methods for providing a pre­
cise solution [66]. For the current study, a novel integration between the
Fuzzy-AHP and Fuzzy-VIKOR approaches was employed for picking out
the single optimal HRSES considering 10 key performance criteria (KPC)
covering the economic, environmental and energy aspects (see Table 2).
First, the Fuzzy-AHP was implemented to measure the significance of
each criterion by determining the relative weights. Then, Fuzzy-VIKOR
was employed for valuation and ranking the viable HRSES alternatives.
The KPC were initially designated and appraised through three expert
decision-makers from the scientific research sector and energy markets
to ascertain the comparative significance of the distinct KPC to each
other and also the significance of KPC to each energy system alternative.
For beneficial criteria, maximum value is the best and minimum value is
the worst. Contrary, the minimum value of non-beneficial criteria is the
best while the maximum value is the worst
3.1.2. Solar energy data
For a better description of the SR data in the investigated site, the
heat map of global horizontal solar ins of the study area is given in Fig. 8
(b). The analysis of the data demonstrates that the annual average value
of the SR is 5.5 kWh/m2/day. Also, the SR intensity is pretty high from
April to September during the summer, where the highest value of SR is
recorded in June with 8.1 kWh/m2/day, while the lowest value is
recorded in December with 2.74 kWh/m2/day, correspondingly. The
site temperature is high during the summer season reaching its
maximum in August with 32.8 ◦ C while the lowest temperature is
recorded in January with 19.9 ◦ C. The temperature heat map of the
studied location is shown in Fig. 8(c).
2.2.1. Fuzzy-AHP for determining the criteria weights
The AHP is the most widespread criteria weighting technique in
decision-making problems [65]. To increase the accuracy of pair-wise
judgments during comparisons, and for efficient handling the fuzzi­
ness and uncertainty of problem nature, a fuzzy set is combined with
AHP, replacing the exact numbers with fuzzy linguistic expressions, and
forming Fuzzy-AHP. Fig. 5 illustrates the steps of the applied FuzzyAHP, while the detailed explanation of each step is provided in the
supplementary materials (Section S1).
3.2. The reverse osmosis desalination plant
In this study, the load demand represents a large-scale RODP of 1000
m3/day, which is constructed to provide the daily freshwater to a beach
resort in Baltim, Egypt. The process flow schematic of the RODP and its
technical specifications are provided in the supplementary materials
(Section S3). The load demand of the RODP was characterized based on
the plant daily electricity requirements, as shown in daily profile in
Fig. 9. Besides, the real data were synthesized by random variabilities of
5% day-to-day and 5% time-step to obtain the annual scaled load profile
2.2.2. Fuzzy-VIKOR for electing the best energy system alternative
The VIKOR technique was introduced as one of the decision-making
7
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
Fig. 7. Pharma Beach Resort (a) location and layout, and (b) Egyptian national grid lines.
8
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
12
0
(a)
Jan
Feb
Mar
Apr
May
24
Jun
Jul
Day of Year
Wind Speed
Aug
Sep
Oct
Nov
Feb
Mar
Apr
May
Jun
Jul
Day of Year
Aug
Sep
Oct
Nov
Hour of Day
Hour of Day
12
0
(e)
Jan
Feb
Mar
Apr
May
Jun
Jul
Day of Year
Aug
Sep
Oct
Nov
Feb
Mar
Apr
May
Jun
Jul
Aug
7
Sep
Oct
Nov
Dec
(d)
6
5
4
3
2
1
0
Jan
°C
34.0
32.4
30.8
29.2
27.6
26.0
24.4
22.8
21.2
19.6
18.0
18
6
3
1
Dec
Ambient Temperature
24
4
Jan
(c)
Jan
5
0
m/s
24.0
21.6
19.2
16.8
14.4
12.0
9.6
7.2
4.8
2.4
0.0
12
0
6
2
Dec
18
6
7
Wind Speed (m/s)
6
(b)
8
Dec
35
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
(f)
30
Temperature (oC)
Hour of Day
18
9
Solar Radiation (kW/m2/d)
kW/m2
1.20
1.08
0.96
0.84
0.72
0.60
0.48
0.36
0.24
0.12
0.00
Global Horizontal Solar Radiation
24
25
20
15
10
5
0
Jan
Feb
Mar
Fig. 8. Meteorological data at the studied location, (a, b) solar radiation, (c, d) wind speed, and (e, f) ambient temperature.
Fig. 9. Load profile of the studied RODP.
9
Energy Conversion and Management 235 (2021) 113962
K.M. Kotb et al.
Fig. 10. Schematic diagram of the studied HRSES using FBAT and TPH storage.
(a)
(b)
Fig. 11. HOMER model for the developed HRESs (a) with FBAT (b) with TPH.
as illustrated in the seasonal profile in Fig. 9. The inspected RODP fa­
cilities consume average energy of 2133.2 kWh/day with a peak value of
333.15 kW, where the plant is designed to operate for 8 h/day from 9:00
am to 5:00 pm.
Table 3
Model constraints.
Item
Value
Item
Value
Average SR
5.5 kWh/m2/
day
5.805 m/s
10%
0%
Nominal discount rate
9.25%
Real discount rate
Inflation rate
Capacity shortage
penalty
5.66%
3.4%
0 $/kWh
Average WS
Operating reserve
Capacity shortage
CO2 emission
penalties
0$
4. System mathematical modelling and description
The proposed HRSES consists of five major components as shown in
Fig. 10: PVs, WTs, DGen, a converter and an energy storage technology
(EST). Two different ESTs named Zinc-Bromine flow battery (FBAT) and
turbine-pumped hydro (TPH), are integrated individually with the
HRSES. The PVs and WTs are the main power supplies of the RO plant.
The FBAT or TPH is utilized to maintain the load deficits and store the
10
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
(a)
(b)
(c)
Fig. 12. DGen performance (a) Average monthly output power, (b) output power heat map, (c) fuel consumption profile.
11
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
5M
4.63E6
4M
Cost ($)
designed with optimal component capacities. The performance analysis
of the four possible HRSES are evaluated and presented in the following
subsections.
Capital
Operating
Replacement
Salvage
Resource
5.2.1. Case-1 (FBAT + wind + diesel)
The optimal capacities obtained for this system are 50-kW DGen
integrated with 14 × 20-kW EO20 WTs, 122 ZB-FBAT units, and 259-kW
converter. This configuration has NPC of $1,290,240, a COE of $0.125/
kWh, RF of 90.55%, and SE of 13.5%, which makes case-1 ranked 3rd in
category-1. Due to the high RF and the low fuel consumption, the CEI is
54,170.4 kg/yr (95.24% less) compared to the base-case. Fig. 14 illus­
trates the monthly energy contribution of both WT and to supply the
RODP. According to the fruitful wind potential of Baltim, the largest
energy production share goes to the WTs by 93.2%. The DGen typically
operates during the unwind hours and battery’s SOC is at minimum
value, particularly in September and October (see Fig. 14-d).
3M
2.41E6
2M
1.48E6
1M
0
200000
-20210
Cost type
5.2.2. Case-2 (FBAT + PV + diesel)
In case-2, 583-kW PV panels accompanied with the DGen to feed the
load over the daytime and any excess electricity is stored in the flow
batteries. The DGen capacity is the same as in case-1 while the batteries
number are reduced to 94 units, and the converter rating increased to
324-kW. Fig. 15 shows the output power and the shared annual energy
for each component in case-2. The PV solar panels dominate the elec­
tricity generations (95.8%), and this results in a high RF (94.48%). In
contrast, the diesel operates as a backup source and contributes by 4.2%
mainly during the winter season when cloud coverage is extreme. Owing
to the less consumption of fuel by DGen as shown in Fig. 15-d, this
configuration generates 31,668.16 kg/yr of CO2 (97.22% less compared
to the case-0); hence, this configuration ranked 2nd in category-1.
Additionally, the NPC and COE of case-2 are $1,148,861 and $ 0.111/
kWh respectively, and the extra energy is 13.7%.
Fig. 13. Cost distribution of the DGen system.
excess power. On the other side, DGen is turned on to cover the peak
load during the cloudy and unwind hours. The detailed mathematical
modelling of system elements and their technical and economic speci­
fication are provided in the supplementary materials (Section S4).
5. Results and discussion
The findings of the performance analysis in this study include a
comparison among nine HRSES configurations, in addition to the basecase system of conventional diesel. The comparison targets to emphasize
the diverse influences of using two different technologies of ZB-FBAT
and TBH on the developed HRSES for Baltim coastal city. The pro­
posed two HRSESs with FBAT and TPH were build using HOMER plat­
form are given in Fig. 11 with technical and cost specifications provided
in Section S4 of the supplementary material, in addition to the model
constraints listed in Table 3. The studied configurations are categorized
to four different classifications (system has only DGen to be treated as a
reference for comparison, configurations include RERs and FBAT stor­
age, configurations include RERs and TPH storage, and configurations
include RERs without ESTs).
5.2.3. Case-3 (FBAT + wind + PV + diesel)
The optimal capacities of case-3 comprise 5 × 20-kW WTs, 328-kW
PV panels, 100-kW DGen, 112 FBAT, and 235-kW converter. This sys­
tem characterizes by the minimum NPC ($1,048,046) and COE ($0.1/
kWh) among all feasible systems of category-1. The cost summary of
every element is described in Fig. 16 in which the principal portion of
the cost goes to the initial capital costs of renewable components, fol­
lowed by the operating costs. Fig. 17(a) presents the monthly contri­
bution of case-3 elements in providing the RODP electricity demand.
Obviously, the PV panels contribute the highest part of the total elec­
trical output by 58% followed by the WTs with a portion of 38.3% while
the DGen shares only 3.7%. Therefore, this configuration achieves the
highest RF and the least CEI among all feasible configurations with
95.6% and 25,426.46 kg/yr, respectively. Besides, the system offers SE
by 6.15%. Compared to other configurations, this system is suggested as
the most cost-effective design for supplying the RODP. Further, Fig. 17
(e) confirms the viability and high-reliability of case-3 to meet the
hourly consumption of the RODP by representing energy dispatch dur­
ing three consecutive days.
5.1. Category-1: Case-0 (Diesel only)
The optimization results yield that a 400-kW diesel generator is
required to provide the RO facilities with annual electricity production
and consumption of 1,334,684 kWh/yr and 778,618 kWh/yr respec­
tively recording a SE of 41.66%. Fig. 12 shows the operations details of
the diesel system including average monthly energy production, oper­
ating hours, and fuel consumption profiles. Obviously, the single and
continuous operation of the DGEN over 8760 hrs results in highest
hourly consumption of fuel of 49.7 L/h and ultimate CEI of 1,139,837
kg/yr among all cases. Also, the DGen achieved the maximum NPC
($8,697,765) and COE ($0.845/kWh). Fig. 13 shows the cost summary
of case-0 in which the highest portion is initiated by the operating cost
(53.2%) while the lowest portion goes to the capital cost by (2.3%).
5.2.4. Case-4 (FBAT + wind + PV)
In this case, the load is supplied from renewable sources, mostly from
PV panels (97.5%) while the WTs contribute only by 2.5% due to the
high potential of solar radiation compared to the wind activity (see
Fig. 18(a,b)). Besides, Fig. 18(c) illustrate that the batteries SOC
remained 100% for most of the annual period except some days in the
winter. Since this configuration does not include DGen, there is no CEI
5.2. Category-2: Configurations include RERs and FBAT storage
In this category, four feasible HRSES scenarios along with FBAT are
12
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
(a)
(b)
(c)
(d)
Fig. 14. Case-1 performance (a) Monthly share (b) WT output power (c) DGen output power (d) electricity dispatch of system components on a typical week.
13
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
(a)
(b)
(c)
Fig. 15. Case-2 performance (a) Monthly share (b) PV output power (c) DGen fuel consumption.
and the RF is 100%. Therefore, it comes in the 1st position among all
systems as the most sustainable and eco-friendly system. However, the
NPC and COE are twice compared to case-3 due to the large capacity
(large initial costs) of PV system as shown in Fig. 19.
results lead to three possible HRSES scenarios integrated with TPH
storage. Besides, the absence impact of ESTs and DGen on the different
system aspects is also presented in case-7 and case-8, respectively.
5.3.1. Case-5 (TPH + wind + diesel)
The optimal specifications of the system elements are 13 × 20 kW
WTs, 100-kW DGen, 13 × 22 kW TGSs, 13,000 m3 water reservoir, and
245-kW converter. According to Fig. 20(a,b), the WTs are considered the
primary source to provide the load with the necessary energy by 89.1%.
Conversely, the dispatchable DGen shares only 10.9% and used for load
feeding during light wind periods. It can be seen from Fig. 20(c) that the
SOC of the TPH upper reservoir reached its lowest value (i.e., very low
5.3. Category-3: Configurations include RERs and TPH storage system
In this category, the extra energy produced during the extreme
availability of renewable resources is employed for pumping up un­
derground water to an upper reservoir. The potential of the stored water
is utilized to run the turbine-generator set (TGS) to generate electricity
when the power from RERs is insufficient. The 3E optimization analysis
14
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
600000
Cost ($)
reaches the minimum allowable limits during the winter season. Also,
the TPH is fully charged during the remaining time of the year except for
March and October in which the normal charging and discharge can be
seen. Overall, this system ranked 2nd in the category-3 due to its NPC
($1,457,158) and COE ($0.141/kWh). Besides, this system has the
highest RF among all HRSESs by 93.74% and produces SE of 13.1%.
ZB flow BAT
System Converter
Sun305 PV
EO20 WT
DGen
800000
5.3.3. Case-7 (TPH + wind + PV + diesel)
This system consists of 5 × 20-kW WTs, 329-kW PV panels, 100-kW
DGen, 222-kW converter, 10 × 22 kW TGS, and the water reservoir of
10,000 m3. The monthly average energy production of case-7 is dis­
played in Fig. 22(a-c) in which the largest and lowest contributions
belong to the PV system (56.4%) and DGen (6.6%), respectively while
the WT contributes by 37%. Moreover, Fig. 22(d) displays the energy
scheduling and storage profiles during a sample of three consecutive
days in June. Moreover, the inclusion of the diesel which has a total
annual fuel consumption of 18,011 L (see Fig. 22(e)) results in a RF of
91.8% and CEI of 47,108 kg/yr which is 95.86% lower than case-0.
Economically, this configuration ranked 1st in category-3 achieving
the least NPC and COE of $1,371,805 and $0.133/kWh, respectively.
Fig. 23 shows the cost involvement of individual parts of the system.
400000
200000
0
Capital
Operating Replacement Salvage
-200000
Resource
Cost type
Fig. 16. Cost summary of case-3 (FBAT + WT + PV + DGen).
water volume) in July, September, and October while during the
remaining months, regular charging and discharging are achieved.
Moreover, the NPC, COE, RF, and SE of this system are $1,698,684,
$0.165/kWh, 85.3%, 16.82%, respectively.
5.3.4. Case-8 (TPH + WT + PV)
In this system, the load is supplied from a 100% renewable supply
where the SE is stored by the TPH and utilized again to operate the
storge unit when the generation from RESs become insufficient. Both the
PV and wind systems contribute equally to supply the load demand (see
Fig. 24(a)). Besides, the upper reservoir hourly SOC is mostly near 100%
and reaches its lowest value in Feb. and Dec. (see Fig. 24(b)). Similar to
case-4, the CEI is zero, and the RF is 100%; however, utilizing TPH
storage instead of FBAT enhanced the economic characteristic of the
5.3.2. Case-6 (TPH + PV + diesel)
This system comprises 558-kW PV panels, 50-kW DGen, 12 × 22 kW
TGS, 12,000 m3 water reservoir, and 322-kW converter. Fig. 21(a-c)
exhibits the PV system and the DGen contributions in supplying the load
demand. Expectedly, the PV panels supply 95% of the plant demand
while the DGen supplies the load during night and bleary hours. Further,
the SOC profile of the TPH is given in Fig. 21(d) in which the SOC
90
Sun305 PV
Production (MWh)
80
EO20 WT
DGen
70
60
50
40
30
20
10
0
(a)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
(b)
Fig. 17. Case-3 performance (a) monthly share (b) PV output power (c) WT output power (d) DGen output power (e) energy dispatch during 3 consecutive days.
15
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
(c)
(d)
(e)
Fig. 17. (continued).
system with NPC of $ 1,535,590 and COE of 0.149$/kWh, yet the con­
verter rating increased by 48.6%.
design, a comparison among case-9, case-3, and case-7 was conducted
and demonstrated in Table 4. Efficiently, the inclusion of the FBAT in
case-3 lessens the NPC, COE, capital cost, and annual operating costs by
almost 61.3%, 61.2%, 37%, 80.74%, respectively. This substantial
reduction can be elucidated from the reduced capacity of DGen (60%).
Consequently, the yearly fuel consumption rate is reduced by 86%, and
hence, the annual emission is also saved by 86%. Meanwhile, utilizing
TPH storage in case-6 results in a significant reduction in NPC and COE
by 49.3% and decreased the DGen capacity by 60% which in turn
shortens both the fuel consumption rate and CEI by 86%.
5.4. Category-4: Configurations include RERs without ESTs (case-9)
In this configuration, the load is supplied mainly by th PV system and
the WTs. Due to the absence of the energy storge elements, the DGen
supplies the load during night and light wind periods. The monthly
contribution of each element including the fuel consumption is shown in
Fig. 25(a-d). It can be seen in Fig. 25(d) that the DGen fuel consumption
rate reached 191 L/day which is considered the 2nd highest rate behind
case-0; therefore, the system has the 2nd highest CEI by 182,542.2 kg/
yr. To highlight the absence of ESTs on the diverse aspects of HRSES
16
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
(a)
(b)
(c)
Fig. 18. Case-4 performance (a) PV output power (b) WT output power (c) batteries SOC.
17
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
evaluated by three strictures (CSR, SE, and UL). From Table 5, it can be
recognized that the FBAT + WT + PV + DGen (case-3) system has the
best economic performance. Nevertheless, it produces the least amount
of SE/yr with only 58134.18 kWh (6.15%) which signifies that for the
unforeseen load variations. As a result, case-3 would not be of high
benefit and may not be able to encounter the future growth in the plant
load demand. Moreover, this system has negligible values of both the
CSR and UL with only 760.87 kWh/yr (0.097%) and 581.0985 kWh/yr
(0.07463%). Despite these negligible percentages, case-3 ranks last
based on the CSR and UL, as indicated in Table 6. Conversely, the
highest amount of SE is produced from the FBAT + WT + PV system
(case-4) with 2,096,764 kWh/yr (71.58%), which signifies that this
configuration can undoubtedly handle any unexpected load fluctuations
and upcoming rise in the plant load demand without increasing the cost
of the system. Besides, this system ranked first and third based on the UL
percentage and the CSR, respectively because of its ability to meet the
load demand with a negligible shortage of 165.507 kWh/yr (~0.021%).
Also, the comparative technical analysis shows that the DGen only,
FBAT + PV + DGen, and TPH + wind DGen systems all together ranked
first regarding CSR and UL percentage as they successfully meet the
occupied load demand without scarcity. Fig. 26 and Table 6 show the
ranking of all configurations based on SE, CSR, and UL.
ZB flow BAT
System Converter
Sun305 PV
EO20 WT
2000000
Cost ($)
1500000
1000000
500000
0
Capital
Operating Replacement Salvage
Cost type
Resource
Fig. 19. Cost summary of case-4 (FBAT + WT + PV).
5.5. Comparative analysis between various feasible HRSES configurations
In the previous subsection, ten feasible configurations are simulated
and discussed with a particular focus given to the impact of employing
FABT and TPH energy storages. Table 5 summarizes the optimization
results for the possible HRSES configurations that can be employed to
supply the RO plant located in Baltim coastal city. The tabulated results
also reveal, for each configuration, the optimum component capacities,
and the values of different KPC. To reveal an alternative manner of
comparisons, Table 6 summarizes the different ranks of the ten config­
urations based on the distinct performance aspects including the NPC,
COE, RF, CSR, UL%, SE%, PbP, and CEI. More investigation on the
comparison between different designs in terms of cost, technical and
emission aspects is discussed in the following subsections.
5.5.3. Ecological aspects comparison
In this study, the environmental comparison evaluates the CEI,
which is inversely proportional with the RF, of the ten feasible config­
urations. As expected, both case-4 and case-8 (i.e., pure renewables
designs) have the highest RF due to the absence of the DGen; conse­
quently, these systems have zero emissions and ranked in the first place
among all configurations regarding both the CEI and RF. From Tables 5
and 6, it can be recognized that the FBAT + Wind + PV + DGen (case-3)
system is positioned at 2nd place among all configurations in terms of
ecological behaviour with 95.55% RF; consequently, it produces a
realistic amount of carbon emissions with only 25426.46 kg/yr. This
configuration displays a lessening of 97.76% in CEI compared to the
base-case of diesel system. Furthermore, emission results illustrate that
the FBAT + PV + DGen is intently positioned in the 3rd place among all
systems (CEI of 31,668.16 kg/yr and RF of 94.48%). Thus, this system
has resulted in a reduction of CEI by 97.22% compared to the base-case.
The worst CEI among all systems (excluding the base-case system) goes
to the WT + PV + DGen (case-9) system with 182,542.2 kg/yr since it
has RF of 71.63% (see Fig. 26).
To summarize, based on the previous evaluation and comparisons,
there is no particular system that has a superior performance in all as­
pects of comparison. Economically, the FBAT + Wind + PV + DGen
(case-3) system is the best. However, from the energy reliability point of
view, it is the severest. The FBAT + Wind + PV (case-4) system comes in
the top rank among all systems regarding the load feeding reliability;
however, economically, this system ranked the 8th due to the high
capital cost. Sustainably and ecologically, both case-4 and case-8 have
100% RF with no CEI; however, economically, they are not the best.
From the previous conclusions, it can be noticed that different perfor­
mance aspects affect the system’s ranking. Therefore, MCDM is applied
in section 5.2 by employing both Fuzzy-AHP and Fuzzy-VIKOR methods
to select the optimum configuration, as illustrated in the next
subsection.
5.5.1. Economic aspects comparison
From the 3E optimization results given in Table 5, it can be recog­
nized that category-2 dominates the first three positions in the final
ranking among all configurations. The FBAT + WT + PV + DGen system
(case-3) is ranked as the cost-effective configuration, thus ranked first,
with minimum NPC, COE and PbP of $1,048,046, $ 0.1019/kWh, 1.1
years, respectively, as well as maximum IRR of 104%. Also, case-2 is
ranked 2nd with NPC, COE, PbP, and IRR equal $1,148,861, $ 0.1116/
kWh, 1.14 and 94.9%, respectively. Moreover, the FBAT + WT + DGen
system (case-1) is ranked as the third most cost-effective configuration
with NPC of $ 1,290,240 and COE of $ 0.1254/kWh. On the other side,
for the category (3), the obtained results revealed that the TPH + WT +
PV + DGen system (case-7) is the least cost design and has NPC, COE,
PbP and IRR of $ 1,371,805 and $ 0.1333/kWh, 1.16 and 92.9%
correspondingly. However, it ranked as the fourth economical of all
cases based on NPC. The results also show that the absence of ESTs has
an uneconomic impact on the values of NPC, and COE, as demonstrated
in the WT PV + DGen system (case-9). This system has the second
expensive NPC and COE of $ 2,705,951 and $0.263 /kWh, respectively.
This mainly attributed to the high capital cost required for both the WT
and PV systems; consequently, it is ranked in the 9th place just before
the base-case system.
5.5.2. Energy aspects comparison
The technical or energy performance of the diverse systems can be
18
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
(a)
(b)
(c)
Fig. 20. Case-5 performance (a) monthly share (b) DGen output power (c) reservoir SOC.
19
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
(a)
(b)
(c)
(d)
Fig. 21. Case-6 performance (a) monthly share (b) PV output power (c) DGen output power (d) reservoir SOC.
20
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
100
Sun305 PV
EO20 WT
DGen
Production (MWh)
80
60
40
20
(a)
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
(b)
(c)
Fig. 22. Case-7 performance (a) monthly share (b) PV output power (c) WTs output power (d) energy scheduling 3 consecutive days (e) fuel consumption.
21
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
(d)
(e)
Fig. 22. (continued).
5.6. Multicriteria decision-making analysis results
600000
Cost ($)
5.6.1. Fuzzy-AHP results
The fuzzy pair-wise comparison matrices of DMs are shown in
Tables S4-S6 in supplementary materials (Section S5). The calculated
values of CR with DM1, DM2 and DM3 are 0.079, 0.082, and 0.080,
respectively, which are less than 0.1, indicating the consistency and
robustness of the preferences. Further, the aggregated fuzzy pair-wise
comparison matrix is shown in Table 7. The final fuzzy geometric
mean, fuzzified and defuzzified weights and normalized weights are
listed in Table 8. The Fuzzy-AHP results show that the economic crite­
rion represented by NPC is the most important with a weight value of
0.248, followed by emission criteria CEI with weight value equals 0.152.
In contrast, the LR and IE criteria have the lowest importance on the
decision of best alternative selection with normalized weights of 0.019
and 0.017, respectively.
System Converter
Sun305 PV
PH storage
EO20 WT
DGen
800000
400000
200000
0
Capital
-200000
Operating Replacement Salvage
Resource
Cost type
5.6.2. Fuzzy-VIKOR results
Depends on the assessment of available DMs, the linguistic variables
for evaluating energy system alternatives are transformed into fuzzy
numbers. Linguistics terms are translated to fuzzy numbers then, the
DMs’ ratings are aggregated, using the geometric mean method, to a
single fuzzy decision-matrix. Tables S7-S8 in the supplementary
Fig. 23. Cost summary of case-7 (TPH + WT + PV + DGen).
22
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
(a)
(b)
Fig. 24. Case-8 performance (a) monthly share (b) reservoir SOC.
23
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
(a)
System Converter
Sun305 PV
PH storage
EO20 WT
DGen
800000
200000
0
Capital
Operating Replacement Salvage
Resource
Cost type
(b)
System Converter
Sun305 PV
PH storage
EO20 WT
DGen
800000
600000
Cost ($)
Cost ($)
600000
400000
-200000
400000
200000
0
Capital
-200000
Operating Replacement Salvage
Resource
Cost type
(c)
(d)
Fig. 25. Case-9 performance (a) monthly share (b) PV output power (c) WTs output power (d) fuel consumption.
24
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
Table 4
Impact of utilizing ESTs on the different systems’ aspects.
Energy storage
System
None
Case-9
FBAT
Case-3
TPH
Case-7
Reduction due to FBAT (%)
Reduction due to TPH (%)
NPC ($M)
COE ($/kWh)
Operating cost ($/yr)
Capital cost ($M)
Fuel consumption (L/yr)
CEI (kg/yr)
DGen size (kW)
2.71
1.05
1.37
61.3%
49.3%
0.263
0.101
0.133
61.2%,
49.3%
113,563.9
21,866.82
41,926.35
80.74%
63%
1.21
0.76
0.82
37%
32.2%
69,790.29
9721.15
18,010.65
86%
74.2%
182,542
25,426.4
47,108.32
86%
74.2%
250
100
100
60%
60%
Table 5
Optimal results of the feasible configurations.
Case
Config./
Item
Case-0
DGen
Optimal
size
WTs (No.)
PV (kW)
DGen
(kW)
PC (kW)
FBAT
(No.)
TGS (No.)
Reservoir
(m3)
WTs
(kWh/yr)
PV (kWh/
yr)
DGen
(kWh/yr)
SE%
CSR%
UL %
NPC ($)
COE
($/kWh)
Capital
cost ($)
Fuel cost
($/yr)
PbP (yr)
IRR (%)
REF %
CEI (kg/
yr)
Energy
aspects
Economic
Ecological
Case-1
FBAT +
WT +
DGen
–
Case-2
FBAT + PV
+ DGen
Case-4
FBAT +
WT + PV
Case-5
TPH + WT
+ DGen
Case-6
TPH + PV
+ DGen
14
Case-3
FBAT + WT
+ PV +
DGen
–
Case-8
TPH + WT
+ PV
Case-9
WT + PV
+ DGen
13
Case-7
TPH + WT
+ PV +
DGen
–
5
1
5
8
10
–
400
–
50
583
50
328
100
1707
–
–
100
558
50
329
100
354
–
568
250
–
–
269
122
324
94
235
112
328
58
245
–
322
–
222
–
330
–
282
–
–
–
–
–
–
–
–
–
–
–
13
13,000
12
12,000
10
10,000
14
14,000
–
–
–
1,012,707
–
361,681.1
72,336.22
940,370.9
–
361,681.1
578,689.8
723,362.2
–
–
975,639.4
548289.7
2,856,877
–
933,913.9
550,767.3
592,941.1
950,542.1
1,334,684
73,576.48
42,936.37
34,550
–
114,462.7
48,710.97
64,050
–
220,847
41.66
0
0
8,697,765
0.8456
13.5
0.018
0
1,290,240
0.1254
13.8
0
0
1,148,861
0.1116
6.15
0.097
0.07463
1,048,046
0.1019
71.58
0.021
0
2,292,340
0.2228
16.82
0
0
1,698,684
0.1651
13.1
0.084
0.004
1,457,158
0.1416
12.4
0.0721
0.006
1,371,805
0.1333
28.12
0.0907
0.0121
1,535,590
0.1493
58.08
0.043
0.002
2,705,951
0.263
200,000
787,591.3
810,999.8
759,184.9
1,923,690
862,822.1
891,917.1
817,957.4
1,016,357
1,205,771
182,159
8657.052
5060.936
4063.441
0
13,475.43
5740.677
7528.452
–
29,172.34
–
–
0
1,139,837
1.12
97.4
90.55
54,170.39
1.14
94.9
94.48
31,668.16
1.1
104
95.55
25,426.46
3.00
34.7
100
0
1.25
84.8
85.3
84,320.76
1.26
83.4
93.74
35,921.55
1.16
92.9
91.77
47,108.32
1.42
72.0
100
0
1.89
51.5
71.63
182,542.2
materials (Section S6) show the DMs ratings, the aggregated decisionmatrix, fuzzy best and worst values. The indices Si, Ri, S*, R*, S-, and
R- shown in Table 9 are then calculated by Eqs (S7-S10). Besides, Table 9
also shows both the fuzzified and defuzzified values of the VIKOR-index
(Q*) for every energy system alternative and the final ranking of
alternatives.
From the Fuzzy-VIKOR outcomes, the case-3 system has identified as
the best configuration, thus ranked 1st, among the ten feasible alter­
natives for supplying the investigated large-scale RO plant of Baltim
coastal city. This result is mainly attributed to the excellent economic
characteristics and realistic environmental and technical of this design.
Besides, case-2 and case-1 are ranked 2nd and 3rd, respectively, fol­
lowed by case-7 (TPH + WT + PV + DGen). It worth to mention that
case-7 is classified as the best alternative in category-2. As expected, due
to the low cost and emission behaviours of the base-case system and
case-9 (Wind + PV + DGen), they have ranked the worst systems with
Table 6
Different rankings of the feasible system based on various performance aspects.
Config.
Rank
NPC
/10
COE/
10
PbP /9
REF
/9
SE
/10
CSR
/8
UL
/6
CEI
/9
Case-0
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
10
3
2
1
8
7
5
4
6
9
10
3
2
1
8
7
5
4
6
9
reference
2
3
1
9
5
6
4
7
8
9
6
3
2
1
7
4
5
1
8
3
6
7
10
1
5
8
9
4
2
1
2
1
8
3
1
6
5
7
4
1
1
1
6
1
1
3
4
5
2
9
6
3
2
1
7
4
5
1
8
25
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
NPC
2500000
0.2
Unmet load
REF
CEI
0.125
8000000
0.100
0.10
125
0.08
100
1000000
0.075
4000000
0.050
0.02
0.00
500000
0
0.04
2000000
0.025
0
0.000
-0.02
75
50
CEI (kg/yr)
1000000
6000000
REF (%)
1500000
Unmet load (%)
0.06
NPC ($)
0.4
Surplus energy (kWh/yr)
COE ($/kWh)
0.6
CSR
10000000
2000000
0.8
Surplus energy
CSR (%)
1.0
COE
500000
25
9
as
e-
C
8
as
e-
C
7
as
e-
C
6
as
e-
C
5
as
e-
C
4
as
e-
C
im
al
)
2
pt
as
eC
0
C
as
e
-3
(o
as
e-
C
0
C
as
e-
0
(b
as
e
-c
as
e)
-500000
1
-0.04
0.0
Fig. 26. Comparison of the feasible systems considering different performance aspects.
9th and 10th positions, respectively.
enhance the system reliability. These results provide undesirable
imprint on the economic feasibility of the designed system for future
expansions. On the other side, the COE values of case-3 and case-7 are
relatively stable against future load growth, which prove the economic
viability and encourage future investment on renewables in the inves­
tigated area. In the view of the ESTs cost, reducing the cost of the TPH
storage by 50% and 70% executes lessening in both NPC and COE by
11.7% and 6.57%, respectively.
Similarly, the reduction of the FBAT cost by 50% and 70% achieve
saving in the NPC and COE by only 2.83% and 1.88%, respectively. Even
with the reduction of FBAT and TPH storages costs, case-3 show better
cost performance than case-7; thus, it remains as the best economic
option for supplying the RO demand. These can give a constructive
impact on the financial feasibility of hybrid systems for upcoming in­
vestments. Later, regarding the interest rate, it can be noticed from
Fig. 27 that the interest rate is inversely proportional with the NPC and
directly proportional with the COE. Thus, the probable decrease in the
interest ratio to 6% performs a significant increase in the NPC using TPH
and FBAT by 15.3% and 6.6%, respectively, and a significant decrease in
the COE to $0.111/kWh and $0.079/kWh, respectively. Inversely, the
projected increase in the interest ratio to 12% decreases the NPC using
TPH and batteries by 8.7% and 6.5% and increases the COE to $0.154/
kWh and $0.122/kWh, respectively. Categorically, the decrease in the
energy storage facilities and the possible load growth in the resort
location necessity be considered by decision-makers for the appropriate
investment choice.
5.7. Sensitivity analysis
Herein, the sensitivity analysis is executed to investigate the
response of the optimal systems in both categories 1 and 2 against the
uncertainties model inputs parameters. In this study, the influence of
three design parameters is considered: (i) growth of the RO plant energy
demand, (ii) the reduction of EST cost, and (iii) the positive and negative
change of the interest rate. These inputs were sensitively selected stand
on the following reasons:
• The effect of plant load growth is considered due to the nature of the
investigated site. The beach resort project is expected to be expanded
to comprise more residential, commercial, and service facilities;
therefore, the necessity of freshwater will be increased. The load
growth was assumed to be 30% and 60% of the specified value.
• Due to the quick-changing in ESTs influence in reducing the storage
facilities cost, the impact of reducing the storage cost by 50% and
70% is examined.
• Since the interest rate (discount rate) mainly depends on the eco­
nomic status of the state, two possibilities were conducted once by
increasing the discount rate to 12% and once again by reducing its
value to 6%.
Fig. 27 shows the response of the NPC and COE against the variations
in the three considered sensitivity variables. First, the analysis reveals
that increasing the load demand by 30% and 60% will increase the NPC
by 29.2% and 59.8%, respectively when using the TPH energy storage.
In contrast, the inclusion of the FBAT results in increasing the system
NPC by 30.18% and 58.5%, respectively. This increase of system cost is
due to the substantial growth of the direct investment for the further PV
panels, WTs, and energy storage units to satisfy the load growth and
5.8. Evaluation of model accuracy compared with relative literature
Nevertheless, the existing study analysis was challenging to be
compared with relative literatures due to alterations in HRSES structure,
climate conditions and load consumption, the economic metrics were
considered satisfactory standard for assessing and contrasting the
26
K.M. Kotb et al.
(3.175,4.217,5.241)
(2.884,3.915,4.932)
(1.587,2.621,3.634)
(3.175,4.217,5.241)
(3.42,4.481,5.518)
(2,3,4)
(0.315,0.362,0.437)
(2,2.466,2.884)
(1,1,1)
(0.168,0.203,0.255)
(3.302,4.309,5.313)
(1.587,2.621,3.634)
(0.874,1.442,2.289)
(1.587,2.154,2.621)
(2.289,3.476,4.579)
(1.26,1.817,2.289)
(0.191,0.243,0.347)
(1,1,1)
(0.347,0.405,0.5)
(0.161,0.193,0.24)
(7.862,8.277,8.653)
(6.868,7.612,8.32)
(4.579,5.593,6.604)
(5.518,6.542,7.56)
(4.327,4.327,4.327)
(3.107,4.38,5.518)
(1,1,1)
(2.884,4.121,5.241)
(2.289,2.756,3.175)
(0.794,1.26,1.817)
(9,9,9)
(6.868,7.612,8.32)
(5.241,6.257,7.268)
(6.868,7.399,7.862)
(6.316,7.319,8.32)
(3.78,5.013,6)
(0.55,0.794,1.26)
(4.16,5.192,6.214)
(3.915,4.932,5.944)
(1,1,1)
SE
CSR
LR
- Using batteries, the 3E optimization outcomes show that WT + PV +
DGen + FBAT system (case-7) is the most cost-effective system with
minimum NPC, COE and PbP of $1,048,046, 0.1019 $/kWhr, and
1.1 yr, respectively. Nevertheless, this configuration has a moderate
performance regarding the energy aspect. Still, excluding the 100%
renewable configurations, case-7 has the best ecological impact
among all alternatives.
- With the inclusion of TPH storage rather than FBAT, the WT + PV +
DGen + TPH configuration is found the best system to supply the
RODP. The system has NPC of $1,371,805 and COE of 0.1333$/kWhr
and highest IRR of 92.9%. However, case-2 with battery storage
shows a better economic performance, achieving ~ 23% in NPC and
COE compared to case-7.
- Two 100% renewable-based HRSESs integrate solar, and wind sup­
plies plus FBAT (case-4) and TPH (case-8) show their feasibility to
supply the reference site with zero-emission. However, they did not
offer competitive cost solutions and ranked 7th and 8th from an
economic point of view due to the substantial increase in solar ar­
rays, storage, and converter sizes. On the other side, the base-case
(diesel only) provides the utmost level of supply reliability
observed by zero values of CSR and UL. However, this case is
considered as the worst energy scenario owing to the poor economic
and emission performances.
- The Fuzzy-AHP and Fuzzy-VIKOR results demonstrate that the case3, which comprises 5 × 20-kW wind turbines, 328-kW photovoltaic
array, 100-kW diesel generator, 112 flow batteries, and 235-kW
converter provide a right trade-off solution supplying the RO facil­
ities and provide the freshwater for the coastal area in Baltim. This
case is ranked 1st among the ten feasible cases with minimum value
defuzzified Q* equals − 2.05.
- The winning HRSES ensure the ability to maintain the load balance
and records the best-compromised values of NPC ($104,8046), COE
(0.1019$/kWh), PbP (1.1 yr), IRR (104%), REF (95.55%), CEI
(25426.46 kg/yr), CSR (761 kWh/yr, i.e., ~ 0.0977%) and UL (581
kWh/yr, i.e., ~ 0.0746%).
- Last, the sensitivity analysis reveals that the proposed HRSES
configuration comprising batteries are more cost-efficient than
(1.26,1.442,1.587)
(0.437,0.585,0.794)
(0.315,0.362,0.437)
(0.693,1,1.442)
(1,1,1)
(0.191,0.273,0.315)
(0.231,0.231,0.231)
(0.218,0.288,0.437)
(0.181,0.223,0.292)
(0.12,0.137,0.158)
(2.884,3.915,4.932)
(2.154,3.302,4.38)
(0.63,0.874,1.145)
(2.621,3.634,4.642)
(3.175,4.217,5.241)
(1,1,1)
(0.181,0.228,0.322)
(0.437,0.55,0.794)
(0.25,0.33,0.5)
(0.167,0.199,0.265)
This study develops a 3E fuzzy decision-making optimization for
design and evaluation of HRSES comprising PV, WT, DGen with two
options of ESTs (FBAT or TPH). The load demand represents a largescale RO plant used as freshwater supply for resort beach in Baltim
coastal city (Egypt), with an average demand of 2133.2-kWh/day and
peak demand of 333.15-kW. The significant outcomes of the in­
vestigations are listed as follows:
(2.154,3.302,4.38)
(0.794,1,1.26)
(0.347,0.511,0.721)
(1,1,1)
(0.693,1,1.442)
(0.215,0.275,0.382)
(0.132,0.153,0.181)
(0.382,0.464,0.63)
(0.191,0.237,0.315)
(0.127,0.135,0.146)
PbP
6. Conclusions
(4.579,5.593,6.604)
(2.289,3.302,4.309)
(1,1,1)
(1.387,1.957,2.884)
(2.289,2.759,3.175)
(0.874,1.145,1.587)
(0.151,0.179,0.218)
(0.437,0.693,1.145)
(0.275,0.382,0.63)
(0.138,0.16,0.191)
OPEX
(1,1,1)
(0.397,0.481,0.63)
(0.151,0.179,0.218)
(0.228,0.303,0.464)
(0.63,0.693,0.794)
(0.203,0.255,0.347)
(0.116,0.121,0.127)
(0.188,0.232,0.303)
(0.191,0.237,0.315)
(0.111,0.111,0.111)
NPC
COE
OPEX
PbP
CEI
REF
LR
CSR
SE
IE
COE
NPC
KPC
Table 7
The aggregated fuzzy pair-wise comparison matrix of all DMs.
attained findings with the results founded in literature. Table 10 pre­
sents a synoptical evaluation between the NPC and COE values of the
current work and absolute values of specific HRSES projects constructed
at different locations over the global. Since the calculation of the NPC
depends on components’ capacities and project capital expenses, its
significance was considerably unbalanced and varied from a region to
other. On the other hand, the COE values should be observed as a
consistent measure of sustainable development cost. According to
Table 10, it can be remarked that the case-study presented in [29] has
the minimum COE (0.062 $/kWh) among other regions while the casestudy of Fuerteventura Island, Spain [31] has the highest value of. 0.478
$/kWh. By evaluating the electricity cost of different case-studies which
leads to cost of energy of 0.209 $/kWh on average, the COE of the
current study was founded below the average, this signifies a robust
alignment with the other global investigations and will extend aware­
ness into the economic viability of the suggested HRSES in the coastal
city of Baltim, Egypt.”
(1.587,2.08,2.52)
(1,1,1)
(0.232,0.303,0.437)
(0.794,1,1.26)
(1.26,1.71,2.289)
(0.228,0.303,0.464)
(0.12,0.131,0.146)
(0.275,0.382,0.630
(0.203,0.255,0.347)
(0.12,0.131,0.146)
CEI
REF
IE
Energy Conversion and Management 235 (2021) 113962
27
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
Table 8
Fuzzified and defuzzified weights and of the studied KPC.
Criterion
The fuzzy geometric mean value
Fuzzy weights (W~i)
De-fuzzified weights
Normalized weights (w*)
NPC
COE
OPEX
PbP
CEI
REF
LR
CSR
SE
IE
(2.901,3.522,4.067)
(1.647,2.119,2.611)
(0.777,1.015,1.3)
(1.563,1.985,2.492)
(1.964,2.417,2.883)
(0.74,0.964,1.25)
(0.228,0.263,0.317)
(0.675,0.866,1.162)
(0.442,0.544,0.704)
(0.2,0.23,0.271)
(0.17,0.253,0.365)
(0.097,0.152,0.234)
(0.046,0.073,0.117)
(0.092,0.143,0.224)
(0.115,0.174,0.259)
(0.043,0.069,0.112)
(0.013,0.019,0.029)
(0.04,0.062,0.104)
(0.026,0.039,0.063)
(0.012,0.017,0.024)
0.263
0.161
0.078
0.153
0.183
0.075
0.020
0.069
0.043
0.018
0.248
0.152
0.074
0.144
0.172
0.071
0.019
0.065
0.040
0.017
Table 9
Values of Si, Ri, Q* and the rank of energy system alternatives.
Config.
Si
Ri
Q*
Defuzzified Q*
Rank
Case-0
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
S*, R*
S-, R-
(− 0.203, 0.153, 0.740)
(0.272, 0.77, 1.971)
(0.313, 0.817, 2.005)
(0.332, 0.89, 2.043)
(− 0.095, 0.301, 1.065)
(− 0.061, 0.402, 1.412)
(0.07, 0.582, 1.692)
(0.171, 0.648, 1.824)
(0.092, 0.574, 1.564)
(− 0.246, 0.139, 0.812)
(− 0.246, 0.139, 0.740)
(0.332, 0.890, 2.043)
(0.034, 0.069, 0.207)
(0.112, 0.236, 0.456)
(0.124, 0.244, 0.456)
(0.136, 0.253, 0.456)
(0.092, 0.174, 0.324)
(0.02, 0.083, 0.293)
(0.051, 0.154, 0.36)
(0.063, 0.172, 0.391)
(0.092, 0.174, 0.36)
(0.012, 0.042, 0.178)
(0.012, 0.042, 0.178)
(0.136, 0.253, 0.456)
(− 3.55, 0.073, 2.878)
(− 8.06, 0.879, 1.357)
(− 8.10, 0.931, 1.171)
(− 8.15, 1, 1)
(− 5.35, 0.420, 2.052)
(− 5.42, 0.271, 2.876)
(− 6.56, 0.56, 2.347)
(− 7.1, 0.6, 2.07)
(− 6.4, 0.6, 1.82)
(− 3.29, 0,3.2)
− 0.20250271
− 1.942742707
− 2.001553769
− 2.050997231
− 0.962507016
− 0.757862554
− 1.21766291
− 1.475019065
− 1.326389738
− 0.029617366
9
3
2
1
7
8
6
4
5
10
0.16
2.0
Interest rate (%)
Average daily load (kWh/day)
Storage cost ($)
with TPH
1.6
1.4
1.2
with TPH
0.13
0.12
0.11
with Batteries
0.10
with Batteries
0.09
1.0
0.08
0.8
0.4
Interest rate (%)
Average daily load (kWh/day)
Storage cost ($)
0.14
1.8
NPC (M$)
0.15
COE($/kWh)
2.2
0.6
0.8
1.0
1.2
1.4
0.07
0.4
1.6
Parameter variation
0.6
0.8
1.0
1.2
Parameter variation
(a)
(b)
Fig. 27. Sensitivity analysis results impact on (a) NPC and (b) COE.
28
1.4
1.6
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
Overall, this study would help researchers and decision-makers to
select the best suitable hybrid energy system design for off-grid areas
where sustainable planning based on energy, economic, and emission
aspects is a significant objective.
Table 10
Obtained results compared with most recent literature.
Reference
Year
City, Location
COE
($/kWh)
NPC ($)
[26]
[27]
[28]
[29]
[30]
[31]
2020
2020
2020
2018
2019
2019
0.145
0.28
0.117
0.308
0.062
0.478
69,049,624
60,562
438,657
152,672
115,649
560,247
[32]
[33]
[34]
[35]
Current
study
2020
2021
2020
2021
2020
Isolated Island
Al Batinah, Oman
Neom city, KSA
Bozcaada Island, Turkey
Minya, Egypt
Fuerteventura Island,
Spain
Lanzarote Island, Spain
Sinai, Egypt
Cairo, Egypt
Abo Ramad, Egypt
Five villages, Iran
Baltim, Egypt
0.404
0.107
0.089
0.164
0.148
0.1019
473,013
502,662
1,540,250
3,120,035
358,350.2
1,048,046
CRediT authorship contribution statement
Kotb M. Kotb: Conceptualization, Methodology, Investigation,
Formal analysis, Software, Validation, Writing - original draft. M.R.
Elkadeem: Methodology, Software, Validation. Ahmed Khalil: . Sherif
M. Imam: . Mohamed A. Hamada: . Swellam W. Sharshir: Formal
analysis. András Dán: Formal analysis, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
pumped hydro against the of the growth of RO demand, reduction of
storage cost and variation of the interest rate.
Appendix
A1 Performance assessment criteria
A1.1 Net present cost
The NPC of a system is the present sum value of all costs experienced over the system lifetime, excepting the present value of all revenue the system
receives. The NPC is determined by Eq. (A1) [56] where InC, OpC, FuC, ReC, SaC, and Nc is the initial capital cost, operating cost, fuel cost,
replacement cost, salvage cost, number of elements, respectively. The discount factor (DF) is a ratio utilized to define the present value of an income (a
series of equal yearly cash flows) which is a function of the real interest rate (ir%) and the number of years (n).
Ny
Nc ∑
∑
NPC =
j=1
[
)]
(
Nj InCj + DF OpCj + FuCj + ReCj − SaCj , DF =
n=1
1
(1 + ir )n
(A1)
A1.2 Cost of energy
The cost of energy is the average cost/kWh, which reflects the average cost/kWh of the useful energy produced (Eg) by the system over its lifespan
(Ny). The COE is evaluated using Eq. (A2), where CRF is the capital recovery factor [57].
NPC. CRF(ir , n)
ir ⋅(1 + ir )Ny
COE = ∑Nc ∑8760
, CRF(ir , Ny ) =
(1 + ir )Ny − 1
t=1 Eg,j (t)
j=1
(A2)
A1.3 Capacity shortage ratio
The CSR which indicated in Eq. (A3) is the total capacity shortage (Ecs) divided by the total electrical demand (ELoad) where EGen,tot is the total
generated energy. HOMER reflects a system to be feasible only when the CSR is less than or equal to the maximum annual capacity shortage [58]. The
CSR is described as a reliability metric in which the zero value indicates that the load is continuously met, while the one value means that the load is
certainly not served.
CSR =
Ecs
, Ecs = ELoad − EGen,tot
ELoad
(A3)
A1.4 Carbon emission impact
The CEI calculates the carbon dioxide emissions emitted from the energy system to the environment in a specific time. The CEI can be determined
using Eq. (A4) based on the annual generated energy [59] where Vol(CO2) is the aggregated quantity of CO2 emission in (tCO2/kWh) and Eg,non-ren is
the energy generated using non-renewable sources (kWh).
∑
CEI =
Vol(CO2 )⋅Eg,non− ren
(A4)
t∈T
29
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
A1.5 Payback period
The PbP is the number of years the system takes for the cumulative income to equal the initial investment. It is also an indication of how long it
would take to recover the difference in investment costs between a system and the base-case system. The PbP can be calculated using Eq. (A5) [60].
PbP =
Total intial investment
Annual Saving
(A5)
A1.6 Internal rate of return
The IRR is a discounted cash method which considers the time value of money. The IRR sets the NPC of the cash flows equal to zero in a given time
as in Eq. (A6) [61] where Cn is the cash flow related to the nth year, and Nsh is the number of individual cash flows. This parameter is utilized to
determine if the deemed investing may be remunerative or not.
Nsh
∑
NPC =
Cn
(1
+
IRR)n
n=0
(A6)
A2. Model constraints
A2.1 Flow battery constraints
These constraints maintain the FBAT existence by preventing both overcharging and over-discharging based on the state of charge (SOC)
boundaries. Additionally, the instant stored energy is restricted by the maximum mounted-storage capacity. The FBAT function is constrained by; (i)
SOC limits to overcharge and over-discharge, as in Eq. (A7) [62]; (ii) the size of energy stored (EFBAT) at each hour as given in Eqs. A8-A9 [63] where
CFBAT is the nominal battery capacity, VFBAT is the battery bank voltage, and DODmax is the maximum depth of discharge.
SOCmin ⩽SOC(t)⩽SOCmax
(A7)
EFBAT,min ⩽EFBAT (t)⩽EFBAT,max
(A8)
EFBAT,max = SOCmax ⋅CFBAT ⋅VFBAT &EFBAT,min = (1 − DODmax )⋅CFBAT ⋅VFBAT
(A9)
A2.2 Renewable energy fraction
Renewable energy fraction (REF) is the percentage of energy that originated from renewables (Eg,ren) to the total load (El). Typically, it is desired to
have a high REF toward zero-emission cities considering their impact on system costs. The REF is expressed using the formula in Eq. (A10) [38].
REF =
Eg,ren
El
(A10)
A2.3 Load satisfaction
This constraint ensures the power equilibrium of the system by safeguarding the uninterrupted operation of the load at any time t considering the
reserve power (PRes) as indicated in Eq. (A11) [64].
∑
PPV (t) + PWT (t) + PDGen (t) = PLoad (t) + PRes (t)
(A11)
t∈T
Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.enconman.2021.113962.
References
[5] UNESCO. World Water Development Report 2015: Water for a Sustainable World.
Extension://Bfdogplmndidlpjfhoijckpakkdjkkil/Pdf/ViewerHtml?File=https%3A
%2F%2FsustainabledevelopmentUnOrg%2Fcontent%2Fdocuments%2F1711Water
%2520for%2520a%2520Sustainable%2520WorldPdf, 2015.
[6] Prisciandaro M, Capocelli M, Piemonte V, Barba D. Process analysis applied to
water reuse for a “closed water cycle” approach. Chem Eng J 2016;304:602–8.
https://doi.org/10.1016/j.cej.2016.06.134.
[7] Okampo EJ, Nwulu N. Optimisation of renewable energy powered reverse osmosis
desalination systems: A state-of-the-art review. Renew Sustain Energy Rev 2021;
140:110712. https://doi.org/10.1016/j.rser.2021.110712.
[8] Fragkos P, Laura van Soest H, Schaeffer R, Reedman L, Köberle AC, Macaluso N,
et al. Energy system transitions and low-carbon pathways in Australia, Brazil,
Canada, China, EU-28, India, Indonesia, Japan, Republic of Korea, Russia and the
United States. Energy 2021;216:119385. https://doi.org/10.1016/j.
energy.2020.119385.
[1] Giannetti BF, Agostinho F, Eras JJC, Yang Z, Almeida CMVB. Cleaner production
for achieving the sustainable development goals. J Clean Prod 2020;271:122127.
https://doi.org/10.1016/j.jclepro.2020.122127.
[2] Zhang Y, Fang J, Wang S, Yao H. Energy-water nexus in electricity trade network:
A case study of interprovincial electricity trade in China. Appl Energy 2020;257:
113685. https://doi.org/10.1016/j.apenergy.2019.113685.
[3] Ali F, Ahmar M, Jiang Y, AlAhmad M. A techno-economic assessment of hybrid
energy systems in rural Pakistan. Energy 2021;215:119103. https://doi.org/
10.1016/j.energy.2020.119103.
[4] Rezk H, Abdelkareem MA, Ghenai C, Ali M, Ghenai C. Performance evaluation and
optimal design of stand-alone solar PV-battery system for irrigation in isolated
regions: A case study in Al Minya (Egypt). Sustain Energy Technol Assess 2019;36:
100556. https://doi.org/10.1016/j.seta.2019.100556.
30
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
[9] Mito MT, Ma X, Albuflasa H, Davies PA. Reverse osmosis (RO) membrane
desalination driven by wind and solar photovoltaic (PV) energy: State of the art
and challenges for large-scale implementation. Renew Sustain Energy Rev 2019;
112:669–85.
[10] Schunke AJ, Hernandez Herrera GA, Padhye L, Berry T-A. Energy recovery in
SWRO desalination: Current status and new possibilities. Front Sustain Cities 2020.
https://doi.org/10.3389/frsc.2020.00009.
[11] Frankfurt School-UNEP Centre/BNEF. Global Trends in Renewable Energy
Investment 2020, http://www.fs-unep-centre.org. n.d. https://doi.org/htt
p://fs-unep-centre.org/sites/default/files/publications/gtr2018v2.pdf.
[12] Adelaja AO. Barriers to national renewable energy policy adoption: Insights from a
case study of Nigeria. Energy Strateg Rev 2020;30:100519. https://doi.org/
10.1016/j.esr.2020.100519.
[13] IRENA, IEA, REN21. Renewable energy policies in a time of transition. Https
://WwwIrenaOrg/Publications/2018/Apr/Renewable-Energy-Policies-in-a-Timeof-Transition, 2018.
[14] IRENA, IEA, REN21. Renewable Energy Policies in a Time of Transition Heating
and Cooling. Https://wwwIrenaOrg/Publications/2020/Nov/Renewable-EnergyPolicies-in-a-Time-of-Transition-Heating-and-Cooling, 2020.
[15] REN21. Renewables 2017 Global Futures Report. 2017.
[16] REN21. Renewables 2019 Global Status Report. 2019.
[17] Mondal MAH, Ringler C, Al-Riffai P, Eldidi H, Breisinger C, Wiebelt M. Long-term
optimization of Egypt’s power sector: Policy implications. Energy 2019;166:
1063–73. https://doi.org/10.1016/j.energy.2018.10.158.
[18] EETC. Egyptian electricity holding company annual report 2018/2019. Htt
p://WwwMoeeGovEg/English_new/ReportAspx, 2019.
[19] Egypt’s vision 2030. Https://WwwGreengrowthknowledgeOrg/National-Docume
nts/Sustainable-Development-Strategy-Egypt-Vision-2030, 2016.
[20] Eldardiry Hisham, Hossain Faisal. A blueprint for adapting high Aswan dam
operation in Egypt to challenges of filling and operation of the Grand Ethiopian
Renaissance dam. J Hydrol 2020:125708. https://doi.org/10.1016/j.
jhydrol.2020.125708.
[21] I. Renewable E. Agency International Renewable Energy Agency 2018 Egypt
Renewable Energy Outlook.
[22] Siksnelyte-Butkiene I, Zavadskas EK, Dalia S, Streimikiene D. Multi-criteria
decision-making (MCDM) for the assessment of renewable energy technologies in a
household: A review. Energies 2020;13:1164.
[23] García-Olivares A, Solé J, Osychenko O. Transportation in a 100% renewable
energy system. Energy Convers Manag 2018;158:266–85. https://doi.org/
10.1016/j.enconman.2017.12.053.
[24] Das Barun K, Hasan Mahmudul. Optimal sizing of a stand-alone hybrid system for
electric and thermal loads using excess energy and waste heat. Energy 2021;214:
119036. https://doi.org/10.1016/j.energy.2020.119036.
[25] Sanaye Sepehr, Sarrafi Ahmadreza. Cleaner production of combined cooling,
heating, power and water for isolated buildings with an innovative hybrid (solar,
wind and LPG fuel) system. J Clean Prod 2021;279:123222. https://doi.org/
10.1016/j.jclepro.2020.123222.
[26] Mehrjerdi Hasan. Modeling and optimization of an island water-energy nexus
powered by a hybrid solar-wind renewable system. Energy 2020;197:117217.
https://doi.org/10.1016/j.energy.2020.117217.
[27] Vakili-Nezhaad G, Mishra SB, Mousa H, Ziaiefar H. Simulation and optimization of
hybrid green energy systems for desalination purposes. Environ Prog Sustain
Energy 2020:1–12. https://doi.org/10.1002/ep.13515.
[28] Rezk Hegazy, Alghassab Mohammed, Ziedan Hamdy A. An optimal sizing of standalone hybrid PV-fuel cell-battery to desalinate seawater at Saudi NEOM city.
Processes 2020;8.;8(4):382. https://doi.org/10.3390/pr8040382.
[29] Gökçek M. Integration of hybrid power (wind-photovoltaic-diesel-battery) and
seawater reverse osmosis systems for small-scale desalination applications.
Desalination 2018;435:210–20. https://doi.org/10.1016/j.desal.2017.07.006.
[30] Rezk Hegazy, Sayed Enas Taha, Al-Dhaifallah Mujahed, Obaid M, El-Sayed Abou
Hashema M, Abdelkareem Mohammad Ali, et al. Fuel cell as an effective energy
storage in reverse osmosis desalination plant powered by photovoltaic system.
Energy 2019;175:423–33. https://doi.org/10.1016/j.energy.2019.02.167.
[31] Padrón I, Avila D, Marichal GN, Rodríguez JA. Assessment of hybrid renewable
energy systems to supplied energy to autonomous desalination systems in two
islands of the Canary Archipelago. Renew Sustain Energy Rev 2019;101:221–30.
https://doi.org/10.1016/j.rser.2018.11.009.
[32] Osman M, Farahat MA, Elsayed M, Atallah MO, Farahat MA, Lotfy ME, et al.
Operation of conventional and unconventional energy sources to drive a reverse
osmosis desalination plant in Sinai Peninsula, Egypt. Renew Energy 2020;145:
141–52. https://doi.org/10.1016/j.renene.2019.05.138.
[33] Elkadeem MR, Kotb Kotb M, Elmaadawy Khaled, Ullah Zia, Elmolla Emad,
Liu Bingchuan, et al. Feasibility analysis and optimization of an energy-water-heat
nexus supplied by an autonomous hybrid renewable power generation system: An
empirical study on airport facilities. Desalination 2021;504:114952. https://doi.
org/10.1016/j.desal.2021.114952.
[34] Elmaadawy Khaled, Kotb Kotb M, Elkadeem MR, Sharshir Swellam W, Dán András,
Moawad Ahmed, et al. Optimal sizing and techno-enviro-economic feasibility
assessment of large-scale reverse osmosis desalination powered with hybrid
renewable energy sources. Energy Convers Manag 2020;224:113377. https://doi.
org/10.1016/j.enconman.2020.113377.
[35] Mousavi Seyed Ali, Zarchi Ruhollah Asayesh, Astaraei Fatemeh Razi,
Ghasempour Roghayeh, Khaninezhad Farshid Mohammad. Decision-making
between renewable energy configurations and grid extension to simultaneously
supply electrical power and fresh water in remote villages for five different climate
zones. J Clean Prod 2021;279:123617. https://doi.org/10.1016/j.
jclepro.2020.123617.
[36] Suresh V, Muralidhar M, Kiranmayi R. Modelling and optimization of an off-grid
hybrid renewable energy system for electrification in a rural areas. Energy Rep
2020;6:594–604. https://doi.org/10.1016/j.egyr.2020.01.013.
[37] Niyonteze Jean De Dieu, Zou Fumin, Norense Osarumwense Asemota Godwin,
Bimenyimana Samuel, Shyirambere Gilbert. Key technology development needs
and applicability analysis of renewable energy hybrid technologies in off-grid areas
for the Rwanda power sector. Heliyon 2020;6(1):e03300. https://doi.org/
10.1016/j.heliyon.2020.e03300.
[38] Kotb Kotb M, Elkadeem MR, Elmorshedy Mahmoud F, Dán András. Coordinated
power management and optimized techno-enviro-economic design of an
autonomous hybrid renewable microgrid: A case study in Egypt. Energy Convers
Manag 2020;221:113185. https://doi.org/10.1016/j.enconman.2020.113185.
[39] Kaya İ, Çolak M, Terzi F. A comprehensive review of fuzzy multi criteria decision
making methodologies for energy policy making. Energy Strateg Rev 2019;24:
207–28. https://doi.org/10.1016/j.esr.2019.03.003.
[40] Ilbahar E, Cebi S, Kahraman C. A state-of-the-art review on multi-attribute
renewable energy decision making. Energy Strateg Rev 2019;25:18–33. https://
doi.org/10.1016/j.esr.2019.04.014.
[41] Kahraman C, Öztayşi B, Uçal Sari I, Turanoǧlu E. Fuzzy analytic hierarchy process
with interval type-2 fuzzy sets. Knowledge Based Syst 2014;59:48–57. https://doi.
org/10.1016/j.knosys.2014.02.001.
[42] Kaya T, Kahraman C. Multicriteria renewable energy planning using an integrated
fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy 2010;35:2517–27.
https://doi.org/10.1016/j.energy.2010.02.051.
[43] Choudhary Devendra, Shankar Ravi. An STEEP-fuzzy AHP-TOPSIS framework for
evaluation and selection of thermal power plant location: A case study from India.
Energy 2012;42(1):510–21. https://doi.org/10.1016/j.energy.2012.03.010.
[44] Wang Z, Xu G, Ren J, Li Z, Zhang B, Ren X. Polygeneration system and
sustainability: Multi-attribute decision-support framework for comprehensive
assessment under uncertainties. J Clean Prod 2017;167:1122–37. https://doi.org/
10.1016/j.jclepro.2017.08.089.
[45] Streimikiene Dalia, Balezentis Tomas, Krisciukaitienė Irena, Balezentis Alvydas.
Prioritizing sustainable electricity production technologies: MCDM approach.
Renew Sustain Energy Rev 2012;16(5):3302–11. https://doi.org/10.1016/j.
rser.2012.02.067.
[46] Balin A, Baraçli H. A fuzzy multi-criteria decision making methodology based upon
the interval Type-2 fuzzy sets for evaluating renewable energy alternatives in
Turkey. Technol Econ Dev Econ 2017;23:742–63. https://doi.org/10.3846/
20294913.2015.1056276.
[47] Kaya Tolga, Kahraman Cengiz. Multicriteria decision making in energy planning
using a modified fuzzy TOPSIS methodology. Expert Syst Appl 2011;38(6):
6577–85. https://doi.org/10.1016/j.eswa.2010.11.081.
[48] Cayir Ervural B, Zaim S, Demirel OF, Aydin Z, Delen D. An ANP and fuzzy TOPSISbased SWOT analysis for Turkey’s energy planning. Renew Sustain Energy Rev
2018;82:1538–50. https://doi.org/10.1016/j.rser.2017.06.095.
[49] Wu Song-Man, Liu Hu-Chen, Wang Li-En. Hesitant fuzzy integrated MCDM
approach for quality function deployment: A case study in electric vehicle. Int J
Prod Res 2017;55(15):4436–49. https://doi.org/10.1080/
00207543.2016.1259670.
[50] Wang Zhixun, Lin Xiangning, Tong Ning, Li Zhengtian, Sun Shitong, Liu Chang.
Optimal planning of a 100 % renewable energy island supply system based on the
integration of a concentrating solar power plant and desalination units. Electr
Power Energy Syst 2020;117:105707. https://doi.org/10.1016/j.
ijepes.2019.105707.
[51] Dahiru Ahmed Tijjani, Tan Chee Wei. Optimal sizing and techno-economic analysis
of grid-connected nanogrid for tropical climates of the Savannah. Sustain Cities Soc
2020;52:101824. https://doi.org/10.1016/j.scs.2019.101824.
[52] Jahangiri M, Haghani A, Alidadi Shamsabadi A, Mostafaeipour A, Pomares LM.
Feasibility study on the provision of electricity and hydrogen for domestic purposes
in the south of Iran using grid-connected renewable energy plants. Energy Strateg
Rev 2019;23:23–32. https://doi.org/10.1016/j.esr.2018.12.003.
[53] Al-Ammar Essam A, Habib Habib Ur Rahman, Kotb Kotb M, Wang Shaorong,
Ko Wonsuk, Elmorshedy Mahmoud F, et al. Residential community load
management based on optimal design of standalone HRES with model predictive
control. IEEE Access 2020;8:12542–72. https://doi.org/10.1109/
Access.628763910.1109/ACCESS.2020.2965250.
[54] Bahramara S, Moghaddam MP, Haghifam MR. Optimal planning of hybrid
renewable energy systems using HOMER: A review. Renew Sustain Energy Rev
2016;62:609–20. https://doi.org/10.1016/j.rser.2016.05.039.
[55] Weinand Jann Michael, Scheller Fabian, McKenna Russell. Reviewing energy
system modelling of decentralized energy autonomy. Energy 2020;203:117817.
https://doi.org/10.1016/j.energy.2020.117817.
[56] Bukar AL, Tan CW. A review on stand-alone photovoltaic-wind energy system with
fuel cell: System optimization and energy management strategy. J Clean Prod
2019;221:73–88. https://doi.org/10.1016/j.jclepro.2019.02.228.
[57] Tezer T, Yaman R, Yaman G. Evaluation of approaches used for optimization of
stand-alone hybrid renewable energy systems. Renew Sustain Energy Rev 2017;73:
840–53. https://doi.org/10.1016/j.rser.2017.01.118.
[58] Khan FA, Pal N, Saeed SH. Review of solar photovoltaic and wind hybrid energy
systems for sizing strategies optimization techniques and cost analysis
methodologies. Renew Sustain Energy Rev 2018;92:937–47. https://doi.org/
10.1016/j.rser.2018.04.107.
31
K.M. Kotb et al.
Energy Conversion and Management 235 (2021) 113962
[59] Eriksson ELV, Gray EM. Optimization and integration of hybrid renewable energy
hydrogen fuel cell energy systems – A critical review. Appl Energy 2017;202:
348–64. https://doi.org/10.1016/J.APENERGY.2017.03.132.
[60] Bilir L, Yildirim N. Modeling and performance analysis of a hybrid system for a
residential application. Energy 2018;163:555–69. https://doi.org/10.1016/j.
energy.2018.08.089.
[61] Orioli A, Di Gangi A. The recent change in the Italian policies for photovoltaics:
Effects on the payback period and levelized cost of electricity of grid-connected
photovoltaic systems installed in urban contexts. Energy 2015;93:1989–2005.
https://doi.org/10.1016/j.energy.2015.10.089.
[62] Kumar J, Suryakiran BV, Verma A, Bhatti TS. Analysis of techno-economic viability
with demand response strategy of a grid-connected microgrid model for enhanced
rural electrification in Uttar Pradesh state, India. Energy 2019;178:176–85.
https://doi.org/10.1016/j.energy.2019.04.105.
[63] Khiareddine Abla, Ben Salah Chokri, Rekioua Djamila, Mimouni Mohamed Faouzi.
Sizing methodology for hybrid photovoltaic /wind/ hydrogen/battery integrated
to energy management strategy for pumping system. Energy 2018;153:743–62.
[64] Elkadeem MRR, Wang S, Sharshir SW, Atia EG. Feasibility analysis and technoeconomic design of grid-isolated hybrid renewable energy system for
electrification of agriculture and irrigation area: A case study in Dongola, Sudan.
Energy Convers Manag 2019;196:1453–78. https://doi.org/10.1016/j.
enconman.2019.06.085.
[65] Kannan D, Khodaverdi R, Olfat L, Jafarian A, Diabat A. Integrated fuzzy multi
criteria decision making method and multiobjective programming approach for
supplier selection and order allocation in a green supply chain. J Clean Prod 2013;
47:355–67. https://doi.org/10.1016/j.jclepro.2013.02.010.
[66] Liu Yan, Eckert Claudia M, Earl Christopher. A review of fuzzy AHP methods for
decision-making with subjective judgements. Expert Syst Appl 2020;161:113738.
https://doi.org/10.1016/j.eswa.2020.113738.
[67] Mardani A, Zavadskas EK, Govindan K, Senin AA, Jusoh A. VIKOR technique: A
systematic review of the state of the art literature on methodologies and
applications. Sustain 2016;8:1–38. https://doi.org/10.3390/su8010037.
[68] Bag S. Fuzzy VIKOR approach for selection of big data analyst in procurement
management. J Transp Supply Chain Manag 2016;10:1–6. https://doi.org/
10.4102/jtscm.v10i1.230.
[69] Alguliyev Rasim M, Aliguliyev Ramiz M, Mahmudova Rasmiyya S. Multicriteria
personnel selection by the modified fuzzy VIKOR method. Sci World J 2015;2015:
1–16. https://doi.org/10.1155/2015/612767.
[70] Ploskas N, Papathanasiou J. A decision support system for multiple criteria
alternative ranking using TOPSIS and VIKOR in fuzzy and nonfuzzy environments.
Fuzzy Sets Syst 2019;377:1–30. https://doi.org/10.1016/j.fss.2019.01.012.
[71] NASA Prediction of Worldwide Energy Resources n.d.
32