Abstract

Penetration of plug-in hybrid electric vehicles (PHEVs) is capable of alleviating numerous global environmental and energy challenges. Utilization of a significant number of PHEVs with significant capacity and control capabilities can increase electrical grid flexibility. However, optimum management of such vehicles with renewable energy sources (RESs) would be one of the primary difficulties needing to be investigated. In the form of a microgrid, the operation of substantial RESs’ and PHEVs’ penetration would be achieved when operating within a microgrid. The problem has been formulated and approached as a single-objective optimization model aiming to minimize the total cost of the grid-tied MG. The converged barnacles mating optimizer (CBMO) algorithm is deployed to tackle the problem. The derived results verify the desired performance of the method compared to well-established ones. In scenario 1, the CBMO method determines the MG operating costs that are lower than those given by some well-established methods including the genetic algorithm (GA), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO). The cost computed by the CBMO is 263.632 €ct/day. Likewise, the values of cost for scenarios 2 and 3 utilizing the hybrid CBMO method are 300.1364 €ct/day and 336.2154 €ct/day, respectively. The findings confirm the usefulness of the proposed CBMO algorithm with an excellent convergence rate. Comparing the average solution time of the CBMO algorithm with those provided by other algorithms reveals the excellent performance of the CBMO method. The obtained results indicate that the mean simulation time of the suggested CBMO approach in the first case is 5.19 s, whereas the time required by the GA, PSO, and ICA is 12.92 s, 10.73 s, and 7.27 s, respectively.

References

1.
Hai
,
T.
,
Said
,
N. M.
,
Zain
,
J. M.
,
Sajadi
,
S. M.
,
Mahmoud
,
M. Z.
, and
Aybar
,
H. Ş.
,
2022
, “
ANN Usefulness in Building Enhanced With PCM: Efficacy of PCM Installation Location
,”
J. Build. Eng.
,
57
, p.
104914
.
2.
Li
,
Y.
,
Mohammed
,
S. Q.
,
Nariman
,
G. S.
,
Aljojo
,
N.
,
Rezvani
,
A.
, and
Dadfar
,
S.
,
2020
, “
Energy Management of Microgrid Considering Renewable Energy Sources and Electric Vehicles Using the Backtracking Search Optimization Algorithm
,”
ASME J. Energy Resour. Technol.
,
142
(
5
), p.
052103
.
3.
Obara
,
S. Y.
, and
Tanno
,
I.
,
2009
, “
Installation Plan of a Fuel Cell Microgrid System Optimized by Maximizing Power Generation Efficiency
,”
ASME J. Energy Resour. Technol.
,
131
(
4
), p.
042601
.
4.
Tao
,
H.
,
Zain
,
J. M.
,
Band
,
S. B.
,
Sundaravadivazhagan
,
B.
,
Mohamed
,
A.
,
Marhoon
,
H. A.
,
Ogbonnia
,
O. O.
, and
Young
,
P.
,
2022
, “
SDN-Assisted Technique for Traffic Control and Information Execution in Vehicular Adhoc Networks
,”
Comput. Electr. Eng.
,
102
, p.
108108
.
5.
Hai
,
T.
,
Zhou
,
J.
, and
Muranaka
,
K.
,
2022
, “
An Efficient Fuzzy-Logic Based MPPT Controller for Grid-Connected PV Systems by Farmland Fertility Optimization Algorithm
,”
Optik
,
267
, p.
169636
.
6.
Moghaddam
,
A. A.
,
Seifi
,
A.
,
Niknam
,
T.
, and
Pahlavani
,
M. R.
,
2011
, “
Multi-Objective Operation Management of a Renewable MG (Micro-Grid) With Back-Up Micro-Turbine/Fuel Cell/Battery Hybrid Power Source
,”
Energy
,
36
(
11
), pp.
6490
6507
.
7.
Moghaddam
,
A. A.
,
Seifi
,
A.
, and
Niknam
,
T.
,
2012
, “
Multi-Operation Management of a Typical Micro-Grids Using Particle Swarm Optimization: A Comparative Study
,”
Renewable Sustainable Energy Rev.
,
16
(
2
), pp.
1268
1281
.
8.
Izadbakhsh
,
M.
,
Gandomkar
,
M.
,
Rezvani
,
A.
, and
Ahmadi
,
A.
,
2015
, “
Short-Term Resource Scheduling of a Renewable Energy Based Micro Grid
,”
Renewable Energy
,
75
, pp.
598
606
.
9.
Rezvani
,
A.
,
Gandomkar
,
M.
,
Izadbakhsh
,
M.
, and
Ahmadi
,
A.
,
2015
, “
Environmental/Economic Scheduling of a Micro-Grid With Renewable Energy Resources
,”
J. Cleaner Prod.
,
87
, pp.
216
226
.
10.
Javadi
,
M. S.
,
Nezhad
,
A. E.
,
Jordehi
,
A. R.
,
Gough
,
M.
,
Santos
,
S. F.
, and
Catalão
,
J. P.
,
2022
, “
Transactive Energy Framework in Multi-Carrier Energy Hubs: A Fully Decentralized Model
,”
Energy
,
238
, p.
121717
.
11.
Esmaeel Nezhad
,
A.
,
Ahmadi
,
A.
,
Javadi
,
M. S.
, and
Janghorbani
,
M.
,
2015
, “
Multi-Objective Decision-Making Framework for an Electricity Retailer in Energy Markets Using Lexicographic Optimization and Augmented Epsilon-Constraint
,”
Int. Trans. Electr. Energy Syst.
,
25
(
12
), pp.
3660
3680
.
12.
Nezhad
,
A. E.
,
Rahimnejad
,
A.
, and
Gadsden
,
S. A.
,
2021
, “
Home Energy Management System for Smart Buildings With Inverter-Based Air Conditioning System
,”
Int. J. Electr. Power Energy Sys.
,
133
, p.
107230
.
13.
Abedini
,
M.
, and
Abedini
,
M.
,
2018
, “
Energy Management and Control Policies of the Islanded Microgrids
,”
Sustain. Cities Soc.
,
38
, pp.
714
722
.
14.
Liu
,
C.
,
Abdulkareem
,
S. S.
,
Rezvani
,
A.
,
Samad
,
S.
,
Aljojo
,
N.
,
Foong
,
L. K.
, and
Nishihara
,
K.
,
2020
, “
Stochastic Scheduling of a Renewable-Based Microgrid in the Presence of Electric Vehicles Using Modified Harmony Search Algorithm With Control Policies
,”
Sustain. Cities Soc.
,
59
, p.
102183
.
15.
Kumar
,
K. P.
,
Saravanan
,
B.
, and
Swarup
,
K. S.
,
2016
, “
Optimization of Renewable Energy Sources in a Microgrid Using Artificial Fish Swarm Algorithm
,”
Energy Procedia
,
90
, pp.
107
113
.
16.
Askarzadeh
,
A.
,
2017
, “
A Memory-Based Genetic Algorithm for Optimization of Power Generation in a Microgrid
,”
IEEE Trans. Sustainable Energy
,
9
(
3
), pp.
1081
1089
.
17.
Maulik
,
A.
, and
Das
,
D.
,
2017
, “
Optimal Operation of Microgrid Using Four Different Optimization Techniques
,”
Sustain. Energy Technol. Assess.
,
21
, pp.
100
120
.
18.
Najibi
,
F.
, and
Niknam
,
T.
,
2015
, “
Stochastic Scheduling of Renewable Micro-Grids Considering Photovoltaic Source Uncertainties
,”
Energy Convers. Manage.
,
98
, pp.
484
499
.
19.
Crisostomi
,
E.
,
Liu
,
M.
,
Raugi
,
M.
, and
Shorten
,
R.
,
2014
, “
Plug-and-Play Distributed Algorithms for Optimized Power Generation in a Microgrid
,”
IEEE Trans. Smart Grid
,
5
(
4
), pp.
2145
2154
.
20.
Nikmehr
,
N.
, and
Ravadanegh
,
S. N.
,
2015
, “
Optimal Power Dispatch of Multi-Microgrids at Future Smart Distribution Grids
,”
IEEE Trans. Smart Grid
,
6
(
4
), pp.
1648
1657
.
21.
Quynh
,
N. V.
,
Ali
,
Z. M.
,
Alhaider
,
M. M.
,
Rezvani
,
A.
, and
Suzuki
,
K.
,
2021
, “
Optimal Energy Management Strategy for a Renewable-Based Microgrid Considering Sizing of Battery Energy Storage With Control Policies
,”
Int. J. Energy Res.
,
45
(
4
), pp.
5766
5780
.
22.
Mortazavi
,
S. M. B.
,
Shiri
,
N.
,
Javadi
,
M. S.
, and
Dehnavi
,
S. D.
,
2015
, “
Optimal Planning and Management of Hybrid Vehicles in Smart Grid
,”
Sci. Nat.
,
37
, pp.
253
263
.
23.
Aghaei
,
J.
,
Esmaeelnezhad
,
A. E.
,
Rabiee
,
A.
, and
Rahimi
,
E.
,
2016
, “
Contribution of Plug-In Hybrid Electric Vehicles in Power System Uncertainty Management
,”
Renewable Sustainable Energy Rev.
,
59
, pp.
450
458
.
24.
Noori
,
M.
, and
Tatari
,
O.
, 2016, “
Development of an Agent-Based Model for Regional Market Penetration Projections of Electric Vehicles in the United States
,”
Energy
,
96
, pp.
215
230
.
25.
Lee
,
H.
, and
Lovellette
,
G.
, “
Will Electric Cars Transform the US Market?
.”
26.
Tan
,
X.
,
Li
,
Q.
, and
Wang
,
H.
,
2013
, “
Advances and Trends of Energy Storage Technology in Microgrid
,”
Int. J. Electr. Power Energy Syst.
,
44
(
1
), pp.
179
191
.
27.
Druitt
,
J.
, and
Früh
,
W. G.
,
2012
, “
Simulation of Demand Management and Grid Balancing With Electric Vehicles
,”
J. Power Sources
,
216
, pp.
104
116
.
28.
Honarmand
,
M.
,
Zakariazadeh
,
A.
, and
Jadid
,
S.
,
2014
, “
Optimal Scheduling of Electric Vehicles in an Intelligent Parking Lot Considering Vehicle-to-Grid Concept and Battery Condition
,”
Energy
,
65
, pp.
572
579
.
29.
Sortomme
,
E.
, and
El-Sharkawi
,
M. A.
,
2011
, “
Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services
,”
IEEE Trans. Smart Grid
,
3
(
1
), pp.
351
359
.
30.
Honarmand
,
M.
,
Zakariazadeh
,
A.
, and
Jadid
,
S.
,
2014
, “
Integrated Scheduling of Renewable Generation and Electric Vehicles Parking Lot in a Smart Microgrid
,”
Energy Convers. Manage.
,
86
, pp.
745
755
.
31.
Zhang
,
Q.
,
Mclellan
,
B. C.
,
Tezuka
,
T.
, and
Ishihara
,
K. N.
,
2013
, “
A Methodology for Economic and Environmental Analysis of Electric Vehicles With Different Operational Conditions
,”
Energy
,
61
, pp.
118
127
.
32.
Hadley
,
S. W.
,
2006
, “Impact of Plug-In Hybrid Vehicles on the Electric Grid,” ORNL Report.
33.
Javadi
,
M. S.
,
Gough
,
M.
,
Nezhad
,
A. E.
,
Santos
,
S. F.
,
Shafie-Khah
,
M.
, and
Catalão
,
J. P.
,
2022
, “
Pool Trading Model Within a Local Energy Community Considering Flexible Loads, Photovoltaic Generation and Energy Storage Systems
,”
Sustain. Cities Soc.
,
79
, p.
103747
.
34.
Rezaee
,
S.
,
Farjah
,
E.
, and
Khorramdel
,
B.
,
2013
, “
Probabilistic Analysis of Plug-In Electric Vehicles Impact on Electrical Grid Through Homes and Parking Lots
,”
IEEE Trans. Sustain. Energy
,
4
(
4
), pp.
1024
1033
.
35.
Rostami
,
M. A.
,
Kavousi-Fard
,
A.
, and
Niknam
,
T.
,
2015
, “
Expected Cost Minimization of Smart Grids With Plug-In Hybrid Electric Vehicles Using Optimal Distribution Feeder Reconfiguration
,”
IEEE Trans. Ind. Inf.
,
11
(
2
), pp.
388
397
.
36.
Qian
,
K.
,
Zhou
,
C.
,
Allan
,
M.
, and
Yuan
,
Y.
,
2010
, “
Modeling of Load Demand Due to EV Battery Charging in Distribution Systems
,”
IEEE Trans. Power Syst.
,
26
(
2
), pp.
802
810
.
37.
Hoch
,
J. M.
,
2008
, “
Variation in Penis Morphology and Mating Ability in the Acorn Barnacle, Semibalanus Balanoides
,”
J. Exp. Mar. Biol. Ecol.
,
359
(
2
), pp.
126
130
.
38.
Sulaiman
,
M. H.
,
Mustaffa
,
Z.
,
Saari
,
M. M.
, and
Daniyal
,
H.
,
2020
, “
Barnacles Mating Optimizer: A New Bio-Inspired Algorithm for Solving Engineering Optimization Problems
,”
Eng. Appl. Artif. Intell.
,
87
, p.
103330
.
39.
Mertens
,
T. R.
,
1992
, “
Introducing Students to Population Genetics & the Hardy–Weinberg Principle
,”
The Am. Biol. Teacher
,
54
(
2
), pp.
103
107
.
40.
Sulaiman
,
M. H.
,
Mustaffa
,
Z.
,
Saari
,
M. M.
,
Daniyal
,
H.
,
Daud
,
M. R.
,
Razali
,
S.
, and
Mohamed
,
A. I.
,
2020
, “
Barnacles Mating Optimizer: A New Bio-Inspired Algorithm for Solving Optimization Problems
,”
Eng. Appl. Artif. Intell.
,
87
, p.
103330
.
41.
Mertens
,
T. R.
,
1992
, “
Introducing Students to Population Genetics & the Hardy–Weinberg Principle
,”
The American Biology Teacher
,
54
(
2
), pp.
103
107
.
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