ORIGINAL_ARTICLE
Direct model predictive speed control strategy for a PMSM fed by a three-level NPC converter
TThis paper presents a direct predictive speed control strategy to control a permanent magnet synchronous motor (PMSM) fed by a three-level neutral-point clamped converter (NPC). A new cost function is proposed by incorporating the speed dynamic, the current dynamic and the system constraints to have a good performance without additional outer-loop PI speed controller. The current dynamic added to cost function is based on the concept of the sliding mode control (SMC). Moreover, a load torque observer is used for better performance of the proposed method and the stability of the observer is presented. By combining new reference value of the current into the cost function, the necessity to multiple horizon in the predictive speed control (PSC) is obviated, the effects of the current dynamic on the transient conditions is considered so current distortion and torque fluctuation is reduced considerably and the controller acts as fast as an original direct speed control without cascade structure. Simulation results using MATLAB/SIMULINK demonstrate the performance of the proposed scheme.
https://www.jemat.org/article_118293_8ee5071f596ea5cfc622e3c2ad799821.pdf
2021-09-01
1
7
10.22109/jemt.2020.236813.1246
Model predictive Control
Sliding mode control
Three-Level NPC
Permanent Magnet Synchronous Motor
Sajad
Saberi
s_saberi@aut.ac.ir
1
Electrical Engineering, PhD candidate, Babol Noshirvani University of Technology, Mazandaran, Babol
AUTHOR
Behrooz
Rezaie
brezaie@nit.ac.ir
2
Faculty of Electrical Engineering, Babol Noshirvani University of Technology, Mazandaran, Babol, Iran
LEAD_AUTHOR
1. R. Vijayapriya, P. Raja, and M.P. Selvan, “Enhanced method of rotor
1
speed and position estimation of permanent magnet synchronous Machine based on stator SRF-PLL” Engineering Science and Technology,
2
an International Journal, vol 20, no 5, pp 1450-1459, 2017.
3
2. J.F. Yang and Y.W. Hu, “Optimal direct torque control of permanent magnet synchronous motor”. Proceedings of Chinese Society of Electrical
4
Engineering, vol. 31, pp. 109–115, 2011.
5
3. J. Holtz, “Advanced PWM and predictive control—An overview,” IEEE
6
Transactions on Industrial Electronics, vol. 63, no. 6, pp. 3837–3844,
7
Jun. 2016.
8
4. H. Abu-Rub, A. Iqbal, and J. Guzinski, High performance control of ac
9
drives with matlab/simulink models. Chichester, U.K.: Wiley, 2012.
10
5. A. Linder, R. Kanchan, P. Stolze, and R. Kennel, Model-based predictive
11
control of electric drives. Göttingen, Germany: Cuvillier Verlag, 2012.
12
6. D. Casdaei, F. Profumo, and G. Serra, “FOC and DTC: two variable
13
schemes for induction motors torque control,” IEEE Transactions on
14
Power Electronics, vol. 17, no. 5, pp. 779-788, 2002.
15
7. M. F. Rahman, M. E. Haque, and L. X. Tang, “Problems associated
16
with the direct torque control of an interior permanent-magnet synchronous motor drive and their remedies,” IEEE Transactions on Power
17
Electronics, vol. 51, no. 4, pp. 799-809, 2004.
18
8. M. Sarailoo, B. Rezaie and Z. Rahmani, “Fuzzy predictive control of
19
three-tank system based on a novel modeling framework of hybrid
20
systems”, Proceedings of the Institution of Mechanical Engineering,
21
Part I: Journal of System and Control Engineering, vol. 228, no. 6, pp.
22
369-384, 2014.
23
9. F. Rajabi, B. Rezaie and Z. Rahmani, “Designing NMPC controller
24
using hybrid PSO-SQP algorithm: Application to an evaporator system”,
25
Transactions of the Institute of Measurement and Control, vol. 38, no.
26
1, pp. 23-32, 2016.
27
10. S. Jalili, B. Rezaie and Z. Rahmani, “A novel hybrid model predictive
28
control design with application to a quadrotor helicopter”, Optimal
29
Control Applications and Methods, vol. 39, no. 4, pp. 1301-1322, 2018.
30
11. M.R. Zamani, Z. Rahmani and B. Rezaie, “A novel model
31
predictive control for a piecewise affine class of hybrid system with repetitive disturbance,” ISA Transactions, 2020. DOI:
32
https://doi.org/10.1016/j.isatra.2020.08.023
33
12. J. Bocker, B. Freudenberg, A. The, and S. Dieckerhoff, “Experimental
34
comparison of model predictive control and cascaded control of the
35
modular multilevel converter,” IEEE Transactions on Power Electronics,
36
vol. 30, no. 1, pp. 422–430, Jan. 2015.
37
13. H. Abu-Rub, J. Guzinski, Z. Krzeminski, and H. A. Toliyat, “Predictive current control of voltage-source inverters,” IEEE Transactions on
38
Industrial Electronics, vol. 51, no. 3, pp. 585–593, Jun. 2004.
39
14. F. Wang, X. Mei, J. Rodriguez and R. Kennel, “Model predictive control for electrical drive systems-an overview,” CES Transactions on
40
Electrical Machines and Systems, vol. 1, no. 3, pp. 219-230, Sept.
41
15. Liuping Wang, Lu Gan, “Integral FCS Predictive Current Control of
42
Induction Motor Drive,” IFAC Proceedings Volumes, vol. 47, no. 3, pp.
43
11956-11961, 2014.
44
16. T. Dragicevi ˇ c, “Dynamic stabilization of DC microgrids with predictive ´
45
control of point-of-load converters,” IEEE Transactions on Power Electronics, vol. 33, no. 12, pp. 10872-10884, Dec. 2018.
46
17. O. Sandre-Hernandez, J. Rangel-Magdaleno and R. Morales-Caporal,
47
"A comparison on finite-set model predictive torque control schemes
48
for PMSMs," in IEEE Transactions on Power Electronics, vol. 33, no.
49
10, pp. 8838-8847, Oct. 2018.
50
18. R. Vargas, J. Rodriguez, U. Ammann, and P. Wheeler, “Predictive
51
current control of an induction machine fed by a matrix converter with
52
reactive power control,” IEEE Transactions on Industrial Electronics,
53
vol. 55, no. 12, pp. 4362– 4371, Dec. 2008.
54
19. F. Ban, G. Lian, J. Zhang, B. Chen and G. Gu, “Study on a novel
55
predictive torque control strategy based on the finite control set for
56
PMSM,” IEEE Transactions on Applied Superconductivity, vol. 29, no.
57
2, pp. 1-6, Mar. 2019.
58
20. J. Rodriguez, R. Kennel, J. Espinoza, M. Trincado, C. Silva, and C.
59
Rojas, “High-performance control strategies for electrical drives: An
60
experimental assessment,” IEEE Transactions on Industrial Electronocs,
61
vol. 59, no. 2, pp. 812–820, Feb. 2012.
62
21. S. Chai, L. Wang, and E. Rogers, “A cascade MPC control structure
63
for a PMSM with speed ripple minimization,” IEEE Trans. Industrial
64
Electronics, vol. 60, no. 8, pp. 2978–2987, Aug. 2013
65
22. E. Fuentes, C. Silva, D. Quevedo, and E. Silva, “Predictive speed
66
control of a synchronous permanent magnet motor,” in Proceedings of
67
IEEE ICIT, Feb. 2009.
68
23. E. J. Fuentes, C. A. Silva and J. I. Yuz, “Predictive speed control
69
of a two-mass system driven by a permanent magnet synchronous
70
motor”, IEEE Transactions on Industrial Electronics, vol. 59, no. 7, pp.
71
2840-2848, Jul. 2012.
72
24. E. Fuentes, D. Kalise, J. Rodríguez and R. M. Kennel, “Cascade-free
73
predictive speed control for electrical drives,” IEEE Transactions on
74
Industrial Electronics, vol. 61, no. 5, pp. 2176-2184, May 2014.
75
25. M. Preindl and S. Bolognani, “Model predictive direct speed control
76
with finite control set of PMSM drive systems,” IEEE Transactions on
77
Power Electronics, vol. 28, no. 2, pp. 1007–1015, Feb. 2013.
78
26. L. Wang, S. Chai, D. Yoo, L. Gan, and K. Ng, PID and predictive
79
control of electrical drives and power converters using matlab/simulink.
80
Hoboken, NJ, USA: Wiley, 2015.
81
27. P. Kakosimos and H. Abu-Rub, “Predictive Speed Control With Short
82
Prediction Horizon for Permanent Magnet Synchronous Motor Drives,”
83
IEEE Transactions on Power Electronics, vol. 33, no. 3, pp. 2740-2750,
84
March 2018.
85
28. N. Kazantzis and C. Kravaris, “Time-discretization of nonlinear control
86
systems via Taylor methods,” Computers & Chemical Engineering, vol.
87
23, no. 6, pp. 763-784, Jan. 1999
88
29. Z. Zhang, F. Wang, J. Wang, J. Rodríguez and R. Kennel, “Nonlinear
89
direct control for three-level NPC back-to-back converter PMSG wind
90
turbine systems: experimental assessment with FPGA,” in IEEE Transactions on Industrial Informatics, vol. 13, no. 3, pp. 1172-1183, June
91
30. S. Bolognani, R. Petrella, A. Prearo, and L. Sgarbossa, “Automatic
92
tracking of MTPA trajectory in ipm motor drives based on ac current
93
injection,” in Proceedings of IEEE Energy Conversion. Congr. Expo.,
94
2009, pp. 2340–2346.
95
31. K. Li and Y. Wang, “Maximum torque per ampere (MTPA) control for
96
IPMSM drives based on a variable-equivalent-parameter MTPA control
97
law,” IEEE Transactions on Power Electronics, vol. 34, no. 7, pp. 7092-
98
7102, July 2019.
99
32. P. Cortes, S. Kouro, B. La Rocca, R. Vargas, J. Rodriguez, J. I. Leon,
100
S. Vazquez, and L.G. Franquello, “Guidelines for weighting factors
101
design in model predictive control of power converters and drives,” in
102
Proceedings of IEEE International Conference on Industrial Technology,
103
33. S. Saberi and B. Rezaie, “A full range permanent magnet synchronous
104
motor position and speed estimation using adaptive non-singular fast
105
terminal sliding mode observer,” Majlesi Journal of Energy Management, vol. 8, no. 4, pp. 17-25, 2019.
106
ORIGINAL_ARTICLE
How does the civilized gravitational search algorithm solve the optimal DG placement?
This study addresses Civilized Gravitational Search Algorithm (CGSA) as a new mass intelligence optimization algorithm for solving optimal single and multiple DG placement problems in the electrical distribution networks. The proposed technique utilizes the modified search procedure of Society Civilization Algorithm (SCA) combining with Newtonian laws of GSA. It mainly consists of two steps. The first step provides a candidate list for DG allocation based on active power loss minimization and the second one searches simultaneously the optimal DG size(s) and location(s) using Self-Adoptive Learning (SAL) strategy. In order to verify the capabilities and effectiveness of the suggested approach, all simulations are conducted through IEEE 33- and 69-bus electrical distribution networks. Moreover, 23 standard functions are examined to verify the stability of the proposed algorithm on different low- and high-dimensional functions. Comparisons demonstrate the superior performance of the presented method to provide better solution quality with a fast convergence characteristic.
https://www.jemat.org/article_119565_224f589b7b68b84df4576a7b25df2892.pdf
2021-09-01
8
20
10.22109/jemt.2020.224858.1234
Optimal DG placement
Distribution network
civilized gravitational search algorithm
active power loss minimization
saman
Hosseini-Hemati
saman.h@live.com
1
Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran.
AUTHOR
Shahram
Karimi
shahramkarimi@razi.ac.ir
2
Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran
LEAD_AUTHOR
Gholam Hossein
Shaeisi
g.sheisi@razi.ac.ir
3
Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran
AUTHOR
1. F. Jabari, S. Asadi, and S. Seyed-barhagh, “A Novel Forward-Backward
1
Sweep Based Optimal DG Placement Approach in Radial Distribution
2
Systems,” Optim. Power Syst. Probl., vol. 262, no. 1, pp. 49–61, 2020.
3
2. K. R. Devabalaj, K. Ravi, and D. P. Kothari, “Optimal location and sizing
4
of capacitor placement in radial distribution system using bacterial
5
foraging optimization algorithm,” Int. J. Electr. Power Energy Syst., vol.
6
71, pp. 383–390, 2015.
7
3. S. Kumar, K. Mandal, and N. Chakraborty, “Optimal DG placement
8
by multi-objective opposition based chaotic differential evolution for
9
techno-economic analysis,” Appl. Soft Comput., vol. 78, pp. 70–83,
10
4. M. . Sujatha, V. Roja, and T. . Prasad, “Multiple DG Placement and
11
Sizing in Radial Distribution System Using Genetic Algorithm and
12
Particle Swarm Optimization,” Comput. Intell. Big Data Anal., pp. 21–36,
13
5. M. . Lalitha and O. Hemakesavulu, “Effect of Load Model and Load
14
Level on DG Placement by Crow Search Algorithm,” Emerg. Trends
15
Electr. Commun. Inf. Technol., vol. 569, pp. 185–198, 2020.
16
6. D. Rama Prabha, T. Jayabarathi, R. Umamageswari, and S. Saranya,
17
“Optimal location and sizing of distributed generation unit using intelligent water drop algorithm,” Sustain. Energy Technol. Assessments, vol.
18
11, pp. 106–113, 2015.
19
7. C. . Prasad, K. Subbaramaiah, and P. Sujatha, “Cost–benefit analysis
20
for optimal DG placement in distribution systems by using elephant
21
herding optimization algorithm,” Renewables Wind. Water, Sol., vol. 6,
22
no. 2, 2019.
23
8. M. Gandomkar, M. Vakilian, and M. Ehsan, “A genetic–based tabu
24
search algorithm for optimal DG allocation in distribution networks,”
25
Electr. Power Components Syst., vol. 33, no. 12, pp. 1351–1362, 2005.
26
9. N. Acharya, P. Mahat, and N. Mithulananthan, “An analytical approach
27
for DG allocation in primary distribution network,” Int. J. Electr. Power
28
Energy Syst., vol. 28, no. 10, pp. 669–678, 2006.
29
10. H. R. Esmaeilian and R. Fadaeinedjad, “Energy loss minimization
30
in distribution systems utilizing an enhanced reconfiguration method
31
integrating distributed generation,” IEEE Syst. J., vol. 9, no. 4, pp.
32
1430–1439, 2015.
33
11. R. Srinivasa Rao, K. Ravindra, K. Satish, and S. V. L. Narasimham,
34
“Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation,” IEEE Trans. Power
35
Syst., vol. 28, no. 1, pp. 317–325, 2013.
36
12. S. Kansal, B. B. R. Sai, B. Tyagi, and V. Kumar, “Optimal placement of
37
distributed generation in distribution networks,” Int. J. Eng. Sci. Technol.,
38
vol. 3, no. 3, pp. 47–55, 2011.
39
13. B. Sookananta, W. Kuanprab, and S. Hanak, “Determination of the
40
optimal location and sizing of distributed generation using particle
41
swarm optimization,” in International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information
42
Technology (ECTI-CON), 2010, pp. 818–822.
43
14. B. Sookananta, P. Utaton, and R. Khongsila, “Determination of
44
the optimal location and sizing of distributed generation using ant
45
colony search,” in International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2010, pp. 814–817.
46
15. M. H. Moradi and M. Abedini, “A combination of genetic algorithm
47
and particle swarm optimization for optimal DG location and sizing in
48
distribution systems,” Int. J. Electr. Power Energy Syst., vol. 34, no. 1,
49
pp. 66–74, 2012.
50
16. U. Sultana, A. B. Khairuddin, A. S. Mokhtar, N. Zareen, and B. Sultana,
51
“Grey wolf optimizer based placement and sizing of multiple distributed
52
generation in the distribution system,” Energy, vol. 111, pp. 525–536,
53
17. A. Mohamed Imran, M. Kowsalya, and D. P. Kothari, “A novel integration
54
technique for optimal network reconfiguration and distributed generation placement in power distribution networks,” Int. J. Electr. Power
55
Energy Syst., vol. 63, pp. 461–472, 2014.
56
18. M. Sedighizadeh, M. Esmaili, and M. Esmaeili, “Application of the
57
hybrid big bang-big crunch algorithm to optimal reconfiguration and
58
distributed generation power allocation in distribution systems,” Energy,
59
vol. 76, pp. 920–930, 2014.
60
19. H. Doagou-Mojarrad, G. B. Gharehpetian, H. Rastegar, and J. Olamaei,
61
“Optimal placement and sizing of DG (distributed generation) units in
62
distribution networks by novel hybrid evolutionary algorithm,” Energy,
63
vol. 54, pp. 129–138, 2013.
64
20. S. S. Tanwar and D. K. Khatod, “Techno-economic and environmental approach for optimal placement and sizing of renewable DGs in
65
distribution system,” Energy, vol. 127, pp. 52–67, 2017.
66
21. S. Golshannavaz, “Optimal simultaneous siting and sizing of DGs and
67
capacitors considering reconfiguration in smart automated distribution
68
systems,” J. Intell. Fuzzy Syst., vol. 27, no. 4, pp. 1719–1729, 2014.
69
22. E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: a gravitational search algorithm,” Inf. Sci. (Ny)., vol. 179, no. 13, pp. 2232–2248,
70
23. A. Bhattacharya and P. K. Roy, “Solution of multi-objective optimal
71
power flow using gravitational search algorithm,” IET Gener. Transm.
72
Distrib., vol. 6, no. 8, pp. 751–763, 2012.
73
24. S. Ghavidel, J. Aghaei, K. M. Muttaqi, and A. Heidari, “Renewable
74
energy management in a remote area using modified gravitational
75
search algorithm,” Energy, vol. 97, pp. 391–399, 2016.
76
25. M. R. Narimani, A. Azizi Vahed, R. Azizipanah-Abarghooee, and
77
M. Javidsharifi, “Enhanced gravitational search algorithm for multiobjective distribution feeder reconfiguration considering reliability, loss
78
and operational cost,” IET Gener. Transm. Distrib., vol. 8, no. 1, pp.
79
55–69, 2013.
80
26. T. Ray and K. M. Liew, “Society and civilization: an optimization algorithm based on the simulation of social behavior,” IEEE Trans. Evol.
81
Comput., vol. 7, no. 4, pp. 386–396, 2003.
82
27. Y. Wang, B. Li, T. Weise, J. Wang, B. Yuan, and Q. Tian, “Self-adaptive
83
learning based particle swarm optimization,” Inf. Sci. (Ny)., vol. 181, no.
84
20, pp. 4515–4538, 2011.
85
28. D. Das, “Optimal placement of capacitors in radial distribution system
86
using a Fuzzy-GA method,” Int. J. Electr. Power Energy Syst., vol. 30,
87
no. 6–7, pp. 361–367, 2008.
88
29. M. E. Baran and F. F. Wu, “Network reconfiguration in distribution
89
systems for loss reduction and load balancing,” IEEE Trans. Power
90
Deliv., vol. 4, no. 2, pp. 1401–1407, 1989.
91
30. A. F. A.Kadir, A. Mohamed, H. Shareef, and M. Z. C.Wanik, “Optimal
92
placement and sizing of distributed generations in distribution systems
93
for minimizing losses and THD_v using evolutionary programming,”
94
Turkish J. Electr. Eng. Comput. Sci., vol. 21, pp. 2269–2282, 2013.
95
31. K. D. Mistry and R. Roy, “Enhancement of loading capacity of distribution system through distributed generator placement considering
96
techno-economic benefits with load growth,” Int. J. Electr. Power Energy
97
Syst., vol. 54, pp. 505–515, 2014.
98
32. S. He, Q. H. Wu, and J. R. Saunders, “Group search optimizer: an
99
optimization algorithm inspired by animal searching behavior,” IEEE
100
Trans. Evol. Comput., vol. 13, no. 5, pp. 973–990, 2009.
101
33. Y. Sun, X. Wang, Y. Chen, and Z. Liu, “A modified whale optimization
102
algorithm for large-scale global optimization problems,” Expert Syst.
103
Appl., vol. 114, pp. 563–577, 2018.
104
34. B. Zhang, M. Zhang, and Y.-J. Zheng, “Improving enhanced fireworks
105
algorithm with new gaussian explosion and population selection strategies,” in International Conference in Swarm Intelligence, 2014, pp.
106
53–63.
107
35. G.-G. Wang, A. H. Gandomi, X. Zhao, and H. C. E. Chu, “Hybridizing
108
harmony search algorithm with cuckoo search for global numerical
109
optimization,” Soft Comput., vol. 20, no. 1, pp. 273–285, 2016.
110
36. S. Ang and U. Leeton, “Optimal placement and size of distributed generation in radial distribution system using whale optimization algorithm,”
111
Suranaree J. Sci. Technol., vol. 26, no. 1, pp. 1–12, 2019.
112
37. H. D. M. Braz and B. A. Souza, “Distribution network reconfiguration
113
using genetic algorithms with sequential encoding: subtractive and
114
additive approaches,” IEEE Trans. Power Syst., vol. 26, no. 2, pp.
115
582–593, 2011.
116
38. J. S. Savier and D. Das, “Impact of network reconfiguration on loss
117
allocation of radial distribution systems,” IEEE Trans. Power Deliv., vol.
118
22, no. 4, pp. 2473–2480, 2007.
119
39. P. D.P.Reddy, V. C. V.Reddy, and T. G.Manohar, “Whale optimization
120
algorithm for optimal sizing of renewable resources for loss reduction
121
in distribution systems,” Renewables Wind. Water, Sol., vol. 4, no. 3,
122
pp. 1–13, 2017.
123
ORIGINAL_ARTICLE
Reliability centered economic dispatch in concept of energy hub considering resource diversity constraint
In an inconsistent view of energy systems, the interaction between different energy carriers is not taken into account. In such a view, the considered problem is not well optimized. The idea of an integrated looking into several carriers has been proposed by creating the energy hub concept. Due to the simultaneous attention of all energy carriers, long-term planning and short-term operation have converted to complicated challenges. To this end, this study focuses on the energy hub operation for cost minimization. In addition to considering reliability indices for different loads, diversity constraint is regarded as a key point to increase energy security. Sensitivity analysis of the degree of diversity and its effects on operation costs and Expected Energy not Supply (EENS), play a vital role in the final decision. LINDOGlobal solver is employed in GAMS to implement Mixed Integer Nonlinear Programming (MINLP) model. A sample energy hub, considering three carriers in the input port and three loads in the output port, is used as a test system, and results are discussed in depth.
https://www.jemat.org/article_118726_c8c52b839d79cbc0bf2cded5ab68efde.pdf
2021-09-01
21
29
10.22109/jemt.2020.231405.1241
Energy hub
Economic Dispatch
Diversity Constraint
Reliability
Seyed Meisam
Ezzati
ezzati_sm@srbiau.ac.ir
1
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Hosein
Mohammadnezhad Shourkaei
h-mohamadnejad@srbiau.ac.ir
2
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Faramarz
Faghihi
faramarz.faghihi@srbiau.ac.ir
3
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Soodabeh
Soleymani
s.soleymani@srbiau.ac.ir
4
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Seyed Babak
Mozafari
mozafari@srbiau.ac.ir
5
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
1. X. Wang, Y. Liu, C. Liu, and J. Liu, “Coordinating energy management
1
for multiple energy hubs: From a transaction perspective,” International
2
Journal of Electrical Power & Energy Systems, vol. 121, p. 106060,
3
2. M. Geidl, G. Koeppel, P. Favre-Perrod, B. Klockl, G. Andersson, and
4
K. Frohlich, “Energy hubs for the future,” IEEE power and energy
5
magazine, vol. 5, no. 1, pp. 24–30, 2006.
6
3. M. Geidl, Integrated modeling and optimization of multi-carrier energy
7
systems. PhD thesis, ETH Zurich, 2007.
8
4. X. Zhang, M. Shahidehpour, A. Alabdulwahab, and A. Abusorrah, “Optimal expansion planning of energy hub with multiple energy infrastructures,” IEEE Transactions on Smart Grid, vol. 6, no. 5, pp. 2302–2311,
9
5. S. Mansouri, A. Ahmarinejad, M. Ansarian, M. Javadi, and J. Catalao, “Stochastic planning and operation of energy hubs considering
10
demand response programs using benders decomposition approach,”
11
International Journal of Electrical Power & Energy Systems, vol. 120, p.
12
106030, 2020.
13
6. M. Alipour, K. Zare, and M. Abapour, “Minlp probabilistic scheduling
14
model for demand response programs integrated energy hubs,” IEEE
15
Transactions on Industrial Informatics, vol. 14, no. 1, pp. 79–88, 2017.
16
7. A. Mansour-Saatloo, M. Agabalaye-Rahvar, M. A. Mirzaei, B.
17
Mohammadi-Ivatloo, M. Abapour, and K. Zare, “Robust scheduling
18
of hydrogen based smart micro energy hub with integrated demand
19
response,” Journal of Cleaner Production, vol. 267, p. 122041, 2020.
20
8. M. A. Mirzaei, M. Nazari-Heris, B. Mohammadi-Ivatloo, K. Zare, M.
21
Marzband, and A. Anvari-Moghaddam, “A novel hybrid framework for
22
co-optimization of power and natural gas networks integrated with
23
emerging technologies,” IEEE Systems Journal, 2020.
24
9. S. M. Ezzati, F. Faghihi, H. M. Shourkaei, S. B. Mozafari, and S. Soleymani, “Optimum operation of multi-energy carriers in the context of
25
an energy hub considering a wind generator based on linear programming,” Journal of Renewable and Sustainable Energy, vol. 10, no. 1, p.
26
014702, 2018.
27
10. A. Dolatabadi, B. Mohammadi-Ivatloo, M. Abapour, and S. Tohidi, “Optimal stochastic design of wind integrated energy hub,” IEEE Transactions on Industrial Informatics, vol. 13, no. 5, pp. 2379–2388, 2017.
28
11. M. Jadidbonab, S. Madadi, and B. Mohammadi-ivatloo, “Hybrid strategy for optimal scheduling of renewable integrated energy hub based
29
on stochastic/robust approach,” Journal of Energy Management and
30
Technology, vol. 2, no. 4, pp. 29–38, 2018.
31
12. M. Jadidbonab, A. Dolatabadi, B. Mohammadi-Ivatloo, M. Abapour,
32
and S. Asadi, “Risk-constrained energy management of pv integrated
33
smart energy hub in the presence of demand response program and
34
compressed air energy storage,” IET Renewable Power Generation,
35
vol. 13, no. 6, pp. 998–1008, 2019.
36
13. E. N. Krapels, “New york as a clean energy hub,” The Electricity Journal,
37
vol. 29, no. 7, pp. 23–29, 2016.
38
14. M. Moeini-Aghtaie, A. Abbaspour, M. Fotuhi-Firuzabad, and P. Dehghanian, “Optimized probabilistic phevs demand management in the
39
context of energy hubs,” IEEE Transactions on Power Delivery, vol. 30,
40
no. 2, pp. 996–1006, 2014.
41
15. A. Shahmohammadi, M. Moradi-Dalvand, H. Ghasemi, and M. Ghazizadeh, “Optimal design of multicarrier energy systems considering
42
reliability constraints,” IEEE Transactions on Power Delivery, vol. 30, no.
43
2, pp. 878–886, 2014.
44
16. Y. Liang, W. Wei, and C. Wang, “A generalized nash equilibrium approach for autonomous energy management of residential energy
45
hubs,” IEEE Transactions on Industrial Informatics, vol. 15, no. 11, pp.
46
5892–5905, 2019.
47
17. S. Paudyal, C. A. Cañizares, and K. Bhattacharya, “Optimal operation
48
of industrial energy hubs in smart grids,” IEEE Transactions on Smart
49
Grid, vol. 6, no. 2, pp. 684–694, 2014.
50
18. M. A. Mirzaei, M. Z. Oskouei, B. Mohammadi-Ivatloo, A. Loni, K. Zare,
51
M. Marzband, and M. Shafiee, “Integrated energy hub system based
52
on power-to-gas and compressed air energy storage technologies in
53
the presence of multiple shiftable loads,” IET Generation, Transmission
54
& Distribution, vol. 14, no. 13, pp. 2510–2519, 2020.
55
19. S. Hoseinzadeh and R. Azadi, “Simulation and optimization of a solarassisted heating and cooling system for a house in northern of iran,”
56
Journal of Renewable and Sustainable Energy, vol. 9, no. 4, p. 045101,
57
20. H. Cong, X. Wang, and C. Jiang, “Robust coalitional game theoretic
58
optimisation for cooperative energy hubs with correlated wind power,”
59
IET Renewable Power Generation, vol. 13, no. 13, pp. 2391–2399,
60
21. S. Fan, Z. Li, J. Wang, L. Piao, and Q. Ai, “Cooperative economic
61
scheduling for multiple energy hubs: a bargaining game theoretic
62
perspective,” IEEE Access, vol. 6, pp. 27777–27789, 2018.
63
22. S. Hoseinzadeh and P. S. Heyns, “Thermo-structural fatigue and lifetime
64
analysis of a heat exchanger as a feedwater heater in power plant,”
65
Engineering Failure Analysis, p. 104548, 2020.
66
23. H. Kariman, S. Hoseinzadeh, A. Shirkhani, P. S. Heyns, and J. Wannenburg, “Energy and economic analysis of evaporative vacuum easy
67
desalination system with brine tank,” Journal of Thermal Analysis and
68
Calorimetry, vol. 140, no. 4, pp. 1935–1944, 2020.
69
24. S. Hoseinzadeh, R. Ghasemiasl, M. Javadi, and P. S. Heyns, “Performance evaluation and economic assessment of a gas power plant with
70
solar and desalination integrated systems,” 2020.
71
25. M. Mohammadi, Y. Noorollahi, B. Mohammadi-Ivatloo, and H. Yousefi,
72
“Energy hub: from a model to a concept–a review,” Renewable and
73
Sustainable Energy Reviews, vol. 80, pp. 1512–1527, 2017.
74
26. S. Walker, T. Labeodan, W. Maassen, and W. Zeiler, “A review study of
75
the current research on energy hub for energy positive neighborhoods,”
76
Energy Procedia, vol. 122, pp. 727–732, 2017.
77
27. M. Mohammadi, Y. Noorollahi, B. Mohammadi-ivatloo, H. Yousefi, and S.
78
Jalilinasrabady, “Optimal scheduling of energy hubs in the presence of
79
uncertainty-a review,” Journal of energy management and technology,
80
vol. 1, no. 1, pp. 1–17, 2017.
81
28. M. Mohammadi, Y. Noorollahi, B. Mohammadi-ivatloo, M. Hosseinzadeh, H. Yousefi, and S. T. Khorasani, “Optimal management of
82
energy hubs and smart energy hubs–a review,” Renewable and Sustainable Energy Reviews, vol. 89, pp. 33–50, 2018.
83
29. M.-H. Shariatkhah, M.-R. Haghifam, M. Parsa-Moghaddam, and P.
84
Siano, “Modeling the reliability of multi-carrier energy systems considering dynamic behavior of thermal loads,” Energy and Buildings, vol.
85
103, pp. 375–383, 2015.
86
30. H. Gharibpour, H. Monsef, and M. Ghanaatian, “The comparison of two
87
control methods of power swing reduction in power system with upfc
88
compensator,” in 20th Iranian Conference on Electrical Engineering
89
(ICEE2012), pp. 386–391, IEEE, 2012.
90
31. M. Z. Gargari and R. Ghaffarpour, “Reliability evaluation of multi-carrier
91
energy system with different level of demands under various weather
92
situation,” Energy, vol. 196, p. 117091, 2020.
93
32. The routledge handbook of energy security.
94
33. L. Martišauskas, J. Augutis, and R. Krikštolaitis, “Methodology for
95
energy security assessment considering energy system resilience to
96
disruptions,” Energy strategy reviews, vol. 22, pp. 106–118, 2018.
97
34. B. W. Ang, W. L. Choong, and T. S. Ng, “Energy security: Definitions,
98
dimensions and indexes,” Renewable and sustainable energy reviews,
99
vol. 42, pp. 1077–1093, 2015.
100
35. P. Grunewald and M. Diakonova, “Flexibility, dynamism and diversity
101
in energy supply and demand: a critical review,” Energy Research &
102
Social Science, vol. 38, pp. 58–66, 2018.
103
36. H. Gharibpour and F. Amini-far, “Electricity market assessment in wind
104
energy integrated power systems with the potential of flexibility: A
105
boundary condition approach,” Scientia Iranica, 2019.
106
37. W. Huang, N. Zhang, Y. Wang, T. Capuder, I. Kuzle, and C. Kang,
107
“Matrix modeling of energy hub with variable energy efficiencies,” International Journal of Electrical Power & Energy Systems, vol. 119, p.
108
105876, 2020.
109
38. S. Moazeni, A. H. Miragha, and B. Defourny, “A risk-averse stochastic
110
dynamic programming approach to energy hub optimal dispatch,” IEEE
111
Transactions on Power Systems, vol. 34, no. 3, pp. 2169–2178, 2018.
112
39. S. M. Ezzati, F. Faghihi, H. Mohammadnezhad Shourkaei, S. B. Mozafari, and S. Soleymani, “Reliability assessment for economic dispatch
113
problem in the energy hub concept,” Energy Sources, Part B: Economics, Planning, and Policy, vol. 13, no. 9-10, pp. 414–428, 2018.
114
40. S. M. Ezzati, F. Faghihi, H. Mohammadnezhad Shourkaei, S. B. Mozafari, and S. Soleymani, “Reliability assessment for economic dispatch
115
problem in the energy hub concept,” Energy Sources, Part B: Economics, Planning, and Policy, vol. 13, no. 9-10, pp. 414–428, 2018.
116
41. S. M. Ezzati, F. Faghihi, H. Mohammadnezhad Shourkaei, S. B. Mozafari, and S. Soleymani, “Reliability assessment for economic dispatch
117
problem in the energy hub concept,” Energy Sources, Part B: Economics, Planning, and Policy, vol. 13, no. 9-10, pp. 414–428, 2018.
118
42. S. M. Ezzati, F. Faghihi, H. Mohammadnezhad Shourkaei, S. B. Mozafari, and S. Soleymani, “Reliability assessment for economic dispatch
119
problem in the energy hub concept,” Energy Sources, Part B: Economics, Planning, and Policy, vol. 13, no. 9-10, pp. 414–428, 2018.
120
ORIGINAL_ARTICLE
Investigating effect of using earth-sheltered architecture on energy conservation in cold and mountainous climate; case study: Yakhchal-e Qaem Maqam, Basement of Sharbat Oqli House, and Cistern of Parvin Etesami House
The limitation of energy resources is becoming a serious crisis in the world. Considering the energy and environmental crises caused by the excessive consumption of energy in the world, it is necessary to revise design methods and use sustainable and valuable models in the design of buildings in order to provide thermal comfort. Architecture in the shadow of the earth is a valuable and sustainable model which has high energy conservation capability acts along the protection of the environment via energy conservation and adapts with the needs of the era. The present research is aimed to explain the concepts of earth-sheltered architecture and determine the effect of design and use of underground spaces in environmental sustainable design, architectural harmony with the climate, and energy conservation. In this research, using descriptive-analytical research method based on library and documentary studies as well as field survey, the earth-sheltered architectural concepts are explored and successful samples of the world and valuable models in Iranian traditional architecture are introduced. In the case study, the physical features of a few samples of underground spaces in Tabriz as well as heat waste or absorption from walls are examined at different depths. The results of the study indicate that heat waste or absorption rate via walls depends on the underground physical properties, contact of walls with the outside air, and burial level of buildings. Benefiting from the potential of underground spaces and use of ground depth in architectural design has led to developing relatively stable conditions against adverse conditions of climate and environmental balance. Utilizing the values of experiences could provide a solution for solving a part of the current energy crisis and creation of responsive environments in terms of climate and application.
https://www.jemat.org/article_114927_e2d5521c7e48804c59f848f16b2e13f6.pdf
2021-09-01
30
42
10.22109/jemt.2020.230539.1240
Earth-Sheltered Architecture
sustainable
energy conservation
climatic comfort
energy consumption
Elnaz
Abizadeh
elnazabizadeh@gmail.com
1
Assistant Professor, Roshdiyeh Higher Education Institute, Tabriz, IRAN
LEAD_AUTHOR
1. Z. Barzegar, and S. Mofidi Shemirani, “How to use ground mass in the
1
world’s local architecture: As a cooling technique in buildings, “Journal
2
of Bagh-e Nazar, vol. 7, no.15, pp. 13-26, 2010.
3
2. M. Schweiker, and S. Masanori, “Comparative effects of building envelope improvements and occupant behavioral changes on the energy
4
consumption for heating and cooling,” Energy Policy, vol. 38, no. 6, pp.
5
2976-2986, 2010.
6
3. K.J. Chua, and S.K.w Chou, “A performance-based method for energy efficiency improvement of buildings,” Energy Conservation and
7
Management, vol. 52, no .4, pp1829-1839, 2011.
8
4. M. Karimi Moshaver, and S. Negin Taji, “Position of underground spaces
9
in urban plans,” City Knowledge, Tehran Planning Studies Center, vol.
10
12, no. 12, pp. 3-21, 2010.
11
5. D. Watson, and L. Kenneth, “Climatic design,” Energy-Efficient Building
12
Principles and Practices, Columbus OH: McGraw-hill, 2006.
13
6. S. G. Golany, and T. Ojima, “Geo-Space Urban Design” New York, John
14
Wiley, 1996.
15
7. R. Sterling, and J. Carmody, “Design of underground spaces,” translated by Mashhad Vahid Ebrahimi, Marandiz, 2009.
16
8. V. Qobadian, “Climatic study of traditional Iranian buildings,” Tehran,
17
University of Tehran, Publication and Printing Institute, 2019.
18
9. Information presented to the authors, Cultural Heritage, Handicrafts,
19
and Tourism Organization, 2017.
20
10. Web-based Simulation Software of Swedish Building Code Energy Calculation (SBCE), 2019, Retrieved from the website:
21
http://www.energiberakning.se/Heated_Basement/Heated_Basement.aspx
22
11. Information presented to the authors, East Azerbaijan Province Iranology Foundation (Sharbat Oghli Basement), 2017.
23
ORIGINAL_ARTICLE
The seventh line: a scenario planning strategic framework for Iranian 7th energy progress plan by 2020-2025
Iran, not just as an oil-exporting country, but also because of its geopolitical position, is one of the world’s most important countries. Since the recent events of the Middle East, the importance of the country has grown globally. Because of this growing importance, the short term future of Iran is significant to be studied. In this study, a descriptive approach has been taken to synthesize the world’s energy portfolio and the global energy balance outlook to provide insights into the development of local energy portfolio scenarios focusing on Iran. The shell company represents two basic scenarios, which are the scramble and blueprint. According to shell scenarios world energy portfolio might be organized in two images in which each image concludes different energy portfolios. Thus, developing energy portfolio scenarios is essential. In this paper, after reviewing the four selected developing countries’ energy portfolios and developed countries, the scenarios of Iran’s energy portfolio in 2020-2025 will be presented. For this purpose, a combination of qualitative and quantitative methods will be used for scenario building.
https://www.jemat.org/article_118294_747f4d8145a47b15cdb01bdb5bc683cf.pdf
2021-09-01
43
53
10.22109/jemt.2020.234795.1243
Energy Policy
Scenario Planning
Public Policy
Strategic Planning
Nima
Norouzi
nima1376@aut.ac.ir
1
Energy engineering and physics department, Amirkabir university, Tehran, Iran
AUTHOR
Maryam
Fani
mfani@aut.ac.ir
2
Energy, Amirkabir university of tech., tehran, iran
LEAD_AUTHOR
1. R. Dunia, F. Thomas, and K. Ahmadi, “Study of heavy crude oil flows in
1
pipelines with electromagnetic heaters,” Energy Fuels, vol. 26, no. 7,
2
pp. 4426–4437, 2012.
3
2. J. Ariga, “Internalizing Environmental Quality in a Simple Endogenous
4
Growth Model,” Working Paper, Department of Agricultural and Resource Economics University of Maryland Collge Park, MD 20742,
5
3. British Petroleum(BP), “Energy Outlook 2035,” 2015.
6
4. M. Bahrami, and P. Abbaszadeh, “An overview of renewable energies
7
in Iran,” Renewable and Sustainable Energy Reviews, Vol. 24, pp.
8
198-208, 2013.
9
5. M.E. Yousef Nezhad, and S. Hoseinzadeh, “Mathematical modelling
10
and simulation of a solar water heater for an aviculture unit using MATLAB/SIMULINK,” Renewable and Sustainable Energy, Vol. 9, 063702,
11
6. S. Hoseinzadeh, M. Hadi Zakeri, A. Shirkhani, and A.J. Chamkha,
12
“Analysis of energy consumption improvements of a zero-energy building in a humid mountainous area,” Renewable and Sustainable Energy,
13
Vol. 11, 015103, 2019.
14
7. S. Hoseinzadeh, and R. Azadi, “Simulation and optimization of a solarassisted heating and cooling system for a house in Northern of Iran,”
15
Renewable and Sustainable Energy, Vol. 9, 045101, 2017.
16
8. M. Dasgupta, and S.K. Mishra, “Least Absolute Deviation Estimation
17
of Linear Econometric Models: A Literature Review,” Working Paper,
18
http://mpra.ub.uni-muenchen.de/1781/, 2007.
19
9. C. Di Maria, and S. Valente, “Hicks Meets Hotelling: The Direction of
20
Technical Change in Capital–resource Economies,” Environment and
21
Development Economics, Vol. 13, pp. 691-717, 2008.
22
10. C. Groth and P. Schou, “Can Non-renewable Resources Alleviate the
23
Knife-edge Character of Endogenous Growth?,” Oxford Economic Papers, Vol. 54, pp. 386-411, 2002.
24
11. H. Mohtadi, “Environment, Growth and Optimal Policy Design,” Public
25
Economics, Vol. 63, pp. 119-140, 1996.
26
12. S. Smulders, and R. Gradus “Pollution Abatement and Long-term
27
Growth,” European Journal of Political Economy, Vol. 12, pp. 505-532,
28
13. IEA, “World Energy Outlook 2016,” OECD/IEA 9 rue de la federation,
29
75739 Paris cedex 15, France, 2016.
30
14. World Energy Council (WEC), “Cost of technologies report”, 2013.
31
15. Shell, “Shell energy scenarios to 2050,” 4th edition, VMS The Hague
32
H8259, 2015.
33
16. W. Weimer-Jehle, “Cross-impact balances: A system-theoretical approach to cross-impact analysis,” Technological Forecasting and Social
34
Change, Vol. 73, pp. 334-361, 2006.
35
17. N. Norouzi, M. Fani, and Z. Karami Ziarani, “The Fall of oil age: A
36
scenario planning approach over the last peak oil of human history by
37
2040,” petroleum science and engineering, Vol. 188, 106827, 2020.
38
18. BP (British Petroleum), “ Energy Outlook 2035,” 2016.
39
19. IPCC, “Intergovermental Pannel on Climate Change Fourth Assesment
40
Report Summery for Policy Makers,” 2015.
41
20. IEA, “World Energy Outlook 2016,” OECD/IEA 9 rue de la federation,
42
75739 Paris cedex 15, France, 2016.
43
21. IEA, “Energy Technology Perspectives 2015,” OECD/IEA 9 rue de la
44
federation, 75739 Paris cedex 15, France, 2015.
45
22. M.R. Lotfalipour, M.A. Falahi, and M. Ashena, “Economic growth, CO2
46
emissions, and fossil fuels consumption in Iran,” Energy, Vol. 35, pp.
47
5115-5120, 2010.
48
23. P.A. Kharecha, and J.E. Hansen, “Implications of“peak oil” for atmospheric CO2 and climate,” Global Biogeochemical Cycles, Vol. 22,
49
GB3012, 2008.
50
24. M. Mirzaei and M. Bekri, “Energy consumption and CO2 emissions in
51
Iran, 2025,” Environmental research, Vol. 154, pp. 345-351, 2017.
52
25. N. Ghorbani, A. Aghahosseini and C. Breyer,“Assessment of a costoptimal power system fully based on renewable energy for Iran by
53
2050-Achieving zero greenhouse gas emissions and overcoming the
54
water crisis,” Renewable Energy, Vol. 146, pp. 125-148, 2020.
55
26. R. Alizadeh, M. Majidpour, R. Maknoon, and J. Salimi, “Iranian energy
56
and climate policies adaptation to the Kyoto protocol,” International
57
Journal of Environmental Research, Vol. 9, pp. 853-864, 2015.
58
27. R. Alizadeh, M. Majidpour, R. Maknoon, and S. Shafiei Kaleibari, “The
59
green development mechanism in Iran: does it need a revival,” International journal of global warming, Vol. 10, pp. 196-215, 2016.
60
28. Z. Farajzadeh, “Emissions tax in Iran: Incorporating pollution disutility
61
in a welfare analysis,”, Journal of the greener Production, Vol. 186, pp.
62
618-631, 2018.
63
29. S. Mafalda, L. Vítor, I. Vítor Oliveira, and M. Horta, “A scenario-based
64
approach for assessing the energy performance of urban development
65
pathways,” Sustainable Cities and Society, Vol. 40, pp. 372-382, 2018.
66
30. L. Huanan, M. Hailin, G. Shusen, and L. Miao, “Scenario analysis for
67
optimal allocation of China’s electricity production system,” Sustainable
68
Cities and Society, Vol. 10, pp. 241-244, 2014
69
31. P. Miguelac, J. Gonçalvesac, L. Nevesab, and A.G. Martins, “Using
70
clustering techniques to provide simulation scenarios for the smart
71
grid,” Sustainable Cities and Society, Vol. 26, pp. 447-455, 2016.
72
32. M. Fani, and N. Norouzi, “Using Social and Economic Indicators for
73
Modeling, Sensitivity Analysis and Forecasting the Gasoline Demand
74
in the Transportation Sector An ANN Approach in case study for Tehran
75
metropolis”, Iranian Journal of Energy, 2019 ‘article in press’.
76
33. T. Kaya, and C. Kahraman, “Multicriteria renewable energy planning
77
using an integrated fuzzy VIKOR & AHP methodology: The case of
78
Istanbul,” Energy, pp. 1–11, 2010.
79
34. A. Elshkakia, and L. Shen, “Energy-material nexus: The impacts of
80
national and international energy scenarios on critical metals use in
81
China up to 2050 and their global implication,” Energy, Vol. 180, pp.
82
903-917, 2019.
83
35. A. Krstic-Furundži ´ c, M. Vujoševi ´ c, and A. Petrovski, “Energy and envi- ´
84
ronmental performance of the office building facade scenario,” Energy,
85
Vol. 183, pp. 437-447, 2019.
86
36. P. Shen, and B. Yang, “Projecting Texas energy use for residential
87
sector under future climate and urbanization scenarios: A bottom-up
88
method based on twenty-year regional energy use data,” Energy, Vol.
89
193, 116694, 2020.
90
ORIGINAL_ARTICLE
Optimal distributed generation placement strategy to enhancing resilience against smoke effect
Climate change raises natural disasters, especially high impact low probability (HILP) events like wildfire. The effect of wildfire on power systems could be investigated based on the flame and smoke of wildfire. Smoke can affect power system resilience, however, this effect on the power system has not yet been fully investigated. In this paper, at first, the smoke effect has been examined, and after that power system resilience has been improved by the optimal placement of distributed generation resources. Since the smoke effect depends on the direction of the wind, and it has stochastic nature, the wind rose curve has been used to reduce possible scenarios. It should be noted that the proposed method has been studied on the IEEE 33-bus distribution system to the multi-objective placement of distributed generation sources. Since the multi-objective solutions have Pareto set answers, it is provided to find a unique answer by using the fuzzy method. Also, a new optimization algorithm has been presented for the first time that is called the handball championship cup algorithm or HCCA algorithm. It is shown that the proposed methods have good accuracy, and are suitable for improving the power system resilience against the smoke effect.
https://www.jemat.org/article_119974_b4d2292afbbb9a5da72d13ca3bbe5e18.pdf
2021-09-01
54
66
10.22109/jemt.2020.226576.1236
Power system Resilience
Smoke Effect
Distributed generation sources placement
HCCA Algorithm
Navid
Javidtash
navid.javidtash@miau.ac.ir
1
Department of Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
AUTHOR
Masoud
Jabbari
jabbari@miau.ac.ir
2
Department of Engineering, Marvdasht Branch , Islamic Azad University, Marvdasht, Iran
LEAD_AUTHOR
Taher
Niknam
taher_nik@yahoo.com
3
Department of Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
AUTHOR
Mehdi
Nafar
mehdinafar@yahoo.com
4
Department of Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
AUTHOR
1. H. Farh, A. Al-shaalan, A. Eltamaly, and A. Abdullrahman, " A Novel
1
Crow Search Algorithm Auto-Drive PSO for Optimal Allocation and
2
Sizing of Renewable Distributed Generation," IEEE Access, Vol. 8 , pp.
3
27807 – 27820, 2020.
4
2. "Office of President Executive Order, Economic benefits of increasing electric grid resilience to weather outages," U.S. Department of
5
Energy’s Office of Electricity Delivery and Energy Reliability, August
6
3. R. Campbell, "Weather-related Power Outages and Electric System
7
Resiliency," Congressional Research Service, Library of Congress, pp.
8
1-8, 2012.
9
4. Y. Wang, C. Chen, J. Wang, and R. Baldick, "Research on Resilience
10
of Power Systems Under Natural Disasters—A Review," IEEE Transactions on Power Systems, vol. 31, no. 2, pp. 1604-1613, 2016.
11
5. A. Berkeley, M. Wallace, and C. Coo, "A Framework For Establishing
12
Critical Infrastructure Resilience Goals," Final Report and Recommendations by the Council, National Infrastructure Advisory Council, pp.
13
18-21, 2010.
14
6. Z. Bie, Y. Lin, G. Li, and F. Li, "Battling the extreme: a study on the
15
power system resilience,"Proceedings of the IEEE, vol. 105, no. 7, pp.
16
1253-1264, 2017.
17
7. M. Panteli, D. Trakas, and P. Mancarella, "Boosting the power grid
18
resilience to extreme weather events using defensive islanding," IEEE
19
Transactions on Smart Grid, vol. 7, no. 6, pp. 2913-2919, 2016.
20
8. S. Lei, J. Wang, C. Chen, and Y. Hou, "Mobile emergency generator
21
pre-positioning and real-time allocation for resilient response to natural
22
disasters," IEEE Transactions on Smart Grid, vol. 9, no. 3, pp. 2030-
23
2039, 2018.
24
9. M. Zare, A. Abbaspour, M. Fotuhi-Firuzabad, and M. Moein-Aghtaei,
25
"Increasing the resilience of distribution systems against hurricane by
26
optimal switch placement," in Conference on Electrical Power Distribution Networks Conference (EPDC), 19-20 April 2017, Semnan, Iran,
27
10. S. Yao, P. Wang, and T. Zhao, "Transportable energy storage for more
28
resilient distribution systems with multiple microgrids," IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3331-3340, 2019.
29
11. S. Ma; B. Chen; Z. Wang, "Resilience Enhancement Strategy for Distribution Systems under Extreme Weather Events," IEEE Transactions
30
on Smart Grid, vol. 9, no. 2, pp. 1442-1449, 2018.
31
12. A. Berkeley, M. Wallace, and C. Coo, "A Framework For Establishing
32
Critical Infrastructure Resilience Goals," Final Report and Recommendations by the Council, National Infrastructure Advisory Council,
33
October 2010.
34
13. D. Cai, X. Li, and Y. Wang, et al., "Impact of natural disasters on the
35
western Hubei power grid and its anti-disaster enhancement measures," The Journal of Engineering, vol. 2019, no. 16, pp. 1976-1980,
36
14. M. Mohamed, T. Chen, W. Su and T. Jin, "Proactive Resilience of Power
37
Systems Against Natural Disasters: A Literature Review," IEEE Access,
38
vol. 7, pp. 163778-163795, 2019.
39
15. P. Karimyan, G.B. Gharehpetian, M. Abedi, and A. Gavili, "Long term
40
scheduling for optimal allocation and sizing of DG unit considering load
41
variations and DG type," International Journal of Electrical Power &
42
Energy Systems, vol. 54, pp. 277-287, 2014.
43
16. P. Li, D. Huang, J. Ruan, and H. Qin et al., "Influence of Forest Fire Particles on the Breakdown Characteristics of Air Gap," IEEE Transactions
44
on Dielectrics and Electrical Insulation, vol. 23, no. 4, pp. 1974-1983,
45
17. K. Charzan, and Z. Wroblebski, "The threat caused by fires under
46
high voltage lines," in 15 st International Conference on Advances in
47
Processing, Testing and Application of Dielectric Materials, Wrocław
48
University of Technology, Wrocław, Poland, Conference 15, no.40,
49
18. D. Trakas, and N. Hatziargyriou, "Optimal Distribution Operation For
50
Enhancing Resilience Against Wildfire," IEEE Transactions on Power
51
Systems, vol. 33 , no. 2, pp.1-12, 2018.
52
19. S. Ma, L. Su, Z. Wang, F. Qiu, and G. Guo, "Resilience enhancement of
53
distribution grids against extreme weather events," IEEE Transactions
54
on Power Systems, vol. 33, no. 5, pp. 4842-4853, 2018.
55
20. M. Panteli, C. Pickering, and S. Wilkinson, et al., "Power system resilience to extreme weather: fragility modeling, probabilistic impact
56
assessment, and adaptation measures," IEEE Transactions on Power
57
Systems, vol.32, no. 5, pp. 3747–3757, 2017.
58
21. A. Bagchi, A. Sprintson and C. Singh, "Modeling the impact of fire
59
spread on the electrical distribution network of a virtual city," 41st North
60
American Power Symp, Starkville, MS, USA, pp. 1-6, 2009.
61
22. M. Choobineh, B. Ansari, and S. Mohagheghi, "Vulnerability assessment of the power grid against progressing wildfires," Fire Safety Journal, vol. 73, pp. 20–28, 2015.
62
23. S. Mohagheghi, and S. Rebennack, "Optimal resilient power grid operation during the course of a progressing wildfire," International Journal
63
of Electrical Power & Energy Systems, vol. 73, pp. 843-852, 2015.
64
24. B. Ansari, and S. Mohagheghi, "Optimal energy dispatch of the power
65
distribution network during the course of a progressing wildfire," International Transactions on Electrical Energy Systems, vol. 25, pp.
66
3422-3438, 2015.
67
25. M. Salehizadeh, M. Koohbijari, H. Nouri, and A. Ta¸scıkaraoglu, et ˘
68
al. "Bi-objective optimization model for optimal placement of thyristorcontrolled series compensator devices," Energies, vol. 12, pp. 1-4,
69
26. K. Karimizadeh, S. Soleymani, and F. Faghihi, "Optimal placement
70
of DG units for the enhancement of MG networks performance using
71
coalition game theory," IET Generation, Transmission & Distribution ,
72
vol. 14 , no. 5, pp. 3-13, 2020.
73
27. M. Aman, J. Jasmon, H. Mokhlis, and A. Bakar, "Optimal placement
74
and sizing of a DG based on a new power stability index and line
75
losses," International Journal of Electrical Power & Energy Systems,
76
vol. 43, no. 1, pp. 1296-1304, 2012.
77
28. S. Naik, D. Khatod, and M. Sharma, "Optimal allocation of combined
78
DG and capacitor for real power loss minimization in distribution networks," International Journal of Electrical Power & Energy Systems,
79
vol. 53, pp. 967-970, 2013.
80
29. A. Abou.El-Ela, S. Allam, and M. Shatla, "Maximal optimal benefits of
81
distributed generation using genetic algorithms," Electric Power System
82
Research, vol. 80, no. 7, pp. 869–877, 2010.
83
30. M. Moradi, and M. Abedini, "A combination of genetic algorithm and
84
particle swarm optimization for optimal dg location and sizing in distribution systems," International Journal of Electrical Power & Energy
85
Systems, vol. 34, no. 1, pp. 66-72, 2012.
86
31. C. Tymstra, B. Stocksb, X. Caia, and M. Flannigan, "Wildfire management in Canada: Review, challenges and opportunities," Progress in
87
Disaster Science, vol.5, 2020.
88
32. Sh. Guan, D.Wong, Y. Gao, T. Zhang, and G. Pouliot, "Impact of wildfire
89
on particulate matter in the southeastern United States in November
90
2016," Science of The Total Environment, vol. 724, 2020.
91
33. I. Silvab, M. Vallea, L. Barrosa, and J. Meyer, "A wildfire warning
92
system applied to the state of Acre in the Brazilian Amazon," Applied
93
Soft Computing, vol. 89, 2020.
94
34. M. Salehizadeh, and S. Soltanian, "Application of fuzzy Q-learning
95
for electricity market modeling by considering renewable power penetration," Renewable and Sustainable Energy Reviews, vol. 56, pp.
96
1172-1176, 2016.
97
35. R. Liang, and J. Liao, "A fuzzy-optimization approach for generation
98
scheduling with wind and solar energy systems," IEEE Transactions on
99
Power Systems, vol. 22, no. 4, pp. 1665-1674, 2007.
100
36. M. Abdi-Siab, and H. Lesani, "Two-stage scenario-based DEP incorpo-
101
rating PEV using Benders’ decomposition," IET Generation, Transmission & distribution, vol. 14, no. 8, pp. 1508-1520, 2020.
102
37. N. Amjady, J. Aghaei, and H.A. Shayanfar, "Stochastic multiobjective
103
market clearing of joint energy and reserves auctions ensuring power
104
system security," IEEE Transactions on Power Systems, vol. 24, no. 4,
105
pp. 1841-1850, 2009.
106
38. F. Castellani, A. Garinei, L. Terzi, D. Astolfi, and M. Gaudiosi, "Improving wind farm operation practice through numerical modelling and
107
supervisory control and data acquisition data analysis," IET Renewable
108
Power Generation, vol. 8 , no. 4 , pp.367-373, 2014.
109
39. N. Duan, W. Xu, T. Yuan, and S. Wang, et al., " Modelling of Hysteresis Phenomenon Based on the Elemental Operator and Wind-Rose
110
Method," in 21st International Conference on Electrical Machines and
111
Systems (ICEMS), 7-10 Oct. 2018, Jeju, South Korea, 2018.
112
40. S. Chavan, V. Saahil, A. Singh, and H. Himanshu, et al., "Application
113
of wind rose for wind turbine installation," International Conference on
114
Circuit ,Power and Computing Technologies (ICCPCT), 19 October
115
2017, Kollam, India, 2017.
116
41. A. Leporea, B. Palumbo, and A. Pievatolo, "A Bayesian approach for
117
site-specific wind rose prediction," Renewable Energy, vol. 150, pp.
118
691-702, 2020.
119
42. N. Arreyndip, and E. Joseph, "Small 500 kW onshore wind farm project
120
in Kribi, Cameroon: Sizing and checkers layout optimization model,"
121
Energy Reports, vol. 4, pp. 528-535, 2018.
122
43. "Local weather information of high quality worldwide for any point on
123
land or sea in the world ", Available: www.meteoblue.com [Accessed:
124
Nov. 23, 2020].
125
44. N. Javidtash, M. Jabbari, T. Niknam, and M. Nafar, "A novel mixture
126
of non-dominated sorting genetic algorithm and fuzzy method to multiobjective placement of distributed generations in Microgrids," Journal
127
of Intelligent and Fuzzy Systems, vol. 33, no.4, pp. 2577-2584, 2017.
128
45. "International Handball Federation," Available: www.ihf.info [Accessed:
129
Nov. 23, 2020].
130
46. E. Almabsout, R. El-Sehiemy, O. Nuri, and O. Bayat, "A Hybrid Local
131
Search-Genetic Algorithm for Simultaneous Placement of DG Units
132
and Shunt Capacitors in Radial Distribution Systems," IEEE Access,
133
vol. 8, pp. 54465 - 54481, 2020.
134
47. H. Doagoo-Mojarrad, G.B. Gharepetian, H. Rastegar, J. Olamaei, "Optimal placement and sizing of DG (distributed generation) units in
135
distribution networks by novel hybrid evolutionary algorithm," Energy,
136
vol. 54, pp. 129-138, 2013.
137
48. T. Niknam, I. Taheri, J. Aghaei, S. Tabatabaei, M. Nayeripour, "A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources," Applied Energy, vol. 88, no.12,
138
PP. 4818-4821, 2011.
139
49. C.A.S. Coello, G.T. Pulidio, and M.S. Salazar, "Handling Multiple Objectives With Particle Swarm Optimization," IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256-268, 2004.
140
ORIGINAL_ARTICLE
Optimal reactive power dispatch problem: A comprehensive study on meta-heuristic algorithms
The main mission of modern power systems is to supply the load in the most economical and reliable methods. One of the most challenging issues in this regard is the Optimal Reactive Power Dispatch (ORPD), since the crucial focus of planning and operation studies is mainly on only supplying the active power. The primary purpose of the ORPD issue, as a complex and nonlinear problem, is to identify the relevant control variables to minimize some objective functions, i.e. active power losses considering the system constraints. As the literature review shows, the application of meta-heuristic techniques to find the optimal solution to the ORPD problem is of great importance in this field. This paper, as a comparative case study, attempts to investigate the capability of some powerful meta-heuristic optimization algorithms to tackle the ORPD problem. The control variables are the generated power by the power plants, the voltage magnitude of PV buses, the installed capacity of parallel capacitors, and on-load transformer tap changers. All the simulations were implemented on the two case study systems, including the IEEE 30-, and 57-buses. The applied meta-heuristic algorithms to the problem are Orthogonal Crossover based Differential Evolution (OXDE), Hybrid Grey Wolf Optimization, and Particle Swarm Optimization Algorithm (HGWO-PSO), Sine Cosine Algorithm (SCA), and Hybrid PSO and Genetic Algorithm (HPSO-GA).
https://www.jemat.org/article_119626_6f907d8bb4d77665c395a0c7d5da1be2.pdf
2021-09-01
67
77
10.22109/jemt.2020.241172.1251
Optimal Reactive Power Dispatch
Optimization techniques
reactive power
meta-heuristic algorithms
Hamdi
Abdi
hamdiabdi@gmail.com
1
Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran
AUTHOR
Mansour
Moradi
mansour.moradi.ir@gmail.com
2
Young Researchers and Elite Club, Islamic Azad University, Kermanshah Branch, Kermanshah, Iran.
LEAD_AUTHOR
Reza
Asadi
rezaasadi268@yahoo.com
3
Department of Electrical Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran.
AUTHOR
Soheil
Naderi
s.naderi69@ut.ac.ir
4
School of Electrical and Computer Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran.
AUTHOR
Bahman
Amirian
bahman_amirian@ghrec.co.ir
5
West Regional Electric Company, Kermanshah, Iran.
AUTHOR
Farhad
Karimi
farhad70karimi1412@gmail.com
6
Department of Electrical Engineering, Faculty of Technical and Vocational, Islamabad-e Gharb Branch, Technical and Vocational University (TVU), Kermanshah, Iran.
AUTHOR
1. L. Shi, C. Wang, L. Yao, Y. Ni, and M. Bazargan, "Optimal power flow
1
solution incorporating wind power," IEEE Systems Journal, vol. 6, no.
2
2, pp. 233-241, 2011.
3
2. G. Chen, L. Liu, P. Song, and Y. Du, "Chaotic improved PSO-based
4
multi-objective optimization for minimization of power losses and L
5
index in power systems," Energy Conversion and Management, vol.
6
86, pp. 548-560, 2014.
7
3. B. Zhao, C. Guo, and Y. Cao, "A multiagent-based particle swarm
8
optimization approach for optimal reactive power dispatch," IEEE
9
transactions on power systems, vol. 20, no. 2, pp. 1070-1078, 2005.
10
4. A. M. Shaheen, S. R. Spea, S. M. Farrag, and M. A. Abido, "A review
11
of meta-heuristic algorithms for reactive power planning problem," Ain
12
Shams Engineering Journal, vol. 9, no. 2, pp. 215-231, 2018.
13
5. D. Chattopadhyay, K. Bhattacharya, and J. Parikh, "Optimal reactive
14
power planning and its spot-pricing: an integrated approach," IEEE
15
Transactions on Power systems, vol. 10, no. 4, pp. 2014-2020, 1995.
16
6. S.-S. Lin and S.-C. Horng, "Iterative simulation optimization approach
17
for optimal volt-ampere reactive sources planning," International
18
Journal of Electrical Power & Energy Systems, vol. 43, no. 1, pp.
19
984-991, 2012.
20
7. A. Mahmoudabadi and M. Rashidinejad, "An application of hybrid
21
heuristic method to solve concurrent transmission network expansion
22
and reactive power planning," International Journal of Electrical Power
23
& Energy Systems, vol. 45, no. 1, pp. 71-77, 2013.
24
8. H. Liu, V. Krishnan, J. D. McCalley, and A. Chowdhury, "Optimal
25
planning of static and dynamic reactive power resources," IET
26
Generation, Transmission & Distribution, vol. 8, no. 12, pp. 1916-1927,
27
9. A. L. Ara, A. Kazemi, S. Gahramani, and M. Behshad, "Optimal
28
reactive power flow using multi-objective mathematical programming,"
29
Scientia Iranica, vol. 19, no. 6, pp. 1829-1836, 2012.
30
10. J. C. Lopez, J. Contreras, J. I. Munoz, and J. Mantovani, "A multi-stage
31
stochastic non-linear model for reactive power planning under
32
contingencies," IEEE Transactions on Power Systems, vol. 28, no. 2,
33
pp. 1503-1514, 2012.
34
11. R. A. Jabr, N. Martins, B. C. Pal, and S. Karaki, "Contingency constrained VAr planning using penalty successive conic programming,"
35
IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 545-553, 2011.
36
12. E. Naderi, M. Pourakbari-Kasmaei, and H. Abdi, "An efficient particle
37
swarm optimization algorithm to solve optimal power flow problem
38
integrated with FACTS devices," Applied Soft Computing, vol. 80, pp.
39
243-262, 2019.
40
13. B. S. Boroujeni, S. M. S. Boroujeni, and A. Memaripour, "Reactive
41
power expansion planning under a deregulated market power system,"
42
Research Journal of Applied Sciences, Engineering and Technology,
43
vol. 4, no. 19, pp. 3755-3759, 2012.
44
14. B. B. Pal, P. Biswas, and A. Mukhopadhyay, "GA based FGP approach
45
for optimal reactive power dispatch," Procedia Technology, vol. 10, pp.
46
464-473, 2013.
47
15. K. Vadivelu and G. Marutheswar, "Soft computing technique based
48
reactive power planning using NVSI," Journal of Electrical Systems,
49
vol. 11, no. 1, pp. 89-101, 2015.
50
16. M. Abdelmoumene, M. Belkacemi, and A. Boubakeur, "Optimal
51
reactive power dispatch using differential evolution algorithm with
52
voltage profile control," International Journal of Intelligent Systems and
53
Applications, vol. 5, no. 10, p. 28, 2013.
54
17. H. R. Shahbazi and M. Kalantar, "The optimal reactive power
55
dispatch using seeker optimization algorithm based different objective
56
functions," Journal of Basic and Applied Scientific Research (JBASR),
57
vol. 3, no. 8, pp. 232-240, 2013.
58
18. S. Padaiyatchi and M. Daniel, "OPF-based reactive power planning and
59
voltage stability limit improvement under single line outage contingency
60
condition through evolutionary algorithms," Turkish Journal of Electrical
61
Engineering & Computer Sciences, vol. 21, no. 4, pp. 1092-1106, 2013.
62
19. A. Abou El-Ela, A. Kinawy, R. El-Sehiemy, and M. Mouwafi, "Optimal
63
reactive power dispatch using ant colony optimization algorithm,"
64
Electrical Engineering, vol. 93, no. 2, pp. 103-116, 2011.
65
20. R. Hemmati, R.-A. Hooshmand, and A. Khodabakhshian, "Market
66
based transmission expansion and reactive power planning with
67
consideration of wind and load uncertainties," Renewable and
68
Sustainable Energy Reviews, vol. 29, pp. 1-10, 2014.
69
21. M. Ghasemi, M. Taghizadeh, S. Ghavidel, J. Aghaei, and A. Abbasian, "Solving optimal reactive power dispatch problem using a
70
novel teaching–learning-based optimization algorithm," Engineering
71
Applications of Artificial Intelligence, vol. 39, pp. 100-108, 2015.
72
22. M. De and S. K. Goswami, "Optimal reactive power procurement with
73
voltage stability consideration in deregulated power system," IEEE
74
transactions on power systems, vol. 29, no. 5, pp. 2078-2086, 2014.
75
23. B. Shaw, V. Mukherjee, and S. Ghoshal, "Solution of reactive power
76
dispatch of power systems by an opposition-based gravitational search
77
algorithm," International Journal of Electrical Power & Energy Systems,
78
vol. 55, pp. 29-40, 2014.
79
24. M. Ghasemi, M. M. Ghanbarian, S. Ghavidel, S. Rahmani, and E.
80
M. Moghaddam, "Modified teaching learning algorithm and double
81
differential evolution algorithm for optimal reactive power dispatch
82
problem: a comparative study," Information Sciences, vol. 278, pp.
83
231-249, 2014.
84
25. S. Dutta, P. K. Roy, and D. Nandi, "Optimal location of STATCOM using
85
chemical reaction optimization for reactive power dispatch problem,"
86
Ain Shams Engineering Journal, vol. 7, no. 1, pp. 233-247, 2016.
87
26. K. Lenin, B. R. Reddy, and M. S. Kalavathi, "Water cycle algorithm for
88
solving optimal reactive power dispatch problem," J Eng Technol Res,
89
vol. 2, no. 2, pp. 1-11, 2014.
90
27. R. N. S. Mei, M. H. Sulaiman, Z. Mustaffa, and H. Daniyal, "Optimal
91
reactive power dispatch solution by loss minimization using moth-flame
92
optimization technique," Applied Soft Computing, vol. 59, pp. 210-222,
93
28. T. T. Nguyen and D. N. Vo, "Improved social spider optimization
94
algorithm for optimal reactive power dispatch problem with different
95
objectives," Neural Computing and Applications, pp. 1-32, 2019.
96
29. E. Davoodi, E. Babaei, B. Mohammadi-Ivatloo, and M. Rasouli, "A
97
novel fast semidefinite programming-based approach for optimal
98
reactive power dispatch," IEEE Transactions on Industrial Informatics,
99
vol. 16, no. 1, pp. 288-298, 2019.
100
30. C. Bingane, M. F. Anjos, and S. Le Digabel, "Tight-and-cheap conic
101
relaxation for the optimal reactive power dispatch problem," IEEE
102
Transactions on Power Systems, vol. 34, no. 6, pp. 4684-4693, 2019.
103
31. T. T. Nguyen, D. N. Vo, H. Van Tran, and L. Van Dai, "Optimal dispatch
104
of reactive power using modified stochastic fractal search algorithm,"
105
Complexity, vol. 2019, 2019.
106
32. Z. Li, Y. Cao, L. V. Dai, X. Yang, and T. T. Nguyen, "Finding solutions for
107
optimal reactive power dispatch problem by a novel improved antlion
108
optimization algorithm," Energies, vol. 12, no. 15, p. 2968, 2019.
109
33. K. ben oualid Medani, S. Sayah, and A. Bekrar, "Whale optimization
110
algorithm based optimal reactive power dispatch: A case study of the
111
Algerian power system," Electric Power Systems Research, vol. 163,
112
pp. 696-705, 2018.
113
34. E. Yalçın, M. C. Taplamacıoglu, and E. Çam, "The adaptive chaotic ˘
114
symbiotic organisms search algorithm proposal for optimal reactive
115
power dispatch problem in power systems," Electrica, vol. 19, no. 1, pp.
116
37-47, 2019.
117
35. J. Radosavljevic, M. Jevti ´ c, and M. Milovanovi ´ c, "A solution to ´
118
the ORPD problem and critical analysis of the results," Electrical
119
Engineering, vol. 100, no. 1, pp. 253-265, 2018.
120
36. A. A. Heidari, R. A. Abbaspour, and A. R. Jordehi, "Gaussian
121
bare-bones water cycle algorithm for optimal reactive power dispatch
122
in electrical power systems," Applied Soft Computing, vol. 57, pp.
123
657-671, 2017.
124
37. Y. Muhammad, R. Khan, F. Ullah, A. ur Rehman, M. S. Aslam, and
125
M. A. Z. Raja, "Design of fractional swarming strategy for solution of
126
optimal reactive power dispatch," Neural Computing and Applications,
127
pp. 1-18, 2019.
128
38. Y. Amrane, M. Boudour, and M. Belazzoug, "A new optimal reactive
129
power planning based on differential search algorithm," International
130
Journal of Electrical Power & Energy Systems, vol. 64, pp. 551-561,
131
39. Y. Wang, Z. Cai, and Q. Zhang, "Enhancing the search ability of
132
differential evolution through orthogonal crossover," Information
133
Sciences, vol. 185, no. 1, pp. 153-177, 2012.
134
40. L. Goel, "An extensive review of computational intelligence-based
135
optimization algorithms: trends and applications," SOFT COMPUTING,
136
41. K. Vadivelu and G. Marutheswar, "Fast voltage stability index based
137
optimal reactive power planning using differential evolution," Electrical
138
and Electronics Engineering: An International Journal (ELELIJ), vol. 3,
139
no. 1, pp. 51-60, 2014.
140
42. B. Bhattacharyya and S. Kumar, "Reactive power planning with FACTS
141
devices using gravitational search algorithm," Ain Shams Engineering
142
Journal, vol. 6, no. 3, pp. 865-871, 2015.
143
43. R. D. Zimmerman, C. E. Murillo-Sánchez, and D. Gan, "MATPOWER: A
144
MATLAB power system simulation package," Manual, Power Systems
145
Engineering Research Center, Ithaca NY, vol. 1, 1997.
146
44. M. Ghasemi, S. Ghavidel, M. M. Ghanbarian, and A. Habibi, "A new
147
hybrid algorithm for optimal reactive power dispatch problem with
148
discrete and continuous control variables," Applied soft computing, vol.
149
22, pp. 126-140, 2014.
150
45. M. Agarwal and G. M. S. Srivastava, "Genetic algorithm-enabled
151
particle swarm optimization (PSOGA)-based task scheduling in
152
cloud computing environment," International Journal of Information
153
Technology & Decision Making, vol. 17, no. 04, pp. 1237-1267, 2018.
154
46. S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances
155
in engineering software, vol. 69, pp. 46-61, 2014.
156
47. "Available at: https://www.mathworks.com/matlabcentral/fileexchange/68776-
157
hybrid-gwopso-optimization."
158
48. S. Mirjalili, "SCA: a sine cosine algorithm for solving optimization
159
problems," Knowledge-based systems, vol. 96, pp. 120-133, 2016.
160
49. A. Khazali and M. Kalantar, "Optimal reactive power dispatch based on
161
harmony search algorithm," International Journal of Electrical Power &
162
Energy Systems, vol. 33, no. 3, pp. 684-692, 2011.
163
50. M. H. Sulaiman, Z. Mustaffa, M. R. Mohamed, and O. Aliman, "Using
164
the gray wolf optimizer for solving optimal reactive power dispatch
165
problem," Applied Soft Computing, vol. 32, pp. 286-292, 2015.
166
51. S. Jeyadevi, S. Baskar, C. Babulal, and M. W. Iruthayarajan, "Solving
167
multiobjective optimal reactive power dispatch using modified NSGAII," International Journal of Electrical Power & Energy Systems, vol. 33,
168
no. 2, pp. 219-228, 2011.
169
52. M. Varadarajan and K. Swarup, "Differential evolution approach for
170
optimal reactive power dispatch," Applied soft computing, vol. 8, no. 4,
171
pp. 1549-1561, 2008.
172
53. A. Khorsandi, A. Alimardani, B. Vahidi, and S. Hosseinian, "Hybrid
173
shuffled frog leaping algorithm and Nelder–Mead simplex search for
174
optimal reactive power dispatch," IET generation, transmission &
175
distribution, vol. 5, no. 2, pp. 249-256, 2011.
176
54. H. Abdi, "Profit-based unit commitment problem: A review of models,
177
methods, challenges, and future directions," Renewable and Sustainable Energy Reviews, p. 110504, 2020.
178