In this paper, the grid-connected operation of microgrid is investigated where the microgrid can exchange power with the main grid. The proposed problem is modeled as the mixed-integer linear programming (MILP) and is solved by an evolutionary algorithm known as the teaching learning-based optimization (TLBO). Finally, the proposed model is tested on a modified IEEE 33 bus test system to show the performance of the method

Bitaraf, Mohammad

Jamaledini, Ashkan

Khazaei, Ehsan

Munich Personal RePEc Archive

MPRA
Munich Personal RePEc Archive
Solving the Grid-Connected Microgrid
Operation by Teaching Learning Based
Optimization Algorithm
Ashkan Jamaledini and Ehsan Khazaei and Mohammad
Bitaraf
Sazeh Sazan Power Company
2 June 2019
Online at https://mpra.ub.uni-muenchen.de/94276/
MPRA Paper No. 94276, posted 5 June 2019 21:06 UTC
Solving the Grid-Connected Microgrid 
Operation by Teaching Learning Based 
Optimization Algorithm 
 
Ashkan Jamaledini, Ehsan Khazaei, Mohammad Hassan Bitaraf 
Electrical and Computer Engineering Department, Sazeh Sazan Power Company, Iran 
 
Abstract: In this paper, the grid-connected operation of microgrid is investigated where 
the microgrid can exchange power with the main grid. The proposed problem is 
modeled as the mixed-integer linear programming (MILP) and is solved by an 
evolutionary algorithm known as the teaching learning-based optimization (TLBO). 
Finally, the proposed model is tested on a modified IEEE 33 bus test system to show 
the performance of the method. 
Keywords: Microgrid, TLBO, Optimization, Grid-connected operation 
I. INTRODUCTION 
 
GGREATION of loads and distributed energy systems in a small network which is 
close to the consumers, known as the microgrid [1-5]. Such a network has some 
significant benefits from two main perspectives: i) technical, ii) economic. This is due to 
closeness to the consumers, less transmission line, higher reliability, higher resiliency, and 
higher power quality [6-7].  
Although there exist lots of benefits, the challenges in protection and operation are not 
addressed completely yet. This paper addressed one of them, which is the optimal 
operation of the grid-connected operation of the microgrid. Islanded and grid-connected 
A 
operation of microgrid are investigated in [6]. Same authors designed a DC/DC boost 
converter for the grid-connected operation of the microgrid in [7]. In [8-11] microgrid in the 
grid-connected mode is solved by several heuristic methods such as CDOA, GA, and 
PSO. The control of grid-connected and islanded microgrid is widely investigated in [12-
15]. Adding the electric vehicles, as well as the security of the microgrid is investigated in 
[16- 19]. Despite the above works, this research studied the effective operation of the grid-
connected microgrid, utilizing a new evolutionary technique known as the teaching 
learning-based optimization algorithm [20].  
II. MATHEMATICAL FORMULATIONS 
This optimization problem contains an objective as (1). 
A. Objective function 
The objective is   
𝑚𝑖𝑛∑ [𝐶𝑖𝑃𝑖𝑡𝐼𝑖𝑡 + 𝑆𝑈𝑖𝑡 + 𝑆𝐷𝑖𝑡]∀𝑖                                                                                                                          (1) 
In which I is a binary variable (0 or 1) that control the status of unit i at time t. Moreover, 
SU and SD are the startup and shutdown costs.  In the rest of the paper, 𝑈𝑇 and 𝐷𝑇 are 
minimum up and down, 𝑇(𝑜𝑛) and 𝑇(𝑜𝑓𝑓) are the number of successive on and off hours, 
and RU and RD are the ramp up and down of the generators.                                    
 
B. Constraints 
The constraints of the problem are defined as  
1. The capacity of the generators  
𝑃𝑖𝑡,𝑚𝑖𝑛 ≤ 𝑃𝑖𝑡 ≤ 𝑃𝑖𝑡,𝑚𝑎𝑥                                                                                                                        (2) 
2. Ramp up and down of the generators 
𝑃𝑖𝑡 − 𝑃𝑖(𝑡−1) ≤ 𝑅𝑈𝑖                                                                                                                           (3) 
𝑃𝑖(𝑡−1) − 𝑃𝑖𝑡 ≤ 𝑅𝐷𝑖                                                                                                                            (4) 
3. Minimum up and down of the generators 
𝑇(𝑜𝑛)𝑖𝑡 ≥ 𝑈𝑇𝑖(𝐼𝑖𝑡 − 𝐼𝑖(𝑡−1)                                                                                                                 (5) 
𝑇(𝑜𝑓𝑓)𝑖𝑡 ≥ 𝐷𝑇𝑖(𝐼𝑖(𝑡−1) − 𝐼𝑖𝑡                                                                                                                (6) 
III. TEACHING LEARNING BASED OPTIMIZATION ALGORITHM 
As mentioned, microgrid in the grid-connected mode is a MILP problem. This paper 
suggested teaching learning-based optimization (TLBO) algorithm to solve the problem. 
This method inspires the students who are in a class and try to learn the course by 
difference methods. More explanation is in [20]. The flowchart of TLBO is shown in Fig. 
1. Overall, heuristic methods are attracted to many considerations because of the fast 
and precise response [21-24].  
 
 
Fig. 1. Flowchart of the proposed method [20]. 
 
IV. RESULTS 
To shows, the perfect performance of the mode, a modified IEEE 30 bus test network 
is tested which includes three generation units. Fig. 2 shows the single line diagram of the 
proposed network. Also, the features of the dispatchable units are shown in Table I. The 
load demand for the next 24 hours has been shown in Fig. 3. 
 Fig. 2. Single line model 
 
 
 
 
Table I 
Dispatchable unit’s features 
 
 Minimum output power Maximum output power 
Unit 1 at bus 25 20 100 
Unit 2 at bus 33 40 50 
Unit 3 at bus 18 1 25 
  
 
 
 
 
 
Fig. 3. Load demand for the next 24 hours 
 
DG1
DG2
DG3
Main
Grid
 
The output power of dispatchable units is shown in Fig. 4. According to the figure, the 
cheapest unit (unit 1) is more participated than other units; that the decision only is based 
on the economic. 
 
 
Fig. 4. Generation units output power 
 
The comparison of the cost between the proposed method and other well-known methods 
such as the particle swarm optimization (PSO) and genetic algorithm (GA) methods is 
investigated in Table II. 
 
Table II 
Cost of operation for several methods 
 Operation cost ($) Computational Time (second)  
PSO 4942.1 17.1 
GA 4839.5 14.9 
Proposed method 4747.7 12.8 
 
 
V. CONCLUSION 
In this paper, the TLBO is developed for optimal operation of grid-connected microgrid. 
The microgrid could exchange power in the grid-connected mode. The results show that 
the proposed method has a very fast response and also lower operation cost, comparing 
to the well-known methods such as PSO and GA.  
  
 
 
Reference 
[1] Dabbaghjamanesh, M., A. Moeini, M. Ashkaboosi, P. Khazaei, and K. Mirzapalangi. 
"High Performance Control of Grid Connected Cascaded H-Bridge Active Rectifier 
Based on Type II-Fuzzy Logic Controller with Low Frequency Modulation 
Technique." International Journal of Electrical & Computer Engineering (2088-
8708) 6, no. 2 (2016). 
[2] Ashkaboosi, Maryam, Seyed Mehdi Nourani, Peyman Khazaei, Morteza 
Dabbaghjamanesh, and Amirhossein Moeini. "An optimization technique based on 
profit of investment and market clearing in wind power systems." American Journal of 
Electrical and Electronic Engineering 4, no. 3 (2016): 85-91. 
[3] A. Azarang, H. E. Manoochehri, and N. Kehtarnavaz, "Convolutional autoencoder-
based multispectral image fusion," in IEEE Access, vol. 7, pp.35673-35683, Apr. 
2019. 
[4] A. Azarang, S. Kamaei, M. Miri, and M.H. Asemani, "A new fractional-order chaotic 
system and its synchronization via Lyapunov and improved Laplacian-based method," 
in Optik, vol. 127, no. 24, pp.11717-11731, Dec. 2016. 
[5] Pourbehzadi, Motahareh, Taher Niknam, Jamshid Aghaei, Geev Mokryani, Miadreza 
Shafie-khah, and João PS Catalão. "Optimal operation of hybrid AC/DC microgrids 
under uncertainty of renewable energy resources: A comprehensive 
review." International Journal of Electrical Power & Energy Systems 109 (2019): 139-
159. 
[6] Rakhshan, Mohsen, Navid Vafamand, Mokhtar Shasadeghi, Morteza 
Dabbaghjamanesh, and Amirhossein Moeini. "Design of networked polynomial control 
systems with random delays: sum of squares approach." International Journal of 
Automation and Control 10, no. 1 (2016): 73-86. 
[7] Khazaei, P., S. M. Modares, M. Dabbaghjamanesh, M. Almousa, and A. Moeini. "A 
high efficiency DC/DC boost converter for photovoltaic applications." International 
Journal of Soft Computing and Engineering (IJSCE) 6, no. 2 (2016): 2231-2307. 
[8] Khazaei, Peyman, Morteza Dabbaghjamanesh, Ali Kalantarzadeh, and Hasan 
Mousavi. "Applying the modified TLBO algorithm to solve the unit commitment 
problem." In 2016 World Automation Congress (WAC), pp. 1-6. IEEE, 2016. 
[9] Dabbaghjamanesh, Morteza, Abdollah Kavousi-Fard, and Shahab Mehraeen. 
"Effective scheduling of reconfigurable microgrids with dynamic thermal line 
rating." IEEE Transactions on Industrial Electronics 66, no. 2 (2019): 1552-1564. 
[10] Dabbaghjamanesh, Morteza, Shahab Mehraeen, Abdollah Kavousifard, and 
Mosayeb Afshari Igder. "Effective scheduling operation of coordinated and 
uncoordinated wind-hydro and pumped-storage in generation units with modified 
JAYA algorithm." In 2017 IEEE Industry Applications Society Annual Meeting, pp. 1-
8. IEEE, 2017. 
[11] Dabbaghjamanesh, Morteza, Shahab Mehraeen, Abdollah Kavousi-Fard, and 
Farzad Ferdowsi. "A New Efficient Stochastic Energy Management Technique for 
Interconnected AC Microgrids." In 2018 IEEE Power & Energy Society General 
Meeting (PESGM), pp. 1-5. IEEE, 2018. 
[12] Jiang, Quanyuan, Meidong Xue, and Guangchao Geng. "Energy management of 
microgrid in grid-connected and stand-alone modes." IEEE transactions on power 
systems 28, no. 3 (2013): 3380-3389. 
[13] Najy, Waleed KA, Hatem H. Zeineldin, and Wei Lee Woon. "Optimal protection 
coordination for microgrids with grid-connected and islanded capability." IEEE 
Transactions on industrial electronics 60, no. 4 (2012): 1668-1677. 
[14] Al-Saedi, Waleed, Stefan W. Lachowicz, Daryoush Habibi, and Octavian Bass. 
"Power flow control in grid-connected microgrid operation using Particle Swarm 
Optimization under variable load conditions." International Journal of Electrical Power 
& Energy Systems 49 (2013): 76-85. 
[15] Savaghebi, Mehdi, Juan C. Vasquez, Alireza Jalilian, Josep M. Guerrero, and Tzung-
Lin Lee. "Selective compensation of voltage harmonics in grid-connected microgrids." 
Mathematics and Computers in Simulation 91 (2013): 211-228. 
[16] Taherzadeh, Erfan, Morteza Dabbaghjamanesh, Mohsen Gitizadeh, and Akbar 
Rahideh. "A new efficient fuel optimization in blended charge depletion/charge 
sustenance control strategy for plug-in hybrid electric vehicles." IEEE Transactions on 
Intelligent Vehicles 3, no. 3 (2018): 374-383. 
[17] Tajalli, Seyede Zahra, Seyed Ali Mohammad Tajalli, Abdollah Kavousi-Fard, Taher 
Niknam, Morteza Dabbaghjamanesh, and Shahab Mehraeen. "A Secure Distributed 
Cloud-Fog Based Framework for Economic Operation of Microgrids." In 2019 IEEE 
Texas Power and Energy Conference (TPEC), pp. 1-6. IEEE, 2019. 
[18] Dabbaghjamanesh, Morteza. "Stochastic Energy Management of Reconfigurable 
Power Grids in the Presence of Renewable Energy by Considering Practical 
Limitations." (2019). 
[19] Taherzadeh, Erfan, Shahram Javadi, and Morteza Dabbaghjamanesh. "New Optimal 
Power Management Strategy for Series Plug-In Hybrid Electric 
Vehicles." International Journal of Automotive Technology 19, no. 6 (2018): 1061-
1069. 
[20] Rao, Ravipudi V., Vimal J. Savsani, and D. P. Vakharia. "Teaching–learning-based 
optimization: a novel method for constrained mechanical design optimization 
problems." Computer-Aided Design 43, no. 3 (2011): 303-315. 
[21] Razavi Arab, Azadeh, S. Abbas Haghshenas, and Farzin Samsami. "Sediment 
transport and morphodynamic changes in Ziarat Estuary and Mond River Delta, the 
Persian Gulf." In EGU General Assembly Conference Abstracts, vol. 16. 2014. 
[22] HAGHSHENAS, SEYED ABBAS, and ARAB AZADEH RAZAVI. "APPLICATION OF 
SEDIMENT CONSTITUENT ANALYSIS FOR CHARACTERIZING LONGSHORE 
SEDIMENT TRANSPORT, CASE STUDY OF RAMIN PORT-IRANIAN COASTLINE 
OF THE OMAN SEA." (2014). 
[23] Arab, Azadeh Razavi, Afshin Danehkar, S. Abbas Haghshenas, and Gita B. 
Ebrahimi. "ASSESSMENT OF COASTAL DEVELOPMENT IMPACTS ON CORAL 
ECOSYSTEMS IN NAIBAND BAY, THE PERSIAN GULF." Coastal Engineering 
Proceedings 1, no. 33 (2012): 31. 
[24] Haghshenas, S. Abbas, Azadeh Razavi Arab, Arash Bakhtiari, Morteza Jedari Attari, 
and Michael John Risk. "Decadal Evolution of Mond River Delta, the Persian 
Gulf." Journal of Coastal Research 75, no. sp1 (2016): 805-810. 
[25] Zhang, Yan, Fanlin Meng, Rui Wang, Behzad Kazemtabrizi, and Jianmai Shi. 
"Uncertainty-resistant Stochastic MPC Approach for Optimal Operation of CHP 
Microgrid." Energy(2019). 
[26] Chen, Yahong, Changhong Deng, Weiwei Yao, Ning Liang, Pei Xia, Peng Cao, 
Yiwang Dong et al. "Impacts of stochastic forecast errors of renewable energy 
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(2019): 442-461. 
[27] A. Azarang, M. Miri, S. Kamaei, and M.H. Asemani, "Nonfragile fuzzy output feedback 
synchronization of a new chaotic system: design and implementation," in Journal of 
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Solving the Grid-Connected Microgrid Operation by Teaching Learning Based Optimization Algorithm

Munich RePEc Personal Archive

Munich Personal RePEc ArchiveSolving the Grid-Connected MicrogridOperation by Teaching Learning BasedOptimization AlgorithmJamaledini, Ashkan and Khazaei, Ehsan and Bitaraf,MohammadSazeh Sazan Power Company2 June 2019Online at https://mpra.ub.uni-muenchen.de/94276/MPRA Paper No. 94276, posted 05 Jun 2019 21:06 UTCSolving the Grid-Connected Microgrid Operation by Teaching Learning Based Optimization Algorithm  Ashkan Jamaledini, Ehsan Khazaei, Mohammad Hassan Bitaraf Electrical and Computer Engineering Department, Sazeh Sazan Power Company, Iran  Abstract: In this paper, the grid-connected operation of microgrid is investigated where the microgrid can exchange power with the main grid. The proposed problem is modeled as the mixed-integer linear programming (MILP) and is solved by an evolutionary algorithm known as the teaching learning-based optimization (TLBO). Finally, the proposed model is tested on a modified IEEE 33 bus test system to show the performance of the method. Keywords: Microgrid, TLBO, Optimization, Grid-connected operation I. INTRODUCTION  GGREATION of loads and distributed energy systems in a small network which is close to the consumers, known as the microgrid [1-5]. Such a network has some significant benefits from two main perspectives: i) technical, ii) economic. This is due to closeness to the consumers, less transmission line, higher reliability, higher resiliency, and higher power quality [6-7].  Although there exist lots of benefits, the challenges in protection and operation are not addressed completely yet. This paper addressed one of them, which is the optimal operation of the grid-connected operation of the microgrid. Islanded and grid-connected A operation of microgrid are investigated in [6]. Same authors designed a DC/DC boost converter for the grid-connected operation of the microgrid in [7]. In [8-11] microgrid in the grid-connected mode is solved by several heuristic methods such as CDOA, GA, and PSO. The control of grid-connected and islanded microgrid is widely investigated in [12-15]. Adding the electric vehicles, as well as the security of the microgrid is investigated in [16- 19]. Despite the above works, this research studied the effective operation of the grid-connected microgrid, utilizing a new evolutionary technique known as the teaching learning-based optimization algorithm [20].  II. MATHEMATICAL FORMULATIONS This optimization problem contains an objective as (1). A. Objective function The objective is   𝑚𝑖𝑛∑ [𝐶𝑖𝑃𝑖𝑡𝐼𝑖𝑡 + 𝑆𝑈𝑖𝑡 + 𝑆𝐷𝑖𝑡]∀𝑖                                                                                                                          (1) In which I is a binary variable (0 or 1) that control the status of unit i at time t. Moreover, SU and SD are the startup and shutdown costs.  In the rest of the paper, 𝑈𝑇 and 𝐷𝑇 are minimum up and down, 𝑇(𝑜𝑛) and 𝑇(𝑜𝑓𝑓) are the number of successive on and off hours, and RU and RD are the ramp up and down of the generators.                                     B. Constraints The constraints of the problem are defined as  1. The capacity of the generators  𝑃𝑖𝑡,𝑚𝑖𝑛 ≤ 𝑃𝑖𝑡 ≤ 𝑃𝑖𝑡,𝑚𝑎𝑥                                                                                                                        (2) 2. Ramp up and down of the generators 𝑃𝑖𝑡 − 𝑃𝑖(𝑡−1) ≤ 𝑅𝑈𝑖                                                                                                                           (3) 𝑃𝑖(𝑡−1) − 𝑃𝑖𝑡 ≤ 𝑅𝐷𝑖                                                                                                                            (4) 3. Minimum up and down of the generators 𝑇(𝑜𝑛)𝑖𝑡 ≥ 𝑈𝑇𝑖(𝐼𝑖𝑡 − 𝐼𝑖(𝑡−1)                                                                                                                 (5) 𝑇(𝑜𝑓𝑓)𝑖𝑡 ≥ 𝐷𝑇𝑖(𝐼𝑖(𝑡−1) − 𝐼𝑖𝑡                                                                                                                (6) III. TEACHING LEARNING BASED OPTIMIZATION ALGORITHM As mentioned, microgrid in the grid-connected mode is a MILP problem. This paper suggested teaching learning-based optimization (TLBO) algorithm to solve the problem. This method inspires the students who are in a class and try to learn the course by difference methods. More explanation is in [20]. The flowchart of TLBO is shown in Fig. 1. Overall, heuristic methods are attracted to many considerations because of the fast and precise response [21-24].    Fig. 1. Flowchart of the proposed method [20].  IV. RESULTS To shows, the perfect performance of the mode, a modified IEEE 30 bus test network is tested which includes three generation units. Fig. 2 shows the single line diagram of the proposed network. Also, the features of the dispatchable units are shown in Table I. The load demand for the next 24 hours has been shown in Fig. 3.  Fig. 2. Single line model     Table I Dispatchable unit’s features   Minimum output power Maximum output power Unit 1 at bus 25 20 100 Unit 2 at bus 33 40 50 Unit 3 at bus 18 1 25        Fig. 3. Load demand for the next 24 hours  DG1DG2DG3MainGrid The output power of dispatchable units is shown in Fig. 4. According to the figure, the cheapest unit (unit 1) is more participated than other units; that the decision only is based on the economic.   Fig. 4. Generation units output power  The comparison of the cost between the proposed method and other well-known methods such as the particle swarm optimization (PSO) and genetic algorithm (GA) methods is investigated in Table II.  Table II Cost of operation for several methods  Operation cost ($) Computational Time (second)  PSO 4942.1 17.1 GA 4839.5 14.9 Proposed method 4747.7 12.8   V. CONCLUSION In this paper, the TLBO is developed for optimal operation of grid-connected microgrid. The microgrid could exchange power in the grid-connected mode. The results show that the proposed method has a very fast response and also lower operation cost, comparing to the well-known methods such as PSO and GA.      Reference [1] Dabbaghjamanesh, M., A. Moeini, M. Ashkaboosi, P. Khazaei, and K. Mirzapalangi. "High Performance Control of Grid Connected Cascaded H-Bridge Active Rectifier Based on Type II-Fuzzy Logic Controller with Low Frequency Modulation Technique." International Journal of Electrical & Computer Engineering (2088-8708) 6, no. 2 (2016). [2] Ashkaboosi, Maryam, Seyed Mehdi Nourani, Peyman Khazaei, Morteza Dabbaghjamanesh, and Amirhossein Moeini. "An optimization technique based on profit of investment and market clearing in wind power systems." American Journal of Electrical and Electronic Engineering 4, no. 3 (2016): 85-91. [3] A. Azarang, H. E. Manoochehri, and N. Kehtarnavaz, "Convolutional autoencoder-based multispectral image fusion," in IEEE Access, vol. 7, pp.35673-35683, Apr. 2019. [4] A. Azarang, S. Kamaei, M. Miri, and M.H. Asemani, "A new fractional-order chaotic system and its synchronization via Lyapunov and improved Laplacian-based method," in Optik, vol. 127, no. 24, pp.11717-11731, Dec. 2016. [5] Pourbehzadi, Motahareh, Taher Niknam, Jamshid Aghaei, Geev Mokryani, Miadreza Shafie-khah, and João PS Catalão. "Optimal operation of hybrid AC/DC microgrids under uncertainty of renewable energy resources: A comprehensive review." International Journal of Electrical Power & Energy Systems 109 (2019): 139-159. [6] Rakhshan, Mohsen, Navid Vafamand, Mokhtar Shasadeghi, Morteza Dabbaghjamanesh, and Amirhossein Moeini. "Design of networked polynomial control systems with random delays: sum of squares approach." International Journal of Automation and Control 10, no. 1 (2016): 73-86. [7] Khazaei, P., S. M. Modares, M. Dabbaghjamanesh, M. Almousa, and A. Moeini. "A high efficiency DC/DC boost converter for photovoltaic applications." International Journal of Soft Computing and Engineering (IJSCE) 6, no. 2 (2016): 2231-2307. [8] Khazaei, Peyman, Morteza Dabbaghjamanesh, Ali Kalantarzadeh, and Hasan Mousavi. "Applying the modified TLBO algorithm to solve the unit commitment problem." In 2016 World Automation Congress (WAC), pp. 1-6. IEEE, 2016. [9] Dabbaghjamanesh, Morteza, Abdollah Kavousi-Fard, and Shahab Mehraeen. "Effective scheduling of reconfigurable microgrids with dynamic thermal line rating." IEEE Transactions on Industrial Electronics 66, no. 2 (2019): 1552-1564. [10] Dabbaghjamanesh, Morteza, Shahab Mehraeen, Abdollah Kavousifard, and Mosayeb Afshari Igder. "Effective scheduling operation of coordinated and uncoordinated wind-hydro and pumped-storage in generation units with modified JAYA algorithm." In 2017 IEEE Industry Applications Society Annual Meeting, pp. 1-8. IEEE, 2017. [11] Dabbaghjamanesh, Morteza, Shahab Mehraeen, Abdollah Kavousi-Fard, and Farzad Ferdowsi. "A New Efficient Stochastic Energy Management Technique for Interconnected AC Microgrids." In 2018 IEEE Power & Energy Society General Meeting (PESGM), pp. 1-5. IEEE, 2018. [12] Jiang, Quanyuan, Meidong Xue, and Guangchao Geng. "Energy management of microgrid in grid-connected and stand-alone modes." IEEE transactions on power systems 28, no. 3 (2013): 3380-3389. [13] Najy, Waleed KA, Hatem H. Zeineldin, and Wei Lee Woon. "Optimal protection coordination for microgrids with grid-connected and islanded capability." IEEE Transactions on industrial electronics 60, no. 4 (2012): 1668-1677. [14] Al-Saedi, Waleed, Stefan W. Lachowicz, Daryoush Habibi, and Octavian Bass. "Power flow control in grid-connected microgrid operation using Particle Swarm Optimization under variable load conditions." International Journal of Electrical Power & Energy Systems 49 (2013): 76-85. [15] Savaghebi, Mehdi, Juan C. Vasquez, Alireza Jalilian, Josep M. Guerrero, and Tzung-Lin Lee. "Selective compensation of voltage harmonics in grid-connected microgrids." Mathematics and Computers in Simulation 91 (2013): 211-228. [16] Taherzadeh, Erfan, Morteza Dabbaghjamanesh, Mohsen Gitizadeh, and Akbar Rahideh. "A new efficient fuel optimization in blended charge depletion/charge sustenance control strategy for plug-in hybrid electric vehicles." IEEE Transactions on Intelligent Vehicles 3, no. 3 (2018): 374-383. [17] Tajalli, Seyede Zahra, Seyed Ali Mohammad Tajalli, Abdollah Kavousi-Fard, Taher Niknam, Morteza Dabbaghjamanesh, and Shahab Mehraeen. "A Secure Distributed Cloud-Fog Based Framework for Economic Operation of Microgrids." In 2019 IEEE Texas Power and Energy Conference (TPEC), pp. 1-6. IEEE, 2019. [18] Dabbaghjamanesh, Morteza. "Stochastic Energy Management of Reconfigurable Power Grids in the Presence of Renewable Energy by Considering Practical Limitations." (2019). [19] Taherzadeh, Erfan, Shahram Javadi, and Morteza Dabbaghjamanesh. "New Optimal Power Management Strategy for Series Plug-In Hybrid Electric Vehicles." International Journal of Automotive Technology 19, no. 6 (2018): 1061-1069. [20] Rao, Ravipudi V., Vimal J. Savsani, and D. P. Vakharia. "Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems." Computer-Aided Design 43, no. 3 (2011): 303-315. [21] Razavi Arab, Azadeh, S. Abbas Haghshenas, and Farzin Samsami. "Sediment transport and morphodynamic changes in Ziarat Estuary and Mond River Delta, the Persian Gulf." In EGU General Assembly Conference Abstracts, vol. 16. 2014. [22] HAGHSHENAS, SEYED ABBAS, and ARAB AZADEH RAZAVI. "APPLICATION OF SEDIMENT CONSTITUENT ANALYSIS FOR CHARACTERIZING LONGSHORE SEDIMENT TRANSPORT, CASE STUDY OF RAMIN PORT-IRANIAN COASTLINE OF THE OMAN SEA." (2014). [23] Arab, Azadeh Razavi, Afshin Danehkar, S. Abbas Haghshenas, and Gita B. Ebrahimi. "ASSESSMENT OF COASTAL DEVELOPMENT IMPACTS ON CORAL ECOSYSTEMS IN NAIBAND BAY, THE PERSIAN GULF." Coastal Engineering Proceedings 1, no. 33 (2012): 31. [24] Haghshenas, S. Abbas, Azadeh Razavi Arab, Arash Bakhtiari, Morteza Jedari Attari, and Michael John Risk. 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Solving the Grid-Connected Microgrid Operation by Teaching Learning Based Optimization Algorithm

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