Islanded microgrid (MG) is a microgrid which is disconnected from the main network and therefore it is safe and has lead to one of the most important challenges in the power system operation. In the case of higher price in the market, the islanded MG offers a lower operational cost. On the other hand, the nonlinear attribute of optimal operation of the islanded MG has made difficulties in using such systems. To overcome this obstacle, a new heuristic method known as the Gravitational Emulation Local Search Algorithm (GELSA) is proposed in this paper in order to solve the problem.  The efficiency of the technique is examined on a modified IEEE 30 bus test network

Jamaledini, Ashkan

Khazaei, Ehsan

Soltani, Ali

Munich RePEc Personal Archive

Munich Personal RePEc ArchiveEconomic Self-Operation of MicrogridsBy Gravitational Emulation Local SearchAlgorithmKhazaei, Ehsan and Jamaledini, Ashkan and Soltani, AliElectrical and Computer Engineering Department, Sazeh SazanPower Company, Iran, Electrical and Computer EngineeringDepartment, Sazeh Sazan Power Company, Iran, Electrical andComputer Engineering Department, Sazeh Sazan Power Company,Iran2019Online at https://mpra.ub.uni-muenchen.de/95895/MPRA Paper No. 95895, posted 11 Sep 2019 05:41 UTCEconomic Self-Operation of Microgrids By Gravitational Emulation Local Search Algorithm  Ehsan Khazaei, Ashkan Jamaledini, Ali Soltani Electrical and Computer Engineering Department, Sazeh Sazan Power Company, Iran  Abstract: Islanded microgrid (MG) is a microgrid which is disconnected from the main network and therefore it is safe and has lead to one of the most important challenges in the power system operation. In the case of higher price in the market, the islanded MG offers a lower operational cost. On the other hand, the nonlinear attribute of optimal operation of the islanded MG has made difficulties in using such systems. To overcome this obstacle, a new heuristic method known as the Gravitational Emulation Local Search Algorithm (GELSA) is proposed in this paper in order to solve the problem.  The efficiency of the technique is examined on a modified IEEE 30 bus test network.  I. INTRODUCTION  Microgrid (MG) is the accumulation of the load and distributed energy resources in which the generators satisfy the power demand requested inside the small network. This capability leads to significant economic and technical advantages [1-5]. A few advantages of these systems involve closeness to costumers, less transmission lien, higher reliability, higher resiliency, lower operation cost, etc. Despite all the advantages, an important challenge is optimal energy management of islanded MG which is a complicated problem. Among advanced methods of optimization like Taguchi, grey, grey-fuzzy, and grey-adaptive neuro fuzzy inference system–integrated approach [6-9]. This paper presents a new technique for optimal energy management of islanded MG. In this study, the small grid is disconnected from the main network and consequently, the generation units only provide the load demand of the small network. As a result, in this mode, power exchange with the main grid is blocked.  A number of researchers have implemented evolutionary algorithms, e.g., CDOA, GA, PSO, and TLABO to study energy management of islanded MG [8-11]. Moreover, some literature has widely studied efficient control of MG operation [12-15]. References [16-20] have investigated utilization of electric vehicles (EVs), MG superconductivity, and MG security. Function of an islanded MG is a nonlinear problem and seems hard to solve. Another important factor in solving nonlinear problems is the convergence speed.  Therefore, in this paper a new method known as the gravitational emulation local search (GELS) algorithm is developed and presented in order to find a solution for nonlinear problem of optimization of MG systems [16-19].   II. MATHEMATICAL FORMULATIONS OF THE PROBLEM A. Objective function Objective function for this problem of minimizing the operational cost is defined as:  𝑚𝑖𝑛 ∑ [𝐶𝑖𝑃𝑖𝑡𝐼𝑖𝑡 + 𝑆𝑈𝑖𝑡 + 𝑆𝐷𝑖𝑡]∀𝑖                                                                                           (1) whsere: I is Binary variable {0,1} SU,SD are startup and shutdown costs   B. Constraints The constraint imposed on this problem are the following items. The limit on capacity of each  generation unit is: 𝑃𝑖𝑡,𝑚𝑖𝑛 ≤ 𝑃𝑖𝑡 ≤ 𝑃𝑖𝑡,𝑚𝑎𝑥                                                                                                        (2) Also the limit on ramp up and down of each generation unit are: 𝑃𝑖𝑡 − 𝑃𝑖(𝑡−1) ≤ 𝑅𝑈𝑖                                                                                                             (3) 𝑃𝑖(𝑡−1) − 𝑃𝑖𝑡 ≤ 𝑅𝐷𝑖                                                                                                             (4) where: 𝑅𝑈𝑖 , 𝑅𝑈𝑖  are ramp up and ramp down of the ith unit Finally, the limited up and down time of each generation unit is considered as: 𝑇(𝑜𝑛)𝑖𝑡 ≥ 𝑈𝑇𝑖(𝐼𝑖𝑡 − 𝐼𝑖(𝑡−1)                                                                                                   (5) 𝑇(𝑜𝑓𝑓)𝑖𝑡 ≥ 𝐷𝑇𝑖(𝐼𝑖(𝑡−1) − 𝐼𝑖𝑡                                                                                                  (6) where: 𝑈𝑇𝑖, 𝐷𝑇𝑖  are minimum up and down rates of the ith unit 𝑇(𝑜𝑛), 𝑇(𝑜𝑓𝑓)  are number of successive on and off hours                                    III. GRAVITATIONAL EMULATION LOCAL SEARCH ALGORITHM As mentioned, the proposed problem is a mixed-integer linear programming (MILP) problem, which is hard to solve by the mathematical models. Also, the optimal solution is not assuring by solving the problem by the mathematical approach. To this end, in the paper, a new evolutionary algorithm known as the gravitational emulation local search (GELS) a is adapted to solve the problem. More detail regarding this algorithm can be found in [20]. It should be noted that evolutionary methods are used widely in many fields because of the fast and precise response [21-23].  IV. RESULTS To show the economic merit of the proposed technique, the modified IEEE 30 bus test system is selected and tested, as shown in Fig. 1. The system has 3 distributed generators (DGs) as explained in Table I. Also, the 24-hours load demand is shown in Fig. 2.   Fig. 1. Single line model   Table I Features of Units  Minimum output power Maximum output power Unit 1 20 80 Unit 2 20 50 Unit 3 1 25     Fig. 2. 24-hour load demand  DG1DG2DG3The DGs output is shown in Fig. 3, where the output of the DGs are based on their economic price. That means cheapest units are more active than others.    Fig. 3. The output power of Units   Table II shows the total operation cost of the proposed problem under difference algorithms. The proposed technique has higher speed and lower operation cost.  Table II Operation cost   Operation cost ($) Computational Time (second)  PSO 5432.1 14.2 GA 5532.5 11.8 Proposed method 5114.7 8.7  V. CONCLUSION In this paper a new heurist technique, known as the GELSA has been developed for solving MILP microgrid operation. The results have been compare with the PSO and GA. Results prove the higher speed of the proposed method. Also, from the economic perspective, the proposed method has lower operation cost. This method can be easily adaptable in the power industrial grids as it has a very high speed and accuracy.    Reference  [1] Zhang, Yan, Fanlin Meng, Rui Wang, Behzad Kazemtabrizi, and Jianmai Shi. "Uncertainty-resistant Stochastic MPC Approach for Optimal Operation of CHP Microgrid." Energy(2019). [2] Chen, Yahong, Changhong Deng, Weiwei Yao, Ning Liang, Pei Xia, Peng Cao, Yiwang Dong et al. "Impacts of stochastic forecast errors of renewable energy generation and load demands on microgrid operation." Renewable Energy 133 (2019): 442-461. [3] 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. [4] Tsikalakis, Antonis G., and Nikos D. Hatziargyriou. "Centralized control for optimizing microgrids operation." In 2011 IEEE power and energy society general meeting, pp. 1-8. IEEE, 2011. [5] Dimeas, Aris L., and Nikos D. Hatziargyriou. "Operation of a multiagent system for microgrid control." IEEE Transactions on Power systems 20, no. 3 (2005): 1447-1455. [6] Reza Bagherian Azhiri, Jalal Fathi Sola, Ramin Mehdizad Tekiyeh, Farid Javidpour, Abolfaz  Salmani Bideskan, “Analyzing of joint strength, impact energy, and angular distortion of the ABS friction stir welded joints reinforced by nanosilica addition”, The International Journal of Advanced Manufacturing Technology, February 2019, Volume 100, Issue 9–12, pp 2269–2282. [7] Reza Bagherian Azhiri, Abolfazl Salmani Bideskan, Farid Javidpour, Ramin Mehdizad Tekiyeh, “Study on material removal rate, surface quality, and residual stress of AISI D2 tool steel in electrical discharge machining in presence of ultrasonic vibration effect”, The International Journal of Advanced Manufacturing Technology, April 2019, Volume 101, Issue 9–12, pp 2849–2860 [8] Olivares, Daniel E., Ali Mehrizi-Sani, Amir H. Etemadi, Claudio A. Cañizares, Reza Iravani, Mehrdad Kazerani, Amir H. Hajimiragha et al. "Trends in microgrid control." IEEE Transactions on smart grid 5, no. 4 (2014): 1905-1919. [9] Basak, Prasenjit, A. K. Saha, S. Chowdhury, and S. P. Chowdhury. "Microgrid: Control techniques and modeling." In 2009 44th International Universities Power Engineering Conference (UPEC), pp. 1-5. IEEE, 2009.  [10] 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. [11] Dabbaghjamanesh, Morteza. "Stochastic Energy Management of Reconfigurable Power Grids in the Presence of Renewable Energy by Considering Practical Limitations." (2019). [12] 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. [13] Sayed, Gehad Ismail, Aboul Ella Hassanien, and Ahmad Taher Azar. "Feature selection via a novel chaotic crow search algorithm." Neural Computing and Applications 31, no. 1 (2019): 171-188. [14] Dabbaghjamanesh, Morteza, Boyu Wang, Shahab Mehraeen, Jie Zhang, and Abdollah Kavousi-Fard. "Networked Microgrid Security and Privacy Enhancement By the Blockchain-enabled Internet of Things Approach." In 2019 IEEE Green Technologies Conference (GreenTech), pp. 1-5. IEEE, 2019. [15] Dabbaghjamanesh, Morteza, Boyu Wang, Abdollah Kavousi-Fard, Shahab Mehraeen, Nikos D. Hatziargyriou, Dimitris Trakas, and Farzad Ferdowsi. "A Novel Two-Stage Multi-Layer Constrained Spectral Clustering Strategy for Intentional Islanding of Power Grids." IEEE Transactions on Power Delivery (2019). [16] Wang, Boyu, Morteza Dabbaghjamanesh, Abdollah Kavousi Fard, and Shahab Mehraeen. "Cybersecurity Enhancement of Power Trading Within the Networked Microgrids Based on Blockchain and Directed Acylic Graph Approach." IEEE Transactions on Industry Applications (2019). [17] Saberi, Hossein, Shahab Mehraeen, and Boyu Wang. "Stability improvement of microgrids using a novel reduced UPFC structure via nonlinear optimal control." In 2018 IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 3294-3300. IEEE, 2018. [18] Kavousi-Fard, Abdollah, Morteza Dabbaghjamanesh, Jie Zhang, and Omid Avatefipour. "Superconducting Fault Current Limiter Allocation in Reconfigurable Smart Grids." arXiv preprint arXiv:1905.02324 (2019). [19] Lasseter, Robert H., and Paolo Piagi. "Microgrid: A conceptual solution." In IEEE Power Electronics Specialists Conference, vol. 6, pp. 4285-4291. 2004. [20] Barzegar, Behnam, Amir Masoud Rahmani, Kamran Zamanifar, and Azadeh Divsalar. "Gravitational emulation local search algorithm for advanced reservation and scheduling in grid computing systems." In 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology, pp. 1240-1245. IEEE, 2009. [21]  Liu, Nian, Qifang Chen, Jie Liu, Xinyi Lu, Peng Li, Jinyong Lei, and Jianhua Zhang. "A heuristic operation strategy for commercial building microgrids containing EVs and PV system." IEEE Transactions on Industrial Electronics 62, no. 4 (2014): 2560-2570. [22] Parisio, Alessandra, Evangelos Rikos, and Luigi Glielmo. "A model predictive control approach to microgrid operation optimization." IEEE Transactions on Control Systems Technology 22, no. 5 (2014): 1813-1827. [23] Almada, J. B., R. P. S. Leão, R. F. Sampaio, and G. C. Barroso. "A centralized and heuristic approach for energy management of an AC microgrid." Renewable and Sustainable Energy Reviews 60 (2016): 1396-1404.        

Economic Self-Operation of Microgrids By Gravitational Emulation Local Search Algorithm

Munich Personal RePEc Archive

Munich Personal RePEc Archive
Economic Self-Operation of Microgrids
By Gravitational Emulation Local Search
Algorithm
Khazaei, Ehsan and Jamaledini, Ashkan and Soltani, Ali
Electrical and Computer Engineering Department, Sazeh Sazan
Power Company, Iran, Electrical and Computer Engineering
Department, Sazeh Sazan Power Company, Iran, Electrical and
Computer Engineering Department, Sazeh Sazan Power Company,
Iran
2019
Online at https://mpra.ub.uni-muenchen.de/95895/
MPRA Paper No. 95895, posted 11 Sep 2019 05:41 UTC
Economic Self-Operation of Microgrids By 
Gravitational Emulation Local Search 
Algorithm 
 
Ehsan Khazaei, Ashkan Jamaledini, Ali Soltani 
Electrical and Computer Engineering Department, Sazeh Sazan Power Company, Iran 
 
Abstract: Islanded microgrid (MG) is a microgrid which is disconnected from the main 
network and therefore it is safe and has lead to one of the most important challenges in 
the power system operation. In the case of higher price in the market, the islanded MG 
offers a lower operational cost. On the other hand, the nonlinear attribute of optimal 
operation of the islanded MG has made difficulties in using such systems. To overcome 
this obstacle, a new heuristic method known as the Gravitational Emulation Local 
Search Algorithm (GELSA) is proposed in this paper in order to solve the problem.  The 
efficiency of the technique is examined on a modified IEEE 30 bus test network. 
 
I. INTRODUCTION 
 
Microgrid (MG) is the accumulation of the load and distributed energy resources in which 
the generators satisfy the power demand requested inside the small network. This 
capability leads to significant economic and technical advantages [1-5]. A few 
advantages of these systems involve closeness to costumers, less transmission lien, 
higher reliability, higher resiliency, lower operation cost, etc. Despite all the advantages, 
an important challenge is optimal energy management of islanded MG which is a 
complicated problem. Among advanced methods of optimization like Taguchi, grey, grey-
fuzzy, and grey-adaptive neuro fuzzy inference system–integrated approach [6-9]. This 
paper presents a new technique for optimal energy management of islanded MG. In this 
study, the small grid is disconnected from the main network and consequently, the 
generation units only provide the load demand of the small network. As a result, in this 
mode, power exchange with the main grid is blocked.  
A number of researchers have implemented evolutionary algorithms, e.g., CDOA, GA, 
PSO, and TLABO to study energy management of islanded MG [8-11]. Moreover, some 
literature has widely studied efficient control of MG operation [12-15]. References [16-20] 
have investigated utilization of electric vehicles (EVs), MG superconductivity, and MG 
security. Function of an islanded MG is a nonlinear problem and seems hard to solve. 
Another important factor in solving nonlinear problems is the convergence speed.  
Therefore, in this paper a new method known as the gravitational emulation local search 
(GELS) algorithm is developed and presented in order to find a solution for nonlinear 
problem of optimization of MG systems [16-19].  
 
II. MATHEMATICAL FORMULATIONS OF THE PROBLEM 
A. Objective function 
Objective function for this problem of minimizing the operational cost is defined as:  𝑚𝑖𝑛 ∑ [𝐶𝑖𝑃𝑖𝑡𝐼𝑖𝑡 + 𝑆𝑈𝑖𝑡 + 𝑆𝐷𝑖𝑡]∀𝑖                                                                                           (1) 
whsere: 
I is Binary variable {0,1} 
SU,SD are startup and shutdown costs   
B. Constraints 
The constraint imposed on this problem are the following items. 
The limit on capacity of each  generation unit is: 𝑃𝑖𝑡,𝑚𝑖𝑛 ≤ 𝑃𝑖𝑡 ≤ 𝑃𝑖𝑡,𝑚𝑎𝑥                                                                                                        (2) 
Also the limit on ramp up and down of each generation unit are: 𝑃𝑖𝑡 − 𝑃𝑖(𝑡−1) ≤ 𝑅𝑈𝑖                                                                                                             (3) 𝑃𝑖(𝑡−1) − 𝑃𝑖𝑡 ≤ 𝑅𝐷𝑖                                                                                                             (4) 
where: 𝑅𝑈𝑖 , 𝑅𝑈𝑖  are ramp up and ramp down of the ith unit 
Finally, the limited up and down time of each generation unit is considered as: 𝑇(𝑜𝑛)𝑖𝑡 ≥ 𝑈𝑇𝑖(𝐼𝑖𝑡 − 𝐼𝑖(𝑡−1)                                                                                                   (5) 𝑇(𝑜𝑓𝑓)𝑖𝑡 ≥ 𝐷𝑇𝑖(𝐼𝑖(𝑡−1) − 𝐼𝑖𝑡                                                                                                  (6) 
where: 𝑈𝑇𝑖, 𝐷𝑇𝑖  are minimum up and down rates of the ith unit 𝑇(𝑜𝑛), 𝑇(𝑜𝑓𝑓)  are number of successive on and off hours                                    
III. GRAVITATIONAL EMULATION LOCAL SEARCH ALGORITHM 
As mentioned, the proposed problem is a mixed-integer linear programming (MILP) 
problem, which is hard to solve by the mathematical models. Also, the optimal solution 
is not assuring by solving the problem by the mathematical approach. To this end, in 
the paper, a new evolutionary algorithm known as the gravitational emulation local 
search (GELS) a is adapted to solve the problem. More detail regarding this algorithm 
can be found in [20]. It should be noted that evolutionary methods are used widely in 
many fields because of the fast and precise response [21-23].  
IV. RESULTS 
To show the economic merit of the proposed technique, the modified IEEE 30 bus test 
system is selected and tested, as shown in Fig. 1. The system has 3 distributed 
generators (DGs) as explained in Table I. Also, the 24-hours load demand is shown in 
Fig. 2.  
 
Fig. 1. Single line model 
 
 
Table I 
Features of Units 
 Minimum output power Maximum output power 
Unit 1 20 80 
Unit 2 20 50 
Unit 3 1 25 
  
 
 
Fig. 2. 24-hour load demand 
 
DG1
DG2
DG3
The DGs output is shown in Fig. 3, where the output of the DGs are based on their 
economic price. That means cheapest units are more active than others.  
 
 
Fig. 3. The output power of Units 
 
 
Table II shows the total operation cost of the proposed problem under difference 
algorithms. The proposed technique has higher speed and lower operation cost.  
Table II 
Operation cost 
 
 Operation cost ($) Computational Time 
(second)  
PSO 5432.1 14.2 
GA 5532.5 11.8 
Proposed method 5114.7 8.7 
 
V. CONCLUSION 
In this paper a new heurist technique, known as the GELSA has been developed for 
solving MILP microgrid operation. The results have been compare with the PSO and GA. 
Results prove the higher speed of the proposed method. Also, from the economic 
perspective, the proposed method has lower operation cost. This method can be easily 
adaptable in the power industrial grids as it has a very high speed and accuracy.  
  
Reference 
 [1] Zhang, Yan, Fanlin Meng, Rui Wang, Behzad Kazemtabrizi, and Jianmai Shi. 
"Uncertainty-resistant Stochastic MPC Approach for Optimal Operation of CHP 
Microgrid." Energy(2019). 
[2] Chen, Yahong, Changhong Deng, Weiwei Yao, Ning Liang, Pei Xia, Peng Cao, 
Yiwang Dong et al. "Impacts of stochastic forecast errors of renewable energy 
generation and load demands on microgrid operation." Renewable Energy 133 
(2019): 442-461. 
[3] 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. 
[4] Tsikalakis, Antonis G., and Nikos D. Hatziargyriou. "Centralized control for optimizing 
microgrids operation." In 2011 IEEE power and energy society general meeting, pp. 
1-8. IEEE, 2011. 
[5] Dimeas, Aris L., and Nikos D. Hatziargyriou. "Operation of a multiagent system for 
microgrid control." IEEE Transactions on Power systems 20, no. 3 (2005): 1447-
1455. 
[6] Reza Bagherian Azhiri, Jalal Fathi Sola, Ramin Mehdizad Tekiyeh, Farid Javidpour, 
Abolfaz  Salmani Bideskan, “Analyzing of joint strength, impact energy, and angular 
distortion of the ABS friction stir welded joints reinforced by nanosilica addition”, The 
International Journal of Advanced Manufacturing Technology, February 2019, 
Volume 100, Issue 9–12, pp 2269–2282. 
[7] Reza Bagherian Azhiri, Abolfazl Salmani Bideskan, Farid Javidpour, Ramin 
Mehdizad Tekiyeh, “Study on material removal rate, surface quality, and residual 
stress of AISI D2 tool steel in electrical discharge machining in presence of 
ultrasonic vibration effect”, The International Journal of Advanced Manufacturing 
Technology, April 2019, Volume 101, Issue 9–12, pp 2849–2860 
[8] Olivares, Daniel E., Ali Mehrizi-Sani, Amir H. Etemadi, Claudio A. Cañizares, Reza 
Iravani, Mehrdad Kazerani, Amir H. Hajimiragha et al. "Trends in microgrid 
control." IEEE Transactions on smart grid 5, no. 4 (2014): 1905-1919. 
[9] Basak, Prasenjit, A. K. Saha, S. Chowdhury, and S. P. Chowdhury. "Microgrid: 
Control techniques and modeling." In 2009 44th International Universities Power 
Engineering Conference (UPEC), pp. 1-5. IEEE, 2009. 
 [10] 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. 
[11] Dabbaghjamanesh, Morteza. "Stochastic Energy Management of Reconfigurable 
Power Grids in the Presence of Renewable Energy by Considering Practical 
Limitations." (2019). 
[12] 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. 
[13] Sayed, Gehad Ismail, Aboul Ella Hassanien, and Ahmad Taher Azar. "Feature 
selection via a novel chaotic crow search algorithm." Neural Computing and 
Applications 31, no. 1 (2019): 171-188. 
[14] Dabbaghjamanesh, Morteza, Boyu Wang, Shahab Mehraeen, Jie Zhang, and 
Abdollah Kavousi-Fard. "Networked Microgrid Security and Privacy Enhancement By 
the Blockchain-enabled Internet of Things Approach." In 2019 IEEE Green 
Technologies Conference (GreenTech), pp. 1-5. IEEE, 2019. 
[15] Dabbaghjamanesh, Morteza, Boyu Wang, Abdollah Kavousi-Fard, Shahab 
Mehraeen, Nikos D. Hatziargyriou, Dimitris Trakas, and Farzad Ferdowsi. "A Novel 
Two-Stage Multi-Layer Constrained Spectral Clustering Strategy for Intentional 
Islanding of Power Grids." IEEE Transactions on Power Delivery (2019). 
[16] Wang, Boyu, Morteza Dabbaghjamanesh, Abdollah Kavousi Fard, and Shahab 
Mehraeen. "Cybersecurity Enhancement of Power Trading Within the Networked 
Microgrids Based on Blockchain and Directed Acylic Graph Approach." IEEE 
Transactions on Industry Applications (2019). 
[17] Saberi, Hossein, Shahab Mehraeen, and Boyu Wang. "Stability improvement of 
microgrids using a novel reduced UPFC structure via nonlinear optimal control." 
In 2018 IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 
3294-3300. IEEE, 2018. 
[18] Kavousi-Fard, Abdollah, Morteza Dabbaghjamanesh, Jie Zhang, and Omid 
Avatefipour. "Superconducting Fault Current Limiter Allocation in Reconfigurable 
Smart Grids." arXiv preprint arXiv:1905.02324 (2019). 
[19] Lasseter, Robert H., and Paolo Piagi. "Microgrid: A conceptual solution." In IEEE 
Power Electronics Specialists Conference, vol. 6, pp. 4285-4291. 2004. 
[20] Barzegar, Behnam, Amir Masoud Rahmani, Kamran Zamanifar, and Azadeh 
Divsalar. "Gravitational emulation local search algorithm for advanced reservation 
and scheduling in grid computing systems." In 2009 Fourth International Conference 
on Computer Sciences and Convergence Information Technology, pp. 1240-1245. 
IEEE, 2009. 
[21]  Liu, Nian, Qifang Chen, Jie Liu, Xinyi Lu, Peng Li, Jinyong Lei, and Jianhua Zhang. 
"A heuristic operation strategy for commercial building microgrids containing EVs 
and PV system." IEEE Transactions on Industrial Electronics 62, no. 4 (2014): 2560-
2570. 
[22] Parisio, Alessandra, Evangelos Rikos, and Luigi Glielmo. "A model predictive 
control approach to microgrid operation optimization." IEEE Transactions on Control 
Systems Technology 22, no. 5 (2014): 1813-1827. 
[23] Almada, J. B., R. P. S. Leão, R. F. Sampaio, and G. C. Barroso. "A centralized and 
heuristic approach for energy management of an AC microgrid." Renewable and 
Sustainable Energy Reviews 60 (2016): 1396-1404. 
 
 
 
 
 
 
 


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Economic Self-Operation of Microgrids By Gravitational Emulation Local Search Algorithm

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