Load frequency control for two-area hybrid microgrids using model predictive control optimized by grey wolf-pattern search algorithm

Load–frequency control for an islanded microgrid is very important. If a disturbance is applied to an islanded microgrid, the frequency of the system starts to fluctuate and these fluctuations must be damped by the load–frequency control system. The load–frequency control system uses different contr...

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Vydáno v:	Soft computing (Berlin, Germany) Ročník 27; číslo 23; s. 18227 - 18243
Hlavní autoři:	Amiri, Farhad, Hatami, Alireza
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023 Springer Nature B.V
Témata:	Alternative energy Application of Soft Computing Artificial Intelligence Computational Intelligence Control Control methods Control systems Controllers Damping Distributed generation Electric industries Energy resources Energy storage Engineering Frequency control Load Mathematical Logic and Foundations Mathematical models Mechatronics Optimization algorithms Pattern search Predictive control Proportional integral derivative Renewable resources Robotics Search algorithms Turbogenerators Wind turbines Two-area microgrid Model predictive control (MPC) Load frequency control (LFC) Hybrid Grey Wolf Optimizer–Pattern Search algorithm (HGWO–PS)
ISSN:	1432-7643, 1433-7479
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Abstract	Load–frequency control for an islanded microgrid is very important. If a disturbance is applied to an islanded microgrid, the frequency of the system starts to fluctuate and these fluctuations must be damped by the load–frequency control system. The load–frequency control system uses different controllers to improve the damping of frequency fluctuations related to the islanded microgrid. In this article, the number of controllers used in the load–frequency control system related to an islanded two-area microgrid has been reduced, which means less complexity and less cost in the control structure of microgrids. Also, a new control method has been used in the load–frequency control structure related to the two-area microgrid, which is used to dampen the frequency fluctuations of each of the areas related to the two-area microgrid and to dampen the power deviations between the two microgrids. For this purpose, a hybrid Grey Wolf Optimizer and Pattern Search Algorithm (HGWO–PS)-based model predictive controller (new control method) is employed for load frequency control of an islanded two-area microgrid. The numerical simulations are conducted to verify the performance of the presented controller. Different scenarios such as changes in loads and/or variations in the generated power of the wind turbine generator and the photovoltaic system are studied in the MATLAB/Simulink environment, and the performance of the presented HGWO–PS-based MPC controller, the GWO-based MPC, and social-spider optimizer-based proportional–integral–derivative controller are compared using some criteria including the settling time, the peak overshoot and the peak undershoot. The results show the effectiveness of the presented controller.
AbstractList	Load–frequency control for an islanded microgrid is very important. If a disturbance is applied to an islanded microgrid, the frequency of the system starts to fluctuate and these fluctuations must be damped by the load–frequency control system. The load–frequency control system uses different controllers to improve the damping of frequency fluctuations related to the islanded microgrid. In this article, the number of controllers used in the load–frequency control system related to an islanded two-area microgrid has been reduced, which means less complexity and less cost in the control structure of microgrids. Also, a new control method has been used in the load–frequency control structure related to the two-area microgrid, which is used to dampen the frequency fluctuations of each of the areas related to the two-area microgrid and to dampen the power deviations between the two microgrids. For this purpose, a hybrid Grey Wolf Optimizer and Pattern Search Algorithm (HGWO–PS)-based model predictive controller (new control method) is employed for load frequency control of an islanded two-area microgrid. The numerical simulations are conducted to verify the performance of the presented controller. Different scenarios such as changes in loads and/or variations in the generated power of the wind turbine generator and the photovoltaic system are studied in the MATLAB/Simulink environment, and the performance of the presented HGWO–PS-based MPC controller, the GWO-based MPC, and social-spider optimizer-based proportional–integral–derivative controller are compared using some criteria including the settling time, the peak overshoot and the peak undershoot. The results show the effectiveness of the presented controller.
Author	Hatami, Alireza Amiri, Farhad
Author_xml	– sequence: 1 givenname: Farhad surname: Amiri fullname: Amiri, Farhad organization: Department of Electrical Engineering, Bu-Ali Sina University – sequence: 2 givenname: Alireza orcidid: 0000-0002-0370-3903 surname: Hatami fullname: Hatami, Alireza email: hatamisharif@basu.ac.ir organization: Department of Electrical Engineering, Bu-Ali Sina University
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SubjectTerms	Alternative energy Application of Soft Computing Artificial Intelligence Computational Intelligence Control Control methods Control systems Controllers Damping Distributed generation Electric industries Energy resources Energy storage Engineering Frequency control Load Mathematical Logic and Foundations Mathematical models Mechatronics Optimization algorithms Pattern search Predictive control Proportional integral derivative Renewable resources Robotics Search algorithms Turbogenerators Wind turbines
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Title	Load frequency control for two-area hybrid microgrids using model predictive control optimized by grey wolf-pattern search algorithm
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