Optimal Operation of Droop Control in Microgrids Using Different Techniques Optimization: Review
Microgrids are small power systems and can operate in two modes: island mode and grid connected. Switching between these two modes may cause a change in the load, which causes disturbances that affect the operation of the microgrid (MG), as the load change leads to a change in the voltage and freq...
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| Language: | English |
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University of Misan College of Engineering
2024-12-01
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| Series: | Misan Journal of Engineering Sciences |
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| Online Access: | https://uomisan.edu.iq/eng/mjes/index.php/eng/article/view/87 |
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| author | Ruqaya Majeed Kareem |
| author_facet | Ruqaya Majeed Kareem |
| author_sort | Ruqaya Majeed Kareem |
| collection | DOAJ |
| description | Microgrids are small power systems and can operate in two modes: island mode and grid connected. Switching between these two modes may cause a change in the load, which causes
disturbances that affect the operation of the microgrid (MG), as the load change leads to a change in
the voltage and frequency of the system so the operating control problem main issue for the microgrids
that is need addressed during operation. A control system is required for accurate synchronization,
system protection, and load reduction in an imbalance scenario, as well as to achieve system stability
while supplying robust and efficient electricity to the microgrids. Droop control is one of the common
methods used in the microgrid (MG) to adjust the real power and reactive power and control the
system voltage and frequency. However, the traditional droop control suffers from problems in the
accuracy of load distribution, line impedance mismatch, and slow dynamic response, as a result,
parameter values must be carefully chosen. To address these issues, many techniques have been used,
one of which is the optimization techniques. This paper reviews five different optimization techniques
based on metaheuristic optimization algorithms applied to microgrids that address some of the
drawbacks of droop control by optimizing droop control parameters for optimal flexible microgrid
(MG) operation. These techniques include Particle Swarm Optimization (PSO), Genetic Algorithm
(GA), Grey Wolf Optimization (GWO), Grasshopper Optimization Algorithm (GOA), and Salp Swarm
Algorithm (SSA) |
| format | Article |
| id | doaj-art-5671c43069654db69fd93f559189d76b |
| institution | Kabale University |
| issn | 2957-4242 2957-4250 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | University of Misan College of Engineering |
| record_format | Article |
| series | Misan Journal of Engineering Sciences |
| spelling | doaj-art-5671c43069654db69fd93f559189d76b2025-01-16T14:57:56ZengUniversity of Misan College of EngineeringMisan Journal of Engineering Sciences2957-42422957-42502024-12-0132489510.61263/mjes.v3i2.8787Optimal Operation of Droop Control in Microgrids Using Different Techniques Optimization: ReviewRuqaya Majeed Kareem0University of Misan/ College of EngineeringMicrogrids are small power systems and can operate in two modes: island mode and grid connected. Switching between these two modes may cause a change in the load, which causes disturbances that affect the operation of the microgrid (MG), as the load change leads to a change in the voltage and frequency of the system so the operating control problem main issue for the microgrids that is need addressed during operation. A control system is required for accurate synchronization, system protection, and load reduction in an imbalance scenario, as well as to achieve system stability while supplying robust and efficient electricity to the microgrids. Droop control is one of the common methods used in the microgrid (MG) to adjust the real power and reactive power and control the system voltage and frequency. However, the traditional droop control suffers from problems in the accuracy of load distribution, line impedance mismatch, and slow dynamic response, as a result, parameter values must be carefully chosen. To address these issues, many techniques have been used, one of which is the optimization techniques. This paper reviews five different optimization techniques based on metaheuristic optimization algorithms applied to microgrids that address some of the drawbacks of droop control by optimizing droop control parameters for optimal flexible microgrid (MG) operation. These techniques include Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Grasshopper Optimization Algorithm (GOA), and Salp Swarm Algorithm (SSA)https://uomisan.edu.iq/eng/mjes/index.php/eng/article/view/87microgrid, droop control, optimization algorithm, load change, voltage regulation. |
| spellingShingle | Ruqaya Majeed Kareem Optimal Operation of Droop Control in Microgrids Using Different Techniques Optimization: Review Misan Journal of Engineering Sciences microgrid, droop control, optimization algorithm, load change, voltage regulation. |
| title | Optimal Operation of Droop Control in Microgrids Using Different Techniques Optimization: Review |
| title_full | Optimal Operation of Droop Control in Microgrids Using Different Techniques Optimization: Review |
| title_fullStr | Optimal Operation of Droop Control in Microgrids Using Different Techniques Optimization: Review |
| title_full_unstemmed | Optimal Operation of Droop Control in Microgrids Using Different Techniques Optimization: Review |
| title_short | Optimal Operation of Droop Control in Microgrids Using Different Techniques Optimization: Review |
| title_sort | optimal operation of droop control in microgrids using different techniques optimization review |
| topic | microgrid, droop control, optimization algorithm, load change, voltage regulation. |
| url | https://uomisan.edu.iq/eng/mjes/index.php/eng/article/view/87 |
| work_keys_str_mv | AT ruqayamajeedkareem optimaloperationofdroopcontrolinmicrogridsusingdifferenttechniquesoptimizationreview |