Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)
Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/15/3847 |
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| author | Mahmoud G. Hemeida Salem Alkhalaf Al-Attar A. Mohamed Abdalla Ahmed Ibrahim Tomonobu Senjyu |
| author_facet | Mahmoud G. Hemeida Salem Alkhalaf Al-Attar A. Mohamed Abdalla Ahmed Ibrahim Tomonobu Senjyu |
| author_sort | Mahmoud G. Hemeida |
| collection | DOAJ |
| description | Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (<i>ε</i> constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems. |
| first_indexed | 2024-03-10T18:10:55Z |
| format | Article |
| id | doaj.art-3825a1565dba4878a412b66324e4b81f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T18:10:55Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3825a1565dba4878a412b66324e4b81f2023-11-20T08:08:26ZengMDPI AGEnergies1996-10732020-07-011315384710.3390/en13153847Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)Mahmoud G. Hemeida0Salem Alkhalaf1Al-Attar A. Mohamed2Abdalla Ahmed Ibrahim3Tomonobu Senjyu4Department of Electrical Engineering, Minia Higher Institute of Engineering, Minia 61111, EgyptDepartment of Computer Science, Arrass College of Science and Arts, Qassim University, Qassim 51431, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81511, EgyptDepartment of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81511, EgyptFaculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-70213, JapanManta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (<i>ε</i> constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.https://www.mdpi.com/1996-1073/13/15/3847optimization techniquesmanta ray foraging optimization algorithmmulti-objective functionradial networksoptimal power flow |
| spellingShingle | Mahmoud G. Hemeida Salem Alkhalaf Al-Attar A. Mohamed Abdalla Ahmed Ibrahim Tomonobu Senjyu Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO) Energies optimization techniques manta ray foraging optimization algorithm multi-objective function radial networks optimal power flow |
| title | Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO) |
| title_full | Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO) |
| title_fullStr | Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO) |
| title_full_unstemmed | Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO) |
| title_short | Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO) |
| title_sort | distributed generators optimization based on multi objective functions using manta rays foraging optimization algorithm mrfo |
| topic | optimization techniques manta ray foraging optimization algorithm multi-objective function radial networks optimal power flow |
| url | https://www.mdpi.com/1996-1073/13/15/3847 |
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