Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

• Published in: Energies (Basel) Vol. 13; no. 15; p. 3847
• Main Authors: Hemeida, Mahmoud G., Alkhalaf, Salem, Mohamed, Al-Attar A., Ibrahim, Abdalla Ahmed, Senjyu, Tomonobu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2020
• Subjects: Algorithms; Alternative energy; Computer simulation; Costs; Deviation; Distributed generation; Electric power distribution; Energy conservation; Evolutionary algorithms; Evolutionary computation; Fractals; Fuel cells; Generators; Genetic algorithms; Genetics; Heuristic; Industrial plant emissions; Load; manta ray foraging optimization algorithm; multi-objective function; Multiple objective analysis; optimal power flow; Optimization; Optimization algorithms; Optimization techniques; Power factor; Power plants; Profit maximization; Radial distribution; radial networks; Renewable resources; Search algorithms; Stochasticity; Turbines; Voltage; Voltage stability
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13153847

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 (ε 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.