Abstract
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.
Highlights
Distributed generation (DG) represents the production of electrical energy from distributed units near the consumers with a small capacity
For the first objective function, the total active power loss reaches its minimum value of 11.918 with a total reduction of 93.86% when the DG units were operated at an optimum power factor
The second objective function, the voltage deviation, was minimized to 0.000338 as the lowest value for all four cases when the DG units were set to an optimum power factor
Summary
Distributed generation (DG) represents the production of electrical energy from distributed units near the consumers with a small capacity. DGs may be renewable or nonrenewable resources such as: wind turbine, solar Photo Voltaic (PV) geothermal, hydro, and diesel generators [1]. Energies 2020, 13, 3847 future, the shape of electrical power systems is expected to be one of four types. These types are centralized integrated with distributed generation, centralized with increased decentralization, partially decentralized, and fully decentralized. This shape will be formed based on many factors that may be summarized into three main categories. High level of uncertainty that includes social factors, demand response, regulatory, policy and political factors; secondly, medium level of uncertainty that includes system challenges and technology requirements, and; low level of uncertainty, which includes geographical, climatic considerations, availability of natural resources, population density and distribution, and existing infrastructure [2]
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