Optimal capacitor placement in a radial distribution system using Bat Algorithm

Optimal capacitor placement in the distribution system plays a significant role in minimizing the energy loss. This paper presents an efficient technique to find optimal size and location of the capacitor with the objective of minimizing the total active power losses of distribution system. The Bat Algorithm (BA) is proposed to solve the optimal capacitor placement problem satisfying the operating constraints. To demonstrate the applicability of the proposed method, it is tested on IEEE 33-bus radial distribution system. The simulation results obtained are compared with the previous method reported in the literature and found to be encouraging

Optimal power flow using Hybrid Particle Swarm Optimization and Moth Flame Optimizer approach

In this study, the most common problem of the current power system named optimal power flow (OPF) is optimized using the recently hybrid meta-heuristic optimization technique Particle Swarm Optimization-Moth Flame Optimizer (PSO-MFO) algorithm. Hybrid PSO-MFO is an incorporation of PSO used for exploitation stage and MFO for exploration stage in an uncertain environment. The position and velocity of the particle are restructured according to Moth and flame location in each iteration. The hybrid PSO-MFO technique is carried out to solve the OPF problem. The performance of this technique is deliberated and evaluated on the standard IEEE 30-bus and IEEE 57-bus test system. The problems considered in the OPF are fuel cost reduction, Voltage stability enhancement and Active power loss minimization. The results obtained with hybrid PSO-MFO technique is compared with original PSO and MFO

Artistic feasibility research on a standalone hybrid solar/wind system based on IncCond algorithm under variable load demands-a case study: South Algeria

The aim of this research study is to describe the hybrid renewable energy resources, the photovoltaic and the wind turbine are utilized to produce AC power for a Sahara Hassi R'Mel region in south of Algeria is optimally designed. Hybrid power generation systems are an operative solution for the variable generated power of renewable energy sources. In the new design, the ability circuit and the surveillance regulation of the presented grid-connected hybrid power system simulation is examined via MATLAB/Simulink. To detect the feasibility of the controlled system, this system is studied under various solar radiation and wind speed profiles. On the basis of the results, good tracking with a high accuracy rate is obtained after using filtering component by enhancing the different topology configurations in the expression of comparison voltage (V), and power (W). Overtime, the overall system efficiency is enhanced compared to the MPPT control system. The obtained simulation results for the incremental conductance PV/Wind MPPT controller have accomplished high effective system achievements. IncCond method is appropriate for working in vastly variable weather conditions with easy design, high tracking velocity, and minimum step count

Interior search algorithm for optimal power flow with non-smooth cost functions

The optimal power flow (OPF) problem considers as an optimization problem, in which the utility strives to reduce its global costs while pleasing all of its constraints. Therefore, Interior Search Algorithm (ISA) is applied to treat this problem. Where, ISA is a specific type of artificial intelligence and a mathematical programming, not a meta- heuristic algorithm. The effectiveness of this method in solving the OPF problem is evaluated on two test power systems, the IEEE 30-bus and the IEEE 57-bus test systems. For the first example, the ISA-OPF algorithm finds an answer that agrees with published results. For the 57-bus system, the ISA-OPF demonstrates its ability to transact with larger systems. Thus, the ISA-OPF algorithm is shown to be a robust tool to treat this optimization compared with other methods. Moreover, the advantage of ISA is that it has only one parameter of control which makes the simplicity in the main algorithm

A Novel Multi-Objective Bat Algorithm for Optimal Placement and Sizing of Distributed Generation in Radial Distributed Systems

In the few last decades, Distribution Generation (DG) has drawn a great attention by researchers around the world in the field of Radial Distributed Systems (RDSs). Generally, the optimal placement is based on the maximization of the Voltage Stability Index (VSI) and the optimal sizing is based on the minimization of the Total Active Power Losses (TAPLs). Hence, a Multi-Objective Optimization Problem (MOOP) is proposed to achieve the both mentioned objectives. For this purpose, a new simple optimization algorithm known as Bat Algorithm (BA) based on Weight Sum Method (WSM) has been used to resolve the MOOP. Then, the Fuzzy Based (FB) technique is employed to find the Best Compromise Solution. This paper also provides a comparison between the proposed algorithm and other recently published methods. From the obtained results, the advantage of the proposed algorithm is clearly observed from multiple points of view such as enhancement of Voltage Profile (VP), decreasing of the TAPL, and the maximization of the VSI. The investigations have been carried out on a standard IEEE 12-bus, 33-bus, 69-bus, and 85-bus test feeders

Optimal Power Flow using the Moth Flam Optimizer: A Case Study of the Algerian Power System

In this paper, a new technique of optimization known as Moth-Flam Optimizer (MFO) has been proposed to solve the problem of the Optimal Power Flow (OPF) in the interconnected power system, taking into account the set of equality and inequality constraints. The proposed algorithm has been presented to the Algerian power system network for a variety of objectives. The obtained results are compared with recently published algorithms such as; as the Artificial Bee Colony (ABC), and other meta-heuristics. Simulation results clearly reveal the effectiveness and the robustness of the proposed algorithm for solving the OPF problem.

A solution to the optimal power flow using multi-verse optimizer

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel meta-heuristic optimization Multi-verse Optimizer(MVO) algorithm. In order to solve the optimal power flow problem, the IEEE 30-bus and IEEE 57-bus systems are used. MVO is applied to solve the proposed problem. The problems considered in the OPF problem are fuel cost reduction, voltage profile improvement, voltage stability enhancement. The obtained results are compared with recently published meta-heuristics. Simulation results clearly reveal the effectiveness and the rapidity of the proposed algorithm for solving the OPF problem

An Efficient Stud Krill Herd Framework for Solving Non-Convex Economic Dispatch Problem

The problem of economic dispatch (ED) is the basic problem of power framework, its main goal is to find the most favorable generation dispatch to generate each unit, reduce the whole power generation cost, and meet all system limitations. A heuristic algorithm, recently developed called Stud Krill Herd (SKH), has been employed in this paper to treat non-convex ED problems. The proposed KH has been modified using Stud selection and crossover (SSC) operator, to enhance the solution quality and avoid local optima. We are demonstrated SKH effects in two case study systems composed of 13-unit and 40-unit test systems to verify its performance and applicability in solving the ED problems. In the above systems, SKH can successfully obtain the best fuel generator and distribute the load requirements for the online generators. The results showed that the use of the proposed SKH method could reduce the total cost of generation and optimize the fulfillment of the load requirements

Elephant Herding Optimization for Solving Non-convex Optimal Power Flow Problem

The optimal power flow (OPF) is becoming the most popular problem encountered in studies related to power system analysis. OPF is formulated as a nonlinear optimization problem with conflicting objectives and subjected to both equality and inequality constraints. In this paper, inspired by the herding behavior of elephant, a new type of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving OPF problem. EHO has a fast convergence rate due to the use of clan updating operator and separating operator. The elitism scheme is also used to save the best elephant during the process when updating the elephant. Case studies based on standard IEEE 30-bus test system are employed to prove the capability of the proposed EHO algorithm. The results clearly show the superiority of EHO in searching for the better function values than other well-known metaheuristic search algorithms that has been already done