scholarly journals Optimal Location and Sizing of Distributed Generators in DC Networks Using a Hybrid Method Based on Parallel PBIL and PSO

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1808
Author(s):  
Luis Fernando Grisales-Noreña ◽  
Oscar Danilo Montoya ◽  
Carlos Andrés Ramos-Paja ◽  
Quetzalcoatl Hernandez-Escobedo ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Distributed Generation ◽  
Power Loss ◽  
Hybrid Methods ◽  
Population Based ◽  
Optimal Location ◽  
Swarm Optimization ◽  
Distributed Generators ◽  
Parallel Version ◽  
Location Methods ◽  
Hybrid Methodology

This paper addresses the problem of the locating and sizing of distributed generators (DGs) in direct current (DC) grids and proposes a hybrid methodology based on a parallel version of the Population-Based Incremental Learning (PPBIL) algorithm and the Particle Swarm Optimization (PSO) method. The objective function of the method is based on the reduction of the power loss by using a master-slave structure and the consideration of the set of restrictions associated with DC grids in a distributed generation environment. In such a structure, the master stage (PPBIL) finds the location of the generators and the slave stage (PSO) finds the corresponding sizes. For the purpose of comparison, eight additional hybrid methods were formed by using two additional location methods and two additional sizing methods, and this helped in the evaluation of the effectiveness of the proposed solution. Such an evaluation is illustrated with the electrical test systems composed of 10, 21 and 69 buses and simulated on the software, MATLAB. Finally, the results of the simulation demonstrated that the PPBIL–PSO method obtains the best balance between the reduction of power loss and the processing time.

Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an Improved Version of Particle Swarm Optimization

2021 ◽  
pp. 15-27
Author(s):  
Mamdouh Kamaleldin AHMED ◽  
◽  
Mohamed Hassan OSMAN ◽  
Nikolay V. KOROVKIN ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distributed Generation ◽  
Particle Swarm ◽  
Optimal Location ◽  
Particle Swarm Algorithm ◽  
Power Losses ◽  
Negative Effects ◽  
Swarm Optimization ◽  
Distributed Generations

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

scholarly journals Particle Swarm Optimization Algorithm Based Reactive Power Optimization in Distribution Network

2019 ◽  
Vol 8 (10) ◽  
pp. 3792-3796
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Fitness Function ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Loss Index

Minimization of power loss is the first priority of the power companies. Generally power loss is directly proportional to the reactive power demand and minimization of this is known as reactive power optimization (RPO). In this paper we are trying to minimize the reactive power loss with help of distributed generation. Distributed generation provides active as well as reactive power locally so, there is no need of taking the reactive power from the generator consequently reactive power loss minimizes. Now problem arises that where to place the distributed generation to have minimum power loss. To find the optimal location of the distributed generation, we have used particle swarm optimization algorithm (PSO). For that we have defined the fitness function as well as constraints. Constraints limits the value of variable within the defined range. Fitness function is sum of real power loss index, reactive power loss index and voltage deviation index. We have also used genetic algorithm just to compare the results and to find which one is better out of genetic algorithm and PSO. RPO increases the power transfer capability, reduces the line loss and boost the system stability therefore it can be applied in the distribution network.

Investigating the Impact of Distributed Generators Power Factor to Simultaneous Optimization Analysis

2015 ◽  
Vol 785 ◽  
pp. 253-257
Author(s):  
Jasrul Jamani Jamian ◽  
M.W. Mustafa ◽  
Mohd Noor Abdullah
Keyword(s):  
Power Factor ◽  
Power Loss ◽  
Simultaneous Optimization ◽  
Loss Reduction ◽  
Optimization Analysis ◽  
Swarm Optimization ◽  
Distributed Generator ◽  
Distributed Generators ◽  
Power Loss Reduction ◽  
The Impact

This paper discusses the optimal Distributed Generator (DG) coordination using the Particle Swarm Optimization (PSO) technique where the DG output and location are determined simultaneously. Furthermore, this study analyzes both single DG and multiple DGs configurations. The influence of DG Power Factor (PF) to the optimal DG placement and the DG output are investigated by varying the DG PF values. Specifically, the PF were configured to five values, which are 0.8, 0.85, 0.9, 0.95 and 1.0. From the results, the optimal DG placements are similar, regardless of the PF condition. For example, in the single DG unit experiment, the optimal DG location is at bus 6 whilst in the triple DG units test, the optimal locations are at busses 14, 24, and 30. In contrast, the value of PF significantly influences the optimal DG output and power loss reduction. This study concludes that the design with three DGs where their PFs are configured to 0.8 has the least power loss.

scholarly journals Optimal Location, Sizing, and Appropriate Technology Selection of Distributed Generators for Minimizing Power Loss Using Genetic Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
T. R. Ayodele ◽  
A. S. O. Ogunjuyigbe ◽  
O. O. Akinola
Keyword(s):  
Genetic Algorithm ◽  
Power Loss ◽  
Optimal Operation ◽  
Optimal Location ◽  
Appropriate Technology ◽  
Technology Selection ◽  
Synchronous Generators ◽  
Distributed Generator ◽  
Induction Generators ◽  
Distributed Generators

Genetic algorithm (GA) is utilized to select most suitable Distributed Generator (DG) technology for optimal operation of power system as well as determine the optimal location and size of the DG to minimize power loss on the network. Three classes of DG technologies, synchronous generators, asynchronous generators, and induction generators, are considered and included as part of the variables for the optimization problem. IEEE 14-bus network is used to test the applicability of the algorithm. The result reveals that the developed algorithm is able to successfully select the most suitable DG technology and optimally size and place the DGs to minimize power loss in the network. Furthermore, optimum multiple placement of DG is considered to see the possible impact on power loss in the network. The result reveals that multiple placements can further reduce the power loss in the network.

scholarly journals Optimal Location and Sizing of DGs in DC Networks Using a Hybrid Methodology Based on the PPBIL Algorithm and the VSA

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1913
Author(s):  
Luis Fernando Grisales-Noreña ◽  
Oscar Danilo Montoya ◽  
Ricardo Alberto Hincapié-Isaza ◽  
Mauricio Granada Echeverri ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Location Problem ◽  
Search Algorithm ◽  
Optimization Method ◽  
Population Based ◽  
Power Losses ◽  
Test Systems ◽  
Processing Times ◽  
Distributed Generators ◽  
Parallel Version

In this paper, we propose a master–slave methodology to address the problem of optimal integration (location and sizing) of Distributed Generators (DGs) in Direct Current (DC) networks. This proposed methodology employs a parallel version of the Population-Based Incremental Learning (PPBIL) optimization method in the master stage to solve the location problem and the Vortex Search Algorithm (VSA) in the slave stage to solve the sizing problem. In addition, it uses the reduction of power losses as the objective function, considering all the constraints associated with the technical conditions specific to DGs and DC networks. To validate its effectiveness and robustness, we use as comparison methods, different solution methodologies that have been reported in the specialized literature, as well as two test systems (the 21 and 69-bus test systems). All simulations were performed in MATLAB. According to the results, the proposed hybrid (PPBIL–VSA) methodology provides the best trade-off between quality of the solution and processing times and exhibits an adequate repeatability every time it is executed.

scholarly journals Congestion management in deregulated power system using adaptive moth swarm optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
R. Ramaporselvi ◽  
G. Geetha
Keyword(s):  
Power System ◽  
Power Loss ◽  
Design Methodology ◽  
Congestion Management ◽  
Optimal Location ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Content Type ◽  
The Past ◽  
Compensation Rate

Purpose The purpose of this paper is to enhance the line congestion and to minimize power loss. Transmission line congestion is considered the most acute trouble during the operation of the power system. Therefore, congestion management acts as an effective tool in using the available power without breaking the system hindrances or limitations. Design/methodology/approach Over the past few years, determining the optimal location and size of the devices have pinched a great deal of consideration. Numerous approaches have been established to mitigate the congestion rate, and this paper aims to enhance the line congestion and minimize power loss by determining the compensation rate and optimal location of a thyristor-switched capacitor (TCSC) using adaptive moth swarm optimization (AMSO) algorithm. Findings An AMSO algorithm uses the performances of moth flame and the chaotic local search-based shrinking scheme of the bacterial foraging optimization algorithm. The proposed AMSO approach is executed and discussed for the IEEE-30 bus system for determining the optimal location of single TCSC and dual TCSC. Originality/value In addition to this, the proposed algorithm is compared with various other existing approaches, and the results thus obtained provide better performances than other techniques.

scholarly journals Supervised evolutionary programming based technique for multi-DG installation in distribution system

2020 ◽  
Vol 9 (1) ◽  
pp. 11
Author(s):  
Muhammad Firdaus Shaari ◽  
Ismail Musirin ◽  
Muhamad Faliq Mohamad Nazer ◽  
Shahrizal Jelani ◽  
Farah Adilah Jamaludin ◽  
...  
Keyword(s):  
Distribution System ◽  
Power Loss ◽  
Distribution Systems ◽  
Optimization Technique ◽  
Evolutionary Programming ◽  
Optimal Location ◽  
Programming Method ◽  
Network System ◽  
Distributed Generators ◽  
System Power

<span lang="EN-US">Installing DG in network system, has supported the distribution system to provide the increasing number of consumer demand and load, in order to achieve that this paper presents an efficient and fast converging optimization technique based on a modification of traditional evolutionary programming method for obtain the finest optimal location and power loss in distribution systems. The proposed algorithm that is supervised evolutionary programming is implemented in MATLAB and apply on the 69-bus feeder system in order to minimize the system power loss and obtaining the best optimal location of the distributed generators. </span>

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