Particle Swarm Optimization for the Minimization of Power Losses in Distribution Networks

2015 ◽  
Author(s):  
Joseph B. Abugri ◽  
Marc Karam
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Power Losses ◽  
Swarm Optimization

scholarly journals Optimal Allocation of Multiple Distributed Generations including Uncertainties in Distribution Networks by k-Means Clustering and Particle Swarm Optimization Algorithms

2021 ◽  
Vol 19 ◽  
pp. 79-84
Author(s):  
Onur Hakki Eyüboglu ◽  
◽  
Ömer Ömer Gül
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Optimal Allocation ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Power Losses ◽  
Swarm Optimization ◽  
Distributed Generations ◽  
Distributed Power Generation ◽  
The One

Climate change is the one of the most important issues faced globally and reasons of it must be reduced immediately in every area. Installing distributed power generation (DG) is one of the powerful options for reducing carbon emissions in power generation. However, improper allocation of these assets has several drawbacks. Efficient, novel and robust algorithm which is combination of both k-Means clustering and Particle Swarm Optimization is proposed in order to allocate DGs. Proposed algorithm clusters distribution network buses and selects to most proper cluster to allocate DG in this way it reduces possible buses. Furthermore, sizing and generation constraints of DGs are quite important for allocation. Therefore, several cases including different DG sizes and types are implemented to obtain the best results. Moreover, multiple DG cases are included in the study. Finally, DGs have considered as wind turbines for best cases and cases have analysed in 24 hourly bases including uncertainties both demand and production side. 33 Bus test feeder power losses are reduced up to 69%, 86%, 90% at best cases and 39%, 53%, 55% at including uncertainties by proposed algorithm for cases 1, 2, 3 DG installed, respectively.

scholarly journals An Improved Particle Swarm Optimization Algorithm Using Eagle Strategy for Power Loss Minimization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Power Losses ◽  
Swarm Optimization ◽  
Eagle Strategy

The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.

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.

Multi-objective optimization of water distribution networks using particle swarm optimization

2021 ◽  
Vol 218 ◽  
pp. 18-31
Author(s):  
Douglas F. Surco ◽  
Diogo H. Macowski ◽  
Flávia A.R. Cardoso ◽  
Thelma P.B. Vecchi ◽  
Mauro A.S.S. Ravagnani
Keyword(s):  
Particle Swarm Optimization ◽  
Water Distribution ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective

scholarly journals A Novel Power Losses Reduction Method Based on a Particle Swarm Optimization Algorithm Using STATCOM

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2851 ◽  
Author(s):  
Valeriya Tuzikova ◽  
Josef Tlusty ◽  
Zdenek Muller
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Particle Swarm ◽  
Electric Power Industry ◽  
Main Function ◽  
Power Losses ◽  
Swarm Optimization ◽  
Flexible Ac Transmission ◽  
Definition Of ◽  
Ac Transmission

In the modern electric power industry, Flexible AC Transmission Systems (FACTS) have a special place. In connection with the increased interest in the development of “smart energy”, the use of such devices is becoming especially urgent. Their main function is the ability to manage modes in real time: maintain the necessary level of voltage in the grids, control the power flow, increase the capacity of power lines and increase the static and dynamic stability of the power grid. The problem of system reliability and stability is related to the task of definitions and optimizations and planning indicators, design and exploitation. The main aim of this article is the definition of the best placement of the STATCOM compensator in case to provide stability and reliability of the grid with the minimization of the power losses, using Particle Swarm Optimization algorithms. All calculations were performed in MATLAB.

scholarly journals An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3052 ◽  
Author(s):  
Ali Ahmadian ◽  
Ali Elkamel ◽  
Abdelkader Mazouz
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Combinatorial Problem ◽  
Distribution Networks ◽  
Swarm Optimization ◽  
Expansion Planning ◽  
Hybrid Particle Swarm Optimization ◽  
Electric Distribution

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.

scholarly journals Calibration of water quality model for distribution networks using genetic algorithm, particle swarm optimization, and hybrid methods

MethodsX ◽  
2019 ◽  
Vol 6 ◽  
pp. 540-548 ◽  
Author(s):  
Roya Peirovi Minaee ◽  
Mojtaba Afsharnia ◽  
Alireza Moghaddam ◽  
Ali Asghar Ebrahimi ◽  
Mohsen Askarishahi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Water Quality ◽  
Particle Swarm Optimization ◽  
Hybrid Methods ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Water Quality Model ◽  
Quality Model ◽  
Swarm Optimization

Optimal Capacitor Allocation in Distribution System Using Particle Swarm Optimization

2014 ◽  
Vol 699 ◽  
pp. 770-775
Author(s):  
Ihsan Jabbar Hasan ◽  
Chin Kim Gan ◽  
Meysam Shamshiri ◽  
Mohd Ruddin Ab Ghani ◽  
Ismadi bin Bugis
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Voltage Profile ◽  
Power Losses ◽  
Swarm Optimization ◽  
Standard Case ◽  
Capacitor Placement ◽  
Penalty Factor ◽  
Ieee Standard

Capacitor installation is one of the most commonly used methods for reactive power compensation in the distribution networks. In this paper, the optimum capacitor placement and its sizing has been applied in the distribution network in terms of power losses minimization and voltage profile improvement. The maximum and minimum bus voltage and the maximum possible capacitor size are the constraints of optimum capacitor placement and sizing problem. There are considered as the penalty factor in the objective function. In order to evaluate the obtained objective function, the Particle Swarm Optimization (PSO) is utilized to find the best possible capacitor placement and capacity. The OpenDSS software has then been utilized to solve the power flow through Matlab coding interface. To validate the functionality of the proposed method, the IEEE 13-bus test system is implemented and the obtained results have been compared with the IEEE standard case without capacitor compensation. The results show that the proposed algorithm is more cost effective and has lower power losses as compared to the IEEE standard case. In addition, the voltage profile has been improved, accordingly.

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