scholarly journals Multi objective reactive power and voltage optimization of distribution network based on adaptive genetic annealing algorithm

2020 ◽  
Vol 185 ◽  
pp. 01042
Author(s):  
Yong Yang ◽  
Lei Chen ◽  
Weiyan Zheng ◽  
Xinquan Wei ◽  
Guangxian Lv ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Optimization Model ◽  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Reactive Power Optimization ◽  
Capacitor Switching ◽  
Annealing Algorithm ◽  
Line Loss ◽  
Node Voltage

In this paper, a multi-objective reactive power optimization model of distribution network is established, which takes the minimum of line loss, reactive compensation capacitor switching loss and node voltage deviation as objective, considering the constraints of node voltage, reactive power of wind turbine, capacitor switching times. The improved adaptive genetic annealing algorithm is used to solve the model. IEEE33 system is taken as an example to verify the effectiveness of the reactive power optimization model. When the shunt capacitor and wind turbine are in the optimal reactive power compensation, the line loss of the distribution network can be minimized.

Reactive Power Optimization in Distribution Network with Wind Farm

2012 ◽  
Vol 614-615 ◽  
pp. 1372-1376 ◽  
Author(s):  
Chuang Li ◽  
Min You Chen ◽  
Yong Wei Zhen ◽  
Ang Fu ◽  
Jun Jie Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Model ◽  
Wind Farm ◽  
Reactive Power ◽  
Power Optimization ◽  
Quantum Particle ◽  
Distribution Network ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

The traditional methods to adjust voltage in distribution network reactive power optimization is discretization，and it is difficult to realize the continuous voltage adjustment. A reactive power optimization model and algorithm in distribution network with wind farm is proposed. The network loss，deviation of voltage and stability of voltage are taken into account in the multi-objective reactive power optimization model. The quantum particle swarm optimization（QPSO）algorithm has been used to solve the reactive power optimization problem. The algorithm described particle state by wave function, not only increase the diversity of population，but also avoid premature convergence. The comparison of the simulation result between QPSO and PSO on the modified IEEE 33-bus system demonstrated the effectiveness and advantage of quantum particle swarm optimization.

scholarly journals Reactive power optimization system of distribution network based on edge calculation

2021 ◽  
Vol 1914 (1) ◽  
pp. 012033
Author(s):  
Jinbo Huang ◽  
Jiangxiao Fang ◽  
Liexiang Hu ◽  
Bolong Shi ◽  
Suirong Li ◽  
...  
Keyword(s):  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Reactive Power Optimization ◽  
Optimization System

Reactive Power Optimization with Chaotic Firefly Algorithm and Particle Swarm Optimization in A Distribution Subsystem Network

2016 ◽  
Vol 12 (1) ◽  
pp. 71-78
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya
Keyword(s):  
Particle Swarm Optimization ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Reactive Power Optimization ◽  
Power Losses ◽  
Swarm Optimization ◽  
Real Distribution

In this paper the minimization of power losses in a real distribution network have been described by solving reactive power optimization problem. The optimization has been performed and tested on Konya Eregli Distribution Network in Turkey, a section of Turkish electric distribution network managed by MEDAŞ (Meram Electricity Distribution Corporation). The network contains about 9 feeders, 1323 buses (including 0.4 kV, 15.8 kV and 31.5 kV buses) and 1311 transformers. This paper prefers a new Chaotic Firefly Algorithm (CFA) and Particle Swarm Optimization (PSO) for the power loss minimization in a real distribution network. The reactive power optimization problem is concluded with minimum active power losses by the optimal value of reactive power. The formulation contains detailed constraints including voltage limits and capacitor boundary. The simulation has been carried out with real data and results have been compared with Simulated Annealing (SA), standard Genetic Algorithm (SGA) and standard Firefly Algorithm (FA). The proposed method has been found the better results than the other algorithms.

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