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

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.

2016 ◽  
Vol 12 (1) ◽  
pp. 71-78
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya

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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