Coordinated Voltage Control in Distribution Systems with Distributed Generations

Author(s):  
Alvi Newaz ◽  
Juan Ospina ◽  
M. Omar Faruque
2019 ◽  
Vol 101 (1) ◽  
pp. 175-187 ◽  
Author(s):  
Eman O. Hasan ◽  
Ahmed Y. Hatata ◽  
Ebrahim A. Badran ◽  
Fathi M. H. Yossef

2019 ◽  
Vol 139 (3) ◽  
pp. 178-185 ◽  
Author(s):  
Naoto Yorino ◽  
Tsubasa Watakabe ◽  
Yuki Nakamura ◽  
Yutaka Sasaki ◽  
Yoshifumi Zoka ◽  
...  

Author(s):  
Muhamad Najib Kamarudin ◽  
Tengku Juhana Tengku Hashim

The operation and control of electricity in distribution networks has faced great challenges as a large number of distributed generations (DGs) are integrated. Connection of distributed generations (DGs) in the distribution system offers advantages in terms of reducing distribution and transmission costs as well as encouraging the use of renewable energy sources. The power flow in the distribution systems is no longer moving in a single direction and this resulted the system to become as active distribution networks (ADN). One of the main problems in ADN is the voltage regulation issue which is to maintain the voltage to be within its permissible limits. Several methods of voltage control methods are available and focus is given in finding the optimal voltage control using artificial intelligence techniques. This paper presents an optimal and coordinated voltage control method while minimizing losses and voltage deviation of the network. The optimal and coordinated voltage control scheme is implemented on an IEEE 13 bus distribution network for loss and voltage deviation minimization in the networks. Firefly Algorithm (FA) which is a known heuristic optimization technique for finding the optimal solution is used in this work. The results are compared with another optimization method known as Backtracking Search Algorithm (BSA) for identifying the best setting for solving the voltage regulation problem. In order to solve the multi-objective optimization issue, the MATPOWER load flow simulation is integrated in the MATLAB environment with the optimization algorithm.


2020 ◽  
Vol 140 (6) ◽  
pp. 456-464
Author(s):  
Naoto Yorino ◽  
Tsubasa Watakabe ◽  
Ahmed Bedawy Khalifa ◽  
Yutaka Sasaki ◽  
Yoshifumi Zoka

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