Congestion management in deregulated power system by optimal choice and allocation of FACTS controllers using multi-objective genetic algorithm

2010 ◽  
Author(s):  
S. Surender Reddy ◽  
M. Sailaja Kumari ◽  
M. Sydulu
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Congestion Management ◽  
Optimal Choice ◽  
Multi Objective ◽  
Deregulated Power System ◽  
Multi Objective Genetic Algorithm ◽  
Facts Controllers

scholarly journals Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm

2020 ◽  
Vol 12 (7) ◽  
pp. 2813 ◽  
Author(s):  
Thang Trung Nguyen ◽  
Fazel Mohammadi
Keyword(s):  
Genetic Algorithm ◽  
Power Loss ◽  
Congestion Management ◽  
Mixed Integer ◽  
Total Power ◽  
World Population ◽  
Loss Reduction ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Power Loss Reduction

Electricity demand has been growing due to the increase in the world population and higher energy usage per capita as compared to the past. As a result, various methods have been proposed to increase the efficiency of power systems in terms of mitigating congestion and minimizing power losses. Power grids operating limitations result in congestion that specifies the final capacity of the system, which decreases the conventional power capabilities between coverage areas. Flexible AC Transmission Systems (FACTS) can help to decrease flows in heavily loaded lines and lead to lines loadability improvements and cost reduction. In this paper, total power loss reduction and line congestion improvement are assessed by determining the optimal locations and compensation rates of Thyristor-Controlled Series Compensator (TCSC) devices using the Multi-Objective Genetic Algorithm (MOGA). The results of applying the proposed method on the IEEE 30-bus test system confirmed the efficiency of the proposed procedure. In addition, to check the performance, applicability, and effectiveness of the proposed method, different heuristic algorithms, such as the multi-objective Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm, and Mixed-Integer Non-Linear Program (MINLP) technique, are used for comparison. The obtained results show the accuracy and fast convergence of the proposed method over the other heuristic techniques.

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