Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm
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...
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doaj-2060238fbd514f81b7c1f151e78bcc102020-11-25T03:10:56ZengMDPI AGSustainability2071-10502020-04-01122813281310.3390/su12072813Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic AlgorithmThang Trung Nguyen0Fazel Mohammadi1Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, VietnamElectrical and Computer Engineering (ECE) Department, University of Windsor, Windsor, ON N9B 1K3, CanadaElectricity 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.https://www.mdpi.com/2071-1050/12/7/2813Congestion ManagementFACTS devicesMulti-Objective Genetic Algorithm (MOGA)Power Loss ReductionThyristor-Controlled Series Compensator (TCSC) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Thang Trung Nguyen Fazel Mohammadi |
spellingShingle |
Thang Trung Nguyen Fazel Mohammadi Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm Sustainability Congestion Management FACTS devices Multi-Objective Genetic Algorithm (MOGA) Power Loss Reduction Thyristor-Controlled Series Compensator (TCSC) |
author_facet |
Thang Trung Nguyen Fazel Mohammadi |
author_sort |
Thang Trung Nguyen |
title |
Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm |
title_short |
Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm |
title_full |
Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm |
title_fullStr |
Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm |
title_full_unstemmed |
Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm |
title_sort |
optimal placement of tcsc for congestion management and power loss reduction using multi-objective genetic algorithm |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2020-04-01 |
description |
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. |
topic |
Congestion Management FACTS devices Multi-Objective Genetic Algorithm (MOGA) Power Loss Reduction Thyristor-Controlled Series Compensator (TCSC) |
url |
https://www.mdpi.com/2071-1050/12/7/2813 |
work_keys_str_mv |
AT thangtrungnguyen optimalplacementoftcscforcongestionmanagementandpowerlossreductionusingmultiobjectivegeneticalgorithm AT fazelmohammadi optimalplacementoftcscforcongestionmanagementandpowerlossreductionusingmultiobjectivegeneticalgorithm |
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