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

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  • Thang Trung Nguyen

    (Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam)

  • Fazel Mohammadi

    (Electrical and Computer Engineering (ECE) Department, University of Windsor, Windsor, ON N9B 1K3, Canada)

Abstract

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.

Suggested Citation

  • Thang Trung Nguyen & Fazel Mohammadi, 2020. "Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm," Sustainability, MDPI, vol. 12(7), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2813-:d:340336
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    References listed on IDEAS

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    1. Fazel Mohammadi & Gholam-Abbas Nazri & Mehrdad Saif, 2019. "A Bidirectional Power Charging Control Strategy for Plug-in Hybrid Electric Vehicles," Sustainability, MDPI, vol. 11(16), pages 1-24, August.
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    Cited by:

    1. Hana Merah & Abdelmalek Gacem & Djilani Ben Attous & Abderezak Lashab & Francisco Jurado & Mariam A. Sameh, 2022. "Sizing and Sitting of Static VAR Compensator (SVC) Using Hybrid Optimization of Combined Cuckoo Search (CS) and Antlion Optimization (ALO) Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
    2. Luay Elkhidir & Khalid Khan & Mohammad Al-Muhaini & Muhammad Khalid, 2022. "Enhancing Transient Response and Voltage Stability of Renewable Integrated Microgrids," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    3. Fazel Mohammadi, 2020. "Integration of AC/DC Microgrids into Power Grids," Sustainability, MDPI, vol. 12(8), pages 1-4, April.
    4. Yang Zhang & Zhenghui Fu & Yulei Xie & Qing Hu & Zheng Li & Huaicheng Guo, 2020. "A Comprehensive Forecasting–Optimization Analysis Framework for Environmental-Oriented Power System Management—A Case Study of Harbin City, China," Sustainability, MDPI, vol. 12(10), pages 1-26, May.
    5. Wenchao Yi & Zhilei Lin & Youbin Lin & Shusheng Xiong & Zitao Yu & Yong Chen, 2023. "Solving Optimal Power Flow Problem via Improved Constrained Adaptive Differential Evolution," Mathematics, MDPI, vol. 11(5), pages 1-13, March.

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