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Improving the Penetration of Wind Power with Dynamic Thermal Rating System, Static VAR Compensator and Multi-Objective Genetic Algorithm

Author

Listed:
  • Jiashen Teh

    (School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), 14300 Nibong Tebal, Penang, Malaysia)

  • Ching-Ming Lai

    (Department of Vehicle Engineering, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Road, Taipei 10608, Taiwan)

  • Yu-Huei Cheng

    (Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan)

Abstract

The integration of renewable energy sources, especially wind energy, has been on the rise throughout power systems worldwide. Due to this relatively new introduction, the integration of wind energy is often not optimized. Moreover, owing to the technical constraints and transmission congestions of the power network, most of the wind energy has to be curtailed. Due to various factors that influence the connectivity of wind energy, this paper proposes a well-organized posterior multi-objective (MO) optimization algorithm for maximizing the connections of wind energy. In this regard, the dynamic thermal rating (DTR) system and the static VAR compensator (SVC) have been identified as effective tools for improving the loadability of the network. The propose MO algorithm in this paper aims to minimize: (1) wind energy curtailment, (2) operation cost of the network considering all investments and operations, also known as the total social cost, and (3) SVC operation cost. The proposed MO problem was solved using the non-dominated sorting genetic algorithm (NSGA) II and it was tested on the modified IEEE reliability test system (IEEE-RTS). The results demonstrate the applicability of the proposed algorithm in aiding power system enhancement planning for integrating wind energy.

Suggested Citation

  • Jiashen Teh & Ching-Ming Lai & Yu-Huei Cheng, 2018. "Improving the Penetration of Wind Power with Dynamic Thermal Rating System, Static VAR Compensator and Multi-Objective Genetic Algorithm," Energies, MDPI, vol. 11(4), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:815-:d:139149
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    Citations

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    Cited by:

    1. Mohamed Khamies & Salah Kamel & Mohamed H. Hassan & Mohamed F. Elnaggar, 2022. "A Developed Frequency Control Strategy for Hybrid Two-Area Power System with Renewable Energy Sources Based on an Improved Social Network Search Algorithm," Mathematics, MDPI, vol. 10(9), pages 1-31, May.
    2. Mohammad Dehghani & Mohammad Mardaneh & Om P. Malik & Josep M. Guerrero & Carlos Sotelo & David Sotelo & Morteza Nazari-Heris & Kamal Al-Haddad & Ricardo A. Ramirez-Mendoza, 2020. "Genetic Algorithm for Energy Commitment in a Power System Supplied by Multiple Energy Carriers," Sustainability, MDPI, vol. 12(23), pages 1-23, December.
    3. Yasir Yaqoob & Arjuna Marzuki & Ching-Ming Lai & Jiashen Teh, 2022. "Fuzzy Dynamic Thermal Rating System-Based Thermal Aging Model for Transmission Lines," Energies, MDPI, vol. 15(12), pages 1-23, June.
    4. Fan Song & Yanling Wang & Hongbo Yan & Xiaofeng Zhou & Zhiqiang Niu, 2019. "Increasing the Utilization of Transmission Lines Capacity by Quasi-Dynamic Thermal Ratings," Energies, MDPI, vol. 12(5), pages 1-13, February.

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