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Locating series FACTS devices for multi-objective congestion management improving voltage and transient stability

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  • Esmaili, Masoud
  • Shayanfar, Heidar Ali
  • Moslemi, Ramin

Abstract

Transmission congestion management is a vital task in electricity markets. Series FACTS devices can be used as effective tools to relieve congestion mostly employing Optimal Power Flow based methods, in which total cost as the objective function is minimized. However, power system stability may be deteriorated after relieving congestion using traditional methods leading to a vulnerable power system against disturbances. In this paper, a multi-objective framework is proposed for congestion management where three competing objective functions including total operating cost, voltage and transient stability margins are simultaneously optimized. This leads to an economical and robust operating point where enough levels of voltage and transient security are included. The proposed method optimally locates and sizes series FACTS devices on the most congested branches determined by a priority list based on Locational Marginal Prices. Individual sets of Pareto solutions, resulted from solving multi-objective congestion management for each location of FACTS devices, are merged together to create the comprehensive Pareto set. Results of testing the proposed method on the well-known New-England test system are discussed in details and confirm efficiency of the proposed method.

Suggested Citation

  • Esmaili, Masoud & Shayanfar, Heidar Ali & Moslemi, Ramin, 2014. "Locating series FACTS devices for multi-objective congestion management improving voltage and transient stability," European Journal of Operational Research, Elsevier, vol. 236(2), pages 763-773.
    • Handle: RePEc:eee:ejores:v:236:y:2014:i:2:p:763-773
    DOI: 10.1016/j.ejor.2014.01.017
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    Cited by:

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    2. Peesapati, Rajagopal & Yadav, Vinod Kumar & Kumar, Niranjan, 2018. "Flower pollination algorithm based multi-objective congestion management considering optimal capacities of distributed generations," Energy, Elsevier, vol. 147(C), pages 980-994.
    3. Chun-Yao Lee & Chang-En Wu, 2020. "Short-Term Electricity Price Forecasting Based on Similar Day-Based Neural Network," Energies, MDPI, vol. 13(17), pages 1-15, August.
    4. Frank, Stephen M. & Rebennack, Steffen, 2015. "Optimal design of mixed AC–DC distribution systems for commercial buildings: A Nonconvex Generalized Benders Decomposition approach," European Journal of Operational Research, Elsevier, vol. 242(3), pages 710-729.
    5. Panda, Mitali & Nayak, Yogesh Kumar, 2022. "Impact analysis of renewable energy Distributed Generation in deregulated electricity markets: A Context of Transmission Congestion Problem," Energy, Elsevier, vol. 254(PC).
    6. Anurag Gautam & Ibraheem & Gulshan Sharma & Mohammad F. Ahmer & Narayanan Krishnan, 2023. "Methods and Methodologies for Congestion Alleviation in the DPS: A Comprehensive Review," Energies, MDPI, vol. 16(4), pages 1-28, February.
    7. Sushil Kumar Gupta & Lalit Kumar & Manoj Kumar Kar & Sanjay Kumar, 2022. "Optimal reactive power dispatch under coordinated active and reactive load variations using FACTS devices," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2672-2682, October.
    8. Ramana Pilla & Ahmad Taher Azar & Tulasichandra Sekhar Gorripotu, 2019. "Impact of Flexible AC Transmission System Devices on Automatic Generation Control with a Metaheuristic Based Fuzzy PID Controller," Energies, MDPI, vol. 12(21), pages 1-19, November.

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