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Optimal Deployment of Mobile MSSSC in Transmission System

Author

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  • Zhehan Zhao

    (School of Electrical and Electronic, University College Dublin, D04 V1W8 Dublin, Ireland
    These authors contributed equally to this work.)

  • Alireza Soroudi

    (School of Electrical and Electronic, University College Dublin, D04 V1W8 Dublin, Ireland
    These authors contributed equally to this work.)

Abstract

With the rapid development of the renewable energy source (RES), network congestion management is increasingly important for transmission system operators (TSOs). The limited transmission network capacity and traditional intervention methods result in high RES curtailment. The near-term, powerful, and flexible solutions, such as advanced flexible AC transmission systems (FACTS), are considered to mitigate the risks. The mobile modular static synchronous series compensator (MSSSC) is one of the grid-enhancing solutions. The mobility of the solution allows it to offer fast deployment and seasonal redeployability with limited cost. The demonstration of the mobile MSSSC solution has shown significant benefits for RES curtailment reduction, network congestion alleviation, and facilitating the demand and RES connection. For unlocking the true value of the mobile solution, they should be optimally allocated in the transmission networks. This paper develops a security-constrained DCOPF-based optimisation tool to investigate the optimal allocation of the mobile MSSSC solution in transmission networks. A linear mobile MSSSC model with the operation dead-band was introduced that can be used in large-scale realistic power system planning. The proposed model was implemented in the IEEE 118-bus system to assess the performance of the mobile MSSSC.

Suggested Citation

  • Zhehan Zhao & Alireza Soroudi, 2022. "Optimal Deployment of Mobile MSSSC in Transmission System," Energies, MDPI, vol. 15(11), pages 1-27, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:3878-:d:823239
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    References listed on IDEAS

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    2. William E. Hart & Carl D. Laird & Jean-Paul Watson & David L. Woodruff & Gabriel A. Hackebeil & Bethany L. Nicholson & John D. Siirola, 2017. "Pyomo — Optimization Modeling in Python," Springer Optimization and Its Applications, Springer, edition 2, number 978-3-319-58821-6, September.
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