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Transmission Expansion Planning Considering Storage, Flexible AC Transmission System, Losses, and Contingencies to Integrate Wind Power

Author

Listed:
  • Dany H. Huanca

    (Department of Electrical Engineering, Federal University of Rio de Janeiro, COPPE, Rio de Janeiro 21941-901, Brazil)

  • Djalma M. Falcão

    (Department of Electrical Engineering, Federal University of Rio de Janeiro, COPPE, Rio de Janeiro 21941-901, Brazil)

  • Murilo E. C. Bento

    (Department of Electrical Engineering, Federal University of Rio de Janeiro, COPPE, Rio de Janeiro 21941-901, Brazil)

Abstract

To meet future load projection with the integration of renewable sources, the transmission system must be planned optimally. Thus, this paper introduces a comparative analysis and comprehensive methodology for transmission expansion planning (TEP), incorporating the combined effects of wind power, losses, N-1 contingency, a FACTS, and storage in a flexible environment. Specifically, the optimal placement of the FACTS, known as series capacitive compensation (SCC) devices, is used. The intraday constraints associated with wind power and energy storage are represented by the methodology of typical days jointly with the load scenarios light, heavy, and medium. The TEP problem is formulated as a mixed-integer nonlinear programming (MINLP) problem through a DC model and is solved using a specialized genetic algorithm. This algorithm is also used to determine the optimal placement of SCC devices and storage systems in expansion planning. The proposed methodology is then used to perform a comparison of the effect of the different technologies on the robustness and cost of the final solution of the TEP problem. Three test systems were used to perform the comparative analyses, namely the Garver system, the IEEE-24 system, and a real-world Colombian power system of 93 buses. The results indicate that energy storage and SCC devices lead to a decrease in transmission requirements and overall investment, enabling the effective integration of wind farms.

Suggested Citation

  • Dany H. Huanca & Djalma M. Falcão & Murilo E. C. Bento, 2024. "Transmission Expansion Planning Considering Storage, Flexible AC Transmission System, Losses, and Contingencies to Integrate Wind Power," Energies, MDPI, vol. 17(7), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1777-:d:1371900
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    References listed on IDEAS

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    1. Das, Choton K. & Bass, Octavian & Kothapalli, Ganesh & Mahmoud, Thair S. & Habibi, Daryoush, 2018. "Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm," Applied Energy, Elsevier, vol. 232(C), pages 212-228.
    2. Neumann, Fabian & Hagenmeyer, Veit & Brown, Tom, 2022. "Assessments of linear power flow and transmission loss approximations in coordinated capacity expansion problems," Applied Energy, Elsevier, vol. 314(C).
    3. Gan, Wei & Ai, Xiaomeng & Fang, Jiakun & Yan, Mingyu & Yao, Wei & Zuo, Wenping & Wen, Jinyu, 2019. "Security constrained co-planning of transmission expansion and energy storage," Applied Energy, Elsevier, vol. 239(C), pages 383-394.
    4. Xingning Han & Shiwu Liao & Xiaomeng Ai & Wei Yao & Jinyu Wen, 2017. "Determining the Minimal Power Capacity of Energy Storage to Accommodate Renewable Generation," Energies, MDPI, vol. 10(4), pages 1-17, April.
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