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Congestion Control Algorithm in Distribution Feeders: Integration in a Distribution Management System

Author

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  • Tine L. Vandoorn

    (Department of Electrical Energy, Systems & Automation, Ghent University, Technologiepark-Zwijnaarde 913, 9052 Gent, Belgium)

  • Jan Van de Vyver

    (Department of Electrical Energy, Systems & Automation, Ghent University, Technologiepark-Zwijnaarde 913, 9052 Gent, Belgium)

  • Louis Gevaert

    (Department of Electrical Energy, Systems & Automation, Ghent University, Technologiepark-Zwijnaarde 913, 9052 Gent, Belgium)

  • Lieven Degroote

    (Eandis, Guldensporenpark 52, 9820 Merelbeke, Belgium)

  • Lieven Vandevelde

    (Department of Electrical Energy, Systems & Automation, Ghent University, Technologiepark-Zwijnaarde 913, 9052 Gent, Belgium)

Abstract

The increasing share of distributed energy resources poses a challenge to the distribution network operator (DNO) to maintain the current availability of the system while limiting the investment costs. Related to this, there is a clear trend in DNOs trying to better monitor their grid by installing a distribution management system (DMS). This DMS enables the DNOs to remotely switch their network or better localize and solve faults. Moreover, the DMS can be used to centrally control the grid assets. Therefore, in this paper, a control strategy is discussed that can be implemented in the DMS for solving current congestion problems posed by the increasing share of renewables in the grid. This control strategy controls wind turbines in order to avoid congestion while mitigating the required investment costs in order to achieve a global cost-efficient solution. Next to the application and objective of the control, the parameter tuning of the control algorithm is discussed.

Suggested Citation

  • Tine L. Vandoorn & Jan Van de Vyver & Louis Gevaert & Lieven Degroote & Lieven Vandevelde, 2015. "Congestion Control Algorithm in Distribution Feeders: Integration in a Distribution Management System," Energies, MDPI, vol. 8(6), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:6:p:6013-6032:d:51340
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    References listed on IDEAS

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    1. Cheol-Hee Yoo & Il-Yop Chung & Hak-Ju Lee & Sung-Soo Hong, 2013. "Intelligent Control of Battery Energy Storage for Multi-Agent Based Microgrid Energy Management," Energies, MDPI, vol. 6(10), pages 1-24, September.
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    3. Hawkes, A.D. & Leach, M.A., 2009. "Modelling high level system design and unit commitment for a microgrid," Applied Energy, Elsevier, vol. 86(7-8), pages 1253-1265, July.
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    Cited by:

    1. Javier Leiva & Rubén Carmona Pardo & José A. Aguado, 2019. "Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks," Energies, MDPI, vol. 12(7), pages 1-20, April.
    2. Fco. Javier Zarco-Soto & Pedro J. Zarco-Periñán & Jose L. Martínez-Ramos, 2021. "Centralized Control of Distribution Networks with High Penetration of Renewable Energies," Energies, MDPI, vol. 14(14), pages 1-13, July.

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