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Simulation of Incidental Distributed Generation Curtailment to Maximize the Integration of Renewable Energy Generation in Power Systems

Author

Listed:
  • Ingo Liere-Netheler

    (DLR Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Frank Schuldt

    (DLR Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Karsten von Maydell

    (DLR Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

  • Carsten Agert

    (DLR Institute of Networked Energy Systems, Carl-von-Ossietzky-Str. 15, 26129 Oldenburg, Germany)

Abstract

Power system security is increasingly endangered due to novel power flow situations caused by the growing integration of distributed generation. Consequently, grid operators are forced to request the curtailment of distributed generators to ensure the compliance with operational limits more often. This research proposes a framework to simulate the incidental amount of renewable energy curtailment based on load flow analysis of the network. Real data from a 110 kV distribution network located in Germany are used to validate the proposed framework by implementing best practice curtailment approaches. Furthermore, novel operational concepts are investigated to improve the practical implementation of distributed generation curtailment. Specifically, smaller curtailment level increments, coordinated selection methods, and an extension of the n-1 security criterion are analyzed. Moreover, combinations of these concepts are considered to depict interdependencies between several operational aspects. The results quantify the potential of the proposed concepts to improve established grid operation practices by minimizing distributed generation curtailment and, thus, maximizing power system integration of renewable energies. In particular, the extension of the n-1 criterion offers significant potential to reduce curtailment by up to 94.8% through a more efficient utilization of grid capacities.

Suggested Citation

  • Ingo Liere-Netheler & Frank Schuldt & Karsten von Maydell & Carsten Agert, 2020. "Simulation of Incidental Distributed Generation Curtailment to Maximize the Integration of Renewable Energy Generation in Power Systems," Energies, MDPI, vol. 13(16), pages 1-22, August.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4173-:d:398138
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    Citations

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    Cited by:

    1. Pedro Macieira & Luis Gomes & Zita Vale, 2021. "Energy Management Model for HVAC Control Supported by Reinforcement Learning," Energies, MDPI, vol. 14(24), pages 1-14, December.
    2. Max Bodenstein & Ingo Liere-Netheler & Frank Schuldt & Karsten von Maydell & Alexander K. Hartmann & Carsten Agert, 2023. "Optimized Power Flow Control to Minimize Congestion in a Modern Power System," Energies, MDPI, vol. 16(12), pages 1-19, June.
    3. Florian Schäfer & Martin Braun, 2020. "Multi-Year High-Voltage Power System Planning Considering Active Power Curtailment," Energies, MDPI, vol. 13(18), pages 1-15, September.
    4. Yuri Bulatov & Andrey Kryukov & Andrey Batuhtin & Konstantin Suslov & Ksenia Korotkova & Denis Sidorov, 2022. "Digital Twin Formation Method for Distributed Generation Plants of Cyber–Physical Power Supply Systems," Mathematics, MDPI, vol. 10(16), pages 1-19, August.

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