IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i12p4753-d1172494.html
   My bibliography  Save this article

New Distributed Optimization Method for TSO–DSO Coordinated Grid Operation Preserving Power System Operator Sovereignty

Author

Listed:
  • Steffen Meinecke

    (Department of Energy Management and Power System Operation (e 2 n), University Kassel, 34121 Kassel, Germany
    Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), 34117 Kassel, Germany)

  • David Sebastian Stock

    (Department of Energy Management and Power System Operation (e 2 n), University Kassel, 34121 Kassel, Germany
    Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), 34117 Kassel, Germany)

  • Martin Braun

    (Department of Energy Management and Power System Operation (e 2 n), University Kassel, 34121 Kassel, Germany
    Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), 34117 Kassel, Germany)

Abstract

Electrical power system operators (SOs) are free to realize grid operations according to their own strategies. However, because resulting power flows also depend on the actions of neighboring SOs, appropriate coordination is needed to improve the resulting system states from an overall perspective and from an individual SO perspective. In this paper, a new method is presented that preserves the data integrity of the SOs and their independent operation of their grids. This method is compared with a non-coordinated local control and another sequential method that has been identified as the most promising distributed optimization method in previous research. The time series simulations use transformer tap positioning as well as generation unit voltage setpoints and reactive power injections as flexibilities. The methods are tested on a multi-voltage, multi-SO, realistic benchmark grid with different objective combinations of the SOs. In conclusion, the results of the new method are much closer to the theoretical optimum represented by central optimization than those of the other two methods. Furthermore, the introduced method integrates a sophisticated procedure to provide fairness between SOs that is missing in other methods.

Suggested Citation

  • Steffen Meinecke & David Sebastian Stock & Martin Braun, 2023. "New Distributed Optimization Method for TSO–DSO Coordinated Grid Operation Preserving Power System Operator Sovereignty," Energies, MDPI, vol. 16(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4753-:d:1172494
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/12/4753/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/12/4753/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Francisco Nogales & Francisco Prieto & Antonio Conejo, 2003. "A Decomposition Methodology Applied to the Multi-Area Optimal Power Flow Problem," Annals of Operations Research, Springer, vol. 120(1), pages 99-116, April.
    2. Steffen Meinecke & Leon Thurner & Martin Braun, 2020. "Review of Steady-State Electric Power Distribution System Datasets," Energies, MDPI, vol. 13(18), pages 1-17, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Lixiao & Jing, Z.X. & Zheng, J.H. & Wu, Q.H. & Wei, Feng, 2018. "Decentralized optimization of coordinated electrical and thermal generations in hierarchical integrated energy systems considering competitive individuals," Energy, Elsevier, vol. 158(C), pages 607-622.
    2. Huang, Jinbo & Li, Zhigang & Wu, Q.H., 2017. "Coordinated dispatch of electric power and district heating networks: A decentralized solution using optimality condition decomposition," Applied Energy, Elsevier, vol. 206(C), pages 1508-1522.
    3. Thomas Bittar & Pierre Carpentier & Jean-Philippe Chancelier & Jérôme Lonchampt, 2022. "A decomposition method by interaction prediction for the optimization of maintenance scheduling," Annals of Operations Research, Springer, vol. 316(1), pages 229-267, September.
    4. Jolando M. Kisse & Martin Braun & Simon Letzgus & Tanja M. Kneiske, 2020. "A GIS-Based Planning Approach for Urban Power and Natural Gas Distribution Grids with Different Heat Pump Scenarios," Energies, MDPI, vol. 13(16), pages 1-31, August.
    5. Liu, Jia & Zeng, Peter Pingliang & Xing, Hao & Li, Yalou & Wu, Qiuwei, 2020. "Hierarchical duality-based planning of transmission networks coordinating active distribution network operation," Energy, Elsevier, vol. 213(C).
    6. Jesús Latorre & Santiago Cerisola & Andrés Ramos & Rafael Palacios, 2009. "Analysis of stochastic problem decomposition algorithms in computational grids," Annals of Operations Research, Springer, vol. 166(1), pages 355-373, February.
    7. Steffen Meinecke & Džanan Sarajlić & Simon Ruben Drauz & Annika Klettke & Lars-Peter Lauven & Christian Rehtanz & Albert Moser & Martin Braun, 2020. "SimBench—A Benchmark Dataset of Electric Power Systems to Compare Innovative Solutions Based on Power Flow Analysis," Energies, MDPI, vol. 13(12), pages 1-19, June.
    8. Robson Porsch Delavechia & Bibiana P. Ferraz & Raul Scapini Weiand & Leonardo Silveira & Maicon Jaderson Silveira Ramos & Laura Lisiane Callai dos Santos & Daniel Pinheiro Bernardon & Rui Anderson Fer, 2023. "Electricity Supply Regulations in South America: A Review of Regulatory Aspects," Energies, MDPI, vol. 16(2), pages 1-33, January.
    9. Qu, Kaiping & Yu, Tao & Huang, Linni & Yang, Bo & Zhang, Xiaoshun, 2018. "Decentralized optimal multi-energy flow of large-scale integrated energy systems in a carbon trading market," Energy, Elsevier, vol. 149(C), pages 779-791.
    10. Jona Maurer & Jochen Illerhaus & Pol Jané Soneira & Sören Hohmann, 2022. "Distributed Optimization of District Heating Networks Using Optimality Condition Decomposition," Energies, MDPI, vol. 15(18), pages 1-21, September.
    11. Lin, Shin-Yeu & Chen, Jyun-Fu, 2013. "Distributed optimal power flow for smart grid transmission system with renewable energy sources," Energy, Elsevier, vol. 56(C), pages 184-192.
    12. Ghasemi, Mojtaba & Aghaei, Jamshid & Akbari, Ebrahim & Ghavidel, Sahand & Li, Li, 2016. "A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems," Energy, Elsevier, vol. 107(C), pages 182-195.
    13. Xinhu Zheng & Dongliang Duan & Liuqing Yang & Haonan Wang, 2020. "Decomposed Iterative Optimal Power Flow with Automatic Regionalization," Energies, MDPI, vol. 13(18), pages 1-22, September.
    14. Lin, Jian & Wang, Zhou-Jing, 2019. "Multi-area economic dispatch using an improved stochastic fractal search algorithm," Energy, Elsevier, vol. 166(C), pages 47-58.
    15. Kim, Tae Hyun & Shin, Hansol & Kwag, Kyuhyeong & Kim, Wook, 2020. "A parallel multi-period optimal scheduling algorithm in microgrids with energy storage systems using decomposed inter-temporal constraints," Energy, Elsevier, vol. 202(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4753-:d:1172494. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.