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A game-theoretic analysis of transmission-distribution system operator coordination

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  • Le Cadre, Hélène
  • Mezghani, Ilyès
  • Papavasiliou, Anthony

Abstract

In this paper, we formulate in a game-theoretic framework three coordination schemes for analyzing DSO-TSO interactions. This framework relies on a reformulation of the power flow equations by introducing linear mappings between the state and the decision variables. The first coordination scheme, used as a benchmark, is a co-optimization problem where an integrated market operator activates jointly resources connected at transmission and distribution levels. We formulate it as a standard constrained optimization problem. The second one, called shared balancing responsibility, assumes bounded rationality of TSO and DSOs which act simultaneously and is formulated as a non-cooperative game. The last one involves rational expectation from the DSOs which anticipate the clearing of the transmission market by the TSO, and is formulated as a Stackelberg game. For each coordination scheme, we determine conditions for existence and uniqueness of solutions. On a network instance from the NICTA NESTA test cases, we span the set of Generalized Nash Equilibria solutions of the decentralized coordination schemes. We determine that the decentralized coordination schemes are more profitable for the TSO and that rational expectations from the DSOs gives rise to a last-mover advantage for the TSO. Highest efficiency level is reached by the centralized co-optimization, followed very closely by the shared balancing responsibility. The mean social welfare is higher for the Stackelberg game than under shared balancing responsibility. Finally, under imperfect information, we check that the Price of Information, measured as the worst-case ratio of the optimal achievable social welfare with full information to the social welfare at an equilibrium with imperfect information, is a stepwise increasing function of the coefficient of variation of the TSO and reaches an upper bound.

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  • Le Cadre, Hélène & Mezghani, Ilyès & Papavasiliou, Anthony, 2019. "A game-theoretic analysis of transmission-distribution system operator coordination," European Journal of Operational Research, Elsevier, vol. 274(1), pages 317-339.
  • Handle: RePEc:eee:ejores:v:274:y:2019:i:1:p:317-339
    DOI: 10.1016/j.ejor.2018.09.043
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    Cited by:

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    2. Zhao, Wei & Liao, Qi & Qiu, Rui & Liu, Chunying & Xu, Ning & Yu, Xiao & Liang, Yongtu, 2024. "Pipe sharing: A bilevel optimization model for the optimal capacity allocation of natural gas network," Applied Energy, Elsevier, vol. 359(C).
    3. Lei, Zhenxing & Liu, Mingbo & Shen, Zhijun & Lu, Wentian & Lu, Zhilin, 2023. "A data-driven Stackelberg game approach applied to analysis of strategic bidding for distributed energy resource aggregator in electricity markets," Renewable Energy, Elsevier, vol. 215(C).
    4. Khojasteh, Meysam & Faria, Pedro & Lezama, Fernando & Vale, Zita, 2023. "A hierarchy model to use local resources by DSO and TSO in the balancing market," Energy, Elsevier, vol. 267(C).
    5. Talal Alazemi & Mohamed Darwish & Mohammed Radi, 2022. "TSO/DSO Coordination for RES Integration: A Systematic Literature Review," Energies, MDPI, vol. 15(19), pages 1-26, October.
    6. Chao, Xiangrui & Kou, Gang & Peng, Yi & Viedma, Enrique Herrera, 2021. "Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion," European Journal of Operational Research, Elsevier, vol. 288(1), pages 271-293.
    7. Longxi Li, 2020. "Optimal Coordination Strategies for Load Service Entity and Community Energy Systems Based on Centralized and Decentralized Approaches," Energies, MDPI, vol. 13(12), pages 1-22, June.
    8. Vijay, Rohit & Mathuria, Parul, 2024. "Common TSO-DSO market framework with no upfront priority to utilize DER flexibility," Energy, Elsevier, vol. 299(C).
    9. Shariat Torbaghan, Shahab & Madani, Mehdi & Sels, Peter & Virag, Ana & Le Cadre, Hélène & Kessels, Kris & Mou, Yuting, 2021. "Designing day-ahead multi-carrier markets for flexibility: Models and clearing algorithms," Applied Energy, Elsevier, vol. 285(C).
    10. Anibal Sanjab & Yuting Mou & Ana Virag & Kris Kessels, 2021. "A Linear Model for Distributed Flexibility Markets and DLMPs: A Comparison with the SOCP Formulation," Papers 2111.02328, arXiv.org.
    11. Le Cadre, Hélène & Bedo, Jean-Sébastien, 2020. "Consensus reaching with heterogeneous user preferences, private input and privacy-preservation output," Operations Research Perspectives, Elsevier, vol. 7(C).
    12. Hermann, Alexander & Jensen, Tue Vissing & Østergaard, Jacob & Kazempour, Jalal, 2022. "A complementarity model for electric power transmission-distribution coordination under uncertainty," European Journal of Operational Research, Elsevier, vol. 299(1), pages 313-329.
    13. Attar, Mehdi & Repo, Sami & Mann, Pierre, 2022. "Congestion management market design- Approach for the Nordics and Central Europe," Applied Energy, Elsevier, vol. 313(C).
    14. Anibal Sanjab & H'el`ene Le Cadre & Yuting Mou, 2021. "TSO-DSOs Stable Cost Allocation for the Joint Procurement of Flexibility: A Cooperative Game Approach," Papers 2111.12830, arXiv.org.
    15. Le Cadre, Hélène & Jacquot, Paulin & Wan, Cheng & Alasseur, Clémence, 2020. "Peer-to-peer electricity market analysis: From variational to Generalized Nash Equilibrium," European Journal of Operational Research, Elsevier, vol. 282(2), pages 753-771.
    16. Li, Longxi, 2021. "Coordination between smart distribution networks and multi-microgrids considering demand side management: A trilevel framework," Omega, Elsevier, vol. 102(C).
    17. Martin Palovic, 2022. "Coordination of power network operators as a game-theoretical problem," Bremen Energy Working Papers 0040, Bremen Energy Research.
    18. Schittekatte, Tim & Meeus, Leonardo, 2020. "Flexibility markets: Q&A with project pioneers," Utilities Policy, Elsevier, vol. 63(C).

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