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Optimal participation of ADN in energy and reserve markets considering TSO-DSO interface and DERs uncertainties

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  • Chen, Houhe
  • Wang, Di
  • Zhang, Rufeng
  • Jiang, Tao
  • Li, Xue

Abstract

The flexibility of distribution networks continues to thrive due to the increasing installment of distributed energy resources (DERs). In addition to meeting the load demand in active distribution networks (ADNs), DERs can also provide energy and reserve for the upper-layer grid (i.e., sub-transmission network) at their connection node by participating in the energy and reserve markets. This paper proposes a novel optimal participation model of ADN in energy and reserve markets that takes into account the uncertainties of DERs and the interface of transmission system operator (TSO) and distribution system operator (DSO). The problem of DSO’s strategic behavior is formulated as a stochastic bi-level optimization model. The upper-level model indicates the market clearing of ADNs managed by the DSO and the lower-level model represents the energy and reserve market clearing of the upper-layer grid managed by the TSO. The nonlinear bi-level model is converted into a mathematical program with equilibrium constraint (MPEC) model, and then the mixed-integer second order cone programming (MISOCP) model based on Karush-Kuhn-Tucker conditions and strong duality theory. The effectiveness of the proposed model on improving the economy of ADN and the utilization rate of DERs is validated by numerical studies.

Suggested Citation

  • Chen, Houhe & Wang, Di & Zhang, Rufeng & Jiang, Tao & Li, Xue, 2022. "Optimal participation of ADN in energy and reserve markets considering TSO-DSO interface and DERs uncertainties," Applied Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:appene:v:308:y:2022:i:c:s0306261921015749
    DOI: 10.1016/j.apenergy.2021.118319
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    Cited by:

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    3. Seong-Hyeon Cha & Sun-Hyeok Kwak & Woong Ko, 2023. "A Robust Optimization Model of Aggregated Resources Considering Serving Ratio for Providing Reserve Power in the Joint Electricity Market," Energies, MDPI, vol. 16(20), pages 1-27, October.
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    6. Mansouri, Seyed Amir & Nematbakhsh, Emad & Jordehi, Ahmad Rezaee & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco, 2023. "An interval-based nested optimization framework for deriving flexibility from smart buildings and electric vehicle fleets in the TSO-DSO coordination," Applied Energy, Elsevier, vol. 341(C).
    7. Shao, Zhen & Yang, Yudie & Zheng, Qingru & Zhou, Kaile & Liu, Chen & Yang, Shanlin, 2022. "A pattern classification methodology for interval forecasts of short-term electricity prices based on hybrid deep neural networks: A comparative analysis," Applied Energy, Elsevier, vol. 327(C).
    8. 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).
    9. Li, Yahui & Sun, Yuanyuan & Wang, Qingyan & Sun, Kaiqi & Li, Ke-Jun & Zhang, Yan, 2023. "Probabilistic harmonic forecasting of the distribution system considering time-varying uncertainties of the distributed energy resources and electrical loads," Applied Energy, Elsevier, vol. 329(C).
    10. Vijay, Rohit & Mathuria, Parul, 2024. "Common TSO-DSO market framework with no upfront priority to utilize DER flexibility," Energy, Elsevier, vol. 299(C).
    11. Jiang, Tao & Dong, Xinru & Zhang, Rufeng & Li, Xue, 2023. "Strategic active and reactive power scheduling of integrated community energy systems in day-ahead distribution electricity market," Applied Energy, Elsevier, vol. 336(C).
    12. Zhang, Rufeng & Li, Xue & Fu, Linbo & Jiang, Tao & Li, Guoqing & Chen, Houhe, 2023. "Network-aware energy management for microgrids in distribution market: A leader-followers approach," Applied Energy, Elsevier, vol. 332(C).
    13. Nie, Yonghui & Qiu, Yu & Yang, Annan & Zhao, Yan, 2024. "Risk-limiting dispatching strategy considering demand response in multi-energy microgrids," Applied Energy, Elsevier, vol. 353(PA).

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