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Strategy-making for a proactive distribution company in the real-time market with demand response

Author

Listed:
  • Zhang, Chunyu
  • Wang, Qi
  • Wang, Jianhui
  • Korpås, Magnus
  • Khodayar, Mohammad E.

Abstract

This paper proposes a methodology to optimize the trading strategies of a proactive distribution company (PDISCO) in the real-time market by mobilizing the demand response. Each distribution-level demand is considered as an elastic one. To capture the interrelation between the PDISCO and the real-time market, a bi-level model is presented for the PDISCO to render continuous offers and bids strategically. The upper-level problem expresses the PDISCO’s profit maximization, while the lower-level problem minimizes the operation cost of the transmission-level real-time market. To solve the proposed model, a primal-dual approach is used to translate this bi-level model into a single-level mathematical program with equilibrium constraints. Results of case studies are reported to show the effectiveness of the proposed model.

Suggested Citation

  • Zhang, Chunyu & Wang, Qi & Wang, Jianhui & Korpås, Magnus & Khodayar, Mohammad E., 2016. "Strategy-making for a proactive distribution company in the real-time market with demand response," Applied Energy, Elsevier, vol. 181(C), pages 540-548.
    • Handle: RePEc:eee:appene:v:181:y:2016:i:c:p:540-548
    DOI: 10.1016/j.apenergy.2016.08.058
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    References listed on IDEAS

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    Cited by:

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    2. Shahmohammadi, Ali & Sioshansi, Ramteen & Conejo, Antonio J. & Afsharnia, Saeed, 2018. "Market equilibria and interactions between strategic generation, wind, and storage," Applied Energy, Elsevier, vol. 220(C), pages 876-892.
    3. 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).
    4. Jiang, Tao & Wu, Chenghao & Zhang, Rufeng & Li, Xue & Li, Fangxing, 2022. "Risk-averse TSO-DSOs coordinated distributed dispatching considering renewable energy and demand response uncertainties," Applied Energy, Elsevier, vol. 327(C).
    5. Francisco Prieto-Castrillo & Amin Shokri Gazafroudi & Javier Prieto & Juan Manuel Corchado, 2018. "An Ising Spin-Based Model to Explore Efficient Flexibility in Distributed Power Systems," Complexity, Hindawi, vol. 2018, pages 1-16, May.
    6. Mishra, Mrityunjay Kumar & Al-Sumaiti, Ameena Saad & Murari, Krishna & Parida, S.K. & Jaafari, Khaled Al, 2024. "Strategic interaction among distribution network operator and residential end-users via distribution use of system charges in demand-side management environment," Applied Energy, Elsevier, vol. 364(C).
    7. Behboodi, Sahand & Chassin, David P. & Djilali, Ned & Crawford, Curran, 2017. "Interconnection-wide hour-ahead scheduling in the presence of intermittent renewables and demand response: A surplus maximizing approach," Applied Energy, Elsevier, vol. 189(C), pages 336-351.
    8. Zhang, Chunyu & Wang, Qi & Wang, Jianhui & Korpås, Magnus & Pinson, Pierre & Østergaard, Jacob & Khodayar, Mohammad E., 2016. "Trading strategies for distribution company with stochastic distributed energy resources," Applied Energy, Elsevier, vol. 177(C), pages 625-635.
    9. Sheikhahmadi, P. & Bahramara, S. & Moshtagh, J. & Yazdani Damavandi, M., 2018. "A risk-based approach for modeling the strategic behavior of a distribution company in wholesale energy market," Applied Energy, Elsevier, vol. 214(C), pages 24-38.
    10. Amin Shokri Gazafroudi & Javier Prieto & Juan Manuel Corchado, 2019. "Virtual Organization Structure for Agent-Based Local Electricity Trading," Energies, MDPI, vol. 12(8), pages 1-11, April.
    11. Yoon, Ah-Yun & Kim, Young-Jin & Zakula, Tea & Moon, Seung-Ill, 2020. "Retail electricity pricing via online-learning of data-driven demand response of HVAC systems," Applied Energy, Elsevier, vol. 265(C).

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