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Risk-averse optimal participation of a DR-intensive microgrid in competitive clusters considering response fatigue

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  • Tostado-Véliz, Marcos
  • Hasanien, Hany M.
  • Jordehi, Ahmad Rezaee
  • Turky, Rania A.
  • Jurado, Francisco

Abstract

The massive integration of renewable generators, energy storage systems, and demand response requires the development of smart power infrastructures. In this upcoming context, microgrids will be essential for the optimal integration of such assets. When different microgrids are located near each other, they can be centrally coordinated within a novel paradigm called Microgrid Cluster. In such structures, the microgrids involved can collaborate in a cooperative way or compete by developing internal market structures. This paper develops a novel optimal bidding strategy for a demand response-intensive microgrid partaking in competitive clusters. The new proposal is envisaged as a three-stage methodology that aims at reducing the effects of response fatigue. Uncertainties related to inflexible demand and renewable generation are modeled via scenarios, while the risk associated with uncertain parameters is handled by enforcing the Conditional Value at Risk. The resulting computational tool is effective and tractable, as shown in the results obtained on a benchmark three-microgrids cluster. Indeed, the developed methodology is able to reduce the total response signals by 88 % in some cases. Moreover, this case study allows analyzing the effect of response fatigue minimization in the overall cluster performance, showing that the collective welfare can be reduced by 32 % when response fatigue is taken into account.

Suggested Citation

  • Tostado-Véliz, Marcos & Hasanien, Hany M. & Jordehi, Ahmad Rezaee & Turky, Rania A. & Jurado, Francisco, 2023. "Risk-averse optimal participation of a DR-intensive microgrid in competitive clusters considering response fatigue," Applied Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:appene:v:339:y:2023:i:c:s0306261923003240
    DOI: 10.1016/j.apenergy.2023.120960
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    References listed on IDEAS

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    1. Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Arévalo, Paul & Turky, Rania A. & Jurado, Francisco, 2022. "A stochastic-interval model for optimal scheduling of PV-assisted multi-mode charging stations," Energy, Elsevier, vol. 253(C).
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    1. Lantao Jing & Enyu Wei & Liang Wang & Jinkuo Li & Qiang Zhang, 2024. "A Multi-Type Dynamic Response Control Strategy for Energy Consumption," Energies, MDPI, vol. 17(13), pages 1-20, June.
    2. Turdybek, Balgynbek & Tostado-Véliz, Marcos & Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Jurado, Francisco, 2024. "A local electricity market mechanism for flexibility provision in industrial parks involving Heterogenous flexible loads," Applied Energy, Elsevier, vol. 359(C).
    3. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Zhou, Yuekuan & Mansouri, Seyed Amir & Jurado, Francisco, 2024. "Best-case-aware planning of photovoltaic-battery systems for multi-mode charging stations," Renewable Energy, Elsevier, vol. 225(C).

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