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Modelling demand response aggregator behavior in wind power offering strategies

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  • Mahmoudi, Nadali
  • Saha, Tapan K.
  • Eghbal, Mehdi

Abstract

This paper proposes a new wind offering strategy in which a wind power producer employs demand response (DR) to cope with the power production uncertainty and market violations. To this end, the wind power producer sets demand response (DR) contracts with a DR aggregator. The DR aggregator behavior is modeled through a revenue function. In this way the aggregator aims to maximize its revenue through trading DR with the wind power producer, other market players and the day-ahead market. The problem is formulated in bilevel programming in which the upper level represents wind power producer decisions and the lower level models the DR aggregator behavior. The given bilevel problem is then transformed into a single-level mathematical program with equilibrium constraints (MPEC) and linearized using proper techniques. The feasibility of the given strategy is assessed on a case of the Nordic market.

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  • Mahmoudi, Nadali & Saha, Tapan K. & Eghbal, Mehdi, 2014. "Modelling demand response aggregator behavior in wind power offering strategies," Applied Energy, Elsevier, vol. 133(C), pages 347-355.
    • Handle: RePEc:eee:appene:v:133:y:2014:i:c:p:347-355
    DOI: 10.1016/j.apenergy.2014.07.108
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    Cited by:

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    5. 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.
    6. Seok, Hyesung & Chen, Chen, 2019. "An intelligent wind power plant coalition formation model achieving balanced market penetration growth and profit increase," Renewable Energy, Elsevier, vol. 138(C), pages 1134-1142.
    7. 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.
    8. Behboodi, Sahand & Chassin, David P. & Crawford, Curran & Djilali, Ned, 2016. "Renewable resources portfolio optimization in the presence of demand response," Applied Energy, Elsevier, vol. 162(C), pages 139-148.
    9. Calvillo, C.F. & Sánchez-Miralles, A. & Villar, J. & Martín, F., 2016. "Optimal planning and operation of aggregated distributed energy resources with market participation," Applied Energy, Elsevier, vol. 182(C), pages 340-357.
    10. Carreiro, Andreia M. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2017. "Energy management systems aggregators: A literature survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1160-1172.
    11. Exizidis, Lazaros & Kazempour, S. Jalal & Pinson, Pierre & de Greve, Zacharie & Vallée, François, 2016. "Sharing wind power forecasts in electricity markets: A numerical analysis," Applied Energy, Elsevier, vol. 176(C), pages 65-73.
    12. Mahboubi-Moghaddam, Esmaeil & Nayeripour, Majid & Aghaei, Jamshid, 2016. "Reliability constrained decision model for energy service provider incorporating demand response programs," Applied Energy, Elsevier, vol. 183(C), pages 552-565.
    13. Fera, M. & Macchiaroli, R. & Iannone, R. & Miranda, S. & Riemma, S., 2016. "Economic evaluation model for the energy Demand Response," Energy, Elsevier, vol. 112(C), pages 457-468.
    14. Gomes, I.L.R. & Melicio, R. & Mendes, V.M.F. & Pousinho, H.M.I., 2019. "Decision making for sustainable aggregation of clean energy in day-ahead market: Uncertainty and risk," Renewable Energy, Elsevier, vol. 133(C), pages 692-702.

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