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Mixed integer non-linear programming and Artificial Neural Network based approach to ancillary services dispatch in competitive electricity markets

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  • Canizes, Bruno
  • Soares, João
  • Faria, Pedro
  • Vale, Zita

Abstract

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization.

Suggested Citation

  • Canizes, Bruno & Soares, João & Faria, Pedro & Vale, Zita, 2013. "Mixed integer non-linear programming and Artificial Neural Network based approach to ancillary services dispatch in competitive electricity markets," Applied Energy, Elsevier, vol. 108(C), pages 261-270.
    • Handle: RePEc:eee:appene:v:108:y:2013:i:c:p:261-270
    DOI: 10.1016/j.apenergy.2013.03.031
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    References listed on IDEAS

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    2. Kumar, Abhishek & Meena, Nand K. & Singh, Arvind R. & Deng, Yan & He, Xiangning & Bansal, R.C. & Kumar, Praveen, 2019. "Strategic integration of battery energy storage systems with the provision of distributed ancillary services in active distribution systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
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    4. Goudarzi, Arman & Swanson, Andrew G. & Van Coller, John & Siano, Pierluigi, 2017. "Smart real-time scheduling of generating units in an electricity market considering environmental aspects and physical constraints of generators," Applied Energy, Elsevier, vol. 189(C), pages 667-696.
    5. Hosseini, Seyyed Ahmad & Toubeau, Jean-François & De Grève, Zacharie & Vallée, François, 2020. "An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision," Applied Energy, Elsevier, vol. 280(C).

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