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Stochastic risk-sensitive market integration for renewable energy: Application to ocean wave power plants

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  • Sedzro, Kwami Senam A.
  • Kishore, Shalinee
  • Lamadrid, Alberto J.
  • Zuluaga, Luis F.

Abstract

With the expected increase of intermittent renewable energy resources on the electric power grid, short-term reserve markets can prove to be a critical reliability asset. This paper introduces and tests a potential market structure that includes a short-term reserve market for renewable resources where the realized energy is drawn within the bounds of the hourly capacity offered. The study presents a risk-aware stochastic model that determines, based on the day-ahead offer derived using a classic newsvendor formula, the best intraday offers for a renewable energy power plant. The stochastic approach takes into account the uncertainties of energy production and market prices. The proposed risk-sensitive model aims to maximize the renewable power plant’s revenues and minimize potential risks of loss in a multi-product multi-timescale market setup. To demonstrate the effectiveness of the proposed formulation, we study the case of a hypothetical 750-kW wave power plant coupled with energy storage. The study shows that the risk of profit loss is not uniformly distributed across the risk-aversion factor space. We also find that the introduction of the short-term reserve market results in a wider range of conditional value at risk while inducing 5% profit increase and a lower profit reduction across the risk range. In addition, the study reveals that the short-term reserve market is profitable for both the system operator and the wave energy power plant considered.

Suggested Citation

  • Sedzro, Kwami Senam A. & Kishore, Shalinee & Lamadrid, Alberto J. & Zuluaga, Luis F., 2018. "Stochastic risk-sensitive market integration for renewable energy: Application to ocean wave power plants," Applied Energy, Elsevier, vol. 229(C), pages 474-481.
    • Handle: RePEc:eee:appene:v:229:y:2018:i:c:p:474-481
    DOI: 10.1016/j.apenergy.2018.07.091
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    References listed on IDEAS

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    1. Crespo-Vazquez, Jose L. & Carrillo, C. & Diaz-Dorado, E. & Martinez-Lorenzo, Jose A. & Noor-E-Alam, Md., 2018. "A machine learning based stochastic optimization framework for a wind and storage power plant participating in energy pool market," Applied Energy, Elsevier, vol. 232(C), pages 341-357.

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