An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision

Seyyed Ahmad Hosseini, Jean-François Toubeau, Zacharie De Grève and François Vallée

Applied Energy, 2020, vol. 280, issue C, No S0306261920314239

Abstract: The current evolutions in electricity market policies combined with the technical progress in wind farm control encourage Wind Power Producers (WPPs) to participate in the joint day-ahead energy and reserve market (JERM). In this paper, an advanced bidding strategy dedicated to optimal dispatch of the WPP in the JERM is proposed. The suggested strategy exploits a novel bi-objective two-stage chance-constrained stochastic model in which various revenue streams, stemming from both day-ahead and real-time stages, are fully accounted for. The first objective of the presented model is to allocate the optimal share of the power assigned to each market floor in the day-ahead stage so as to maximize the WPP’s profit. Then, the formulation considers the confidence level of delivering the contracted reserve power in real-time through an additional competing objective. Meanwhile, the presented method allows us to also illustrate the effect of reserve availability as a probabilistic measure on WPP’s profit. In this regard, the wind speed and system frequency uncertainties are introduced as stochastic inputs in the proposed framework. The obtained revenue streams regarding each market floor as well as the total revenue of the WPP are then evaluated in a Monte Carlo out-of-sample analysis. The ex-post samples contain system frequency data from the Belgian market along with wind speed data representing the wind turbulence intensity level. Outcomes reveal not only the effectiveness and flexibility of the proposed model for enhancing WPPs’ revenues, but also the importance of properly considering the uncertain real-time delivery of the contracted reserve power.

Keywords: Bidding strategy; Energy and reserve market; Chance-constrained programming; Stochastic optimization; Wind turbulence intensity (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.apenergy.2020.115973

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