Summary:
This paper proposes a novel approach for the offering strategy of a virtual power plant that participates in the day-ahead and the real-time energy markets. The virtual power plant comprises a conventional power plant, a wind-power unit, a storage facility, and flexible demands, which participate in the day-ahead and the real-time markets as a single entity in order to optimize their energy resources. We model the uncertainty in the wind-power production and in the market prices using confidence bounds and scenarios, respectively, which allows us to formul-ate the strategic offering problem as a stochastic adaptive robust optimization model. Results of a case study are provided to show the applicability of the proposed approach.
Keywords: Robust optimization, stochastic programming, strategic offering, uncertainty, virtual power plant.
JCR Impact Factor and WoS quartile: 5,255 - Q1 (2017); 6,600 - Q1 (2022)
DOI reference:
https://doi.org/10.1109/TPWRS.2016.2633546
Published on paper: September 2017.
Published on-line: December 2016.
Citation:
A. Baringo, L. Baringo, A stochastic adaptive robust optimization approach for the offering strategy of a virtual power plant. IEEE Transactions on Power Systems. Vol. 32, nº. 5, pp. 3492 - 3504, September 2017. [Online: December 2016]
